Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition Models in Ontology Networks

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies allowing for various data contextualisation, while edges are general-purpose computational procedures elaborating data. Arianna+ provides a flexible interface between the inputs and outputs of procedures and statements, which are atomic representations of ontological knowledge. Arianna+ schedules procedures on the basis of events by employing logic-based reasoning, that is, by checking the classification of certain statements in the ontologies. Each procedure involves input and output statements that are differently contextualized in the ontologies based on specific prior knowledge. Arianna+ allows to design networks that encode data within multiple contexts and, as a reference scenario, we present a modular network based on a spatial context shared among all activities and a temporal context specialized for each activity to be recognized. In the article, we argue that a network of small ontologies is more intelligible and has a reduced computational load than a single ontology encoding the same knowledge. Arianna+ integrates in the same architecture heterogeneous data processing techniques, which may be better suited to different contexts. Thus, we do not propose a new algorithmic approach to activity recognition, instead, we focus on the architectural aspects for accommodating logic-based and data-driven activity models in a context-oriented way. Also, we discuss how to leverage data contextualization and reasoning for activity recognition, and to support an iterative development process driven by domain experts.
Classification of Cervical Intraepithelial Neoplasia (CIN) using fine-tuned Convolutional Neural Networks

Convolutional Neural Network (CNN) is considered one of the most successful deep learning techniques used in classification or diagnosis of medical images. However, CNN requires a high computational resource and time; and a large dataset which most medical images (cervix) do not possess. In order to compensate for these shortcomings, we propose an optimized fine-tuned CNN model to classify cervix images into Cervical Intraepithelial Neoplasia grades (CIN 1,2,3) normal and cancerous cervix images. This classification ensures that patients are diagnosed correctly, and appropriate treatments are administered. Deep learning techniques such as Data Augmentation, 1 cycle policy for optimal learning rates selection, Discriminative Fine-Tuning, Mixed Precision Training were used to optimize the fine-tuned DenseNet CNN model. The model achieved 96.3% accuracy, the specificity of 98.86%, and sensitivity of 94.97% on the datasets.
QoE-Aware Traffic Aggregation Using Preference Logic for Edge Intelligence

Traffic flows with different requirements of quality of service (QoS requirements) are aggregated into different QoS classes to provide differentiated services (Diffserv) and better quality of experience (QoE) for users. The existing aggregation approaches/QoS mapping methods are based on quantitative QoS requirements and static QoS classes. However, they are typically qualitative and time-varying at the edge of the beyond fifth generation (B5G) networks. Therefore, the artificial intelligence technology of preference logic is applied in this paper to achieve an intelligent method for edge computing, called the preference logic based aggregation model (PLM), which effectively groups flows with qualitative requirements into dynamic classes. First, PLM uses preferences to describe QoS requirements of flows, and thus can deal with both quantitative and qualitative cases. Next, the potential conflicts in these preferences are eliminated. According to the preferences, traffic flows are finally mapped into dynamic QoS classes by logic reasoning. The experimental results show that PLM presents better performance in terms of QoE satisfaction compared with the existing aggregation methods. Utilizing preference logic to group flows, PLM implements a novel way of edge intelligence to deal with dynamic classes and improves the Diffserv for massive B5G traffic with quantitative and qualitative requirements.
Central Bank Digital Currency with Asymmetric Privacy

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Courts in Victoria, Australia, During COVID: Will Digital Innovation Stick?

We present a case-study of the swift digital response to COVID-19 restrictions by the courts in the State of Victoria, Australia's second-largest jurisdiction. We analyse the extent to which the management of this crisis (Step 1 in John Kotter's model of innovation) can serve as the catalyst for digital innovation in these courts. We contend that the history of innovation in Australia is of quick, pragmatic fixes which do not translate into systematic change. For example, although Australian courts are often credited with being pioneers in court technology, recourse to apparent 'virtual courts' before and during COVID is probably not truly innovative. Applying Boschma's theory about the 5 'proximities' which promote innovation — geographical, social, cognitive, institutional and organisational — we maintain that for these courts, those factors have, paradoxically, worked in the opposite direction to undermine technological innovation. However COVID has seen critical changes in a number of these elements, supported by ideological and practical concerns for courts. Taken together, we are cautiously optimistic that post-COVID, Kotter's final stage of "Making it Stick" through a technologically friendly legal culture which supports systematic and sustained court innovation, might just be possible if government is willing to fund a grander innovation agenda and has confidence in the courts' ability to carry it through.
Attributes Aware Face Generation with Generative Adversarial Networks

Recent studies have shown remarkable success in face image generations. However, most of the existing methods only generate face images from random noise, and cannot generate face images according to the specific attributes. In this paper, we focus on the problem of face synthesis from attributes, which aims at generating faces with specific characteristics corresponding to the given attributes. To this end, we propose a novel attributes aware face image generator method with generative adversarial networks called AFGAN. Specifically, we firstly propose a two-path embedding layer and self-attention mechanism to convert binary attribute vector to rich attribute features. Then three stacked generators generate 64 × 64, 128 × 128 and 256 × 256 resolution face images respectively by taking the attribute features as input. In addition, an image-attribute matching loss is proposed to enhance the correlation between the generated images and input attributes. Extensive experiments on CelebA demonstrate the superiority of our AFGAN in terms of both qualitative and quantitative evaluations.
Measurement of radius of curvature directly in the interferometer confocal position

This paper presents a new method for radius of curvature measurement by interferometers. The radius measurement is carried out directly in the interferometer confocal position without the need for a specific hardware and thus allows us to measure a much more diverse range of optical surfaces than standard methods. The method is based on measuring a number of phase maps and displacements at several steps through the confocal null position. Radius of curvature is then computed as the tangent slope of the measured defocus–displacement pair values in the confocal position. A relative accuracy of the method is approximately 0.05%, which makes the method suitable for a vast number of applications. Results of the method are verified using standard confocal cat’s eye technique.
Big data and democratic speech: Predicting deliberative quality using machine learning techniques

This article explores techniques for using supervised machine learning to study discourse quality in large datasets. We explain and illustrate the computational techniques that we have developed to facilitate a large-scale study of deliberative quality in Canada’s three northern territories: Yukon, Northwest Territories, and Nunavut. This larger study involves conducting comparative analyses of hundreds of thousands of parliamentary speech acts since the creation of Nunavut 20 years ago. Without computational techniques, we would be unable to conduct such an ambitious and comprehensive analysis of deliberative quality. The purpose of this article is to demonstrate the machine learning techniques that we have developed with the hope that they might be used and improved by other communications scholars who are interested in conducting textual analyses using large datasets. Other possible applications of these techniques might include analyses of campaign speeches, party platforms, legislation, judicial rulings, online comments, newspaper articles, and television or radio commentaries.
Deep learning for skeleton-based action recognition

Abstract In order to further improve the recognition rate of skeleton action recognition, and to break the limitation that most of the previous deep learning-based methods have the input content of human joint coordinates, we propose a human skeleton action recognition algorithm combining geometric features and LSTM networks. In this paper, we propose a human skeleton action recognition algorithm that combines geometric features with LSTM network. The algorithm selects geometric features based on the distances between joints and selected lines as the input of the network, and introduces a time-selective LSTM network for training. The ability to select the most recognizable temporal features using the time-selective LSTM network is demonstrated in the SBU Interaction dataset and UT Kinect dataset, achieving 99.46% and 99.30% recognition rates, respectively. The experimental results demonstrate the effectiveness of the method for human skeleton-based action recognition.
Using Facebook to Discuss Aspects of Industry Safety: How Women Journalists Enact Ethics of Care in Online Professional Space

This survey-based case study explored members’ motives for using a closed Facebook group specifically for women journalists. Particularly, we explored a motivation based on the feminist ethics of care and members’ experiences of harassment, job satisfaction, and confidence in discussing safety issues with supervisors. Results indicated that gender-specific professional support and the ethics of care were primary motives for group use, and the ethics of care motive positively predicted job satisfaction. Additionally, those who experienced in-person harassment were more likely to be motivated by ethics of care, and the gender-specific professional support motive was related to confidence in discussing safety threats.
Tarsier: Evolving Noise Injection in Super-Resolution GANs

Super-resolution aims at increasing the resolution and level of detail within an image. The current state of the art in general single-image super-resolution is held by NESRGAN+, which injects a Gaussian noise after each residual layer at training time. In this paper, we harness evolutionary methods to improve NESRGAN+ by optimizing the noise injection at inference time. More precisely, we use Diagonal CMA to optimize the injected noise according to a novel criterion combining quality assessment and realism. Our results are validated by the PIRM perceptual score and a human study. Our method outperforms NESRGAN+ on several standard super-resolution datasets. More generally, our approach can be used to optimize any method based on noise injection.
English to Bengali Neural Machine Translation Using Global Attention Mechanism

Neural Machine Translation (NMT), increased the accuracy of machine translation and recently become more popular in machine translation research community. An attention mechanism has been added with NMT and the result of MT is improving, respectively. Using different attention mechanism in encoder-decoder, along with NMT models are achieving good results considering mentionable improvements in source sentences during the translation. Translating from English to another language is a widely used machine translation problem. For this regard, English-Bengali translation is a researchable area in Bengali machine translation. Bengali is one of the usable languages in the world. Therefore, conversion of Bengali to other language is very essential for people. The translator is a way to understand the meaning of different language. But sometimes the translator does not provide the accurate Bengali translation for the corresponding English text. So, generating the correct English to Bengali translation requires more research. In this research, we build an attention-based NMT model for English to Bengali machine translation instead of the normal NMT model and that achieves 22.3 BELU for model evaluation. Also, we build a seq2seq model without attention for checking the machine performance on translation. That achieves 15.3 BELU for the applied dataset. The analysis shows that the attention-based seq2seq model performs better than seq2seq learning for English to Bengali translation.
Cross-People Mobile-Phone Based Airwriting Character Recognition

Airwriting using mobile phones has many applications in human-computer interaction. However, the recognition of airwriting character needs a lot of training data from user, which brings great difficulties to the pratical application. The model learnt from a specific person often cannot yield satisfied results when used on another person. The data gap between people is mainly caused by the following factors: personal writing styles, mobile phone sensors, and ways to hold mobile phones. To address the cross-people problem, we propose a deep neural network(DNN) that combines convolutional neural network(CNN) and bilateral long short-term memory(BLSTM). In each layer of the network, we also add an AdaBN layer which is able to increase the generalization ability of the DNN. Different from the original AdaBN method, we explore the feasibility for semi-supervised learning. We implement it to our design and conduct comprehensive experiments. The evaluation results show that our system can achieve an accuracy of 99% for recognition and an improvement of 10% on average for transfer learning between various factors such as people, devices and postures. To the best of our knowledge, our work is the first to implement cross-people airwriting recognition via motion sensor signal, which is a fundamental step towards ubiquitous sensing <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> This work is partially supported by The National Key Research and Development Program of China (Grant No. 2016YFB0502201), and the National Natural Science Foundation of China(Grant No. 61971316)..
Mathematical models for the improvement of detection techniques of industrial noise sources from acoustic images

In this paper, a procedure for the detection of the sources of industrial noise and the evaluation of their distances is introduced. The above method is based on the analysis of acoustic and optical data recorded by an acoustic camera. In order to improve the resolution of the data, interpolation and quasi interpolation algorithms for digital data processing have been used, such as the bilinear, bicubic, and sampling Kantorovich (SK). The experimental tests show that the SK algorithm allows to perform the above task more accurately than the other considered methods.
Automatic On-Chip Clock Network Optimization for Electromagnetic Side-Channel Protection

Commercial electronic design automation (EDA) tools typically focus on optimizing the power, area, and speed of integrated circuits (ICs). They rarely consider hardware security requirements. As such, existing EDA tools often directly or indirectly introduce security vulnerabilities. These security vulnerabilities can later be exploited by attackers to leak information or compromise the hardware root-of-trust. In this paper, we show how traditional EDA tools optimize power, area and speed (PAS) metrics in cryptographic circuits at the cost of introducing vulnerabilities to side-channel analysis (SCA) attacks. To balance hardware security with traditional performance metrics, we propose an automatic tool, called CAD4EM-CLK, to secure ICs against power and electromagnetic (EM) SCA attacks. The tool optimizes clock networks for both traditional design requirements and security constraints. To achieve this goal, we first theoretically analyze and model the relationship between on-chip clock networks and side-channel security. The developed model will then guide the CAD4EM-CLK tool to adjust clock network structures to spread the leakage out temporally, also lower its amplitude proportion, so as to help reduce the leaked information. The proposed automatic tool is then validated on various cryptographic circuits. We use layout-level simulation to assess side-channel leakage and the experimental results prove the effectiveness of our proposed tool for power and EM side-channel protection.
Machine Learning Based Product Classification for eCommerce

Product research in web shops involves categories as a helper system for search and browsing. The categorized offer reduces user’s mistakes and misled presentation. Hence, learning a product classifier to work with high precision and proper recall is of fundamental importance in order to provide high-quality shopping experience. However, products can be perfectly described they can miss users’ vocabulary when they do research. Thus, users’ content in form of search phrases has been utilized in order to extent a product data. Thus, original product names and classes have been supplemented with users’ search phrases, which changed classification accuracy in significantly positive direction but also slightly influencing on data consistency. The article presents new knowledge, research framework, machine learning classifiers selection and result analysis with implication for academia and some suggestions for business practitioners.
Memory Optimization Techniques for Computing Discrete Logarithms in Compressed SIKE

The supersingular isogeny-based key encapsulation (SIKE) suite stands as an attractive post- quantum cryptosystem with its relatively small public keys. Public key sizes in SIKE can further be compressed by computing pairings and solving discrete logarithms in certain subgroups of finite fields. This comes at a cost of precomputing and storing large discrete logarithm tables. In this paper, we propose several techniques to optimize memory requirements in computing discrete logarithms in SIKE, and achieve to reduce table sizes by a factor of 4. We implement our techniques and verify our theoretical findings.
Virtual reality as a psychotherapeutic tool: current uses and limitations

Virtual Reality (VR) has been defined as the use of technological interfaces to simulate the behaviour of 3D entities that interact in real time with a user immersed via sensorimotor channels. The aim of this study is to explore the possibilities and limitations in the use of VR systems in mental health treatment and research. We conducted a review of articles addressing the qualities of the current state of research regarding the efficacy and clinical applications of VR as a therapeutic tool. Despite finding general consensus about anxiety disorders, there's a lack of published empirical evidence regarding other mental health disorders. Nevertheless VR components are being steadily used more often for the assessment, treatment and research for more pathologies.
Adapting Behaviour of Socially Interactive Robot Based on Text Sentiment

The assessment of emotion is a complex process that encompasses information from various channels or sources, e.g., verbal, para-linguistic, non-verbal or even textual cues. Understanding the emotion of an interaction partner and exhibiting proper bodily reactions play a crucial role in establishing an engaging interaction. The evaluation of emotion, in general, is significant as far as emotionally intelligent human-robot interaction is concerned. In this paper, a text-based interaction system has been introduced to establish affective communication. A chatbot system has been implemented on a humanoid robot named ROBIN. The sentiment of the user input texts has been analyzed with MITIE machine learning toolkit. The robot considers emotional states of the users' text utterances and reacts with appropriate speech, gesture, and facial expressions. This triggers real-time affective behavior from the robot's perspective. The proposed system has been tested on several interaction scenarios to validate the approach from a human-centered perspective.
LIBRA: Clearing the Cloud Through Dynamic Memory Bandwidth Management

Modern Cloud Service Providers (CSP) heavily co-schedule tasks with different priorities on the same computing node to increase server utilization. To ensure the performance of high priority jobs, CSPs usually employ Quality-of-Service (QoS) mechanisms to manage or regulate the usage of shared hardware resources. Among the critical shared hardware resources, there has been very limited analysis on effective sharing of memory bandwidth among co-scheduled jobs, mainly for two reasons: (1) The correlation between application performance and its memory bandwidth allocation is complicated. (2) An effective hardware throttling mechanism for precise memory bandwidth control is unavailable. These limitations drive CSPs to design conservative policies to ensure the performance of the high priority tasks, which significantly degrades the throughput of batch jobs and reduces the overall benefits of workload co-scheduling. This paper proposes LIBRA, a holistic framework for dynamic memory bandwidth management in production data centers. LIBRA incorporates a novel hardware throttling mechanism, Dynamic Resource Control, to support self-adaptive memory bandwidth regulation. It also employs a lightweight control policy to further enhance the bandwidth scalability for the throttled tasks. Our evaluation results on a cluster demonstrate that LIBRA is capable of increasing the performance of batch jobs by up to 52.8% compared to existing QoS schemes.
A Deep Motion Deblurring Network Using Channel Adaptive Residual Module

In this paper, we solve the problem of dynamic scenes deblurring with motion blur.Restoration of images in the presence of motion blur necessitates a network design that the receptive field can completely cover all areas that need to be deblurred, while the existing network increases the receptive field by continuously stacking the ordinary convolutional layer or increasing the size of the convolution kernel.However, these methods inevitably increase the computational burden of the network.We propose a novel architecture consisting of a channel adaptive residual module.Different features of the blurred image are extracted and distributed on each feature channel.Our network can calculate the weight of each channel through learning, and extract the image features adaptively according to different degrees of blurring and importance of information.We embed the module in a modified encoder-decoder design with skip connections to achieve multi-scale feature fusion for further performance improvement.The extensive comparison with the existing techniques in the baseline dynamic scene deblurring dataset shows that the proposed network can effectively realize image deblurring, and the accuracy and speed are comparable with the existing techniques.