Dynamic Programming for Linear-Time Incremental Parsing

Incremental parsing techniques such as shift-reduce have gained popularity thanks to their efficiency, but there remains a major problem: the search is greedyand only explores a tiny fraction of the whole space (even with beam search) as opposed to dynamic programming. We show that, surprisingly, dynamic programming is in fact possible for many shift-reduce parsers, by merging “equivalent” stacks based on feature values. Empirically, our algorithm yields up to a five-fold speedup over a state-of-the-art shift-reduce dependency parser with no loss in accuracy. Better search also leads to better learning, and our final parser outperforms all previously reported dependency parsers for English and Chinese, yet is much faster.


Photorealistic Video Super Resolution

With the advent of perceptual loss functions, new possibilities in super-resolution have emerged, and we currently have models that successfully generate near-photorealistic high-resolution images from their low-resolution observations. Up to now, however, such approaches have been exclusively limited to single image super-resolution. The application of perceptual loss functions on video processing still entails several challenges, mostly related to the lack of temporal consistency of the generated images, i.e., flickering artifacts. In this work, we present a novel adversarial recurrent network for video upscaling that is able to produce realistic textures in a temporally consistent way. The proposed architecture naturally leverages information from previous frames due to its recurrent architecture, i.e. the input to the generator is composed of the low-resolution image and, additionally, the warped output of the network at the previous step. We also propose an additional loss function to further reinforce temporal consistency in the generated sequences. The experimental validation of our algorithm shows the effectiveness of our approach which obtains competitive samples in terms of perceptual quality with improved temporal consistency.


Automatic Conversational Helpdesk Solution using Seq2Seq and Slot-filling Models

Helpdesk is a key component of any large IT organization, where users can log a ticket about any issue they face related to IT infrastructure, administrative services, human resource services, etc. Normally, users have to assign appropriate set of labels to a ticket so that it could be routed to right domain expert who can help resolve the issue. In practice, the number of labels are very large and organized in form of a tree. It is non-trivial to describe the issue completely and attach appropriate labels unless one knows the cause of the problem and the related labels. Sometimes domain experts discuss the issue with the users and change the ticket labels accordingly, without modifying the ticket description. This results in inconsistent and badly labeled data, making it hard for supervised algorithms to learn from. In this paper, we propose a novel approach of creating a conversational helpdesk system, which will ask relevant questions to the user, for identification of the right category and will then raise a ticket on users' behalf. We use attention based seq2seq model to assign the hierarchical categories to tickets. We use a slot filling model to help us decide what questions to ask to the user, if the top-k model predictions are not consistent. We also present a novel approach to generate training data for the slot filling model automatically based on attention in the hierarchical classification model. We demonstrate via a simulated user that the proposed approach can give us a significant gain in accuracy on ticket-data without asking too many questions to users. Finally, we also show that our seq2seq model is as versatile as other approaches on publicly available datasets, as state of the art approaches.


Unequal Wilkinson Power Divider With Reduced Arm Length For Size Miniaturization

This paper presents a compact unequal Wilkinson power divider (WPD) with a reduced arm length. The unequal power divider consists of four arms and the isolation elements, including a compensating capacitor and isolation resistor. Compared with the conventional WPD, the electric length θ of the arms between the input port and the isolation elements is much shorter, and thus, compact size can be obtained. A theoretical analysis is carried out, and design equations are obtained. In addition, a study is carried out to characterize the isolation bandwidth and output port return loss bandwidth with different values of θ. For validation, two power dividers operating at 1.5 GHz are implemented with various size and isolation bandwidths. Around 50% size reduction and good performance are obtained.


Analysis of a laboratory scale three-phase FC-TCR-based static VAr compensator

This paper presents the design and steady-state analysis of a Fixed Capacitor-Thyristor Controlled Reactor (FC-TCR)-based Static VAr Compensator (SVC). Reactive power compensation is demonstrated through the fundamental frequency analysis of the samples acquired from the designed system. The performance of the SVC in the presence of line reactance is also discussed. National Instrument (NI) based data acquisition system is used to perform the steady-state analysis. Besides, a few transient responses are also captured using the data acquisition system.


New clock-gating techniques for low-power flip-flops

Two novel low power flip-flops are presented in the paper. Proposed flip-flops use new gating techniques that reduce power dissipation deactivating the clock signal. Presented circuits overcome the clock duty-cycle limitation of previously reported gated flip-flops.
Circuit simulations with the inclusion of parasitics show that sensible power dissipation reduction is possible if input signal has reduced switching activity. A 16-bit counter is presented as a simple low power application.


Bone-Conduction-Based Brain Computer Interface Paradigm -- EEG Signal Processing, Feature Extraction and Classification

The paper presents a novel bone-conduction based brain-computer interface paradigm. Four sub-threshold acoustic frequency stimulus patterns are presented to the subjects in an oddball paradigm allowing for "aha-responses" generation to the attended targets. This allows for successful implementation of the bone-conduction based brain-computer interface (BCI) paradigm. The concept is confirmed with seven subjects in online bone-conducted auditory Morse-code patterns spelling BCI paradigm. We report also brain electrophysiological signal processing and classification steps taken to achieve the successful BCI paradigm. We also present a finding of the response latency variability in a function of stimulus difficulty.


Smart Car Parking System Solution for the Internet of Things in Smart Cities

The Internet of Things (IoT) is able to connect billions of devices and services at anytime in any place, with various applications. Recently, the IoT became an emerging technology. One of the most significant current research discussion topics on the IoT is about the smart car parking. A modern urban city has over a million of cars on its roads but it does not have enough parking space. Moreover, most of the contemporary researchers propose management of the data on cloud. However, this method may be considered as an issue since the raw data is sent promptly from distributed sensors to the parking area via cloud and then received back after it is processed. This is considered as an expensive technique in terms of the data transmission as well as the energy cost and consumption. While the majority of proposed solutions address the problem of finding unoccupied parking space and ignore some other critical issues such as information about the nearest car parking and the roads traffic congestion, this paper goes beyond and proposes the alternative method. The paper proposes a smart car parking system that will assist users to solve the issue of finding a parking space and to minimise the time spent in searching for the nearest available car park. In addition, it provides users with roads traffic congestion status. Moreover, the proposed system collects the raw data locally and extracts features by applying data filtering and fusion techniques to reduce the transmitted data over the network. After that, the transformed data is sent to the cloud for processing and evaluating by using machine learning algorithms.


CrowdDB: answering queries with crowdsourcing

Some queries cannot be answered by machines only. Processing such queries requires human input for providing information that is missing from the database, for performing computationally difficult functions, and for matching, ranking, or aggregating results based on fuzzy criteria. CrowdDB uses human input via crowdsourcing to process queries that neither database systems nor search engines can adequately answer. It uses SQL both as a language for posing complex queries and as a way to model data. While CrowdDB leverages many aspects of traditional database systems, there are also important differences. Conceptually, a major change is that the traditional closed-world assumption for query processing does not hold for human input. From an implementation perspective, human-oriented query operators are needed to solicit, integrate and cleanse crowdsourced data. Furthermore, performance and cost depend on a number of new factors including worker affinity, training, fatigue, motivation and location. We describe the design of CrowdDB, report on an initial set of experiments using Amazon Mechanical Turk, and outline important avenues for future work in the development of crowdsourced query processing systems.


AIDE: Fast and Communication Efficient Distributed Optimization

In this paper, we present two new communication-efficient methods for distributed minimization of an average of functions. The first algorithm is an inexact variant of the DANE algorithm [20] that allows any local algorithm to return an approximate solution to a local subproblem. We show that such a strategy does not affect the theoretical guarantees of DANE significantly. In fact, our approach can be viewed as a robustification strategy since the method is substantially better behaved than DANE on data partition arising in practice. It is well known that DANE algorithm does not match the communication complexity lower bounds. To bridge this gap, we propose an accelerated variant of the first method, called AIDE, that not only matches the communication lower bounds but can also be implemented using a purely first-order oracle. Our empirical results show that AIDE is superior to other communication efficient algorithms in settings that naturally arise in machine learning applications.


Data Distillation: Towards Omni-Supervised Learning

We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data. Omni-supervised learning is lower-bounded by performance on existing labeled datasets, offering the potential to surpass state-of-the-art fully supervised methods. To exploit the omni-supervised setting, we propose data distillation, a method that ensembles predictions from multiple transformations of unlabeled data, using a single model, to automatically generate new training annotations. We argue that visual recognition models have recently become accurate enough that it is now possible to apply classic ideas about self-training to challenging real-world data. Our experimental results show that in the cases of human keypoint detection and general object detection, state-of-the-art models trained with data distillation surpass the performance of using labeled data from the COCO dataset alone.


Autonomous lift of a cable-suspended load by an unmanned aerial robot

In this paper, we address the problem of lifting from the ground a cable-suspended load by a quadrotor aerial vehicle. Furthermore, we consider that the mass of the load is unknown. The lift maneuver is a critical step before proceeding with the transportation of a given cargo. However, it has received little attention in the literature so far. To deal with this problem, we break down the lift maneuver into simpler modes which represent the dynamics of the quadrotor-load system at particular operating regimes. From this decomposition, we obtain a series of waypoints that the aerial vehicle has to reach to accomplish the task. We combine geometric control with a least-squares estimation method to design an adaptive controller that follows a prescribed trajectory planned based on the waypoints. The effectiveness of the proposed control scheme is demonstrated by numerical simulations.


Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces

Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-label few- and zero-shot label prediction is mostly unexplored on datasets with large label spaces, especially for text classification. In this paper, we perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III. For few-shot labels we achieve improvements of 6.2% and 4.8% in R@10 for MIMIC II and MIMIC III, respectively, over prior efforts; the corresponding R@10 improvements for zero-shot labels are 17.3% and 19%.


Decentralized control: An overview

The paper reviews the past and present results in the area of decentralized control of large-scale complex systems. An emphasis is laid on decentralization, decomposition, and robustness. These methodologies serve as effective tools to overcome specific difficulties arising in largescale complex systems such as high dimensionality, information structure constraints, uncertainty, and delays. Several prospective topics for future research are introduced in this contents. The overview is focused on recent decomposition approaches in interconnected dynamic systems due to their potential in providing the extension of decentralized control into networked control systems. # 2008 Elsevier Ltd. All rights reserved.


LexIt: A Computational Resource on Italian Argument Structure

The aim of this paper is to introduce LexIt, a computational framework for the automatic acquisition and exploration of distributional information about Italian verbs, nouns and adjectives, freely available through a web interface at the address http://sesia.humnet.unipi.it/lexit. LexIt is the first large-scale resource for Italian in which subcategorization and semantic selection properties are characterized fully on distributional ground: in the paper we describe both the process of data extraction and the evaluation of the subcategorization frames extracted with LexIt.


A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

Spatial Databases Require to detect knowledge from great amount of data Need to handle with arbitrary shape Requirements of Clustering in Data Mining • Scalability • Dealing with different types of attributes • Discovery of Clusters with arbitrary shape • Minimal requirements for domain knowledge to determine input parameters • Able to deal with noise and outliers • Insensitive to the order of input data • High dimensionality of data • Interpretability and usability Introduction(cont..)


Using grid for accelerating density-based clustering

Clustering analysis is a primary method for data mining. The ever increasing volumes of data in different applications forces clustering algorithms to cope with it. DBSCAN is a well-known algorithm for density-based clustering. It is both effective so it can detect arbitrary shaped clusters of dense regions and efficient especially in existence of spatial indexes to perform the neighborhood queries efficiently. In this paper we introduce a new algorithm GriDBSCAN to enhance the performance of DBSCAN using grid partitioning and merging, yielding a high performance with the advantage of high degree of parallelism. We verified the correctness of the algorithm theoretically and experimentally, studied the performance theoretically and using experiments on both real and synthetic data. It proved to run much faster than original DBSCAN. We compared the algorithm with a similar algorithm, EnhancedDBSCAN, which is also an enhancement to DBSCAN using partitioning. Experiments showed the new algorithm's superiority in performance and degree of parallelism.


Active Learning Literature Survey

The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer labeled training instances if it is allowed to choose the data from which is learns. An active learner may ask queries in the form of unlabeled instances to be labeled by an oracle (e.g., a human annotator). Active learning is well-motivated in many modern machine learning problems, where unlabeled data may be abundant but labels are difficult, time-consuming, or expensive to obtain. This report provides a general introduction to active learning and a survey of the literature. This includes a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date. An analysis of the empirical and theoretical evidence for active learning, a summary of several problem setting variants, and a discussion of related topics in machine learning research are also presented.


An improved sampling-based DBSCAN for large spatial databases

Spatial data clustering is one of the important data mining techniques for extracting knowledge from large amount of spatial data collected in various applications, such as remote sensing, GIS, computer cartography, environmental assessment and planning, etc. Several useful and popular spatial data clustering algorithms have been proposed in the past decade. DBSCAN is one of them, which can discover clusters of any arbitrary shape and can handle the noise points effectively. However, DBSCAN requires large volume of memory support because it operates on the entire database. This paper presents an improved sampling-based DBSCAN which can cluster large-scale spatial databases effectively. Experimental results included to establish that the proposed sampling-based DBSCAN outperforms DBSCAN as well as its other counterparts, in terms of execution time, without losing the quality of clustering.


Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications

Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a clustering algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimensionality. It generates cluster descriptions in the form of DNF expressions that are minimized for ease of comprehension. It produces identical results irrespective of the order in which input records are presented and does not presume any specific mathematical form for data distribution. Through experiments, we show that CLIQUE efficiently finds accurate cluster in large high dimensional datasets.


An improved NSGA-III algorithm for feature selection used in intrusion detection

Feature selection can improve classification accuracy and decrease the computational complexity of classification. Data features in intrusion detection systems (IDS) always present the problem of imbalanced classification in which some classifications only have a few instances while others have many instances. This imbalance can obviously limit classification efficiency, but few effort s have been made to address it. In this paper, a scheme for the many-objective problem is proposed for feature selection in IDS, which uses two strategies, namely, a special domination method and a predefined multiple targeted search, for population evolution. It can differentiate traffic not only between normal and abnormal but also by abnormality type. Based on our scheme, NSGA-III is used to obtain an adequate feature subset with good performance. An improved many-objective optimization algorithm (I-NSGA-III) is further proposed using a novel niche preservation procedure. It consists of a bias-selection process that selects the individual with the fewest selected features and a fit-selection process that selects the individual with the maximum sum weight of its objectives. Experimental results show that I-NSGA-III can alleviate the imbalance problem with higher classification accuracy for classes having fewer instances. Moreover, it can achieve both higher classification accuracy and lower computational complexity. © 2016 Published by Elsevier B.V.


A Low Radar Cross Section and Low Profile Antenna Co-Designed With Absorbent Frequency Selective Radome

A low radar cross section (RCS) and low profile antenna co-designed with absorbent frequency selective radome (AFSR) is investigated. A pair of circular slot resonators is embedded on surface of the AFSR to realize a transmission window in the vertical polarization, while the wide absorption band is still maintained in the horizontal polarization. When a patch antenna is etched within the AFSR, where the metal grounds of the patch antenna and AFSR are co-used, the co-designed antenna with low RCS and low profile is thus realized. For demonstration, an AFSR is designed with its transmission window has a minimal insertion loss of 0.45 dB at 8.9 GHz, and two separate absorption bands (a lower absorption band from 4.8 to 7.5 GHz and an upper absorption band from 10 to 13 GHz) in the vertical polarization, a wide absorption band (from 4.5 to 12.5 GHz) in the horizontal polarization. A patch antenna etched within the AFSR is optimized to operate at 8.9 GHz, then it is simulated and fabricated. The measured results demonstrate that the proposed antenna not only has good radiation patterns, but also obtains significant RCS reduction.


Hierarchical Disentangled Representations

Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. A number of recent efforts have focused on learning representations that disentangle statistically independent axes of variation, often by introducing suitable modifications of the objective function. We synthesize this growing body of literature by formulating a generalization of the evidence lower bound that explicitly represents the trade-offs between sparsity of the latent code, bijectivity of representations, and coverage of the support of the empirical data distribution. Our objective is also suitable to learning hierarchical representations that disentangle blocks of variables whilst allowing for some degree of correlations within blocks. Experiments on a range of datasets demonstrate that learned representations contain interpretable features, are able to learn discrete attributes, and generalize to unseen combinations of factors.


Stochastic Models , Estimation and Control


A lightweight method for virtual machine introspection

A method for the introspection of virtual machines is proposed. The main distinctive feature of this method is that it makes it possible to obtain information about the system operation using the minimum knowledge about its internal organization. The proposed approach uses rarely changing parts of the application binary interface, such as identifiers and parameters of system calls, calling conventions, and the formats of executable files. The lightweight property of the introspection method is due to the minimization of the knowledge about the system and by its high performance. The introspection infrastructure is based on the QEMU emulator, version 2.8. Presently, monitoring the file operations, processes, and API function calls are implemented. The available introspection tools (RTKDSM, Panda, and DECAF) get data for the analysis using kernel structures. All the data obtained (addresses of structures, etc.) is written to special profiles. Since the addresses and offsets strongly depend not only on the version of the operating system but also on the parameters of its assembly, these tools have to store a large number of profiles. We propose to use parts of the application binary interface because they are rarely modified and it is often possible to use one profile for a family of OSs. The main idea underlying the proposed method is to intercept the system and library function calls and read parameters and returned values. The processor provides special instructions for calling system and user defined functions. The capabilities of QEMU are extended by an instrumentation mechanism to enable one to track each executed instruction and find the instructions of interest among them. When a system call occurs, the control is passed to the system call detector that checks the number of the call and decides to which module the job should be forwarded. In the case of an API function call, the situation is similar, but the API function detector checks the function address. An introspection tool consisting of a set of modules is developed. These modules are dynamic libraries that are plugged in QEMU. The modules can interact by exchanging data.


International experiences with the Hospital Anxiety and Depression Scale--a review of validation data and clinical results.

More than 200 published studies from most medical settings worldwide have reported experiences with the Hospital Anxiety and Depression Scale (HADS) which was specifically developed by Zigmond and Snaith for use with physically ill patients. Although introduced in 1983, there is still no comprehensive documentation of its psychometric properties. The present review summarizes available data on reliability and validity and gives an overview of clinical studies conducted with this instrument and their most important findings. The HADS gives clinically meaningful results as a psychological screening tool, in clinical group comparisons and in correlational studies with several aspects of disease and quality of life. It is sensitive to changes both during the course of diseases and in response to psychotherapeutic and psychopharmacological intervention. Finally, HADS scores predict psychosocial and possibly also physical outcome.


Addressing the Winograd Schema Challenge as a Sequence Ranking Task

The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.


Inter-AS traffic engineering with SDN

Egress selection in an Internet Service Provider (ISP) is the process of selecting an egress router to route interdomain traffic across the ISP such that a traffic engineering objective is achieved. In traditional ISP networks, traffic through the ISP is carried through the network and exits via an egress that is closest to the source in an attempt to minimize network resources for transit traffic. This exit strategy is known as Hot Potato Routing (HPR). The emerging field of Software-Defined Networking (SDN) has opened up many possibilities and promised to bring new flexibility to the rigid traditional paradigm of networking. In an ISP network, however, completely replacing legacy network devices with SDN nodes is neither simple nor straightforward. This has led to the idea of incremental and selective deployment of SDN nodes in an ISP network. Such a hybrid network gives us control over traffic flows that pass through the SDN nodes without requiring extensive changes to an existing ISP network. In this paper, we look at the problem of choosing an optimal set of egress routers to route inter-domain transit traffic in a hybrid SDN network such that the maximum link utilization of the egress links is minimized. We formulate the optimization problem, show that it is related to the makespan scheduling problem of unrelated parallel machines, and propose heuristics to solve it. We perform simulations to evaluate our heuristic on a real ISP topology and show that even with a small number of SDN nodes in the network, the maximum link utilization on the egress links can be lower than that of traditional HPR.


Graption: A graph-based P2P traffic classification framework for the internet backbone

1 The authors thank CAIDA for providing this set of traffic traces. Additional information for these traces can be found in the DatCat, Internet Measurement Data Catalog [8], indexed under the label ‘‘PAIX’’. 1910 M. Iliofotou et al. / Computer Networks 55 (2011) 1909–1920 behavior. Flow-level and payload-based classification methods require per application training and will thus not detect P2P traffic from emerging protocols. Behavioral-host-based approaches such as BLINC [25] can detect traffic from new protocols [25], but have weak performance when applied at the backbone [26]. In addition, most tools including BLINC [25] require fine-tuning and careful selection of parameters [26]. We discuss the limitations of previous methods in more detail in Section 4. In this paper, we use the network-wide behavior of an application to assist in classifying its traffic. To model this behavior, we use graphs where each node is an IP address, and each edge represents a type of interaction between two nodes. We use the term Traffic Dispersion Graph or TDG to refer to such a graph [19]. Intuitively, with TDGs we enable the detection of network-wide behavior (e.g., highly connected graphs) that is common among P2P applications and different from other traffic (e.g., Web). While we recognize that some previous efforts [6,9] have used graphs to detect worm activity, they have not explored the full capabilities of TDGs for application classification. This paper is an extension of a workshop paper [18] and the differences will be clarified in the related work section (Section 4). We propose a classification framework, dubbed Graption (Graph-based classification), as a systematic way to combine network-wide behavior and flow-level characteristics of network applications. Graption first groups flows using flow-level features, in an unsupervised and agnostic way, i.e., without using application-specific knowledge. It then uses TDGs to classify each group of flows. As a proof of concept, we instantiate our framework and develop a P2P detection method, which we call Graption-P2P. Compared to other methods (e.g., BLINC [25]), Graption-P2P is easier to configure and requires fewer parameters. The highlights of our work can be summarized in the following points: Distinguishing between P2P and client–server TDGs. We use real-world backbone traces and derive graph theoretic metrics that can distinguish between the TDGs formed by client–server (e.g., Web) and P2P (e.g., eDonkey) applications (Section 2.2). Practical considerations for TDGs. We show that even a single backbone link contains enough information to generate TDGs that can be used to classify traffic. In addition, TDGs of the same application seem fairly consistent across time (Section 2.3). High P2P classification accuracy. Our framework instantiation (Graption-P2P) classifies 90% of P2P traffic with 95% accuracy when applied at the backbone. Such traces are particularly challenging for other methods (Section 3.2.2). Comparison with a behavioral-host-based method. Graption-P2P performs better than BLINC [25] in P2P identification at the backbone. For example, Graption-P2P identifies 95% of BitTorrent traffic while BLINC identifies only 25% (Section 3.3). Identifying the unknown. Using Graption, we identified a P2P overlay of the Slapper worm. The TDG of Slapper was never used to train our classifier. This is a promising result showing that our approach can be used to detect both known and unknown P2P applications (Section 3.4). The rest of the paper is organized as follows. In Section 2 we define TDGs, and identify TDG-based metrics that differentiate between applications. In Section 3 we present the Graption framework and our instantiation, GraptionP2P. In Section 5 we discuss various practical issues. In Section 4 we discuss related work. Finally, in Section 6 we conclude the paper. 2. Studying the TDGs of P2P applications 2.1. Traffic dispersion graphs (TDGs) Definition. Throughout this paper, we assume that packets can be grouped into flows using the standard 5-tuple {srcIP, srcPort, dstIP, dstPort, protocol}. Given a group of flows S, collected over a fixed-length time interval, we define the corresponding TDG to be a directed graph G(V,E), where the set of nodes V corresponds to the set of IP addresses in S, and there is a link (u,v) 2 E from u to v if there is a flow f 2 S between them. In this paper, we consider bidirectional flows. We define a TCP flow to start on the first packet with the SYN-flag set and the ACK-flag not set, so that the initiator and the recipient of the flow are defined for the purposes of direction. For UDP flows, direction is decided upon the first packet of the flow. Visualization examples. In Fig. 1, we show TDG examples from two different applications. In order to motivate the discussion in the rest of the paper, we show the contrast between a P2P and a client–server TDG. From the figure we see that P2P traffic forms more connected and more dense graphs compared to client–server TDGs. In Section 2.2, we show how we can translate the visual intuition of Fig. 1 into quantitative measures that can be used to classify TDGs that correspond to different applications. Data set. To study TDGs, we use three backbone traces from a Tier-1 ISP and the Abilene (Internet2) network. These traces are summarized in Table 1. All data are IP anonymized and contain traffic from both directions of the link. The TR-PAY1 and TR-PAY2 traces were collected from an OC48 link of a commercial US Tier-1 ISP at the Palo Alto Internet eXchange (PAIX). To the best of our knowledge, these are the most recent backbone traces with payload that are available to researchers by CAIDA [5]. The TR-ABIL trace is a publicly available data set collected from the Abilene (Internet2) academic network connecting Indianapolis with Kansas City. The Abilene trace consists of five randomly selected five-minute samples taken every day for one month, and covers both day and night hours as well as weekdays and weekends. Extractingground truth. We used a Payload-based Classifier (PC) to establish the ground truth of flows for the 1 2 2 1 0 3 1 9 4 3 3 4 5 4 8 1 4 8 2 9 3 4 1 4 6 1 1 0 7 1 1 5 5 5 6 6 3 4 7 4 5 1 0 5 8 1 1 6 9 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 8 6 3 1 0 3 2 4 4 1 8 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 7 7 1 1 0 9 6 2 5 2 6 4 1 2 4 6 3 2 7 2 8 2 9 3 0 3 1 3 2 3 3 3 4 3 5 3 6 1 0 1 3 7 3 8 7 3 1 6 6 2 0 6 2 3 2 3 4 2 3 4 5 4 0 9 4 8 6 5 3 0 6 3 0 6 3 5 7 3 4 7 4 8 7 4 9 7 9 4 7 9 6 8 5 2 8 5 5 9 1 6 1 0 7 0 1 1 3 4 1 2 4 2 3 9 4 0 8 1 8 5 1 1 6 1 2 3 1 3 0 1 3 2 1 3 5 1 3 9 1 4 8 1 6 1 1 6 4 1 6 5 1 8 6 1 9 1 2 2 5 2 2 7 2 3 5 2 4 3 2 5 7 2 6 8 2 9 2 3 0 3 3 1 3 3 1 6 3 4 9 3 5 0 3 6 2 3 7 6 3 8 3 4 0 3 4 1 3 4 1 4 4 4 5 4 5 5 4 6 1 4 7 7 4 9 3 5 1 7 5 2 4 5 3 7 5 7 0 5 9 1 6 1 4 6 1 9 6 4 0 6 5 7 6 6 6 6 6 8 6 7 4 7 3 1 7 3 6 7 3 7 8 0 0 8 0 7 8 0 8 8 3 1 8 3 9 8 4 1 8 4 4 8 5 6 8 7 0 8 7 6 8 8 8 8 9 2 8 9 8 9 0 0 9 0 8 9 1 4 9 1 5 9 2 5 9 7 4 1 0 0 0 1 0 0 5 1 0 0 9 1 0 1 8 1 0 2 2 1 0 2 7 1 0 3 5 1 0 5 6 1 0 9 2 1 0 9 9 1 1 0 3 1 1 0 5 1 1 0 6 1 1 2 8 1 1 3 3 1 1 6 5 1 1 6 6 1 1 7 0 1 1 8 3 1 1 8 6 1 2 0 9 1 2 5 6 1 2 6 7 1 2 7 2 1 2 8 8 1 2 8 9 1 2 9 1 1 3 3 4 1 3 5 2 1 3 7 2 4 1 4 2 3 0 9 3 2 0 3 5 6 6 0 2 6 3 2 7 1 4 8 7 2 1 0 6 5 4 3 4 4 4 5 4 6 1 1 5 4 7 4 8 2 5 6 7 7 2 4 9 5 0 1 3 7 1 6 0 1 8 1 5 4 5 7 1 5 7 4 1 1 0 0 4 5 1 5 2 5 3 5 4 1 4 2 1 0 0 7 5 5 5 6 5 7 2 9 1 7 6 0 8 8 3 5 8 5 9 6 0 6 1 2 7 7 4 3 2 6 2 6 3 2 9 9 5 7 2 7 3 5 1 3 7 0 6 4 6 5 9 0 2 6 1 2 8 2 3 4 1 5 8 2 6 3 3 1 3 6 4 6 6 6 7 9 2 9 1 0 8 0 1 0 8 2 6 8 7 5 5 1 1 3 5 1 2 2 8 6 9 7 0 7 1 2 7 0 7 2 7 4 7 5 7 6 7 7 7 8 7 9 8 0 8 2 8 3 7 2 3 8 4 8 7 3 6 1 8 8 8 9 1 7 5 9 1 9 2


Design and development of a low cost EMG signal acquisition system using surface EMG electrode

Electromyogram or EMG signal is a very small signal; it requires a system to enhance for display purpose or for further analysis process. This paper presents the development of low cost physiotherapy EMG signal acquisition system with two channel input. In the acquisition system, both input signals are amplified with a differential amplifier and undergo signal pre-processing to obtain the linear envelope of EMG signal. Obtained EMG signal is then digitalized and sent to the computer to be plotted.


Security and Privacy in the Internet of Vehicles

Internet of Vehicles (IoV) is a typical application of Internet of things in the field of transportation, which aims at achieving an integrated intelligent transportation system to enhance traffics, to avoid accidents, to ensure road safety, and to improve driving experiences. Due to its characteristics of dynamic topological structures, huge network scale, non-uniform distribution of nodes, and mobile limitation, IoV systems face various types of attacks, such as authentication and identification attacks, availability attacks, confidentiality attacks, routing attacks, data authenticity attacks and etc., which result in several challenging requirements in security and privacy. Many security scientists made numerous efforts to ensure the security and privacy for the Internet of Vehicles in recent years. This paper aims to review the advances on issues of security and privacy in IoV, including security and privacy requirements, attack types, and the relevant solutions, and discuss challenges and future trends in this area.


The meaning of compassion fatigue to student nurses: an interpretive phenomenological study

Background: Compassion fatigue is a form of occupational stress which occurs when individuals are exposed to suffering and trauma on an ongoing basis. The purpose of this study was to explore the experiences of compassion fatigue among student nurses following their first clinical placement in a UK health care setting during 2015. Methods: The aim of this study was to explore students’ thoughts and feelings about compassion fatigue using reflective poems as a source of data. An interpretive phenomenological approach was taken using a purposeful sampling strategy which aimed to explore in depth meaning of the concept as experienced by the students. Results: From this study it is clear that students experience compassion fatigue and this has a psychological effect on their wellbeing and ability to learn in the clinical practice setting. Reflective poetry writing enabled articulation of feelings which were at times negative and linked to the student’s status as a novice nurse. Conclusions: Students experience compassion fatigue and educators need to find ways to provide support in both clinical and university settings. Positive practices such as shared reflection and the use of creative teaching methods might be beneficial, to support exploration of feelings, build resilience and effective ways of coping.


A New Framework for Distributed Submodular Maximization

A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. A lot of recent effort has been devoted to developing distributed algorithms for these problems. However, these results suffer from high number of rounds, suboptimal approximation ratios, or both. We develop a framework for bringing existing algorithms in the sequential setting to the distributed setting, achieving near optimal approximation ratios for many settings in only a constant number of MapReduce rounds. Our techniques also give a fast sequential algorithm for non-monotone maximization subject to a matroid constraint.


Image Retrieval using Harris Corners and Histogram of Oriented Gradients

Content based image retrieval is the technique to retrieve similar images from a database that are visually similar to a given query image. It is an active and emerging research field in computer vision. In our proposed system, the Interest points based Histogram of Oriented Gradients (HOG) feature descriptor is used to retrieve the relevant images from the database. The dimensionality of the HOG feature vector is reduced by Principle Component analysis (PCA). To improve the retrieval accuracy of the system the Colour Moments along with HOG feature descriptor are used in this system. The Interest points are detected using the Harris-corner detector in order to extract the image features. The KD-tree is used for matching and indexing the features of the query image with the database images.


HanGuard: SDN-driven protection of smart home WiFi devices from malicious mobile apps

A new development of smart-home systems is to use mobile apps to control IoT devices across a Home Area Network (HAN). As verified in our study, those systems tend to rely on the Wi-Fi router to authenticate other devices. This treatment exposes them to the attack from malicious apps, particularly those running on authorized phones, which the router does not have information to control. Mitigating this threat cannot solely rely on IoT manufacturers, which may need to change the hardware on the devices to support encryption, increasing the cost of the device, or software developers who we need to trust to implement security correctly. In this work, we present a new technique to control the communication between the IoT devices and their apps in a unified, backward-compatible way. Our approach, called HanGuard, does not require any changes to the IoT devices themselves, the IoT apps or the OS of the participating phones. HanGuard uses an SDN-like approach to offer fine-grained protection: each phone runs a non-system userspace Monitor app to identify the party that attempts to access the protected IoT device and inform the router through a control plane of its access decision; the router enforces the decision on the data plane after verifying whether the phone should be allowed to talk to the device. We implemented our design over both Android and iOS (> 95% of mobile OS market share) and a popular router. Our study shows that HanGuard is both efficient and effective in practice.


Digital Beamforming on Receive: Techniques and Optimization Strategies for High-Resolution Wide-Swath SAR Imaging

Synthetic Aperture Radar (SAR) is a well-proven imaging technique for remote sensing of the Earth. However, conventional SAR systems are not capable of fulfilling the increasing demands for improved spatial resolution and wider swath coverage. To overcome these inherent limitations, several innovative techniques have been suggested which employ multiple receive-apertures to gather additional information along the synthetic aperture. These digital beamforming (DBF) on receive techniques are reviewed with particular emphasis on the multi-aperture signal processing in azimuth and a multi-aperture reconstruction algorithm is presented that allows for the unambiguous recovery of the Doppler spectrum. The impact of Doppler aliasing is investigated and an analytic expression for the residual azimuth ambiguities is derived. Further, the influence of the processing on the signal-to-noise ratio (SNR) is analyzed, resulting in a pulse repetition frequency (PRF) dependent factor describing the SNR scaling of the multi-aperture beamforming network. The focus is then turned to a complete high-resolution wide-swath SAR system design example which demonstrates the intricate connection between multi-aperture azimuth processing and the system architecture. In this regard, alternative processing approaches are compared with the multi-aperture reconstruction algorithm. In a next step, optimization strategies are discussed as pattern tapering, prebeamshaping-on-receive, and modified processing algorithms. In this context, the analytic expressions for both the residual ambiguities and the SNR scaling factor are generalized to cascaded beamforming networks. The suggested techniques can moreover be extended in many ways. Examples discussed are a combination with ScanSAR burst mode operation and the transfer to multistatic sparse array configurations.


Effects of domain on measures of semantic relatedness

Measures of semantic relatedness have been used in a variety of applications in information retrieval and language technology, such as measuring document similarity and cohesion of text. Definitions of such measures have ranged from using distance-based calculations over WordNet or other taxonomies to statistical distributional metrics over document collections such as Wikipedia or the Web. Existing measures do not explicitly consider the domain associations of terms when calculating relatedness: This article demonstrates that domain matters. We construct a data set of pairs of terms with associated domain information and extract pairs that are scored nearly identical by a sample of existing semantic-relatedness measures. We show that human judgments reliably score those pairs containing terms from the same domain as significantly more related than cross-domain pairs, even though the semantic-relatedness measures assign the pairs similar scores. We provide further evidence for this result using a machine learning setting by demonstrating that domain is an informative feature when learning a metric. We conclude that existing relatedness measures do not account for domain in the same way or to the same extent as do human judges.


A Lossy Image Codec Based on Index Coding

In this paper we propose a new lossy image codec based on index coding. Both J. Shapiro’s embedded zerotree wavelet algorithm, and A. Said and W. A. Pearlman’s codetree algorithm (which is the state-of-the-art method today) use spatial orientation tree structures to implicitly locate the significant wavelet transform coefficients. Here a direct approach to find the positions of these significant coefficients is presented. The new algorithm combines the discrete wavelet transform, differential coding, variable-length coding of integers, ordered bit plane transmission, and adaptive arithmetic coding. The encoding can be stopped at any point, which allows a target rate or distortion metric to be met exactly. The bits in the bit stream are generated in the order of importance, yielding a fully embedded code to successively approximate the original image source; thus it’s well suited for progressive image transmission. The decoder can also terminate the decoding at any point, and produce a lower bit rate reconstruction image. Our algorithm is very simple in its form (which will make the encoding and decoding very fast), requires no training of any kind or prior knowledge of image sources, and has a clear geometric structure. The image coding results of it are quite competitive with almost all previous reported image compression algorithms on standard test images. 456 1068.0314/96$5.00


Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation

In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.


Contextual feature based one-class classifier approach for detecting video response spam on YouTube

YouTube is one of the largest video sharing websites (with social networking features) on the Internet. The immense popularity of YouTube, anonymity and low publication barrier has resulted in several forms of misuse and video pollution such as uploading of malicious, copyright violated and spam video or content. YouTube has a popular and commonly used feature called as video response which allows users to post a video response to an uploaded or existing video. Some of the popular videos on YouTube receive thousands of video responses. We observe presence of opportunistic users posting unrelated, promotional, pornographic videos (spam videos posted manually or using automated scripts) as video responses to existing videos. We present a method of mining YouTube to automatically detect video response spam. We formulate the problem of video response spam detection as a one-class classification problem (a recognition task) and divide the problem into three sub-problems: promotional video recognition, pornographic or dirty video recognition and automated script or botnet uploader recognition. We create a sample dataset of target class videos for each of the three sub-problems and identify contextual features (meta-data based or non-content based features) characterizing the target class. Our empirical analysis reveals that certain linguistic features (presence of certain terms in the title or description of the YouTube video), temporal features, popularity based features, time based features can be used to predict the video type. We identify features with discriminatory powers and use it within a one-class classification framework to recognize video response spam. We conduct a series of experiments to validate the proposed approach and present evidences to demonstrate the effectiveness of the proposed solution with more than 80% accuracy.


A Generic Toolkit for the Successful Management of Delphi Studies

This paper presents the case of a non-traditional use of the Delphi method for theory evaluation. On the basis of experience gained through secondary and primary research, a generic decision toolkit for Delphi studies is proposed, comprising of taxonomy of Delphi design choices, a stage model and critical methodological decisions. These research tools will help to increase confidence when adopting the Delphi alternative and allow for a wider and more comprehensive recognition of the method within both scientific and interpretivist studies.


A Developmental Model of Critical Thinking

The critical thinking movement, it is suggested, has much to gain from conceptualizing its subject matter in a developmental framework. Most instructional programs designed to teach critical thinking do not draw on contemporary empirical research in cognitive development as a potential resource. The developmental model of critical thinking outlined here derives from contemporary empirical research on directions and processes of intellectual development in children and adolescents. It identifies three forms of second-order cognition (meta-knowing)--metacognitive, metastrategic, and epistemological--that constitute an essential part of what develops cognitively to make critical thinking possible.


Extension of Soft-Switching Region of Dual-Active-Bridge Converter by a Tunable Resonant Tank

Hard-switching-induced switching loss can contribute significantly to the power loss of an isolated bidirectional dual-active-bridge (DAB) dc–dc converter operating at high frequency. An <inline-formula> <tex-math notation="LaTeX">$LC$</tex-math></inline-formula>-type series resonant DAB converter based on a switch-controlled-inductor (SCI) is proposed to mitigate the loss arising from hard switching under wide-range variations in output voltage and current. Zero-voltage switching is achieved at the primary side (high voltage), while at the secondary side (low voltage), zero-current switching is preferred to reduce excessive ringing due to circulating current and switching loss. In order to achieve reduced conduction loss, a nominal operating point is chosen where the root-mean-square resonant tank current is the minimum. To validate the proposed topology and modulation scheme, an <inline-formula><tex-math notation="LaTeX">$LC$</tex-math></inline-formula>-type series resonant DAB converter based on SCI operating at 100 kHz is designed to interface a 400-V dc bus to a supercapacitor-based energy storage. Simulation and experimental results validate the effectiveness of the proposed topology for charging/discharging a supercapacitor with an output voltage variation of between 10 and 48 V and a maximum rated power of 480 W. A maximum efficiency of 94.6% is achieved using the proposed topology and modulation scheme.


WordNet Atlas : a web application for visualizing WordNet as a zoomable map

The English WordNet is a lexical database containing more than 206,900 wordconcept pairs and more than 377,500 semantic and lexical links. Trying to visualize them all at once as a node-link diagram results in a representation which can be too big and complex for the user to grasp, and which cannot be easily processed by current web technologies. We propose a visualization technique based on the concept of semantic zooming, usually found in popular web map applications. This technique makes it feasible to handle large and complex graphs, and display them in an interactive representation. By zooming, it is possible to switch from an overview visualization, quickly and clearly presenting the global characteristics of the structure, to a detailed one, displaying a classic node-link diagram. WordNet Atlas is a web application that leverages this method to aid the user in the exploration of the WordNet data set, from its global taxonomy structure to the detail of words and synonym sets.


Reduced-Order Model and Stability Analysis of Low-Voltage DC Microgrid

Depleting fossil fuels, increasing energy demand, and need for high-reliability power supply motivate the use of dc microgrids. This paper analyzes the stability of low-voltage dc microgrid systems. Sources are controlled using a droop-based decentralized controller. Various components of the system have been modeled. A linearized system model is derived using small-signal approximation. The stability of the system is analyzed by identifying the eigenvalues of the system matrix. The sufficiency condition for stable operation of the system is derived. It provides upper bound on droop constants and is useful during planning and designing of dc microgrids. Furthermore, the sensitivity of system poles to variation in cable resistance and inductance is identified. It is proved that the poles move further inside the negative real plane with a decrease in inductance or an increase in resistance. The method proposed in this paper is applicable to any interconnecting structure of sources and loads. The results obtained by analysis are verified by detailed simulation study. Root locus plots are included to confirm the movement of system poles. The viability of the model is confirmed by experimental results from a scaled-down laboratory prototype of a dc microgrid developed for the purpose.


Hierarchical Control of Droop-Controlled AC and DC Microgrids—A General Approach Toward Standardization

AC and dc microgrids (MGs) are key elements for integrating renewable and distributed energy resources as well as distributed energy-storage systems. In the last several years, efforts toward the standardization of these MGs have been made. In this sense, this paper presents the hierarchical control derived from ISA-95 and electrical dispatching standards to endow smartness and flexibility to MGs. The hierarchical control proposed consists of three levels: 1) The primary control is based on the droop method, including an output-impedance virtual loop; 2) the secondary control allows the restoration of the deviations produced by the primary control; and 3) the tertiary control manages the power flow between the MG and the external electrical distribution system. Results from a hierarchical-controlled MG are provided to show the feasibility of the proposed approach.


Secondary Load-Frequency Control for MicroGrids in Islanded Operation

The objective of this paper is to present novel control strategies for MicroGrid operation, especially in islanded mode. The control strategies involve mainly the coordination of secondary load-frequency control by a MicroGrid Central Controller that heads a hierarchical control system able to assure stable and secure operation when the islanding of the MicroGrid occurs and in load-following situations in islanded mode.


Decentralized Control for Parallel Operation of Distributed Generation Inverters Using Resistive Output Impedance

In this paper, a novel wireless load-sharing controller for islanding parallel inverters in an ac-distributed system is proposed. This paper explores the resistive output impedance of the parallel-connected inverters in an island microgrid. The control loops are devised and analyzed, taking into account the special nature of a low-voltage microgrid, in which the line impedance is mainly resistive and the distance between the inverters makes the control intercommunication between them difficult. In contrast with the conventional droop-control method, the proposed controller uses resistive output impedance, and as a result, a different control law is obtained. The controller is implemented by using a digital signal processor board, which only uses local measurements of the unit, thus increasing the modularity, reliability, and flexibility of the distributed system. Experimental results are provided from two 6-kVA inverters connected in parallel, showing the features of the proposed wireless control


Eval all, trust a few, do wrong to none: Comparing sentence generation models

In this paper, we study recent neural generative models for text generation related to variational autoencoders. These models employ various techniques to match the posterior and prior distributions, which is important to ensure a high sample quality and a low reconstruction error. In our study, we follow a rigorous evaluation protocol using a large set of previously used and novel automatic metrics and human evaluation of both generated samples and reconstructions. We hope that it will become the new evaluation standard when comparing neural generative models for text.


DEEP LEARNING AND INTELLIGENT AUDIO MIXING

Mixing multitrack audio is a crucial part of music production. With recent advances in machine learning techniques such as deep learning, it is of great importance to conduct research on the applications of these methods in the field of automatic mixing. In this paper, we present a survey of intelligent audio mixing systems and their recent incorporation of deep neural networks. We propose to the community a research trajectory in the field of deep learning applied to intelligent music production systems. We conclude with a proof of concept based on stem audio mixing as a contentbased transformation using a deep autoencoder.


Coordination and Geometric Optimization via Distributed Dynamical Systems

Emerging applications for networked and cooperative robots motivate the study of motion coordination for groups of agents. For example, it is envisioned that groups of agents will perform a variety of useful tasks including surveillance, exploration, and environmental monitoring. This paper deals with basic interactions among mobile agents such as “move away from the closest other agent” or “move toward the furthest vertex of your own Voronoi polygon.” These simple interactions amount to distributed dynamical systems because their implementation requires only minimal information about neighboring agents. We characterize the close relationship between these distributed dynamical systems and the disk-covering and sphere-packing cost functions from geometric optimization. Our main results are: (i) we characterize the smoothness properties of these geometric cost functions, (ii) we show that the interaction laws are variations of the nonsmooth gradient of the cost functions, and (iii) we establish various asymptotic convergence properties of the laws. The technical approach relies on concepts from computational geometry, nonsmooth analysis, and nonsmooth stability theory.


Online fuzzy c means

Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present an online fuzzy clustering algorithm which can be used to cluster streaming data, as well as very large data sets which might be treated as streaming data. Results on several large volumes of magnetic resonance images show that the new algorithm produces partitions which are very close to what you could get if you clustered all the data at one time. So, the algorithm is an accurate approach for online clustering.


The architecture of subterranean ant nests: beauty and mystery underfoot

Over the 100 million years of their evolution, ants have constructed or occupied nests in a wide range of materials and situations. A large number of ant species excavate nests in the soil, and these subterranean nests have evolved into a wide range of sizes and architectures. On the basis of casts made of such nests, this variation and the patterns that govern it are described. The possible functions of architectural features are discussed, as are the behavioral “rules” through which the nests are created by worker ants.


A Reorder Buffer Design for High Performance Processors

Modern reorder buffers (ROBs) were conceived to improve processor performance by allowing instruction execution out of the original program order and run ahead of sequential instruction code exploiting existing instruction level parallelism (ILP). The ROB is a functional structure of a processor execution engine that supports speculative execution, physical register recycling, and precise exception recovering. Traditionally, the ROB is considered as a monolithic circular buffer with incoming instructions at the tail pointer after the decoding stage and completing instructions at the head pointer after the commitment stage. The latter stage verifies instructions that have been dispatched, issued, executed, and are not completed speculatively. This paper presents a design of distributed reorder buffer microarchitecture by using small structures near building blocks which work together, using the same tail and head pointer values on all structures for synchronization. The reduction of area, and therefore, the reduction of power and delay make this design suitable for both embedded and high performance microprocessors.


Achieving ICS Resilience and Security through Granular Data Flow Management

Modern Industrial Control Systems (ICS) rely on enterprise to plant floor connectivity. Where the size, diversity, and therefore complexity of ICS increase, operational requirements, goals, and challenges defined by users across various sub-systems follow. Recent trends in Information Technology (IT) and Operational Technology (OT) convergence may cause operators to lose a comprehensive understanding of end-to-end data flow requirements. This presents a risk to system security and resilience. Sensors were once solely applied for operational process use, but now act as inputs supporting a diverse set of organisational requirements. If these are not fully understood, incomplete risk assessment, and inappropriate implementation of security controls could occur. In search of a solution, operators may turn to standards and guidelines. This paper reviews popular standards and guidelines, prior to the presentation of a case study and conceptual tool, highlighting the importance of data flows, critical data processing points, and system-to-user relationships. The proposed approach forms a basis for risk assessment and security control implementation, aiding the evolution of ICS security and resilience.


Adversarial Frontier Stitching for Remote Neural Network Watermarking

The state of the art performance of deep learning models comes at a high cost for companies and institutions, due to the tedious data collection and the heavy processing requirements. Recently, Uchida et al. (2017) proposed to watermark convolutional neural networks by embedding information into their weights. While this is a clear progress towards model protection, this technique solely allows for extracting the watermark from a network that one accesses locally and entirely. This is a clear impediment, as leaked models can be re-used privately, and thus not released publicly for ownership inspection. Instead, we aim at allowing the extraction of the watermark from a neural network (or any other machine learning model) that is operated remotely, and available through a service API. To this end, we propose to operate on the model’s action itself, tweaking slightly its decision frontiers so that a set of specific queries convey the desired information. In present paper, we formally introduce the problem and propose a novel zerobit watermarking algorithm that makes use of adversarial model examples (called adversaries for short). While limiting the loss of performance of the protected model, this algorithm allows subsequent extraction of the watermark using only few remote queries. We experiment this approach on the MNIST dataset with three types of neural networks, demonstrating that e.g., watermarking with 100 images incurs a slight accuracy degradation, while being resilient to most removal attacks.


Strokelets: A Learned Multi-scale Representation for Scene Text Recognition

Driven by the wide range of applications, scene text detection and recognition have become active research topics in computer vision. Though extensively studied, localizing and reading text in uncontrolled environments remain extremely challenging, due to various interference factors. In this paper, we propose a novel multi-scale representation for scene text recognition. This representation consists of a set of detectable primitives, termed as strokelets, which capture the essential substructures of characters at different granularities. Strokelets possess four distinctive advantages: (1) Usability: automatically learned from bounding box labels, (2) Robustness: insensitive to interference factors, (3) Generality: applicable to variant languages, and (4) Expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of strokelets and demonstrate the effectiveness of the proposed algorithm for text recognition.


Unsupervised Footwear Impression Analysis and Retrieval from Crime Scene Data

Footwear impressions are one of the most frequently secured types of evidence at crime scenes. For the investigation of crime series they are among the major investigative notes. In this paper, we introduce an unsupervised footwear retrieval algorithm that is able to cope with unconstrained noise conditions and is invariant to rigid transformations. A main challenge for the automated impression analysis is the separation of the actual shoe sole information from the structured background noise. We approach this issue by the analysis of periodic patterns. Given unconstrained noise conditions, the redundancy within periodic patterns makes them the most reliable information source in the image. In this work, we present four main contributions: First, we robustly measure local periodicity by fitting a periodic pattern model to the image. Second, based on the model, we normalize the orientation of the image and compute the window size for a local Fourier transformation. In this way, we avoid distortions of the frequency spectrum through other structures or boundary artefacts. Third, we segment the pattern through robust point-wise classification, making use of the property that the amplitudes of the frequency spectrum are constant for each position in a periodic pattern. Finally, the similarity between footwear impressions is measured by comparing the Fourier representations of the periodic patterns. We demonstrate robustness against severe noise distortions as well as rigid transformations on a database with real crime scene impressions. Moreover, we make our database available to the public, thus enabling standardized benchmarking for the first time.


An approach to online identification of Takagi-Sugeno fuzzy models

An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the TS model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. In this way, the rule-base structure is inherited and up-dated when new data become available. By applying this learning concept to the TS model we arrive at a new type adaptive model called the Evolving Takagi-Sugeno model (ETS). The adaptive nature of these evolving TS models in combination with the highly transparent and compact form of fuzzy rules makes them a promising candidate for online modeling and control of complex processes, competitive to neural networks. The approach has been tested on data from an air-conditioning installation serving a real building. The results illustrate the viability and efficiency of the approach. The proposed concept, however, has significantly wider implications in a number of fields, including adaptive nonlinear control, fault detection and diagnostics, performance analysis, forecasting, knowledge extraction, robotics, behavior modeling.


Automatic speech recognition and speech variability: A review

Major progress is being recorded regularly on both the technology and exploitation of automatic speech recognition (ASR) and spoken language systems. However, there are still technological barriers to flexible solutions and user satisfaction under some circumstances. This is related to several factors, such as the sensitivity to the environment (background noise), or the weak representation of grammatical and semantic knowledge. Current research is also emphasizing deficiencies in dealing with variation naturally present in speech. For instance, the lack of robustness to foreign accents precludes the use by specific populations. Also, some applications, like directory assistance, particularly stress the core recognition technology due to the very high active vocabulary (application perplexity). There are actually many factors affecting the speech realization: regional, sociolinguistic, or related to the environment or the speaker herself. These create a wide range of variations that may not be modeled correctly (speaker, gender, speaking rate, vocal effort, regional accent, speaking style, non-stationarity, etc.), especially when resources for system training are scarce. This paper outlines current advances related to these topics. 2007 Elsevier B.V. All rights reserved.


Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks

Early detection of pulmonary cancer is the most promising way to enhance a patient’s chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional DCNN is presented for the subsequent false positive reduction. Experimental results of the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior detection performance of the proposed approach on nodule detection(average FROC-score of 0.891, ranking the 1st place over all submitted results).


Kalman Particle Filter for lane recognition on rural roads

Despite the availability of lane departure and lane keeping systems for highway assistance, unmarked and winding rural roads still pose challenges to lane recognition systems. To detect an upcoming curve as soon as possible, the viewing range of image-based lane recognition systems has to be extended. This is done by evaluating 3D information obtained from stereo vision or imaging radar in this paper. Both sensors deliver evidence grids as the basis for road course estimation. Besides known Kalman Filter approaches, Particle Filters have recently gained interest since they offer the possibility to employ cues of a road, which can not be described as measurements needed for a Kalman Filter approach. We propose to combine both principles and their benefits in a Kalman Particle Filter. The comparison between the results gained from this recently published filter scheme and the classical approaches using real world data proves the advantages of the Kalman Particle Filter.


A risk taxonomy proposal for software maintenance

There can be no doubt that risk management is an important activity in the software engineering area. One proof of this is the large body of work existing in this area. However, when one takes a closer look at it, one perceives that almost all this work is concerned with risk management for software development projects. The literature on risk management for software maintenance is much scarcer. On the other hand, software maintenance projects do present specificities that imply they offer different risks than development. This suggests that maintenance projects could greatly benefit from better risk management tools. One step in this direction would be to help identifying potential risk factors at the beginning of a maintenance project. For this, we propose taxonomy of possible risks for software management projects. The ontology was created from: i) an extensive survey of risk management literature, to list known risk factors for software development; and, ii) an extensive survey of maintenance literature, to list known problems that may occur during maintenance.


An industrial study on the risk of software changes

Modelling and understanding bugs has been the focus of much of the Software Engineering research today. However, organizations are interested in more than just bugs. In particular, they are more concerned about managing risk, i.e., the likelihood that a code or design change will cause a negative impact on their products and processes, regardless of whether or not it introduces a bug. In this paper, we conduct a year-long study involving more than 450 developers of a large enterprise, spanning more than 60 teams, to better understand risky changes, i.e., changes for which developers believe that additional attention is needed in the form of careful code or design reviewing and/or more testing. Our findings show that different developers and different teams have their own criteria for determining risky changes. Using factors extracted from the changes and the history of the files modified by the changes, we are able to accurately identify risky changes with a recall of more than 67%, and a precision improvement of 87% (using developer specific models) and 37% (using team specific models), over a random model. We find that the number of lines and chunks of code added by the change, the bugginess of the files being changed, the number of bug reports linked to a change and the developer experience are the best indicators of change risk. In addition, we find that when a change has many related changes, the reliability of developers in marking risky changes is negatively affected. Our findings and models are being used today in practice to manage the risk of software projects.


Generative Modeling with Conditional Autoencoders: Building an Integrated Cell

We present a conditional generative model to learn variation in cell and nuclear morphology and the location of subcellular structures from microscopy images. Our model generalizes to a wide range of subcellular localization and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of our approach by producing photorealistic cell images using our generative model. The conditional nature of the model provides the ability to predict the localization of unobserved structures given cell and nuclear morphology.


Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System with Market Indicators in the Indian Context

Stock price movement prediction has been one of the most challenging issues in finance since the time immemorial. Many researchers in past have carried out extensive studies with the intention of investigating the approaches that uncover the hidden information in stock market data. As a result of which, Artificial Intelligence and data mining techniques have come to the forefront because of their ability to map nonlinear data. The study encapsulates market indicators with AI techniques to generate useful extracts to improve decisions under conditions of uncertainty. Three approaches (fundamental model, technical indicators model and hybrid model) have been tested using the standalone and integrated machine learning algorithms viz. SVM, ANN, GA-SVM, and GA-ANN and the results of all the three approaches have been compared in the four above mentioned methods. The core objective of this paper is to identify an approach from the above mentioned algorithms that best predicts the Indian stocks price movement. It is observed from the results that the use of GA significantly increases the accuracy of ANN and that the use of technical analysis with SVM and ANN is well suited for Indian stocks and can help investors and traders maximize their quarterly profits.


Computation of the characteristics of a claw pole alternator through the finite element method

The paper presents the analysis of a 3-D numerical model developed for a claw pole alternator. The complex structure of the claw-pole magnetic circuit required a 3D FEM model and a double scalar potential magnetic φ-φred formulation, in order to reduce computing time and memory. The no-load and magnetization characteristics and the e.m.f. time dependence have been calculated. Working characteristics and the induced voltage in the static winding can be calculated by knowing the 3D distribution of the field in stationary magnetic regime for successive positions of the rotor considering the stator.


Deep Reinforcement Learning for Optimal Control of Space Heating

Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement learning algorithm which can control space heating in buildings in a computationally efficient manner, and benchmarks it against other known techniques. The proposed algorithm outperforms rule based control by between 5-10% in a simulation environment for a number of price signals. We conclude that, while not optimal, the proposed algorithm offers additional practical advantages such as faster computation times and increased robustness to non-stationarities in building dynamics.


Star Ratings versus Sentiment Analysis -- A Comparison of Explicit and Implicit Measures of Opinions

A typical trade-off in decision making is between the cost of acquiring information and the decline in decision quality caused by insufficient information. Consumers regularly face this trade-off in purchase decisions. Online product/service reviews serve as sources of product/service related information. Meanwhile, modern technology has led to an abundance of such content, which makes it prohibitively costly (if possible at all) to exhaust all available information. Consumers need to decide what subset of available information to use. Star ratings are excellent cues for this decision as they provide a quick indication of the tone of a review. However there are cases where such ratings are not available or detailed enough. Sentiment analysis -text analytic techniques that automatically detect the polarity of text- can help in these situations with more refined analysis. In this study, we compare sentiment analysis results with star ratings in three different domains to explore the promise of this technique.


Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring

Long-term wildfire monitoring using distributed in situ temperature sensors is an accurate, yet demanding environmental monitoring application, which requires long-life, low-maintenance, low-cost sensors and a simple, fast, error-proof deployment procedure. We present in this paper the most important design considerations and optimizations of all elements of a low-cost WSN platform prototype for long-term, low-maintenance pervasive wildfire monitoring, its preparation for a nearly three-month field test, the analysis of the causes of failure during the test and the lessons learned for platform improvement. The main components of the total cost of the platform (nodes, deployment and maintenance) are carefully analyzed and optimized for this application. The gateways are designed to operate with resources that are generally used for sensor nodes, while the requirements and cost of the sensor nodes are significantly lower. We define and test in simulation and in the field experiment a simple, but effective communication protocol for this application. It helps to lower the cost of the nodes and field deployment procedure, while extending the theoretical lifetime of the sensor nodes to over 16 years on a single 1 Ah lithium battery.


Building Natural Language Interfaces to Web APIs

As the Web evolves towards a service-oriented architecture, application program interfaces (APIs) are becoming an increasingly important way to provide access to data, services, and devices. We study the problem of natural language interface to APIs (NL2APIs), with a focus on web APIs for web services. Such NL2APIs have many potential benefits, for example, facilitating the integration of web services into virtual assistants.
 We propose the first end-to-end framework to build an NL2API for a given web API. A key challenge is to collect training data, i.e., NL command-API call pairs, from which an NL2API can learn the semantic mapping from ambiguous, informal NL commands to formal API calls. We propose a novel approach to collect training data for NL2API via crowdsourcing, where crowd workers are employed to generate diversified NL commands. We optimize the crowdsourcing process to further reduce the cost. More specifically, we propose a novel hierarchical probabilistic model for the crowdsourcing process, which guides us to allocate budget to those API calls that have a high value for training NL2APIs. We apply our framework to real-world APIs, and show that it can collect high-quality training data at a low cost, and build NL2APIs with good performance from scratch. We also show that our modeling of the crowdsourcing process can improve its effectiveness, such that the training data collected via our approach leads to better performance of NL2APIs than a strong baseline.


Contrast-Enhanced Black and White Images

This paper investigates contrast enhancement as an approach to tone reduction, aiming to convert a photograph to black and white. Using a filter-based approach to strengthen contrast, we avoid making a hard decision about how to assign tones to segmented regions. Our method is inspired by sticks filtering, used to enhance medical images but not previously used in non-photorealistic rendering. We amplify contrast of pixels along the direction of greatest local difference from the mean, strengthening even weak features if they are most prominent. A final thresholding step converts the contrast-enhanced image to black and white. Local smoothing and contrast enhancement balances abstraction and structure preservation; the main advantage of our method is its faithful depiction of image detail. Our method can create a set of effects: line drawing, hatching, and black and white, all having superior details to previous black and white methods.


The City Browser : Utilizing Massive Call Data to Infer City Mobility Dynamics

This paper presents the City Browser, a tool developed to analyze the complexities underlying human mobility at the city scale. The tool uses data generated from mobile phones as a proxy to provide several insights with regards to the commuting patterns of the population within the bounds of a city. The three major components of the browser are the data warehouse, modules and algorithm, and the visualization interface. The modules and algorithm component utilizes Call Detail Records (CDRs) stored within the data warehouse to infer mobility patterns that are then communicated through the visualization interface. The modules and algorithm component consists of four modules: the spatial-temporal decomposition module, the home/work capturing module, the community detection module, and the flow estimation module. The visualization interface manages the output of each module to provide a comprehensive view of a city’s mobility dynamics over varying time scales. A case study is presented on the city of Riyadh in Saudi Arabia, where the browser was developed to better understand city mobility patterns.


A comprehensive framework for testing graphical user interfaces

The widespread recognition of the usefulness of graphical user interfaces GUIs has established their importance as critical components of today's software. Although the use of GUIs continues to grow, GUI testing has remained a neglected research area. Since GUIs have c haracteristics that are diierent from those of conventional software, such as user events for input and graphical output, techniques developed to test conventional software cannot bedirectly applied to test GUIs. This thesis develops a uniied solution to the GUI testing problem with the particular goals of automation and integration of tools and techniques used in various phases of GUI testing. These goals are accomplished by developing a GUI testing framework with a GUI model as its central component. For eeciency and scalability, a GUI is represented as a hierarchy of components, each used as a basic unit of testing. The framework also includes a test coverage evaluator, test case generator, test oracle, test executor, and regression tester. The test coverage evaluator employs hierarchical, event-based coverage criteria to automatically specify what to test in a GUI and to determine whether the test suite has adequately tested the GUI. The test case generator employs plan generation techniques from artiicial intelligence to automatically generate a test suite. A test executor automatically executes all the test cases on the GUI. As test cases are being executed, a test oracle automatically determines the correctness of the GUI. The test oracle employs a model of the expected state of the GUI in terms of its constituent objects and their properties. After changes are made to a GUI, a regression tester partitions the original GUI test suite into valid test cases that represent correct inputtoutput for the modiied GUI and invalid test cases that no longer represent correct inputtoutput. The regression tester employs a new technique to reuse some of the invalid test cases by repairing them. A cursory exploration of extending the framework to handle the new testing requirements of web-user interfaces WUIs is also done. The framework iv has been implemented and experiments have demonstrated that the developed techniques are both practical and useful. v Acknowledgements I w ould like to thank my parents whose constant eeorts, encouragement and hard work made achieving the goal of obtaining a Ph.D. possible. I thank all my teachers in schools, colleges, and universities whose dedication and hard work helped lay the foundation for this work. Special thanks …


A Safe, Efficient Regression Test Selection Technique

Regression testing is an expensive but necessary maintenance activity performed on modified software to provide confidence that changes are correct and do not adversely affect other portions of the softwore. A regression test selection technique choses, from an existing test set, thests that are deemed necessary to validate modified software. We present a new technique for regression test selection. Our algorithms construct control flow graphs for a precedure or program and its modified version and use these graphs to select tests that execute changed code from the original test suite. We prove that, under certain conditions, the set of tests our technique selects includes every test from the original test suite that con expose faults in the modified procedfdure or program. Under these conditions our algorithms are safe. Moreover, although our algorithms may select some tests that cannot expose faults, they are at lease as precise as other safe regression test selection algorithms. Unlike many other regression test selection algorithms, our algorithms handle all language constructs and all types of program modifications. We have implemented our algorithms; initial empirical studies indicate that our technique can significantly reduce the cost of regression testing modified software.


Coverage criteria for GUI testing

A widespread recognition of the usefulness of graphical user interfaces (GUIs) has established their importance as critical components of today's software. GUIs have characteristics different from traditional software, and conventional testing techniques do not directly apply to GUIs. This paper's focus is on coverage critieria for GUIs, important rules that provide an objective measure of test quality. We present new coverage criteria to help determine whether a GUI has been adequately tested. These coverage criteria use events and event sequences to specify a measure of test adequacy. Since the total number of permutations of event sequences in any non-trivial GUI is extremely large, the GUI's hierarchical structure is exploited to identify the important event sequences to be tested. A GUI is decomposed into GUI components, each of which is used as a basic unit of testing. A representation of a GUI component, called an event-flow graph, identifies the interaction of events within a component and intra-component criteria are used to evaluate the adequacy of tests on these events. The hierarchical relationship among components is represented by an integration tree, and inter-component coverage criteria are used to evaluate the adequacy of test sequences that cross components. Algorithms are given to construct event-flow graphs and an integration tree for a given GUI, and to evaluate the coverage of a given test suite with respect to the new coverage criteria. A case study illustrates the usefulness of the coverage report to guide further testing and an important correlation between event-based coverage of a GUI and statement coverage of its software's underlying code.


A Safe, Efficient Algorithm for Regression Test Selection

Regression testing is a necessary but costly maintenance activity aimed at demonstrating that code has not been adversely aaected by changes. A selective approach to regression testing selects tests for a modi-ed program from an existing test suite. We present a new technique for selective regression testing. Our algorithm constructs control dependence graphs for program versions, and uses these graphs to determine which tests from the existing test suite may exhibit changed behavior on the new version. Unlike most previous techniques for selective retest, our algorithm selects every test from the original test suite that might expose errors in the modiied program, and does this without prior knowledge of program modiications. Our algorithm handles all language constructs and program modiications, and is easily automated.


An Introduction to Least Commitment Planning

Recent developments have clari ed the process of generating partially ordered, partially speci ed sequences of actions whose execution will achive an agent's goal. This paper summarizes a progression of least commitment planners, starting with one that handles the simple strips representation, and ending with one that manages actions with disjunctive precondition, conditional e ects and universal quanti cation over dynamic universes. Along the way we explain how Chapman's formulation of the Modal Truth Criterion is misleading and why his NP-completeness result for reasoning about plans with conditional e ects does not apply to our planner. 1 I thank Franz Amador, Tony Barrett, Darren Cronquist, Denise Draper, Ernie Davis, Oren Etzioni, Nort Fowler, Rao Kambhampati, Craig Knoblock, Nick Kushmerick, Neal Lesh, Karen Lochbaum, Drew McDermott, Ramesh Patil, Kari Pulli, Ying Sun, Austin Tate and Mike Williamson for helpful comments, but retain sole responsibility for errors. This research was funded in part by O ce of Naval Research Grant 90-J-1904 and by National Science Foundation Grant IRI-8957302


Polygenic Influence on Educational Attainment: New evidence from The National Longitudinal Study of Adolescent to Adult Health.

Recent studies have begun to uncover the genetic architecture of educational attainment. We build on this work using genome-wide data from siblings in the National Longitudinal Study of Adolescent to Adult Health (Add Health). We measure the genetic predisposition of siblings to educational attainment using polygenic scores. We then test how polygenic scores are related to social environments and educational outcomes. In Add Health, genetic predisposition to educational attainment is patterned across the social environment. Participants with higher polygenic scores were more likely to grow up in socially advantaged families. Even so, the previously published genetic associations appear to be causal. Among pairs of siblings, the sibling with the higher polygenic score typically went on to complete more years of schooling as compared to their lower-scored co-sibling. We found subtle differences between sibling fixed effect estimates of the genetic effect versus those based on unrelated individuals.


A 4-µW 0.8-V rail-to-rail input/output CMOS fully differential OpAmp

This paper presents an ultra low power rail-to-rail input/output operational amplifier (OpAmp) designed in a low cost 0.18 µm CMOS technology. In this OpAmp, rail-to-rail input operation is enabled by using complementary input pairs with gm control. To maximize the output swing a rail-to-rail output stage is employed. For low-voltage low-power operation, the operating transistors in the input and output stage are biased in the sub-threshold region. The simulated DC open loop gain is 51 dB, and the slew-rate is 0.04 V/µs with a 10 pF capacitive load connected to each of the amplifier outputs. For the same load, the simulated unity gain frequency is 131 kHz with a 64° phase margin. A common-mode feed-forward circuit (CMFF) increases CMRR, reducing drastically the variations in the output common mode voltage and keeping the DC gain almost constant. In fact, their relative error remains below 1.2 % for a (−20°C, +120°C) temperature span. In addition, the proposed OpAmp is very simple and consumes only 4 µW at 0.8 V supply.


An embarrassingly simple approach to zero-shot learning

Zero-shot learning consists in learning how to recognise new concepts by just having a description of them. Many sophisticated approaches have been proposed to address the challenges this problem comprises. In this paper we describe a zero-shot learning approach that can be implemented in just one line of code, yet it is able to outperform state of the art approaches on standard datasets. The approach is based on a more general framework which models the relationships between features, attributes, and classes as a two linear layers network, where the weights of the top layer are not learned but are given by the environment. We further provide a learning bound on the generalisation error of this kind of approaches, by casting them as domain adaptation methods. In experiments carried out on three standard real datasets, we found that our approach is able to perform significantly better than the state of art on all of them, obtaining a ratio of improvement up to 17%.