Reviewerly/exHarmony huggingface dataset:
Behavioural biometrics in action

The importance of identifying and authenticating individuals has increased significantly with the rising levels of terrorism and cyber-crime. At the same time, the growth in the Internet of Things (IoT) will ensure an increase in the number of connections, meaning that billions of connected devices will need to be secured in the years ahead.
QA2Explanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph

In the era of Big Knowledge Graphs, Question Answering (QA) systems have reached a milestone in their performance and feasibility.However, their applicability, particularly in specific domains such as the biomedical domain, has not gained wide acceptance due to their "black box" nature, which hinders transparency, fairness, and accountability of QA systems.Therefore, users are unable to understand how and why particular questions have been answered, whereas some others fail.To address this challenge, in this paper, we develop an automatic approach for generating explanations during various stages of a pipeline-based QA system.Our approach is a supervised and automatic approach which considers three classes (i.e., success, no answer, and wrong answer) for annotating the output of involved QA components.Upon our prediction, a template explanation is chosen and integrated into the output of the corresponding component.To measure the effectiveness of the approach, we conducted a user survey as to how non-expert users perceive our generated explanations.The results of our study show a significant increase in the four dimensions of the human factor from the Human-computer interaction community.
Post treatment of anaerobic effluents: common applications and future perspectives for municipal wastewater treatment

The anaerobic process is considered to be a sustainable technology for organic waste treatment mainly due to its lower energy consumption and production of residual solids coupled with the prospect of energy recovery from the biogas generated. However, the anaerobic process cannot be seen as providing the ‘complete’ solution as its treated effluents would typically not meet the desired discharge limits in terms of residual carbon, nutrients and pathogens. This has given impetus to subsequent post treatment in order to meet the environmental legislations and protect the receiving water bodies and environment.This book discusses anaerobic treatment from the perspective of organic wastes and wastewaters (municipal and industrial) followed by various post-treatment options for anaerobic effluent polishing and resource recovery. Coverage will also be from the perspective of future trends and thoughts on anaerobic technologies being able to support meeting the increasingly stringent disposal standards. The resource recovery angle is particularly interesting as this can arguably help achieve the circular economy. It is intended the information can be used to identify appropriate solutions for anaerobic effluent treatment and possible alternative approaches to the commonly applied post-treatment techniques. The succeeding discussion is intended to lead on to identification of opportunities for further research and development.This book can be used as a standard reference book and textbook in universities for Master and Doctoral students. The academic community relevant to the subject, namely faculty, researchers, scientists, and practicing engineers, will find the book both informative and as a useful source of successful case studies.ISBN: 9781780409733 (paperback)ISBN: 9781780409740 (eBook)ISBN: 9781780409757 (ePub)
Application of VR and AR Technologies in Educational Process

Education system is constantly developing and now people can use new AR and VR technologies to improve their learning process.Augmented reality, abbreviated AR, is a combination of the real world and additional data "entered" in the area that the user sees.VR is generally defined as an interactive computer simulation.They have many advantages and can be used both for training in simulators through special VR devices and for designing something new.There are many platforms for developing augmented reality: 8th Wall, ARToolKit X, VUFORIA, ARCore, Unreal Engine.The list of all possible platforms is still incomplete, because the list is growing every year.T-FLEX is a Russian computer-aided design system.It includes CAD/CAM/CAE/CAPP/PDM /CRM/PM/MDM systems.T-FLEX CAD has all the necessary modules that are not available in other systems.For example, the T-FLEX VR module can contribute to product development at all stages of creation.This technology allows studenys to take a different look at the details and entire design process.
Dual Inference for Improving Language Understanding and Generation

Natural language understanding (NLU) and Natural language generation (NLG) tasks hold a strong dual relationship, where NLU aims at predicting semantic labels based on natural language utterances and NLG does the opposite.The prior work mainly focused on exploiting the duality in model training in order to obtain the models with better performance.However, regarding the fast-growing scale of models in the current NLP area, sometimes we may have difficulty retraining whole NLU and NLG models.To better address the issue, this paper proposes to leverage the duality in the inference stage without the need of retraining.The experiments on three benchmark datasets demonstrate the effectiveness of the proposed method in both NLU and NLG, providing the great potential of practical usage. 1
Conversational Document Prediction to Assist Customer Care Agents

A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users' needs.We study the task of predicting the documents that customer care agents can use to facilitate users' needs.We also introduce a new public dataset 1 which supports the aforementioned problem.Using this dataset and two others, we investigate state-of-the-art deep learning (DL) and information retrieval (IR) models for the task.We also analyze the practicality of such systems in terms of inference time complexity.Our results show that an hybrid IR+DL approach provides the best of both worlds.
Linear Disentangled Representations and Unsupervised Action Estimation

Disentangled representation learning has seen a surge in interest over recent times, generally focusing on new models to optimise one of many disparate disentan- glement metrics. It was only with Symmetry Based Disentangled Representation Learning that a robust mathematical framework was introduced to define precisely what is meant by a “linear disentangled representation”. This framework deter- mines that such representations would depend on a particular decomposition of the symmetry group acting on the data, showing that actions would manifest through irreducible group representations acting on independent representational subspaces. Caselles-Dupre et al. [2019] subsequently proposed the first model to induce and demonstrate a linear disentangled representation in a VAE model. In this work we empirically show that linear disentangled representations are not present in standard VAE models and that they instead require altering the loss landscape to induce them. We proceed to show that such representations are a desirable property with regard to classical disentanglement metrics. Finally we propose a method to induce irreducible representations which forgoes the need for labelled action sequences, as was required by prior work. We explore a number of properties of this method, including the ability to learn from action sequences without knowledge of intermediate states and robustness under visual noise. We also demonstrate that it can successfully learn 4 different symmetries directly from pixels.
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration

Discrete structures play an important role in applications like program language modeling and software engineering. Current approaches to predicting complex structures typically consider autoregressive models for their tractability, with some sacrifice in flexibility. Energy-based models (EBMs) on the other hand offer a more flexible and thus more powerful approach to modeling such distributions, but require partition function estimation. In this paper we propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data, where parameter gradients are estimated using a learned sampler that mimics local search. We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration, achieving a better trade-off between flexibility and tractability. Experimentally, we show that learning local search leads to significant improvements in challenging application domains. Most notably, we present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
AlgoLabel: A Large Dataset for Multi-Label Classification of Algorithmic Challenges

While semantic parsing has been an important problem in natural language processing for decades, recent years have seen a wide interest in automatic generation of code from text. We propose an alternative problem to code generation: labelling the algorithmic solution for programming challenges. While this may seem an easier task, we highlight that current deep learning techniques are still far from offering a reliable solution. The contributions of the paper are twofold. First, we propose a large multi-modal dataset of text and code pairs consisting of algorithmic challenges and their solutions, called AlgoLabel. Second, we show that vanilla deep learning solutions need to be greatly improved to solve this task and we propose a dual text-code neural model for detecting the algorithmic solution type for a programming challenge. While the proposed text-code model increases the performance of using the text or code alone, the improvement is rather small highlighting that we require better methods to combine text and code features.
Moving Vehicle Tracking Optimization Method Based on SPF

In the intelligent transportation system, the license information can be automatically recognized by the computer and the vehicle can be tracked. Red light running, illegal change of lanes, vehicle retrograde, and other illegal driving events are reasonably recorded. This is undoubtedly an effective help for the traffic police to relieve the huge work pressure. However, in China, a considerable number of vehicle tracking methods have certain limitations in resisting complex external environmental influences. The external environmental factors include but not limited to variable factors such as camera movement, jitter, and severe rain and snow. These factors cannot be controlled well, so the tracking accuracy is greatly reduced. In regard to this, this paper proposes an optimization method for moving vehicle tracking based on SPF. First, according to the size of the overlapping area of the motion area between the two images, the researcher can construct and simplify the vertex adjacency matrix that reflects the characteristics of the undirected bipartite graph. Then according to the corresponding relationship between the vertex adjacency matrix and the regional behavior and vehicle behavior, the researcher completes the regional behavior analysis and vehicle behavior analysis. On this basis, a particle filter vehicle tracking algorithm based on segmentation compensation is introduced, and the vector sum of the tracked segmentation area is used as the final position of the target vehicle. In this way, as many scattered particles fall on the target area as possible, which will greatly improve the efficiency of particle utilization, enhance tracking accuracy, and avoid the problem of tracking failure caused by too fast vehicle movement. Through experimental simulation, it can be seen that the method proposed in this paper can greatly enhance the vehicle tracking ability when tracking vehicles in “complex environments.”
Classification using ensemble learning under weighted misclassification loss

Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy (ART) requires periodic assessment of treatment failure, defined as having a viral load (VL) value above a certain threshold. In some resource limited settings, VL tests may be limited by cost or technology, and diagnoses are based on other clinical markers. Depending on scenario, higher premium may be placed on avoiding false-positives which brings greater cost and reduced treatment options. Here, the optimal rule is determined by minimizing a weighted misclassification loss/risk. We propose a method for finding and cross-validating optimal binary classification rules under weighted misclassification loss. We focus on rules comprising a prediction score and an associated threshold, where the score is derived using an ensemble learner. Simulations and examples show that our method, which derives the score and threshold jointly, more accurately estimates overall risk and has better operating characteristics compared with methods that derive the score first and the cutoff conditionally on the score especially for finite samples.
Agent Assist through Conversation Analysis

Customer support agents play a crucial role as an interface between an organization and its end-users.We propose CAIRAA: Conversational Approach to Information Retrieval for Agent Assistance, to reduce the cognitive workload of support agents who engage with users through conversation systems.CAIRAA monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client.We combine traditional information retrieval (IR) approaches with more recent Deep Learning (DL) models to ensure high accuracy and efficient run-time performance in the deployed system.Here, we describe the CAIRAA system and demonstrate its effectiveness in a pilot study via a short video 1 .
Analysis of The Effect of Covid-19 Towards L2 English Speaking Performance

Speaking is one of important factors in communicating. Like English Speaking as a international language and student and lecturer must know how to apply it . Students need to practice after found the treatment from lecturer, face to face. But, teaching Speaking in current situation of Pandemic Covid-19, face some obstacle on it. The pandemic made as have to social distancing and online learning. This research aims to analysis the effect of Covid-19 toward L2 speaking performance which can improve speaking ability on this pandemic, even in difficult situations like learning by virtual. This research method is descriptive research. Conducted by using google form or online. The findings result of this research, was English speaking class in Pandemic Covid-19, more difficult, then normal era. Students or teacher cannot here clearly, about discussion. It was depending on signal as a technology product. Besides, students lack of vocabularies and did not know of pronounce some vocabularies.
Rough‐set and machine learning‐based approach for optimised virtual machine utilisation in cloud computing

To meet rising demands for computing resources, information technology service providers need to select cloud-based services for their vitality and elasticity. Enormous numbers of data centres are designed to meet customer needs. Burning up energy by data centre is very high with the large-scale deployment of cloud data centres. Virtual machine consolidation strategy implementation reduces the data centre energy consumption and guarantees service level agreements. This study proposes a machine learning-based method in cloud computing for the automated use of virtual machines. Machine learning-based virtual machine selection approach integrates the migration control mechanism that enhances selection strategy efficiency. The experiment is performed with various real machine workload circumstances to provide proof and effectiveness of the proposed method. The exploratory outcome shows that the proposed approach streamlines the utilisation of the virtual machine and diminishes the consumption of energy and improves infringement of service level agreements to accomplish better performance.
The trend of artificial intelligence method in computer aided diagnosis

Computer aided diagnosis (CAD) has become an important aspect of medical imagology development. This technology has advantages in precise quantitative analysis, reproducibility and physician workload reduction. In this paper, the research literature of computer aided diagnosis based on artificial intelligence has been reviewed, where the typical artificial intelligence algorithm procedure and related technologies in medical image-based CAD system have also been introduced. We also analysed the weaknesses and challenges of the current CAD system, and proposed the solutions and suggestions to counter these deficiencies. The recent study indicates that compared with the traditional supervised learning method, the semi-supervised active learning is more suitable to the practical requirements of clinical tasks. It will significantly reduce the implementation cost of the CAD system. Therefore, we believe that the research and development semi-supervised active learning method are of great significance to the medical image-based CAD system.
Construction and Practice of Public Service Platform Based on Trusted Blockchain

Abstract In recent years, the digital economy has experienced the era of digital economy 1.0, the era of all-round electronization brought about by the rise of personal computers, and the era of 2.0, the era of all-round data brought about by the popularity of the Internet and mobile Internet, and entered the era of 3.0, the era of “digital right economy”. In the era of digital rights economy, all aspects of social and economic life will create new ecology and models. Digital identity and digital governance, digital asset transaction circulation, digital payment and settlement, and digital industry integration constitute the core sector of digital rights economic development. Blockchains has become the preferred model for establishing digital identity and governance due to its characteristics of multi-party co-governance, distributed storage, transparent rules, and permanent and true data persistence. This paper proposes to solve the problem of economic governance derived from the era of digital economy by building a blockchain service platform. The main achievements of this paper are to create new business models, actively explore the deepening application of blockchain technology in the field of energy and power, break through data barriers, activate data value, develop digital economy, and promote data fusion and data resource integration in the field of energy and power.
FleXOR: Trainable Fractional Quantization

Quantization based on the binary codes is gaining attention because each quantized bit can be directly utilized for computations without dequantization using look-up tables. Previous attempts, however, only allow for integer numbers of quantization bits, which ends up restricting the search space for compression ratio and accuracy. In this paper, we propose an encryption algorithm/architecture to compress quantized weights so as to achieve fractional numbers of bits per weight. Decryption during inference is implemented by digital XOR-gate networks added into the neural network model while XOR gates are described by utilizing $\tanh(x)$ for backward propagation to enable gradient calculations. We perform experiments using MNIST, CIFAR-10, and ImageNet to show that inserting XOR gates learns quantization/encrypted bit decisions through training and obtains high accuracy even for fractional sub 1-bit weights. As a result, our proposed method yields smaller size and higher model accuracy compared to binary neural networks.
Promoting innovation and application of Internet of Things in an organization; Case Study from Petrochemical Commercial Company (PCC)

Objective: The purpose of the study was to investigate factors promoting innovation and application of internet of things (IOT) in Petrochemical Commercial Company (PCC) which was as an organization in which massive various data is daily exchanged and processed. Methodology: Information professionals, information system technologists and queuing functions that normally consume big data and technological resources were involved in the process of data collection using structured questionnaire and content analysis. Results: The results of this study showed that the software development, potential opportunities and capabilities, specific features of IOT, auxiliary technologies, effective technologies and management strategies have tremendous impact on internet of things. Conclusion: Moreover, fitting test showed that the research model has properly fitted the collected data; however, this study suffered from some limitations which were basically beyond the control of the research.
A Superpixel-Wise Just Noticeable Distortion Model

The just noticeable distortion (JND) model reveals the visibility limitation. Human eyes hold different attention and sensitivity to different regions owing to different contributions to the perceptual quality. In this paper, a superpixel-wise JND model based on region (RJND) is proposed. First, an image is segmented into superpixels by the simple linear iterative clustering (SLIC). Then, region color contrast is calculated for each region and foveation regions are selected for the image. Based on the human visual perception, a region weighting model is established by incorporating region color contrast and foveation regions modulation. Considering the contrast masking (CM) effect is not perfect, we introduce the texture coarseness combined with CM effect for a more accurate visual masking effect. Finally, a new region JND model is established by combining the region weighting model and the coarseness modulation. The experimental results demonstrate the proposed RJND model can decrease PSNR more efficiently compared with some existing JND models when achieving nearly the same subjective perceptual quality. It can remove much more visual redundancy.
Adversarial Text Generation via Probability Determined Word Saliency

Deep learning (DL) technology has been widely deployed in many fields and achieved great success, but it is not absolutely safe and reliable. It has been proved that research on adversarial attacks can reveal the vulnerability of deep neural networks (DNN). Although many methods of adversarial attack and defense have been proposed in the field of images, the research on textual adversarial samples is still few. It is challenging because text samples are sparse and discrete and the added perturbation might lead to grammatical errors and semantic changes. Thus, there are some special restrictions on textual adversarial samples. We propose a synonyms substitution-based adversarial text generation via Probability Determined Word Saliency (PDWS). In our method PDWS, the word saliency and the optimal substitution word are determined by the optimal replace-ment effect. The replacement effect is the probability change caused by replacing one word with its substitution word. We evaluate our attack method on two popular text classification tasks using CNN and LSTM. The experimental results show that our method gets higher misleading rate and less perturbation rate than the baseline methods.