Reviewerly/exHarmony huggingface dataset:
Shining a Light on Policing of the Dark Web: An Analysis of UK Investigatory Powers

The dark web and the proliferation of criminals who have exploited its cryptographic protocols to commit crimes anonymously has created major challenges for law enforcement around the world. Traditional policing techniques have required amendment and new techniques have been developed to break the dark web’s use of encryption. As with all new technology, the law has been slow to catch up and police have historically needed to use legislation which was not designed with the available technology in mind. This paper discusses the tools and techniques police use to investigate and prosecute criminals operating on the dark web in the UK and the legal framework in which they are deployed. There are two specific areas which are examined in depth: the use of covert policing and hacking tools, known in the UK as equipment interference. The operation of these investigatory methods within the context of dark web investigations has not previously been considered in UK literature, although this has received greater analysis in the United States and Australia. The effectiveness of UK investigatory powers in the investigation of crimes committed on the dark web are analysed and recommendations are made in relation to both the law and the relevant Codes of Practice. The article concludes that while the UK has recently introduced legislation which adequately sets out the powers police can use during online covert operations and when hacking, the Codes of Practice need to specifically address the role these investigative tools play in dark web investigations. Highlighted as areas of particular concern are the risks of jurisdiction forum shopping and hacking overseas. Recommendations are made for reform of the Investigatory Powers Act 2016 to ensure clarity as to when equipment interference can be used to search equipment when the location of that equipment is unknown.
Cybersecurity threats in the auto industry: Tensions in the knowledge environment

The automotive industry is undergoing probably the most far-reaching changes that have ever affected it. The outward manifestations of change are the emergence of the connected vehicle as the norm rather than the province of up-market cars competing on the presence of innovative ICT-driven features, the race to develop fully autonomous vehicles (AVs) and the switch to more environmentally sustainable alternative fuel vehicles. The main enabler of these fundamental developments is technological change. But technology is also a driver of change, this is nowhere more obvious than in the application of ICTs in the transport arena. But technological changes of this magnitude also lead to changes in industry structure, a shift in the basis of competition from features and performance to functionalities, and a growing reliance on knowledge that is new to the traditional industry. This paper addresses one aspect of this milieu of developments, that is the emergence of cybersecurity threats to the modern highly-computerised vehicle. Countering such problems requires a high degree of knowledge sharing in the industry and particularly between car manufacturers and their supply networks. This article collects, synthesises and analyses some primary evidence from cybersecurity experts in the industry. The overall conclusion reached is that, in the view of auto-cybersecurity specialists, the level of knowledge-sharing is inadequate. The article presents a number of potential explanations for this phenomenon; the common feature of these explanations is the existence of significant tensions and lack of trust in the knowledge environment.
Multi-Stream and Enhanced Spatial-Temporal Graph Convolution Network for Skeleton-Based Action Recognition

In skeleton-based human action recognition, spatial-temporal graph convolution networks (ST-GCNs) have achieved remarkable performances recently. However, how to explore more discriminative spatial and temporal features is still an open problem. The temporal graph convolution of the traditional ST-GCNs utilizes only one fixed kernel which cannot completely cover all the important stages of each action execution. Besides, the spatial and temporal graph convolution layers (GCLs) are serial connected, which mixes information of different domains and limits the feature extraction capability. In addition, the input features like joints, bones, and their motions are modeled in existing methods, but more input features are needed for better performance. To this end, this article proposes a novel multi-stream and enhanced spatial-temporal graph convolution network (MS-ESTGCN). For each basic block of MS-ESTGCN, densely connected multiple temporal GCLs with different kernel sizes are employed to aggregate more temporal features. To eliminate the adverse impact of information mixing, an additional spatial GCL branch is added to the block and the spatial features can be enhanced. Furthermore, we extend the input features by employing relative positions of joints and bones. Consequently, there are totally six data modalities (joints, bones, their motions and relative positions) that can be fed into the network independently with a six-stream paradigm. The proposed method is evaluated on two large scale datasets: NTU-RGB+D and Kinetics-Skeleton. The experimental results show that our method using only two data modalities delivers state-of-the-art performance, and our methods using four and six data modalities further exceed other methods with a significant margin.
Time to Get Personal: Individualised Virtual Reality for Mental Health

Mental health conditions pose a major challenge to healthcare providers and society at large. Early intervention can have significant positive impact on a person's prognosis, particularly important in improving mental health outcomes and functioning for young people. Virtual Reality (VR) in mental health is an emerging and innovative field. Recent studies support the use of VR technology in the treatment of anxiety, phobia, eating disorders, addiction, and pain management. However, there is little research on using VR for supporting, treatment and prevention of depression - a field that is very much emerging. There is also very little work done in offering individualised VR experience to users with mental health issues. This paper proposes iVR, a novel individualised VR for improving users' self-compassion, and in the long run, their positive mental health. We describe the concept, design, architecture and implementation of iVR and outline future work. We believe this contribution will pave the way for large-scale efficacy testing, clinical use, and potentially cost-effective delivery of VR technology for mental health therapy in future.
Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization

Abstract Recommender systems have been an efficient strategy to deal with information overload by producing personalized predictions. Recommendation systems based on deep learning have accomplished magnificent results, but most of these systems are traditional recommender systems that use a single rating. In this work, we introduce a multi-criteria collaborative filtering recommender by combining deep neural network and matrix factorization. Our model consists of two parts: the first part uses a fused model of deep neural network and matrix factorization to predict the criteria ratings and the second one employs a deep neural network to predict the overall rating. The experimental results on two datasets, including a real-world dataset, show that the proposed model outperformed several state-of-the-art methods across different datasets and performance evaluation metrics.
Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis

Background Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients’ attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data. Objective This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. Methods The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data. Results The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse. Conclusions The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids.
Stable High-Speed Encryption Key Distribution via Synchronization of Chaotic Optoelectronic Oscillators

Synchronization of chaotic photonic systems can be used, practically indefinitely, to create and distribute unique encryption keys ($o\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}e\ensuremath{-}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}m\phantom{\rule{0}{0ex}}e$ $p\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}s$). However, typical photonic systems are highly susceptible to phase fluctuations and fundamentally limited in their key-generation rate. In this work, the authors demonstrate a fundamentally different approach based on chaotic optoelectronic oscillators (OEOs), with which highly stable synchronization can be achieved through commercial fiber-optic links. Contrary to current photonic systems, OEOs are $n\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}t$ limited by relaxation oscillations and allow significantly increased rates of key generation, for fast and stable data encryption.
Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm

With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches.
Combinatorial optimization via highly efficient quantum walks

This paper presents an approach to quantum approximate combinatorial optimization using interleaved continuous-time quantum walks and solution quality-dependent phase shifts. Combinatorial indexing functions are used to implement a quantum walk over the subspace of feasible solutions.
Knowledge Graph Embedding Compression

Knowledge graph (KG) representation learning techniques that learn continuous embeddings of entities and relations in the KG have become popular in many AI applications.With a large KG, the embeddings consume a large amount of storage and memory.This is problematic and prohibits the deployment of these techniques in many real world settings.Thus, we propose an approach that compresses the KG embedding layer by representing each entity in the KG as a vector of discrete codes and then composes the embeddings from these codes.The approach can be trained end-toend with simple modifications to any existing KG embedding technique.We evaluate the approach on various standard KG embedding evaluations and show that it achieves 50-1000x compression of embeddings with a minor loss in performance.The compressed embeddings also retain the ability to perform various reasoning tasks such as KG inference.
Application of Big Data Technology in Blended Teaching of College Students: A Case Study on Rain Classroom

Rain classroom is a big data tool that effectively connects the teacher with students throughout the teaching process. This paper mainly applies rain classroom in blended teaching of college students, and evaluates the application effect. Firstly, the authors set up a model of rain classroom, covering all three phases of the teaching process: before-class (B), in-class (I) and after-class (A). Next, the BIA model was applied to the course Film and Television Appreciation, and the key issues in each phase were explained. To evaluate the effect of the BIA model, two questionnaire surveys were carried out among engineering students in Chongqing Three Gorges University. The results show that rain classroom can greatly improve the learning effect of the target course in various aspects: the teacher could arouse the students’ learning interest by sending red packets, make students more attentive through limited-time quiz, and reduce the absence through random roll call; the students were actively involved in group activities and confident in presenting their findings; however, many students most students switched to other apps in the class. The research results provide new insights to the application of big data technology in college education.
To Pretrain or Not to Pretrain: Examining the Benefits of Pretrainng on Resource Rich Tasks

Pretraining NLP models with variants of Masked Language Model (MLM) objectives has recently led to a significant improvements on many tasks.This paper examines the benefits of pretrained models as a function of the number of training samples used in the downstream task.On several text classification tasks, we show that as the number of training examples grow into the millions, the accuracy gap between finetuning BERT-based model and training vanilla LSTM from scratch narrows to within 1%.Our findings indicate that MLM-based models might reach a diminishing return point as the supervised data size increases significantly.
Hypernymy Detection for Low-Resource Languages via Meta Learning

Hypernymy detection, a.k.a.lexical entailment, is a fundamental sub-task of many natural language understanding tasks.Previous explorations mostly focus on monolingual hypernymy detection on high-resource languages, e.g., English, but few investigate the lowresource scenarios.This paper addresses the problem of low-resource hypernymy detection by combining high-resource languages.We extensively compare three joint training paradigms and for the first time propose applying meta learning to relieve the low-resource issue.Experiments demonstrate the superiority of our method among the three settings, which substantially improves the performance of extremely low-resource languages by preventing over-fitting on small datasets.
A Generative Model for Joint Natural Language Understanding and Generation

Natural language understanding (NLU) and natural language generation (NLG) are two fundamental and related tasks in building task-oriented dialogue systems with opposite objectives: NLU tackles the transformation from natural language to formal representations, whereas NLG does the reverse.A key to success in either task is parallel training data which is expensive to obtain at a large scale.In this work, we propose a generative model which couples NLU and NLG through a shared latent variable.This approach allows us to explore both spaces of natural language and formal representations, and facilitates information sharing through the latent space to eventually benefit NLU and NLG.Our model achieves state-of-the-art performance on two dialogue datasets with both flat and tree-structured formal representations.We also show that the model can be trained in a semi-supervised fashion by utilising unlabelled data to boost its performance.
Generating Person Images with Appearance-aware Pose Stylizer

Generation of high-quality person images is challenging, due to the sophisticated entanglements among image factors, e.g., appearance, pose, foreground, background, local details, global structures, etc. In this paper, we present a novel end-to-end framework to generate realistic person images based on given person poses and appearances. The core of our framework is a novel generator called Appearance-aware Pose Stylizer (APS) which generates human images by coupling the target pose with the conditioned person appearance progressively. The framework is highly flexible and controllable by effectively decoupling various complex person image factors in the encoding phase, followed by re-coupling them in the decoding phase. In addition, we present a new normalization method named adaptive patch normalization, which enables region-specific normalization and shows a good performance when adopted in person image generation model. Experiments on two benchmark datasets show that our method is capable of generating visually appealing and realistic-looking results using arbitrary image and pose inputs.
Harvesting and Refining Question-Answer Pairs for Unsupervised QA

Question Answering (QA) has shown great success thanks to the availability of largescale datasets and the effectiveness of neural models.Recent research works have attempted to extend these successes to the settings with few or no labeled data available.In this work, we introduce two approaches to improve unsupervised QA.First, we harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a corpus of question-answer pairs (named as REFQA).Second, we take advantage of the QA model to extract more appropriate answers, which iteratively refines data over RE-FQA.We conduct experiments 1 on SQuAD 1.1, and NewsQA by fine-tuning BERT without access to manually annotated data.Our approach outperforms previous unsupervised approaches by a large margin and is competitive with early supervised models.We also show the effectiveness of our approach in the fewshot learning setting.
Effective Estimation of Deep Generative Language Models

Advances in variational inference enable parameterisation of probabilistic models by deep neural networks.This combines the statistical transparency of the probabilistic modelling framework with the representational power of deep learning.Yet, due to a problem known as posterior collapse, it is difficult to estimate such models in the context of language modelling effectively.We concentrate on one such model, the variational auto-encoder, which we argue is an important building block in hierarchical probabilistic models of language.This paper contributes a sober view of the problem, a survey of techniques to address it, novel techniques, and extensions to the model.To establish a ranking of techniques, we perform a systematic comparison using Bayesian optimisation and find that many techniques perform reasonably similar, given enough resources.Still, a favourite can be named based on convenience.We also make several empirical observations and recommendations of best practices that should help researchers interested in this exciting field.
Effects of Trunk Motion, Touch, and Articulation on Upper-Limb Velocities and on Joint Contribution to Endpoint Velocities During the Production of Loud Piano Tones

Piano performance involves several levels of motor abundancy. Identification of kinematic strategies that enhance performance and reduce risks of practice-related musculoskeletal disorders (PRMD) represents an important research topic since more than half of professional pianists might suffer from PRMD during their career. Studies in biomechanics have highlighted the benefits of using proximal upper-limb joints to reduce the load on distal segments by effectively creating velocity and force at the finger-key interaction. If scientific research has documented postural and expressive features of pianists' trunk motion, there is currently a lack of scientific evidence assessing the role of trunk motion in sound production and in injury prevention. We address this gap by integrating motion of the pelvis and thorax in the analysis of both upper-limb linear velocities and joint angular contribution to endpoint velocities. Specifically, this study aims to assess kinematic features of different types of touch and articulation and the impact of trunk motion on these features. Twelve pianists performed repetitive loud and slow-paced keystrokes. They were asked to vary (i) body implication (use of trunk and upper-limb motion or use of only upper-limb motion), (ii) touch (struck touch, initiating the attack with the fingertip at a certain distance from the key surface, or pressed touch, initiating the attack with the fingertip in contact with the key surface), and (iii) articulation (staccato, short finger-key contact time, or tenuto, sustained finger-key contact time). Data were collected using a 3D motion capture system and a sound recording device. Results show that body implication, touch, and articulation modified kinematic features of loud keystrokes, which exhibited not only downward but also important forward segmental velocities (particularly in pressed touch and staccato articulation). Pelvic anterior rotation had a prominent role in the production of loud tones as it effectively contributed to creating forward linear velocities at the upper limb. The reported findings have implications for the performance, teaching, and research domains since they provide evidence of how pianists' trunk motion can actively contribute to the sound production and might not only be associated with either postural or expressive features.