Reviewerly/exHarmony huggingface dataset:
Resilient Mechanism Against Byzantine Failure for Distributed Deep Reinforcement Learning

Distributed deep reinforcement learning(DDRL) has been used in distributed systems to better improve the adaptability. However, DDRL-based systems are also inevitably under the threat of Byzantine workers. There is an urgent need to enhance the resilience of the DDRL-based system against Byzantine failures. This paper proposes a resilient mechanism for mitigating the influence of Byzantine workers on DDRL-based systems. First, we formalize the DDRL-based system as a multi-armed bandit model for well capturing the collective effect of workers on the whole learning process, and then transforming the resilient mechanism design problem into the sampling policy optimization problem. Second, we propose a self-adaptation process for filtering out the harmful data generated by Byzantine workers and theoretically give a mathematical analysis of the understanding, demonstrating its effectiveness under ideal conditions. Third, based on a typical DDRL-based system (i.e., Asynchronous Advantage Actor-Critic, A3C), we implement a resilient distributed A3C (ReD-A3C). With extensive experiments on the DDRL benchmark tasks, we show that ReD-A3C outperforms available Byzantine tolerant approaches.
COVID-19’s Impact on Geospatial Data: Ethics and Values

Mobile location data became a center point of debate in the midst of the COVID-19 pandemic. Unlike pandemics of the past, the advancement of cellular phone technology and its adoption by a large proportion of the world population caused many governments, health authorities, and companies to explore how phone-based geospatial data could be applied to prevent disease transmission. This chapter examines the values and ethics involved in the debates over whether and/or how to apply mobile location data to prevention efforts through contact tracing, quarantine enforcement, symptom checking, and flow modelling. Questions over the efficacy of these programs and their appropriateness to the public health crisis remain. The rollout of mandatory location-tracking applications in South Korea, Israel, and Taiwan, as well as the participation of data brokers in supplying governments and researchers with widespread location data, challenge the values of privacy, freedom of movement, and informed consent. The precedents set during the COVID-19 pandemic for the use of personal geospatial data and the infrastructure established to support its collection will follow us into the next world challenge.
Complex Event Summarization Using Multi-Social Attribute Correlation

Complex social event summarization is a problem which has been shown having great utility for real-world applications, including crisis management, rumor control and government policy tracking. In recent years there has been significant research effort spent on effectively extracting meaningful textual descriptions of an event. However, in many critical situations, social events are complex and context-sensitive, which demands the online summarization of social events in an integrated manner. In this paper, we propose the first online complex social event summarization approach, namely SOMA, which summarizes the complex social events over multiple attributes including media content and contexts simultaneously. Specifically, we first propose a deep learning model that comprehensively summarizes events in regards to the text description and locations that they appear in, by utilizing their hidden connections in posts. We then propose a summary generator over time, text and location to achieve a maximal coverage of the summary over the original social event and minimal redundancy of the summary. Furthermore, we propose a location estimation method to address the location sparsity issue of complex events by mining the correlation between text and location. The evaluation over four real-event datasets and three benchmark datasets shows that our proposed approach outperforms the existing solutions for event summarizaiton in terms of effectiveness and efficiency.
Mobile-Assisted Language Learning

Learning in today's world is equipped with novel ways and techniques to make learners attracted towards learning. Mobile-assisted language learning (MALL) has been considered as a primary tool for enhancing the performance of learners. However, this phenomenon is missing in developing nations. This chapter, therefore, will attempt to investigate the efficacy of MALL on Pakistani English language learners. Data were collected from the population of 120 learners belonging to private institutes in Lahore, Punjab, Pakistan. Firstly, a pre-test was performed, and two groups were formed, which were equally divided into 60 learners (experimental and controlled). After that, a post-test was conducted, and performance of conventional group and MALL learners was compared. It was found that the experimental group showed significant variations in their performance as compared to conventional learners. Therefore, from the results, it can be concluded that MALL can be integrated and effectively utilized in a local context.
Interact2Vec: Neural Item and User Embedding for Collaborative Filtering

Recommender systems gained great popularity in the last decade. However, despite the significant advances, they still have open problems, such as high data dimensionality and sparseness. Among several alternatives proposed to address these problems, the state-of-the-art solutions aim to represent items and users as dense vectors in a reduced dimensionality space. In this context, one of the most contemporary techniques is neural embeddings-based models, i.e., distributed vector representations generated through artificial neural networks. Many of the latest advances in this area have shown promising results compared to established approaches. However, most existing proposals demand complex neural architectures or content data, often unavailable. This paper presents the Interact2Vec, a new neural network-based model for concomitantly generating a distributed representation of users and items. It has the main advantage of being computationally efficient and only requires implicit user feedback. The results indicate a high performance comparable to other neural embeddings models that demand more significant computational power.
Role of corporate governance in human resource management with reference to Hindustan Shipyard Limited

Corporate governance is a critical component of effective human resource management practices. Since nothing can be said about the appropriate function of corporate governance regulations and standards without mentioning HRM, which are one of the grounds for achieving the organization’s planned objectives. In this context, it is necessary to consider an organization’s human resources, which include both corporate governance and employees, and can think of them as a subset of the settings where a business would try to boost investments in order to accomplish added value. In this purpose the present paper aims to study the role of corporate governance in human resource management with reference to employee perceptions in Hindustan Shipyard Limited. In this purpose a purposive sampling method has been followed to collect data from the selected respondents working in study organization. To reach the objectives of the study and test the hypothesis, descriptive and regression analyses have been done. Thus, the findings demonstrate corporate governance and human resources are interdependent, with each influencing and being influenced by the other.
Ramifications of Ease-of-Use, Access to, and Acceptance of 4IR Technologies in Science Teacher Preparation

Regardless of the need to re-direct Science, Technology, Engineering, and Mathematics (STEM) education toward rural-based Science teacher preparation, factors such as ease-of-use, access to, and acceptance of fourth industrial revolution (4IR) technologies receive minimal to no integration, compared to other industries (manufacturing, aviation, medicine, astrophysics, and cosmology). The commencement of the 4IR and its associated technologies is however, transforming virtually all facets of business and industries. Informed by the lack of adequate application of STEM in rural-based settings, the current research examines influencers of rural-based Science teachers’ preparedness in the context of 4IR technologies. Positioned upon the research aim, the selected methodology was primarily a systematic review. The review assessed both past and present literature via Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Anchored on the stated aim, the research develops a multi-level influence explanation in exploring STEM education which is fit for the rural-based Science teacher preparation. Because of the stated conclusion, the recommendation is that; acceptance, ease-of-use, and access as well as 4IR-related skills are significant influencers of rural-based Science teacher preparation and hence South Africa’s teacher training in STEM.
Gaussian Process-based Visual Pursuit Control with Automatic Gain Tuning

In this paper, we propose a vision-based pursuit control law with uncertainty estimates of the target motion by Gaussian process (GP) regression. We consider a situation where a robot equipped with a visual sensor pursues a target whose velocity is unknown. First, we introduce a GP-based target motion estimation. In addition, we propose an observer-based controller that automatically tunes the feedback gains by quantifying the upper bound on the uncertainty of the target motion with a GP estimate. Second, we provide the theoretical guarantee that the control error and the estimation error are uniformly ultimately bounded with high probability. Finally, we verify the effectiveness of the proposed controller via simulations and experiments.
Image Captioning State-of-the-Art: Is It Enough for the Guidance of Visually Impaired in an Environment?

Although image captioning has made great progress in describing images, current methods are limited in 2D recognition of salient and moving objects. This leads to sentences that lack information about static and background objects, with poor performance on words’ order and prepositions, which cannot be enough for blind people to completely understand a scene. They also don’t give precise information about spatial relationships of the detected objects, reducing by that the amount of information that the scene contains. In this paper, we will first explore the existing methods of state-of-the-art for image captioning, we will learn about the approaches, and highlight their shortcomings considering the egocentric guidance of blind and visually impaired people. We will explore and test some tools that may be needed in the future to augment them with new information and new functionalities that are needed by the visually impaired to be able to not only understand their environment, but to also move around it and have a complete awareness of the objects present in their scene.
Bayesian Computation Methods

In this chapter, we suppose that the reader is familiar with a brief description of Markov chains given in Chap. 2 . In Bayesian methods, the goal is often to compute the posterior distribution, a task made easier whenever conjugate priors are used as seen in Sect. 3.3 .
Detailed Study of a Porphyry Copper Deposit Using Airborne Imaging Spectroscopic Data

Porphyry copper deposits represent very large differentiated hydrothermal alteration footprints resulting from convective fluid circulation. Hyperspectral imaging spectroscopy can provide a rapid and effective tool for systematic mapping of mineralogical footprints of many mineral systems including the porphyry copper deposits at local to regional scales. To demonstrate the capability of airborne hyperspectral imagery in deposit scale exploration, a portion of the HyMap data collected over a porphyry copper deposit named Shadan in eastern Iran was selected. By using a combination of spectral unmixing techniques and feature extraction methods, a large suite of alteration minerals was detected over this deposit. This includes white mica abundance, composition, and crystallinity, kaolinite abundance and crystallinity, ferric oxide content and composition, together with jarosite, chlorite-epidote, amphiboles, and tourmaline abundances and distribution. The results of this study indicate that airborne hyperspectral imagery and their mineral map products can be used to delineate porphyry copper systems and target the most promising areas for further exploration activities.
Efficient Neural Networks with Spatial Wise Sparsity Using Unified Importance Map

Exploiting neural network sparsity is one of the most important directions to accelerate CNN executions. Plenty of techniques are proposed to exploit neural network sparsity, where spatial-wise pruning is quite effective for input image. However, previous spatial-wise pruning methods need nontrivial hardware overhead for dynamic execution, due to layer-by-layer binary sampling and online scheduling. This paper proposes a structured configured, spatial-wise pruning technique. Numerous computation will be saved by skipping unimportant region. By using a unified importance map, the computing graph could be compiled in advance to make it more hardware friendly. Additionally, due to multi-level measurement of importance for each region, our method can have a better performance on various tasks. On image classification task, the method can have around 50% fewer top-1 accuracy drop than previous spatialwise pruning methods at similar sparse level. On super resolution and image deraining task, the method can bring $5 \times$ to $19 \times$ acceleration while causing neglectable effect on reconstruction quality. Hardware implementation is also included.
Stochastic Consensus: Enhancing Semi-Supervised Learning with Consistency of Stochastic Classifiers

Semi-supervised learning (SSL) has achieved new progress recently with the emerging framework of self-training deep networks, where the criteria for selection of unlabeled samples with pseudo labels play a key role in the empirical success. In this work, we propose such a new criterion based on consistency among multiple, stochastic classifiers, termed Stochastic Consensus (STOCO). Specifically, we model parameters of the classifiers as a Gaussian distribution whose mean and standard deviation are jointly optimized during training. Due to the scarcity of labels in SSL, modeling classifiers as a distribution itself provides additional regularization that mitigates overfitting to the labeled samples. We technically generate pseudo labels using a simple but flexible framework of deep discriminative clustering, which benefits from the overall structure of data distribution. We also provide theoretical analysis of our criterion by connecting with the theory of learning from noisy data. Our proposed criterion can be readily applied to self-training based SSL frameworks. By choosing the representative FixMatch as the baseline, our method with multiple stochastic classifiers achieves the state of the art on popular SSL benchmarks, especially in label-scarce cases.
Measuring Global, Direct, Diffuse, and Ground-Reflected Irradiance Using a Reference Cell Array

We report on an updated field trial of a novel method for measuring global horizontal irradiance (GHI), direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and reflected horizontal irradiance (RHI) using an array of reference cells with no moving parts. The technique, similar to previously explored methods using multi-pyranometer array concepts, uses multiple reference cells facing different directions together with an analysis model that fits the data from the sensors to determine GHI, DNI, DHI, and RHI. In previous work we reported on a first test of this approach at a single site. In this work we report further progress with this method, including an updated configuration of the reference cell array that offers improved sensitivity, an updated analysis that does not require site-specific training, and early field test results from prototypes installed at two different locations with different latitude.
Sustainable Personalisation and Explainability in Dyadic Data Systems

Systems that rely on dyadic data, which relate entities of two types together, have become ubiquitously used in fields such as media services, tourism business, e-commerce, and others. However, these systems have had a tendency to be black-box systems, despite their objective of influencing people's decisions. There is a lack of research on providing personalised explanations to the outputs of systems that make use of such data, that is, integrating the idea of Explainable Artificial Intelligence into the field of dyadic data. Moreover, the existing approaches rely heavily on Deep Learning models for their training, reducing their overall sustainability. In this work, we propose a computationally efficient model which provides personalisation by generating explanations based on user-created images. In the context of a particular dyadic data system, the restaurant review platform TripAdvisor, we predict, for any (user, restaurant) pair, the review of the restaurant that is most adequate to present it to the user, based on their personal preferences. This model exploits the usage of efficient Matrix Factorisation techniques combined with feature-rich embeddings of the pre-trained Image Classification models, developing a method capable of providing transparency to dyadic data systems while reducing as much as 80% the carbon emissions of training compared to alternative approaches.
Disinformation in the Deployment and Mitigation of CBRNE Attacks in the Age of New Social Media (NSM)

The use of new social media in spreading disinformation has been highlighted as a potential weapon or at least an Impact Multiplier in their use of Chemical, Biological, Radiological, Nuclear and Explosive weaponry (hereafter “CBRNE”).
A Temporal Scores Network for Basketball Foul Classification

Deep learning has developed rapidly in recent years, not only in image recognition, but now also in action recognition. The research on action recognition started with 3D-CNN, which has achieved good results on many tasks. But most action recognition networks have room for improvement in fine-grained action recognition. The reason is that there is only a slight difference between categories in the fine-grained classification task. e.g. basketball fouls only occur in a few frames and a small region. This situation may lead to some errors with 3D-CNN methods because these models tend to merge all temporal features. To identify these fouls, it is necessary to strengthen the detection of small periods. In this paper, we propose a temporal score network suitable for existing networks, including 3D-Resnet50, 3D-wide-Resnet50, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}\mathbf{(}\mathbf{2}\mathbf{+}\mathbf{1}\mathbf{)}$</tex> D-Resnet50, and I3D-50 to improve the accuracy of fine-grained action recognition. The experimental results show that the accuracy of various models is improved by 3.85% to 6% after adding the proposed network. Since there is no relevant public dataset, we collect the data ourselves to create a basketball foul dataset.
Optimal Fusion-based Asymmetric Two-stream Networks for Multispectral Image Pedestrian Detection

As the fast developing of computer vision, pedestrian detection has become a hot topic of current research. Accurate and real-time detection of pedestrians from video sequences is of great practical importance in the field of intelligent video surveillance. Currently, most pedestrian detection systems are built on RGB images. However, RGB images are easily affected by light changes and weather conditions. In low-light scenes the quality is poor. Infrared information, however, relies only on the thermal radiation of the object and can clearly show the outline of the pedestrian. This paper therefore proposes a multispectral based pedestrian detection algorithm (multispectral images include RGB and IR images). Specifically, firstly, design an asymmetric two-stream network structure to extract RGB image and infrared image features respectively. Then a multimodal fusion mechanism is proposed to fuse RGB feature maps and IR feature maps for channel fusion. Experimental results demonstrate that our method improves the target detection properties. The map value reaches 0.955 on the dataset GTOT and the own proposed dataset School.
The use of famous paintings as means of cognitive visualization in teaching language

The relevance of the research topic is due to close attention of scientific and pedagogical public to the issues of cognitive education. Over the past years there has been sustainable interest to various methods of cognitive visualization and a search for new forms of work for the formation of cognitive interest and development of reflective mental activity. Purpose: to substantiate the effectiveness of the use of famous paintings as cognitive visual material to enhance students’ learning and cognitive activity at English classes and to present the types of tasks with them. Research methods: research methods are based on an analytical review of scientific sources on issues of cognitive education, the use of visual aids in the educational process and generalization of personal pedagogical experience of using the proposed tasks at English classes in the 1st year of the Faculty of Linguistics and Intercultural Communication of the Odintsovo branch of the Moscow State Institute of International Relations (University) of Ministry of Foreign Affairs of Russian Federation. Results: the article presents examples of tasks which generate interest, stimulate cognitive activity and contribute to the development of all components of communicative competence. It is concluded that within the framework of the communicative and cognitive approach, the use of paintings as visual visualization has great potential in the field of motivation, creation of a positive attitude towards learning and contributes to the effective solution of several educational and pedagogical tasks.
Efficient and Privacy-Preserving Outsourced Calculation with Multiple Keys Using Chinese Remainder Theorem

We propose a new scheme for privacy-preserving outsourced calculation. With the help of this scheme, a user on a local host can securely transfer his sensitive information to a cloud platform for processing and storage avoiding disclosing the original information to the unreliable cloud and prying eyes. In addition, the Cloud Platform (CP) can carry out specifical operations on ciphertexts with the help of a Cryptographic Service Provider (CSP) according to the user's requests and send the results to user in the form of ciphertext. Furthermore, data encrypted with multiple keys can also be calculated together, and the computed results will be decrypted by an authorized receiver. We not only implement addition and multiplication operations on ciphertexts, but also design an efficient comparison protocol on ciphertexts (the operations are all in encrypted domain). We then show the security of our encryption and the suggested secure computation sub-protocols. We use a one-way trapdoor permutation and a symmetric somewhat homomorphic encryption as the framework's building blocks rather than the fully homomorphic encryption (FHE) technique. Finally, we use simulations to show the effectiveness and extraordinary efficiency of our suggested scheme.