Reviewerly/exHarmony huggingface dataset:
Distributed Multi-Agent Reinforcement Learning by Actor-Critic Method

We investigate the problem of multi-agent reinforcement learning, in which each agent only has access to its local reward and can only communicate with its nearby neighbors. A distributed algorithm based on actor-critic method has been developed to enable all agents to cooperatively learn a control policy that maximizes the global objective function. Simulations are also provided to validate the proposed algorithm.
Depth Map Enhancement by Revisiting Multi-Scale Intensity Guidance Within Coarse-to-Fine Stages

Being different from the most methods of guided depth map enhancement based on deep convolutional neural network which focus on increasing the depth of networks, this paper is to improve the effectiveness of intensity guidance when the network goes deep. Overall, the proposed network upsamples the low-resolution depth maps from coarse to fine. Within each refinement stage of certain-scale depth features, the current-scale and all coarse-scales of the guidance features are revisited by dense connection. Therefore, the multi-scale guidance is efficiently maintained as the propagation of features. Furthermore, the proposed network maintains the intensity features in the high-resolution domain from which the multi-scale guidance is directly extracted. This design further improves the quality of intensity guidance. In addition, the shallow depth features upsampled via transposed convolution layer are directly transferred to the final depth features for reconstruction, which is called global residual learning in feature domain. Similarly, the global residual learning in pixel domain learns the difference between the depth ground truth and the coarsely upsampled depth map. Also, the local residual learning is to maintain the low frequency within each refinement stage and progressively recover the high frequency. The proposed method is tested for noise-free and noisy cases which compares against 16 state-of-the-art methods. Our experimental results show the improved performances based on the qualitative and quantitative evaluations.
Vabis: Video Adaptation Bitrate System for Time-Critical Live Streaming

With the rise of time-critical and interactive scenarios, ultra-low latency has become the most urgent requirement. Adaptive bitrate (ABR) schemes have been widely used in reducing latency for live streaming services. However, the traditional solutions suffer from a key limitation: they only utilize coarse-grained chunk to solve the I-frame misalignment problem in different bitrate switching process at the cost of increasing latency. As a result, existing schemes are difficult to guarantee the timeliness and granularity of control in essence. In this paper, we use a frame-based approach to solve the I-frame misalignment problem and propose a video adaptation bitrate system (Vabis) in units of the frame for time-critical live streaming to obtain the optimal quality of experience (QoE). On the server-side, a Few-Wait ABR algorithm based on Reinforcement Learning (RL) is designed to adaptively select the bitrate of future frames by state information that can be observed, which can subtly solve the problem of I-frame misalignment. A rule-based ABR algorithm is designed to optimize the Vabis system for the weak network. On the client-side, three delay control mechanisms are designed to achieve frame-based fine-grained control. We construct a trace-driven simulator and the real live platform to evaluate the comprehensive live streaming performance. The results show that Vabis is significantly better than the existing methods with decreases in an average delay of 32%-77% and improvements in average QoE of 28-67%.
Language-Agnostic Representation Learning for Product Search on E-Commerce Platforms

Product search forms an indispensable component of any e-commerce service, and helps customers find products of their interest from a large catalog on these websites. When products that are irrelevant to the search query are surfaced, it leads to a poor customer experience, thus reducing user trust and increasing the likelihood of churn. While identifying and removing such results from product search is crucial, doing so is a burdensome task that requires large amounts of human annotated data to train accurate models. This problem is exacerbated when products are cross-listed across countries that speak multiple languages, and customers specify queries in multiple languages and from different cultural contexts. In this work, we propose a novel multi-lingual multi-task learning framework, to jointly train product search models on multiple languages, with limited amount of training data from each language. By aligning the query and product representations from different languages into a language-independent vector space of queries and products, respectively, the proposed model improves the performance over baseline search models in any given language. We evaluate the performance of our model on real data collected from a leading e-commerce service. Our experimental evaluation demonstrates up to 23% relative improvement in the classification F1-score compared to the state-of-the-art baseline models.
Down-Sampling Based Video Coding with Degradation-Aware Restoration-Reconstruction Deep Neural Network

Recently deep learning techniques have shown remarkable progress in image/video super-resolution. These techniques can be employed in a video coding system for improving the quality of the decoded frames. However, different from the conventional super-resolution works, the compression artifacts in the decoded frames should be concerned with. The straightforward solution is to integrate the artifacts removing techniques before super-resolution. Nevertheless, some helpful features may be removed together with the artifacts, and remaining artifacts can be exaggerated. To address these problems, we design an end-to-end restoration-reconstruction deep neural network (RR-DnCNN) using the degradation-aware techniques. RR-DnCNN is applied to the down-sampling based video coding system. In the encoder side, the original video is down-sampled and compressed. In the decoder side, the decompressed down-sampled video is fed to the RR-DnCNN to get the original video by removing the compression artifacts and super-resolution. Moreover, in order to enhance the network learning capabilities, uncompressed low-resolution images/videos are utilized as a ground-truth. The experimental results show that our work can obtain over 8% BD-rate reduction compared to the standard H.265/HEVC. Furthermore, our method also outperforms in reducing compression artifacts in subjective comparison. Our work is available at https://github.com/minhmanho/rrdncnn .
Blockchain Characteristics and Green Supply Chain Advancement

Blockchain is an emerging technology that has been widely hyped for addressing many business issues. Blockchain's disruptive technological capabilities have the potential to revolutionize global supply chain management processes, and impact green supply chain initiatives. Blockchain technology incorporates four major characteristics: transparency, reliability, smart execution, and tokenization. Blockchain characteristics have implications for green practices in the upstream supply chain, focal company, and downstream supply chain. This chapter provides insights, exemplary practices, and use cases on how blockchain features can enhance green supply chain activities. Research concerns and directions are proposed to advance the discussion and research on this emergent field.
Modelling adaptive information security for SMEs in a cluster

Purpose This paper presents a method for adapting an Information Security Focus Area Maturity (ISFAM) model to the organizational characteristics (OCs) of a small- and medium-sized enterprise (SME) cluster. The purpose of this paper is to provide SMEs with a tailored maturity model enabling them to capture and improve their information security capabilities. Design/methodology/approach Design Science Research was followed to design and evaluate the method as a design artifact. Findings The method has successfully been used to adapt the ISFAM model to a group of SMEs within a regional cluster resulting in a model that is aligned with the OCs of the cluster. Areas for further investigation and improvements were identified. Research limitations/implications The study is based on applying the proposed method for the SMEs active in the transport, logistics and packaging sector in the Port of Rotterdam. Future research can focus on different sectors and regions. The method can be used for adapting other focus area maturity models. Practical implications The resulting adapted maturity model can facilitate the creation and further development of a base of common or shared knowledge in the cluster. The adapted maturity model can cut the cost of over implementation of information security capabilities for the SMEs with scarce resources. Originality/value The resulting adapted maturity model can facilitate the creation and further development of a base of common or shared knowledge in the cluster. The adapted maturity model can cut the cost of over implementation of information security capabilities for the SMEs with scarce resources.
An End-to-End Mammogram Diagnosis: A New Multi-Instance and Multiscale Method Based on Single-Image Feature

Mammography is the most common modality used in breast cancer detection. Most diagnostic mammography studies, however, are based on single-image training with little attention to the fact that the size of breast lesions varies significantly and the overall condition of the breast from different views. Therefore, the methodology is not in accord with the clinical requirement. To overcome this problem, we propose a new end-to-end method for mammographic diagnosis. As part of this process, we construct a data set of patients from West China Hospital to validate the new method. Furthermore, a multiscale module is proposed for the acquisition of complex breast features in a single image, enabling the screening of unique features in variably sized lesions. Finally, a multi-instance module is proposed for realistic hospital requirements to calculate the contribution of each mammogram in reaching the final diagnosis. Guidance by the single-image features can ameliorate the problem of weak one-case labeling. The new method yielded both a public data set and a realistic hospital data set.
Exploring the design of avatars for users from Arabian culture through a hybrid approach of deductive and inductive reasoning

While avatars are frequently employed as a user interface (UI) element for improving user experience in human-computer interaction, the current design of avatars is primarily dominated by non-Arabian cultures. To the best of our knowledge, no previous research or guidelines (based on empirical evidence) can be found for avatar design in the context of Arabian culture. Aiming to address this gap, we conducted an exploratory study to investigate how avatars can be designed for Arab users. Following a hybrid approach of deductive and inductive reasoning, we reviewed the literature on UI design for Arabian culture, (non-Arabian) avatar design in human-computer interaction research, and social response theory. Subsequently, we conducted 32 semi-structured interviews with Arabian culture experts, psychologists, and potential users. Based on thematic analysis of the interviews, we developed a set of six general guidelines for the design of avatars for users from Arabian culture. These six guidelines are expected to provide system or UI designers the ability to design and employ appropriate avatars that can promote Arab users’ experience and engagement.
Collaborative deep recommendation with global and local item correlations

Many recommendation methods have introduced item correlation information to alleviate the data sparsity and cold-start problems. However, existing approaches exploit either global or local item correlations, rarely consider both global and local item correlations, and thus they cannot provide advanced recommendation performance. Inspired by this, we propose a novel collaborative deep framework called GLICR to simultaneously incorporate the global and local item correlations into the model. More specifically, our proposed GLICR model tightly couples deep neural network with matrix factorization (MF), and jointly learns the deep feature representations of item content information in deep neural network and the rating matrix in MF. In addition, we introduce manifold regularization to learn the global and local item correlations directly from data. We conduct comprehensive experiments on real-world datasets at three different degrees of sparsity to confirm that our approach can effectively alleviate data sparsity problem and is superior to existing state-of-the-art recommendation techniques. This work is the first attempt that considers the global and local item correlations by manifold regularization in recommendation scenario.
Blended Learning System Using Social Media for College Student: A Case of Tahsin Education

Tahsin is a method to improve Al Qur'an recitation in a way that enables reflection and pleasant voice, according to the rules. Learning Tahsin is usually done by face-to-face involving learners and instructors, as also held as extracurricular in STT Nurul Fikri, a higher education institution focusing on IT education and character building. However, several problems occur regarding schedules, many other activities, curriculum, less organized, and low enthusiasm. Thus, the level of participation and time efficiency in learning Tahsin process is low and required improvement. Meanwhile, social media has the potential to tackle this issue through blended learning, bringing the different learning experience to ease the learning process. Naturally, the application of a new learning system is not immediately successful. The continuous evaluation is needed. This background problem motivates this study to conduct a quasi-experimental in applying blended learning to the learning Tahsin. The objective is to produce an optimal learning system consists of a fitted curriculum and learning materials, increased level of participation, time efficiency, and an established supporting organization for the learning process. The research steps include a preliminary study, data collection, learning system design, and several evaluation iterations. As a result, this study produces a blended learning system strategy for learning Tahsin using social media, with several issues to be concerned. They are learning the curriculum and material design, strategy to maintain participants commitments through providing rewards mechanism, and encouraging collaboration in the process, such as reading the Qur'an together. The evaluations show positive results with excellent predicate intervals.
Deciding the Confusability of Words under Tandem Repeats in Linear Time

Tandem duplication in DNA is the process of inserting a copy of a segment of DNA adjacent to the original position. Motivated by applications that store data in living organisms, Jain et al. (2016) proposed the study of codes that correct tandem duplications to improve the reliability of data storage. We investigate algorithms associated with the study of these codes. Two words are said to be ⩽-confusable if there exists a sequence of tandem duplications for each word, where each duplication is of length at most k , such that the resulting two words after duplications are equal. For k =3, we demonstrate that the problem of deciding whether two words is ⩽3-confusable is linear-time solvable through a characterisation that can be checked efficiently. Combining with previous results, the decision problem is linear-time solvable for k ⩽ 3. We conjecture that this problem is undecidable for k &gt; 3. Using insights gained from the algorithm, we study the size of tandem-duplication codes. We improve the previous known upper bound and then construct codes with larger sizes as compared to the previous constructions. We determine the sizes of optimal tandem-duplication codes for lengths up to 20, develop recursive methods to construct tandem-duplication codes for all word lengths, and compute explicit lower bounds for the size of optimal tandem-duplication codes for lengths from 21 to 30.
A new one time password mechanism for client-server applications

AbstractAbstractOTP (One Time Passwords) are very important in today's scenario in various applications like in financial transactions, e-commerce etc. They are used for enhancing the security of data communication. Many security applications are based on the strength of OTP. Any prediction in OTP generation can create serious threats to information security. Many new methods have been presented by researchers for OTP generation in the past decade. In this paper, we present a new authenticated OTP generation procedure between client and server. We also provide illustration with complete security analysis in order to prove its utility in various applications. Our procedure is implementable very easily and has embedded authentication and randomness.Subject Classification: Primary 93A30Secondary 49K15Keywords: AuthenticationHOTP (HMAC based One Time Password)OTP (One Time Password)SHA (Secure Hash Algorithm)TOTP (Time based One Time Password)
Multivariate Statistical Analysis of Trace Elements in Pyrite: Prediction, Bias and Artefacts in Defining Mineral Signatures

Pyrite is the most common sulphide in a wide range of ore deposits and well known to host numerous trace elements, with implications for recovery of valuable metals and for generation of clean concentrates. Trace element signatures of pyrite are also widely used to understand ore-forming processes. Pyrite is an important component of the Olympic Dam Cu–U–Au–Ag orebody, South Australia. Using a multivariate statistical approach applied to a large trace element dataset derived from analysis of random pyrite grains, trace element signatures in Olympic Dam pyrite are assessed. Pyrite is characterised by: (i) a Ag–Bi–Pb signature predicting inclusions of tellurides (as PC1); and (ii) highly variable Co–Ni ratios likely representing an oscillatory zonation pattern in pyrite (as PC2). Pyrite is a major host for As, Co and probably also Ni. These three elements do not correlate well at the grain-scale, indicating high variability in zonation patterns. Arsenic is not, however, a good predictor for invisible Au at Olympic Dam. Most pyrites contain only negligible Au, suggesting that invisible gold in pyrite is not commonplace within the deposit. A minority of pyrite grains analysed do, however, contain Au which correlates with Ag, Bi and Te. The results are interpreted to reflect not only primary patterns but also the effects of multi-stage overprinting, including cycles of partial replacement and recrystallisation. The latter may have caused element release from the pyrite lattice and entrapment as mineral inclusions, as widely observed for other ore and gangue minerals within the deposit. Results also show the critical impact on predictive interpretations made from statistical analysis of large datasets containing a large percentage of left-censored values (i.e., those falling below the minimum limits of detection). The treatment of such values in large datasets is critical as the number of these values impacts on the cluster results. Trimming of datasets to eliminate artefacts introduced by left-censored data should be performed with caution lest bias be unintentionally introduced. The practice may, however, reveal meaningful correlations that might be diluted using the complete dataset.
A Multi-Task Representation Learning Architecture for Enhanced Graph Classification

Composed of nodes and edges, graph structured data are organized in the non-Euclidean geometric space and ubiquitous especially in chemical compounds, proteins, etc. They usually contain rich structure information, and how to effectively extract inherent features of them is of great significance on the determination of function or traits in medicine and biology. Recently, there is a growing interest in learning graph-level representations for graph classification. Existing graph classification strategies based on graph neural networks broadly follow a single-task learning framework and manage to learn graph-level representations through aggregating node-level representations. However, they lack the efficient utilization of labels of nodes in a graph. In this paper, we propose a novel multi-task representation learning architecture coupled with the task of supervised node classification for enhanced graph classification. Specifically, the node classification task enforces node-level representations to take full advantage of node labels available in the graph and the graph classification task allows for learning graph-level representations in an end-to-end manner. Experimental results on multiple benchmark datasets demonstrate that the proposed architecture performs significantly better than various single-task graph neural network methods for graph classification.
Smart Autopilot Drone System for Surface Surveillance and Anomaly Detection via Customizable Deep Neural Network

Copter-based unmanned aerial vehicle (drone) systems are being utilized for surveillance, inspection and security purposes for well sites, gathering centers, pipelines, refineries, and other surface facilities. However, most of the practices largely rely on humans, including drone operation, data transfer, image analysis, etc. In this paper, we present a comprehensive, cloud-enabled, human-free autopilot drone system and its application in field surveillance and anomaly detection powered by customizable deep neural network and computer vision models. The proposed system consists of customized quadcopter drones equipped with high-definition cameras, thermal imaging and gas sensing devices, autopiloted by cloud-connected onboard computers. A series of advanced algorithms are developed and deployed onboard and over the Cloud for processing and diagnosing the image/thermal/gas sensing data collected by the drones in real-time or near real-time, including accurate 2D geospatial aerial mapping, anomaly detection and classification for events like oil leak, gas leak, facility failure, human activities, etc. Object detection deep learning models are customized and parallelized for low-profile multi-core single board computers. In our case study, a pre-configured drone flew along the same path twice at a 6-month gap. A robust, iterative image registering algorithm is developed to precisely align and overlay images taken at different days at the same or similar GPS locations, even with significant changes to the environment due to season shift, human activities, camera angles or height variations. Local changes are filtered and selected based on their sizes and magnitudes in the residual images by subtracting pairs of perfectly overlaid scenes. Pre-trained Residual Convolutional Neural network (He et al. 2015) is rapidly re-trained to further classify the type of changes using the techniques of transfer learning and data augmentation. An ROC of 99% was achieved in the multi-task binary classification, wherein the detected changes are divided into positive anomalies (such as oil/gas leak, facility failures, unauthored human activities) and negative (natural/insignificant) signals. Comparing against a support vector machine baseline with a ROC=92%, the ResNet model demonstrates significant, more promising detection accuracy at a faster training time. This innovative integrated platform is presented that combines physical drone, onboard imaging/sensing devices, cloud connectivity, onboard and back-end control system, deep learning and computer vision architecture for situational awareness of oil &amp; gas fields and the mining industry. It achieves full automation of mass surveillance, data acquisition and storage, diagnostics and asset situational understanding. The system architecture, especially the onboard and cloud computation engines, can be readily transferred and applied to other common drone platforms.