Reviewerly/exHarmony huggingface dataset:
READ: Reciprocal Attention Discriminator for Image-to-Video Re-identification

Person re-identification (re-ID) is the problem of visually identifying a person given a database of identities. In this work, we focus on image-to-video re-ID which compares a single query image to videos in the gallery. The main challenge is the asymmetry association of an image and a video, and overcoming the difference caused by the additional temporal dimension. To this end, we propose an attention-aware discriminator architecture. The attention occurs across different modalities, and even different identities to aggregate useful spatio-temporal information for comparison. The information is effectively fused into a united feature, followed by the final prediction of a similarity score. The performance of the method is shown with image-to-video person re-identification benchmarks (DukeMTMC-VideoReID, and MARS).
Jumanji: The Case for Dynamic NUCA in the Datacenter

The datacenter introduces new challenges for computer systems around tail latency and security. This paper argues that dynamic NUCA techniques are a better solution to these challenges than prior cache designs. We show that dynamic NUCA designs can meet tail-latency deadlines with much less cache space than prior work, and that they also provide a natural defense against cache attacks. Unfortunately, prior dynamic NUCAs have missed these opportunities because they focus exclusively on reducing data movement.We present Jumanji, a dynamic NUCA technique designed for tail latency and security. We show that prior last-level cache designs are vulnerable to new attacks and offer imperfect performance isolation. Jumanji solves these problems while significantly improving performance of co-running batch applications. Moreover, Jumanji only requires lightweight hardware and a few simple changes to system software, similar to prior D-NUCAs. At 20 cores, Jumanji improves batch weighted speedup by 14% on average, vs. just 2% for a non-NUCA design with weaker security, and is within 2% of an idealized design.
Understanding the Source of Semantic Regularities in Word Embeddings

Semantic relations are core to how humans understand and express concepts in the real world using language.Recently, there has been a thread of research aimed at modeling these relations by learning vector representations from text corpora.Most of these approaches focus strictly on leveraging the co-occurrences of relationship word pairs within sentences.In this paper, we investigate the hypothesis that examples of a lexical relation in a corpus are fundamental to a neural word embedding's ability to complete analogies involving the relation.Our experiments, in which we remove all known examples of a relation from training corpora, show only marginal degradation in analogy completion performance involving the removed relation.This finding enhances our understanding of neural word embeddings, showing that co-occurrence information of a particular semantic relation is the not the main source of their structural regularity.
Combinatorial Code Classification &amp; Vulnerability Rating

Empirical analysis of source code of Android Fluoride Bluetooth stack demonstrates a novel approach of classification of source code and rating for vulnerability. A workflow that combines deep learning and combinatorial techniques with a straightforward random forest regression is presented. Two kinds of embedding are used: code2vec and LSTM, resulting in a distance matrix that is interpreted as a (combinatorial) graph whose vertices represent code components, functions and methods. Cluster Editing is then applied to partition the vertex set of the graph into subsets representing nearly complete subgraphs. Finally, the vectors representing the components are used as features to model the components for vulnerability risk.
On the classification of quaternary optimal Hermitian LCD codes

We propose a method for classifying quaternary Hermitian LCD codes having large minimum weights. For example, we classify quaternary optimal Hermitian LCD codes of dimension 3.
Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports

The search for Participants, Interventions, and Outcomes (PIO) in clinical trial reports is a critical task in Evidence Based Medicine.For an automatic PIO extraction, high-quality corpora are needed.Obtaining such a corpus from crowdworkers, however, has been shown to be ineffective since (i) workers usually lack domain-specific expertise to conduct the task with sufficient quality, and (ii) the standard approach of annotating entire abstracts of trial reports as one task-instance (i.e.HIT) leads to an uneven distribution in task effort.In this paper, we switch from entire abstract to sentence annotation, referred to as the SEN-BASE approach.We build upon SENBASE in SENSUPPORT, where we compensate the lack of domain-specific expertise of crowdworkers by showing for each task-instance similar sentences that are already annotated by experts.Such tailored task-instance examples are retrieved via unsupervised semantic shorttext similarity (SSTS) method -and we evaluate nine methods to find an effective solution for SENSUPPORT.We compute the Cohen's Kappa agreement between crowd-annotations and gold standard annotations and show that (i) both sentence-based approaches outperform a BASELINE approach where entire abstracts are annotated; (ii) supporting annotators with tailored task-instance examples is the best performing approach with Kappa agreements of 0.78/0.75/0.69 for P, I, and O respectively.
tsegGAN: A Generative Adversarial Network for Segmenting Touching Nontext Components From Text Ones in Handwriting

Segmentation of a touching component to separate its constituent text and nontext parts is always a very crucial but challenging task toward developing a comprehensive document image processing (DIP) system. This is because, irrespective of document types, either printed or handwritten, the nontext parts need to be suppressed first before processing the text parts through an optical character recognition (OCR) system. Although a good number of attempts have been made to address this issue for printed documents, the same for regular handwritten document images is almost none. However, the appearance of touching components where a nontext part gets joined with a text part is a common issue in freestyle handwriting. To this end, in this work, we tailor-make a generative adversarial network (GAN)-based model with a suitable loss function that we name tsegGAN. We also prepare an in-house data set by collecting touching components from different real-world handwritten documents to evaluate our model. The performance comparison of our model with state-of-the-art GAN models shows that tsegGAN has outperformed the others with a significant margin.
Ammonia: an approach for deriving project-specific bug patterns

Finding and fixing buggy code is an important and cost-intensive maintenance task, and static analysis (SA) is one of the methods developers use to perform it. SA tools warn developers about potential bugs by scanning their source code for commonly occurring bug patterns, thus giving those developers opportunities to fix the warnings (potential bugs) before they release the software. Typically, SA tools scan for general bug patterns that are common to any software project (such as null pointer dereference), and not for project specific patterns. However, past research has pointed to this lack of customizability as a severe limiting issue in SA. Accordingly, in this paper, we propose an approach called Ammonia, which is based on statically analyzing changes across the development history of a project, as a means to identify project-specific bug patterns. Furthermore, the bug patterns identified by our tool do not relate to just one developer or one specific commit, they reflect the project as a whole and compliment the warnings from other SA tools that identify general bug patterns. Herein, we report on the application of our implemented tool and approach to four Java projects: Ant, Camel, POI, and Wicket. The results obtained show that our tool could detect 19 project specific bug patterns across those four projects. Next, through manual analysis, we determined that six of those change patterns were actual bugs and submitted pull requests based on those bug patterns. As a result, five of the pull requests were merged.
Local Differential Privacy for Multi-Agent Distributed Optimal Power Flow

Real-time data-driven optimization and control problems over networks, such as in traffic or energy systems, may require sensitive information of participating agents to calculate solutions and decision variables. Adversaries with access to coordination signals may potentially decode information on individual agents and put privacy at risk. We use the Inexact Alternating Minimization Algorithm to instantiate local differential privacy for distributed optimization, addressing situations in which individual agents need to protect their individual data, in the form of optimization parameters, from all other agents and any central authority. This mechanism allows agents to customize their own privacy level based on local needs and parameter sensitivities. The resulting algorithm works across a large family of convex distributed optimization problems. We implement the method on a distributed optimal power flow problem that aims to prevent overload on critical branches in a radial network.
Defining success for a public library makerspace: Implications of participant-defined, individualized evaluative criteria

Public libraries are increasingly investing in makerspaces and seeking to evaluate their offerings. Defining success is the first step of the evaluation process yet proves difficult due to the heterogeneity of making participants and purposes and the individualized, self-directed nature of makerspace participation. This study identified and compared seven evaluative criteria that represented participants' and library definitions of success for one public library makerspace. Findings revealed that at least one criterion drawn from library objectives was relevant for each participant in the sample, yet none of the seven criteria was relevant for every participant. In an evaluation, drawing criteria exclusively from library objectives and/or applying criteria uniformly could underestimate the benefits of the makerspace. Drawing criteria from both library and participant perspectives and using individualized criteria that vary across the population could yield an assessment that reflects the breadth of purposes and benefits associated with the makerspace.
Data-driven image restoration with option-driven learning for big and small astronomical image data sets

Image restoration methods are commonly used to improve the quality of astronomical images. In recent years, developments of deep neural networks and increments of the number of astronomical images have evoked a lot of data--driven image restoration methods. However, most of these methods belong to supervised learning algorithms, which require paired images either from real observations or simulated data as training set. For some applications, it is hard to get enough paired images from real observations and simulated images are quite different from real observed ones. In this paper, we propose a new data--driven image restoration method based on generative adversarial networks with option--driven learning. Our method uses several high resolution images as references and applies different learning strategies when the number of reference images is different. For sky surveys with variable observation conditions, our method can obtain very stable image restoration results, regardless of the number of reference images.
Probabilistic inductive constraint logic

Abstract Probabilistic logical models deal effectively with uncertain relations and entities typical of many real world domains. In the field of probabilistic logic programming usually the aim is to learn these kinds of models to predict specific atoms or predicates of the domain, called target atoms/predicates. However, it might also be useful to learn classifiers for interpretations as a whole: to this end, we consider the models produced by the inductive constraint logic system, represented by sets of integrity constraints , and we propose a probabilistic version of them. Each integrity constraint is annotated with a probability, and the resulting probabilistic logical constraint model assigns a probability of being positive to interpretations. To learn both the structure and the parameters of such probabilistic models we propose the system PASCAL for “probabilistic inductive constraint logic”. Parameter learning can be performed using gradient descent or L-BFGS. PASCAL has been tested on 11 datasets and compared with a few statistical relational systems and a system that builds relational decision trees (TILDE): we demonstrate that this system achieves better or comparable results in terms of area under the precision–recall and receiver operating characteristic curves, in a comparable execution time.
Delegating Quantum Computation in the Quantum Random Oracle Model

A delegation scheme allows a computationally weak client to use a server's resources to help it evaluate a complex circuit without leaking any information about the input (other than its length) to the server. In this paper, we consider delegation schemes for quantum circuits, where we try to minimize the quantum operations needed by the client. We construct a new scheme for delegating a large circuit family, which we call "C+P circuits". "C+P" circuits are the circuits composed of Toffoli gates and diagonal gates. Our scheme is non-interactive, requires small amount of quantum computation from the client (proportional to input length but independent of the circuit size), and can be proved secure in the quantum random oracle model, without relying on additional assumptions, such as the existence of fully homomorphic encryption. In practice the random oracle can be replaced by an appropriate hash function or block cipher, for example, SHA-3, AES. This protocol allows a client to delegate the most expensive part of some quantum algorithms, for example, Shor's algorithm. The previous protocols that are powerful enough to delegate Shor's algorithm require either many client side quantum operations or the existence of FHE. The protocol requires asymptotically fewer quantum gates on the client side compared to running Shor's algorithm locally. To hide the inputs, our scheme uses an encoding that maps one input qubit to multiple qubits. We then provide a novel generalization of classical garbled circuits ("reversible garbled circuits") to allow the computation of Toffoli circuits on this encoding. We also give a technique that can support the computation of phase gates on this encoding. To prove the security of this protocol, we study key dependent message (KDM) security in the quantum random oracle model. KDM security was not previously studied in quantum settings.
Dynamic principal projection for cost-sensitive online multi-label classification

We study multi-label classification (MLC) with three important real-world issues: online updating, label space dimension reduction (LSDR), and cost-sensitivity. Current MLC algorithms have not been designed to address these three issues simultaneously. In this paper, we propose a novel algorithm, cost-sensitive dynamic principal projection (CS-DPP) that resolves all three issues. The foundation of CS-DPP is an online LSDR framework derived from a leading LSDR algorithm. In particular, CS-DPP is equipped with an efficient online dimension reducer motivated by matrix stochastic gradient, and establishes its theoretical backbone when coupled with a carefully-designed online regression learner. In addition, CS-DPP embeds the cost information into label weights to achieve cost-sensitivity along with theoretical guarantees. Experimental results verify that CS-DPP achieves better practical performance than current MLC algorithms across different evaluation criteria, and demonstrate the importance of resolving the three issues simultaneously.
Distributed Computation Offloading using Deep Reinforcement Learning in Internet of Vehicles

In this paper, we first take the moving vehicles as a RP (resource pool), by which we proposed a distributed computation offloading scheme to fully utilize the available resources and reduce task execution time in I0V (Internet of Vehicles). After that, we divide a complex task into many sub-tasks and indicate that how to assign these small tasks to satisfy the task execution time in RP is a NP problem. The executing time of a task is modeled as the longest calculation time among all small tasks, which is actually a min-max problem. For a dynamically vehicular environment, a distributed computing offloading strategy based on deep reinforcement learning is proposed to find the best offloading scheme to minimize the execution time of a task. Numerical results demonstrate that our scheme is better than Partial Flooding Algorithm and can make full use of the available computing resources of surrounding vehicles by considering the mobility of vehicles, the delay of communication transmission, and the separability of the tasks, thus greatly reducing the execution time of the computing tasks.
Enhancing keyphrase extraction from microblogs using human reading time

The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading features. In this article, we aim to leverage human reading time to extract keyphrases from microblog posts. There are two main tasks in this study. One is to determine how to measure the time spent by a human on reading a word. We use eye fixation durations extracted from an open source eye-tracking corpus (OSEC). Moreover, we propose strategies to make eye fixation duration more effective on keyphrase extraction. The other task is to determine how to integrate human reading time into keyphrase extraction models. We propose two novel neural network models. The first is a model in which the human reading time is used as the ground truth of the attention mechanism. In the second model, we use human reading time as the external feature. Quantitative and qualitative experiments show that our proposed models yield better performance than the baseline models on two microblog datasets.
Best variable identification by means of data-mining and cooperative game theory

To develop and evaluate methods to assess single and grouped variables impact on measuring intervention severities and support a search for most expressive variables. Datasets of cohort studies are analyzed automatically based on algorithms. For this, a metric is developed to compare measured variables in different cohorts in a data-mining process. Variables are measured in all possible combinations to detect possible synergies of certain variable constellations and allow for a ranking of the combinations’ expressiveness. Such ranking serves as a basis for a wide range of algorithmic data analysis. In an exemplary application, every group member’s impact on the total result is determined based on the principle of the cooperative game theory besides to the total expressiveness of the variable groups. For different types of interventions, the method is applied to experimental data containing multiple recorded medical lab values. The expressiveness of variable combinations to indicate severity is ranked by means of a metric. Within each combination, any variable’s contribution to the total effect is determined and accumulated over whole datasets to yield local and global variable importance measures. The computed results have been successfully matched with clinical expectations to prove their plausibility. Algorithmic evaluation shows to be a promising approach in automatized quantification of variable expressiveness. It can assess descriptive power of measurements, help to improve future study designs and expose worthwhile research issues. • Collaborative databases accumulate data and allow re-usage for large scale analyses. • Identification of expressive variables to assess treatment effects is intended. • Variable expressiveness ranking is performed by data mining. • Variable relevance weighting is based on data mining results.
Survey on Hybrid Classical-Quantum Machine Learning Models

Optimizing for a classification problem with a 0-1 loss function is an NP-hard problem [1] even for a simple binary classification task. As Preskill stated “We don't expect a quantum computer to solve worst case instances of NP-hard problems, but it might find better approximate solution or find it faster” [2]. Hybrid Quantum Classical Platforms harness the full capacity of Near-Term Quantum Computers. In this study, a focus is given on the variational circuits based approach accomplishing various machine learning tasks. We will survey a variety of experimental demonstrations conducted on actual quantum hardware and actively developing simulation software, anticipated to have a broad range of real-world applications in this increasingly growing field.
Sign Language Recognition with CW Radar and Machine Learning

Sign language is the primary communication medium for the deaf-mute community. However, the literacy of understanding and using sign language is hard to gain without professional training. In this paper, we explore the use of low power frequency modulated continuous wave radar for automatic sign language recognition. The proposed system is composed of a radar, a sound cluster and a computer for transforming signals to spectrograms. Furthermore, as the time-frequency spectrograms are high-dimensional data with redundant information, we then perform dimensionality reduction by extracting the histogram of oriented gradients features from these spectrograms. The features are finally classified by the k-Nearest Neighbour algorithm and a classification result of 95.8% is achieved on the five testing signs. The impact of the k value in the k-Nearest Neighbour is also investigated.
Pre-Processing and Emoji Classification of WhatsApp Chats for Sentiment Analysis

WhatsApp is among the popular social media service with over 2 billion registered users. WhatsApp is integral in people's life as a medium of communication. They use WhatsApp to share their feelings through text messages. With WhatsApp present in over 180 countries, code-switching is common. Along with this, with the increase in usage and prevalence of emojis, emojis have become indispensable during sentiment analysis. Throughout this paper, our approach to convert unstructured WhatsApp messages to a structured form is discussed on which various data mining techniques for sentiment analysis can be performed. Our approach to deal with code-mixing, different emojis and the emotions they depict, and finally, perform basic analysis using this algorithm is discussed.