Reviewerly/exHarmony huggingface dataset:
A Multi-Attention Network for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis (ASA) aims at determining the sentiment polarity of specific aspect term with a given sentence. Recent advances in attention mechanisms suggest that attention models are useful in ASA tasks and can help identify focus words. Or combining attention mechanisms with neural networks are also common methods. However, according to the latest research, they often fail to extract text representations efficiently and to achieve interaction between aspect terms and contexts. In order to solve the complete task of ASA, this paper proposes a Multi-Attention Network (MAN) model which adopts several attention networks. This model not only preprocesses data by Bidirectional Encoder Representations from Transformers (BERT), but a number of measures have been taken. First, the MAN model utilizes the partial Transformer after transformation to obtain hidden sequence information. Second, because words in different location have different effects on aspect terms, we introduce location encoding to analyze the impact on distance from ASA tasks, then we obtain the influence of different words with aspect terms through the bidirectional attention network. From the experimental results of three datasets, we could find that the proposed model could achieve consistently superior results.
Multi-anatomy localization in fetal echocardiography videos

Fetal heart motion is an important diagnostic indicator for structural detection and functional assessment of congenital heart disease. We propose an approach towards integrating deep convolutional and recurrent architectures that utilize localized spatial and temporal features of different anatomical substructures within a global spatiotemporal context for interpretation of fetal echocardiography videos. We formulate our task as a cardiac structure localization problem with convolutional architectures for aggregating global spatial context and detecting anatomical structures on spatial region proposals. This information is aggregated temporally by recurrent architectures to quantify the progressive motion patterns. We experimentally show that the resulting architecture combines anatomical landmark detection at the frame-level over multiple video sequences-with temporal progress of the associated anatomical motions to encode local spatiotemporal fetal heart dynamics and is validated on a real-world clinical dataset.
Hand Gestures for the Human-Car Interaction: The Briareo Dataset

Natural User Interfaces can be an effective way to reduce driver’s inattention during the driving activity. To this end, in this paper we propose a new dataset, called Briareo, specifically collected for the hand gesture recognition task in the automotive context. The dataset is acquired from an innovative point of view, exploiting different kinds of cameras, i.e. RGB, infrared stereo, and depth, that provide various types of images and 3D hand joints. Moreover, the dataset contains a significant amount of hand gesture samples, performed by several subjects, allowing the use of deep learning-based approaches. Finally, a framework for hand gesture segmentation and classification is presented, exploiting a method introduced to assess the quality of the proposed dataset.
Age &amp; Gender Classifier for Edge Computing

Deep learning models are known for being large and computationally expensive. It is a challenge to fit these models into edge devices which usually have frugal memory. A striking feature about neural networks is their enormous size. Embedded devices in edge computing scenario typically cannot handle large neural networks. We present two models of Convolutional Neural Networks (VGG16 type), with a modification in the prediction layer, potentially suitable for edge computing devices. Both networks have been designed for the classification of a human face by gender and age. The first one (VGG16/10) considers gender and age as two related characteristics and the final neurons have been conceived to hold these aspects together at the same time. The second one (VGG16/8+1) in the prediction layer has got a neuron for the prediction of gender and another eight for the prediction of age (according to the Adience benchmark). Such networks have been conceived to provide simultaneously information on both the gender and age of the person identified in the image, without the need to build two dedicated networks. The aim is to develop a solution that can be suitable in edge-computing scenario.
A common framework for the evaluation of psychophysiological visual quality assessment

The assessment of perceived quality based on psychophysiological methods recently gained attraction as it potentially overcomes certain flaws of psychophysical approaches. Although studies report promising results, it is not possible to arrive at decisive and comparable conclusions that recommend the use of one or another method for a specific application or research question. The video quality expert group started a project on psychophysiological quality assessment to study these novel approaches and to develop a test plan that enables more systematic research. This test plan comprises of a specifically designed set of quality annotated video sequences, suggestions for psychophysiological methods to be studied in quality assessment, and recommendations for the documentation and publications of test results. The test plan is presented in this article.
PerRNN: Personalized Recurrent Neural Networks for Acceleration-Based Human Activity Recognition

The ever-growing proliferation of mobile devices equipped with accelerometers has provided new opportunities to capture the semantic meanings of human activities and improve user experience with behavior-based recommendations, which heavily rely on the accuracy of the recognition of daily human activities. Acceleration-based human activity recognition (HAR) is a challenging problem because each accelerometer records multi-dimensional signals in both spatial and temporal domains that have different attributes for representing different activities or even the same activity. Thus we cannot directly compare these signals with each other, because they are embedded in a non-metric space. In this paper, we present a Personalized Recurrent Neural Network (PerRNN) to dynamically segment and recognize the human activities based on accelerometer data. Enlightened by the idea of spatiotemporal predictive learning, the proposed architecture is capable of memorizing different acceleration signals' appearances and temporal variations in a unified memory pool. We evaluate the performance of the proposed framework on a commonly used dataset, WISDM. Experiment results show that compared with state-of-the-art schemes, our proposed PerRNN system recognizes 6 different human activities with the highest overall accuracy of 96.44%.
A Collaborative Filtering Recommendation System for Rating Prediction

Recommendation system is a subclass of information filtering system to help users find relevant items of interest from a large set of possible selections. Model-based collaborative filtering utilized the ratings of the user–item matrix dataset to generate a prediction. Essentially, this type of intelligent system plays a critical role in e-commerce, social network, and popular domains increasingly. In this research work, we present the comparison of the two widely used efficient techniques such as Biased Matrix Factorization and a regular Matrix Factorization, both using Stochastic Gradient Descent (SGD). We have conducted experiments on two real-world public datasets: Book Crossing and Movie Lens 100 K and evaluated by two metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Our experiments demonstrated that Biased Matrix Factorization used SGD technique results in a substantial increase in recommendation accuracy for rating prediction in experimental both datasets. Compute with a regular Matrix Factorization technique, Biased Matrix Factorization produced the reduction of the RMSE by 25.78% and MAE by 19.69% for Book Crossing dataset and RMSE by 19.69% and MAE by 14.08% for Movie Lens 100 K dataset. As expected when comparing the results of different datasets, Biased Matrix Factorization using SGD materialize less prediction error.
An effective method to determine whether a point is within a convex hull and its generalized convex polyhedron classifier

A convex polyhedron classifier that encloses the minority class using a combination of hyperplanes is potentially effective in imbalanced classification. To construct an easy-to-use convex polyhedron classifier, this paper first presents a theoretical foundation for determining whether a point is within the convex hull of a finite point set. This foundation corresponds to a geometric method in which the result is expressed as a separating hyperplane. If the given point and the given convex hull are located on either side of the learned hyperplane, this indicates that the point is outside of the convex hull. Otherwise, the conclusion that the point is within the convex hull can be obtained. As a generalization of the geometric method, a convex polyhedron classifier is further proposed for binary classification. If two finite point sets are polyhedrally separable, a series of hyperplanes can be learned as a combined (piecewise linear) classifier, which surrounds a point set that is inside using a convex polyhedron and excludes the other point set that is outside. Experimental results on twelve real-world datasets show that the proposed classifier is generally better than the other two piecewise linear classifiers. Moreover, a comparison with several types of support vector machines confirms its competitiveness.
A Design of Deep Learning Based Optical Fiber Ethernet Device Fingerprint Identification System

This paper proposes a novel deep learning based hardware fingerprint identification method for optical fiber Ethernet devices. An adjacent constellation trance figure (ACTF) feature extraction method is firstly introduced for baseband modulation system with only amplitude waveform. A 2-dimensional convolutional neural network (2D-CNN) is designed to classify different optical fiber Ethernet devices via ACTF features. An intensity modulation / direct detection (IM/DD) experimental system with 24 optical fiber Ethernet devices is designed for evaluations. We optimize the ACTF parameter setups and compare the classification accuracy with another deep learning based long short-term memory (LSTM) network and classical statistical feature methods. Experimental results show that our proposed ACTF-CNN can achieve a classification accuracy as high as 99.49% and 96.29% under SNR levels of 30 dB and 10 dB, respectively, which significantly outperforms LSTM network and statistical feature based methods.
Combining dynamic finite state machines and text-based similarities to represent human behavior

The analysis of human behavior is a popular topic of research since it allows obtaining specific information about individuals, their motivations, and the problems and difficulties they can encounter. Human behavior can be grouped to elaborate profiles that would enable the classification of individuals. Nevertheless, the elaboration of profiles related to human behaviors presents some difficulties associated with the volume of data and the number of parameters typically considered. Thus, the development of software able to automatize the manipulation of data through graphical assistants to produce understandable visualizations of the human behaviors is crucial. In this paper, the VISUVER framework is presented. It uses finite state machines to represent and visualize the dynamic human behavior automatically. This behavior could be provided by real data collected by specific sensors or simulated data. The state machines are built in sequential steps in order to illustrate the dynamic evolution of the behavior over time. VISUVER also includes similarity metrics based on text mining techniques to establish possible profiles among the analyzed behaviors. The Intelligent Transportation Systems (ITS) domain has been considered in order to validate the proposal.
Real-Time Conflict Resolution of Task-Constrained Manipulator Motion in Unforeseen Dynamic Environments

This paper introduces conflict resolution in task-constrained real-time adaptive motion planning (RAMP) to enable a robot manipulator performing tasks in an environment with dynamically unknown obstacles. The method continuously improves and maintains diverse task constrained as well as unconstrained robot trajectories to allow the manipulator switching to a better trajectory at any time and seamlessly resolving conflicts between satisfying task constraints and avoiding dynamically unknown obstacles. If dynamic obstacles block all available task-constrained trajectories, the algorithm allows the manipulator to change goals on the fly to be free of task constraints and resume the task whenever there is a collision-free, task-constrained trajectory. The method is validated in different dynamic environments with different task constraints in both simulation and real-world experiments.
KleinPAT

We propose a new modal sound synthesis method that rapidly estimates all acoustic transfer fields of a linear modal vibration model, and greatly reduces preprocessing costs. Instead of performing a separate frequency-domain Helmholtz radiation analysis for each mode, our method partitions vibration modes into chords using optimal mode conflation , then performs a single time-domain wave simulation for each chord. We then perform transfer deconflation on each chord's time-domain radiation field using a specialized QR solver, and thereby extract the frequency-domain transfer functions of each mode. The precomputed transfer functions are represented for fast far-field evaluation, e.g., using multipole expansions. In this paper, we propose to use a single scalar-valued Far-field Acoustic Transfer (FFAT) cube map. We describe a GPU-accelerated vector wavesolver that achieves high-throughput acoustic transfer computation at accuracy sufficient for sound synthesis. Our implementation, KleinPAT, can achieve hundred- to thousand-fold speedups compared to existing Helmholtz-based transfer solvers, thereby enabling large-scale generation of modal sound models for audio-visual applications.
Characteristics of Bitcoin Transactions on Cryptomarkets

Cryptomarkets (or darknet markets) are commercial hidden-service websites that operate on The Onion Router (Tor) anonymity network. Cryptomarkets accept primarily bitcoin as payment since bitcoin is pseudonymous. Understanding bitcoin transaction patterns in cryptomarkets is important for analyzing vulnerabilities of privacy protection models in cryptocurrecies. It is also important for law enforcement to track illicit online crime activities in cryptomarkets. In this paper, we discover interesting characteristics of bitcoin transaction patterns in cryptomarkets. The results demonstrate that the privacy protection mechanism in cryptomarkets and bitcoin is vulnerable. Adversaries can easily gain valuable information for analyzing trading activities in cryptomarkets.
Investigating the Potential Effectiveness of Allocentric Mixed Reality Deictic Gesture

Mixed reality technologies offer interactive robots many new ways to communicate their beliefs, desires, and intentions to human teammates. In previous work, we identified several categories of visualizations that when displayed to users through mixed reality technologies serve the same role as traditional deictic gestures (e.g., pointing). In this work, we experimentally investigate the potential utility of one of these categories, allocentric gestures, in which circles or arrows are rendered to enable human teammates to pick out the robot's target referents. Specifically, through two human subject experiments, we examine the objective and subjective performance of such gestures alone as compared to language alone and the combination of language and allocentric gesture. Our results suggest that allocentric gestures are more effective than language alone, but to maintain high robot likability allocentric gestures should be used to complement rather than replace complex referring expressions.
Visual Analysis of Place Connectedness by Public Transport

The concept of place connectedness (traditionally termed `accessibility') refers to the ability of people to reach various services and to participate in activities. Connectedness by public transport is especially important for underprivileged and elderly people while the active use of public transport by the general population contributes in reducing traffic congestions and air pollution in cities. Place connectedness analyses are performed for a variety of purposes. In communication with transportation experts, we performed the conceptual modeling of the domain of problems related to place connectedness, defined the system of analysis tasks, and matched the tasks to visual analytics techniques that are capable to support them. In this paper, we introduce the task typology and present the visual analytics techniques using several example scenarios of place connectedness analyses.
Singular value decomposition assisted multicarrier continuous-variable quantum key distribution

We define the singular value decomposition (SVD) assisted multicarrier continuous-variable quantum key distribution (CVQKD) protocol. The proposed protocol uses the singular value decomposition of the Gaussian quantum channel, which yields an additional degree of freedom for the phase space transmission. This additional degree of freedom can further be exploited in a multiple-access scenario. The SVD-assistance defines the eigenchannels of the Gaussian physical link, which can be used for the simultaneous reliable transmission of multiple user data streams. Our transmission model also includes the singular interference avoider (SIA) precoding scheme. The proposed SIA precoding scheme prevents the eigenchannel interference to reach an optimal transmission over a Gaussian link. We demonstrate the results through the adaptive multicarrier quadrature division–multiuser quadrature allocation (AMQD-MQA) CVQKD multiple-access scheme. The singular value assisted transmission provides improved simultaneous transmission rates for the users, particularly in crucial low signal-to-noise ratio regimes.
Provability logic and the completeness principle

The logic iGLC is the intuitionistic version of Löb's Logic plus the completeness principle A→□A. In this paper, we prove an arithmetical completeness theorems for iGLC for theories equipped with two provability predicates □ and △ that prove the schemes A→△A and □△S→□S for S∈Σ1. We provide two salient instances of the theorem. In the first, □ is fast provability and △ is ordinary provability and, in the second, □ is ordinary provability and △ is slow provability. Using the second instance, we reprove a theorem previously obtained by Mohammad Ardeshir and Mojtaba Mojtahedi [1] determining the Σ1-provability logic of Heyting Arithmetic.
ECoST

Datacenters provide high performance and flexibility for users and cost efficiency for operators. Hyperscale datacenters are harnessing massively scalable computer resources for large-scale data analysis. However, cloud/datacenter infrastructure does not scale as fast as the input data volume and computational requirements of big data and analytics technologies. Thus, more applications need to share CPU at the node level that could have large impact on performance and operational cost. To address this challenge, in this paper we show that, concurrently fine-tune parameters at the application, microarchitecture, and system levels are creating opportunities to co-locate applications at the node level and improve energy-efficiency of the server while maintaining performance. Co-locating and self-tuning of unknown applications are challenging problems, especially when co-locating multiple big data applications concurrently with many tuning knobs, potentially requiring exhaustive brute-force search to find the right settings. This research challenge upsurges an imminent need to develop a technique that co-locates applications at a node level and predict the optimal system, architecture and application level configure parameters to achieve the maximum energy efficiency. It promotes the scale-down of computational nodes by presenting the Energy-Efficient Co-Locating and Self-Tuning (ECoST) technique for data intensive applications. ECoST proof of concept was successfully tested on MapReduce platform. ECoST can also be deployed on other data-intensive frameworks where there are several parameters for power and performance tuning optimizations. ECoST collects run-time hardware performance counter data and implements various machine learning models from as simple as a lookup table or decision tree based to as complex as neural network based to predict the energy-efficiency of co-located applications. Experimental data show energy efficiency is achieved within 4% of the upper bound results when co-locating multiple applications at a node level. ECoST is also scalable, being within 8% of upper bound on an 8-node server.
DoCA: A Content-Based Automatic Classification System Over Digital Documents

Regardless of industry, the overload of information facing most organizations today is a drain on both individuals and the enterprise itself. The increasing volume of this information, which is stored in different electronic formats, requires new sophisticated systems to analyse and classify them. In this paper, we attempt to implement a framework Document Classification and Analysis (DoCA) that can simplify and automate such tasks for different file types, namely: office documents (text, spreadsheets, and presentations), scanned documents (images and PDFs), multimedia files (video and audio). Each file type requires different methods for pre-processing, analysis, and classification. The efficiency and feasibility of the DoCA are examined on HAVELSAN dataset and accuracy of different tasks shows that the DoCA is a promising tool for document analysis and classification.
Circular (Yet Sound) Proofs

We introduce a new way of composing proofs in rule-based proof systems that generalizes tree-like and dag-like proofs. In the new definition, proofs are directed graphs of derived formulas, in which cycles are allowed as long as every formula is derived at least as many times as it is required as a premise. We call such proofs circular. We show that, for all sets of standard inference rules, circular proofs are sound. We first focus on the circular version of Resolution, and see that it is stronger than Resolution since, as we show, the pigeonhole principle has circular Resolution proofs of polynomial size. Surprisingly, as proof systems for deriving clauses from clauses, Circular Resolution turns out to be equivalent to Sherali-Adams, a proof system for reasoning through polynomial inequalities that has linear programming at its base. As corollaries we get: (1) polynomial-time (LP-based) algorithms that find circular Resolution proofs of constant width, (2) examples that separate circular from dag-like Resolution, such as the pigeonhole principle and its variants, and (3) exponentially hard cases for circular Resolution. Contrary to the case of circular resolution, for Frege we show that circular proofs can be converted into tree-like ones with at most polynomial overhead.