Reviewerly/exHarmony huggingface dataset:
COLIEE-2018: Evaluation of the Competition on Legal Information Extraction and Entailment

We summarize the evaluation of the 5th Competition on Legal Information Extraction/Entailment 2018 (COLIEE-2018). The COLIEE-2018 tasks include two tasks in each of statute law and case law. The case law component includes an information retrieval (Task 1), and the confirmation of an entailment relation between an existing case and an unseen case (Task 2). The statute law component includes information retrieval (Task 3) and entailment/question answering (Task 4). Participation was open to any group based on any approach. 13 teams participated in the case law competition, and we received results from 7 teams where 6 submissions to Task 1 (12 runs), and 4 submissions to Task 2 (8 runs). Regarding the statute law, there were submissions of 17 runs from 8 teams (including 2 organizers' runs) for Task 3 and 7 runs from 3 teams for Task 4. We describe each team's approaches, our official evaluation, and analysis on our data and submission results. We also discuss possibilities for future competition tasks.
Graph Convolution Based Attention Model for Personalized Disease Prediction

Clinicians implicitly incorporate the complementarity of multi-modal data for disease diagnosis. Often a varied order of importance for this heterogeneous data is considered for personalized decisions. Current learning-based methods have achieved better performance with uniform attention to individual information, but a very few have focused on patient-specific attention learning schemes for each modality. Towards this, we introduce a model which not only improves the disease prediction but also focuses on learning patient-specific order of importance for multi-modal data elements. In order to achieve this, we take advantage of LSTM-based attention mechanism and graph convolutional networks (GCNs) to design our model. GCNs learn multi-modal but class-specific features from the entire population of patients, whereas the attention mechanism optimally fuses these multi-modal features into a final decision, separately for each patient. In this paper, we apply the proposed approach for disease prediction task for Parkinson’s and Alzheimer’s using two public medical datasets.
Multi-view adaptive semi-supervised feature selection with the self-paced learning

In recent decades, semi-supervised feature selection (SSFS) has gained extensive research in the field of machine learning and computer vision. Most SSFS algorithms are based on graph-based semi-supervised learning (GSSL), and their performance depend heavily on the quality of the Laplacian weight graph. However, the Laplacian weight graph can't be changed once it is constructed, which greatly restricts the performance of SSFS. To address this defect, in this paper we propose a novel Multi-view Adaptive Semi-supervised Feature Selection (MASFS) algorithm, which introduces the self-paced learning (SPL) into SSFS to make the Laplacian weight graph adaptively change according to the current predicted information. Meanwhile, the MASFS algorithm utilizes the multi-view learning to effectively explore the complementary and related information contained in different views to enhance SSFS performance. We propose a valid iterative algorithm for optimizing the objective function, followed by the convergence analysis and the complexity analysis. To illustrate the effectiveness of the MASFS algorithm, some experiments are carried out on NUS-WIDE dataset and MSRA-MM2.0 dataset and the experimental results indicate that MASFS has better performance than other SSFS algorithms based on GSSL.
Negative Log Likelihood Ratio Loss for Deep Neural Network Classification

In deep neural network, the cross-entropy loss function is commonly used for classification. Minimizing cross-entropy is equivalent to maximizing likelihood under assumptions of uniform feature and class distributions. It belongs to generative training criteria which does not directly discriminate correct class from competing classes. We propose a discriminative loss function with negative log likelihood ratio between correct and competing classes. It significantly outperforms the cross-entropy loss on the CIFAR-10 image classification task.
Expert Finding Systems: A Systematic Review

The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.
How to securely outsource the extended euclidean algorithm for large-scale polynomials over finite fields

Cloud computing gives resource-constrained clients great conveniences to outsource exorbitant computations to a public cloud. The extended Euclidean algorithm with large-scale polynomials over finite fields is fundamental and widespread in computer science and cryptography, yet it is computationally overloaded for quantities of lightweight devices emerged with the dawn of internet of things (IoT). In this respect, we design an efficient outsourcing algorithm that enables a lightweight client to achieve this heavy computation with the assistance of a remote cloud server. By comprehensively employing the variable substitution, random polynomial-based blind technique and unimodular matrix transformation, our algorithm achieves the goals of cheating resistance, privacy preservation, and high efficiency. Concretely, our algorithm is based on single untrusted program model which avoids the too strong security assumption between multiple servers, and it enables the client to detect the cloud's misbehaviors with (optimal) probability 1. Also, Thorough theoretical analysis indicates that the algorithm provides robust input/output privacy. Moreover, the algorithm only needs one round interaction between the client and the cloud. Strict theoretical analysis and extensive experimental evaluations demonstrate our algorithm's practical efficiency and effectiveness. Finally, an important application of our algorithm on securely outsourcing the expensive key generation step of NTRU cryptosystem is given.
A systematic review of crime facilitated by the consumer Internet of Things

The nature of crime is changing—estimates suggest that at least half of all crime is now committed online. Once everyday objects (e.g. televisions, baby monitors, door locks) that are now internet connected, collectively referred to as the Internet of Things (IoT), have the potential to transform society, but this increase in connectivity may generate new crime opportunities. Here, we conducted a systematic review to inform understanding of these risks. We identify a number of high-level mechanisms through which offenders may exploit the consumer IoT including profiling, physical access control and the control of device audio/visual outputs. The types of crimes identified that could be facilitated by the IoT were wide ranging and included burglary, stalking, and sex crimes through to state level crimes including political subjugation. Our review suggests that the IoT presents substantial new opportunities for offending and intervention is needed now to prevent an IoT crime harvest.
Plane, Ray, and Point

We present Plane, Ray, and Point, a set of interaction techniques that utilizes shape constraints to enable quick and precise object alignment and manipulation in virtual reality. Users create the three types of shape constraints, Plane, Ray, and Point, by using symbolic gestures. The shape constraints are used like scaffoldings and limit and guide the movement of virtual objects that collide or intersect with them. The same set of gestures can be performed with the other hand, which allow users to further control the degrees of freedom for precise and constrained manipulation. The combination of shape constraints and bimanual gestures yield a rich set of interaction techniques to support object transformation. An exploratory study conducted with 3D design experts and novice users found the techniques to be useful in 3D scene design workflows and easy to learn and use.
Non-sparse label specific features selection for multi-label classification

In multi-label learning, extracting specific features for each label is a new strategy to construct discriminative classification models in recent years. Existing approaches usually consider that each label is only associated with a small subset of the original features. But in some applications this sparsity assumption does not hold, where non-sparse label specific features are more discriminative than sparse label specific features. In this paper, a novel feature selection-based approach is proposed to extract non-sparse label specific features. Firstly, we translate the logic labels to the numerical ones to convey more semantic information and embed the label correlations. Secondly, a linear regression is modeled to describe the discrimination of label specific features based on the numerical labels. To our best knowledge, it is one of the first attempts to utilize the numerical labels for extracting label specific features. Comprehensive experiments on several multi-label data sets clearly manifest that the superiority of our proposed algorithm.
AirContour

Recognizing in-air hand gestures will benefit a wide range of applications such as sign-language recognition, remote control with hand gestures, and “writing” in the air as a new way of text input. This article presents AirContour, which focuses on in-air writing gesture recognition with a wrist-worn device. We propose a novel contour-based gesture model that converts human gestures to contours in 3D space and then recognizes the contours as characters. Different from 2D contours, the 3D contours may have the problems such as contour distortion caused by different viewing angles, contour difference caused by different writing directions, and the contour distribution across different planes. To address the above problem, we introduce Principal Component Analysis (PCA) to detect the principal/writing plane in 3D space, and then tune the projected 2D contour in the principal plane through reversing, rotating, and normalizing operations, to make the 2D contour in right orientation and normalized size under a uniform view. After that, we propose both an online approach, AC-Vec, and an offline approach, AC-CNN, for character recognition. The experimental results show that AC-Vec achieves an accuracy of 91.6% and AC-CNN achieves an accuracy of 94.3% for gesture/character recognition, both outperforming the existing approaches.
Gaze-Assisted Typing for Smart Glasses

Text entry is expected to be a common task for smart glass users, which is generally performed using a touchpad on the temple or by a promising approach using eye tracking. However, each approach has its own limitations. For more efficient text entry, we present the concept of gaze-assisted typing (GAT), which uses both a touchpad and eye tracking. We initially examined GAT with a minimal eye input load, and demonstrated that the GAT technology was 51% faster than a two-step touch input typing method (i.e.,M-SwipeBoard: 5.85 words per minute (wpm) and GAT: 8.87 wpm). We also compared GAT methods with varying numbers of touch gestures. The results showed that a GAT requiring five different touch gestures was the most preferred, although all GAT techniques were equally efficient. Finally, we compared GAT with touch-only typing (SwipeZone) and eye-only typing (adjustable dwell) using an eye-trackable head-worn display. The results demonstrate that the most preferred technique, GAT, was 25.4% faster than the eye-only typing and 29.4% faster than the touch-only typing (GAT: 11.04 wpm, eye-only typing: 8.81 wpm, and touch-only typing: 8.53 wpm).
Numerically Stable Coded Matrix Computations via Circulant and Rotation Matrix Embeddings

Polynomial based methods have recently been used in several works for mitigating the effect of stragglers (slow or failed nodes) in distributed matrix computations. For a system with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> worker nodes where <inline-formula> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> can be stragglers, these approaches allow for an optimal recovery threshold, whereby the intended result can be decoded as long as any <inline-formula> <tex-math notation="LaTeX">$(n-s)$ </tex-math></inline-formula> worker nodes complete their tasks. However, they suffer from serious numerical issues owing to the condition number of the corresponding real Vandermonde-structured recovery matrices; this condition number grows exponentially in <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. We present a novel approach that leverages the properties of circulant permutation matrices and rotation matrices for coded matrix computation. In addition to having an optimal recovery threshold, we demonstrate an upper bound on the worst-case condition number of our recovery matrices which grows as <inline-formula> <tex-math notation="LaTeX">$\approx O(n^{s+5.5})$ </tex-math></inline-formula>; in the practical scenario where <inline-formula> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> is a constant, this grows polynomially in <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. Our schemes leverage the well-behaved conditioning of complex Vandermonde matrices with parameters on the complex unit circle, while still working with computation over the reals. Exhaustive experimental results demonstrate that our proposed method has condition numbers that are orders of magnitude lower than prior work.
ATM: An Attentive Translation Model for Next-Item Recommendation

Predicting what items a user will consume in the next time (i.e., next-item recommendation) is a crucial task for recommender systems. While the factorization method is a popular choice in recommendation, several recent efforts have shown that the inner product does not satisfy the triangle inequality, which may hurt the model's generalization ability. TransRec is a promising method to overcome this issue, which learns a distance metric to predict the strength of user-item interactions. Nevertheless, such method only uses the latest consumed item to model a user's short-term preference, which is insufficient for modeling fidelity. In this article, we propose a simple yet effective method named attentive translation model, to explicitly exploit high-order sequential information for next-item recommendation. Specifically, we construct a user-specific translation vector by accounting for multiple recent items, which encode more information about a user's short-term preference than the latest item. To aggregate multiple items into one representation, we devise a position-aware attention mechanism, learning different weights on items at different orders in a personalized way. Extensive experiments on four real-world datasets show that our method significantly outperforms several state-of-the-art methods.
A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps

In the aluminum reduction process, aluminum fluoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic efficiency. Making the decision on the amount of AlF3 addition (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into consideration a variety of interrelated functions; in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is difficult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have difficulty covering these complex causalities. In this work, a data and knowledge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy decision trees, which are used to amend the initial structure provided by experts. The state transition algorithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.
Knowledge Repository of Ontology Learning Tools from Text

Ontologies are one of the fundamental elements of the Semantic Web, and they have gained a lot of popularity and recognition because they are viewed as the answer to the need for interoperable semantics in modern information systems. The intermingling of techniques in areas such as natural language processing, information retrieval, machine learning, data mining, and knowledge representation provide a lot of possibilities for development of ontology learning approaches. A rise in focus on the ability to cope with the scale of Web data required for ontology learning forces the potential growth of cross-language research, emphasizing the automatic or semi-automatic generation of the tools dedicated to text mining and information extraction. This paper presents the integration of ontology learning tools from text in the knowledge repository to incorporate the applied techniques and outputs of an ontology learning algorithm into the one complex multifunctional solution. The proposed knowledge repository covers various applicability of existing techniques of learning ontologies from text, and offers competency question-based reasoning mechanism for individuals to specify their profiles of ontology learning tools. The validation stage is also provided in the form of applied reasoning.
Response activation and inhibition after exposure to virtual reality

The widespread availability of affordable head-mounted displays and easy access to virtual reality (VR) applications and games has significantly increased the use of such technology by the general public. Thus there is increasing interest in determining any risks of using such technology and any aftereffect from exposure. Head-mounted display manufacturers provide general usage guidance but this is ad hoc and there is limited recent evidence comparing early virtual environment studies with experiences from modern head-mounted displays. The primary objective of this study was to explore response activation and inhibition after participants experienced a typical virtual environment in a head-mounted display. Reaction times were collected with a robust cued go/nogo test as pre- and post-tests. Participants (n ​= ​22, female ​= ​11) played Minecraft VR for 15 ​minutes using an Oculus Rift headset. In contrast to other studies, the results showed no significant impact on reaction times across response activation or inhibition. However, evidence of participant fatigue in the reaction time tests was found. This work confirms safe use of virtual reality experiences in modern head-mounted displays for short duration exposures and identifies issues with reaction time testing that are in need of further investigation.
A quantum secure direct communication protocol using entangled beam pairs

We propose a feasible high-dimensional two-step quantum secure direct communication scheme by using the Einstein-Podolsky-Rosen entangled beam pair. Accoding to the quality of the two superposition forms of the EPR entangled state (two-mode squeezed vacuum state), we select the mutually unbiased bases for quantum measurement. With the unitary operation, we can subtly encode the confidential information in the quantum state. We also conducted a more detailed analysis of the security of this protocol.
Human Posture Recognition and fall detection Using Kinect V2 Camera

Based on the depth information and skeleton tracking technology of Microsoft Kinectv2 sensor, this paper performs human body gesture recognition and realizes human fall detection based on this. First, the depth of the Kinect V2 sensor is used to process the human joints produced by the skeleton tracker. Then the optimized BP neural network is used for posture recognition, and the fall is detected on this basis. The neural network is trained using the dataset generated by the Kinect tracker,but use a different body tracker (NITE) for testing. Finally, the posture recognition and fall detection were experimentally verified. We performed real-time testing (up to 3.5 meters) over the entire working range of the sensor. Through experiments, the overall accuracy of the NITE tracker for fall testing was 98.5%, and the worst accuracy was 97.3%.
Artificial neural networks based techniques for anomaly detection in Apache Spark

Abstract Late detection and manual resolutions of performance anomalies in Cloud Computing and Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose an artificial neural network based methodology for anomaly detection tailored to the Apache Spark in-memory processing platform. Apache Spark is widely adopted by industry because of its speed and generality, however there is still a shortage of comprehensive performance anomaly detection methods applicable to this platform. We propose an artificial neural networks driven methodology to quickly sift through Spark logs data and operating system monitoring metrics to accurately detect and classify anomalous behaviors based on the Spark resilient distributed dataset characteristics. The proposed method is evaluated against three popular machine learning algorithms, decision trees, nearest neighbor, and support vector machine, as well as against four variants that consider different monitoring datasets. The results prove that our proposed method outperforms other methods, typically achieving 98–99% F-scores, and offering much greater accuracy than alternative techniques to detect both the period in which anomalies occurred and their type.