Reviewerly/exHarmony huggingface dataset:
Data recovery in a case of fire-damaged Hard Disk Drives and Solid-State Drives

During a building fire and the subsequent firefighting operation, delicate internals of personal computers may be exposed to a variety of destructive factors, thereby increasing the risk of data loss. So far, there have been only a limited number of studies concerning the problem of data recovery from physically damaged Hard Disk Drives (HDDs) and Solid-State Drives (SSDs). It may be expected that memory chips installed inside a modern Solid-State Drive are less prone to vibrations and falls compared to the internals of a Hard Disk Drive. However, as this study shows, this is not always the case: the memory modules of a Solid-State Drive could be damaged when the drive is exposed to extremely high temperatures. There are many myths and misconceptions concerning data recovery in the case of physically damaged drives. Police first responders often discard badly damaged Hard Disk Drives based solely on their external appearance. While it may be true in some cases, it is nothing more than an incorrect general assumption. The internal design of modern Hard Disk Drives can prevent or reduce the risk of data loss even in the extreme circumstances accompanying a fire-fighting operation. Besides, there are often other contributing factors, which prevent data loss, such as a laptop screen and keyboard creating additional layers of protection. In this article, we describe the case of data recovery from Hard Disk Drives and Solid-State Drives of different manufactures and models with fire-related damage. A detailed account of all operations was included to provide necessary guidance.
Maoqin @ DravidianLangTech-EACL2021: The Application of Transformer-Based Model

This paper describes the result of team-Maoqin at DravidianLangTech-EACL2021. The provided task consists of three languages(Tamil, Malayalam, and Kannada), I only participate in one of the language task-Malayalam. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages (Tamil-English, Malayalam-English, and Kannada-English) collected from social media. This is a classification task at the comment/post level. Given a Youtube comment, systems have to classify it into Not-offensive, Offensive-untargeted, Offensive-targeted-individual, Offensive-targeted-group, Offensive-targeted-other, or Not-in-indented-language. I use the transformer-based language model with BiGRU-Attention to complete this task. To prove the validity of the model, I also use some other neural network models for comparison. And finally, the team ranks 5th in this task with a weighted average F1 score of 0.93 on the private leader board.
Combined Depth Space based Architecture Search For Person Re-identification

Most works on person re-identification (ReID) take advantage of large backbone networks such as ResNet, which are designed for image classification instead of ReID, for feature extraction. However, these backbones may not be computationally efficient or the most suitable architectures for ReID. In this work, we aim to design a lightweight and suitable network for ReID. We propose a novel search space called Combined Depth Space (CDS), based on which we search for an efficient network architecture, which we call CDNet, via a differentiable architecture search algorithm. Through the use of the combined basic building blocks in CDS, CDNet tends to focus on combined pattern information that is typically found in images of pedestrians. We then propose a low-cost search strategy named the Top-k Sample Search strategy to make full use of the search space and avoid trapping in local optimal result. Furthermore, an effective Fine-grained Balance Neck (FBLNeck), which is removable at the inference time, is presented to balance the effects of triplet loss and softmax loss during the training process. Extensive experiments show that our CDNet (~1.8M parameters) has comparable performance with state-of-the-art lightweight networks.
Night-to-Day Road Scene Translation Using Generative Adversarial Network with Structural Similarity Loss for Night Driving Safety

Inadequate road infrastructures in developing countries, such as road lighting, is a real life problem affecting their transportation system. A poor road lighting hinders the ability of drivers to perceive the road conditions as well as its objects which may lead to the accident. We address a problem of synthesizing day-time appearance from the night-time road scene using loss-modified generative adversarial network. Our approach basically makes a balance between domain regularization and human perceptual restoration. By taking the night-time image into our enhanced adversarial network, which employs structural similarity loss, we increase its visibility so that the driver can clearly see the road scene like they are driving during the day-time. Experimental results show the benefit of our approach for enhancing the visibility of the night-time view of the road, measured by the image quality metrics.
Cost reduction on space tests through optimal control

The main goal of this work is to reduce the liquid/gaseous nitrogen (LN2 / GN2) waste on thermal-vacuum space tests. In this paper, we present the results of a feasibility investigation of the optimal control techniques in this issue. It is found that it is possible to reduce the overall consumption by approximately 5% during thermal balance/thermal cycling space test campaigns. Therefore, optimal control techniques improve the best practices on those activities.
Hand Sign Recognition using Infrared Imagery Provided by Leap Motion Controller and Computer Vision

Speech Impairment and conversion of sign language to human re-engineered audio signals is something computer science has always been interested in. However, the architectural robustness and extraction of features on a very insignificant area of change have posed decade long problems to achieve this idea. The paper proposes a Convolutional Neural network based on a deep belief model on Data imagery collected by leap motion controllers on hand sign recognition. The database is composed of 10 different hand-gestures that were performed by 10 different subjects (5 men and 5 women) which is presented, composed by a set of near-infrared images acquired by the Leap Motion sensor. The paper tries to achieve high accuracy on the pertaining training set inorder to create and form a robust model. It embraces the first step towards image understanding of human signs and aid specially-abled people. We have implemented and tested the algorithm for 2000 images each class. The paper achieves the accuracy and precision of 99.4% and 99.68% respectively. The implications of the study intend to enhance understanding of infrared imagery for small areas of localization feature detection and intend to help the idea of human audio re-engineering a resurgence by using the same.
GraphSVX: Shapley Value Explanations for Graph Neural Networks

Graph Neural Networks (GNNs) achieve significant performance for various learning tasks on geometric data due to the incorporation of graph structure into the learning of node representations, which renders their comprehension challenging. In this paper, we first propose a unified framework satisfied by most existing GNN explainers. Then, we introduce GraphSVX, a post hoc local model-agnostic explanation method specifically designed for GNNs. GraphSVX is a decomposition technique that captures the fair contribution of each feature and node towards the explained prediction by constructing a surrogate model on a perturbed dataset. It extends to graphs and ultimately provides as explanation the Shapley Values from game theory. Experiments on real-world and synthetic datasets demonstrate that GraphSVX achieves state-of-the-art performance compared to baseline models while presenting core theoretical and human-centric properties.
ML Based Social Media Data Emotion Analyzer and Sentiment Classifier with Enriched Preprocessor

Sentiment Analysis or opinion mining is NLP's method to computationally identify and categorize user opinions expressed in textual data.  Mainly it is used to determine the user's opinions, emotions, appraisals, or judgments towards a specific event, topic, product, etc. is positive, negative, or neutral. In this approach, a huge amount of digital data generated online from blogs and social media websites is gathered and analyzed to discover the insights and help make business decisions. Social media is web-based applications that are designed and developed to allow people to share digital content in real-time quickly and efficiently.  Many people define social media as apps on their Smartphone or tablet, but the truth is, this communication tool started with computers. It became an essential and inseparable part of human life. Most business uses social media to market products, promote brands, and connect to current customers and foster new business. Online social media data is pervasive. It allows people to post their opinions and sentiments about products, events, and other people in the form of short text messages. For example, Twitter is an online social networking service where users post and interact with short messages, called tweets. Hence, currently, social media has become a prospective source for businesses to discover people's sentiments and opinions about a particular event or product. This paper focuses on the development of a Multinomial Naive Bayes Based social media data emotion analyzer and sentiment classifier. This paper also explains various enriched methods used in pre-processing techniques. This paper also focuses on various Machine Learning Techniques and steps to use the text classifier and different types of language models.
Lord of the Ring(s): Side Channel Attacks on the {CPU} On-Chip Ring Interconnect Are Practical

We introduce the first microarchitectural side channel attacks that leverage contention on the CPU ring interconnect. There are two challenges that make it uniquely difficult to exploit this channel. First, little is known about the ring interconnect's functioning and architecture. Second, information that can be learned by an attacker through ring contention is noisy by nature and has coarse spatial granularity. To address the first challenge, we perform a thorough reverse engineering of the sophisticated protocols that handle communication on the ring interconnect. With this knowledge, we build a cross-core covert channel over the ring interconnect with a capacity of over 4 Mbps from a single thread, the largest to date for a cross-core channel not relying on shared memory. To address the second challenge, we leverage the fine-grained temporal patterns of ring contention to infer a victim program's secrets. We demonstrate our attack by extracting key bits from vulnerable EdDSA and RSA implementations, as well as inferring the precise timing of keystrokes typed by a victim user.
Kubernetes WANWide: a Deployment Scenario to Expose and Use Edge Computing Resources?

Cloud Computing have highlighted the importance of container orchestration to manage distributed applicationy's life-cycle. With the advent of Edge Computing, DevOps expect to find the features of containers in the cloud, also at the edge. However, orchestration systems have not been designed to deal with geo-distribution aspects such as latency, intermittent networks, etc. In other words, it is unclear whether they could be directly used on top of massively distributed edge infrastructures without revision. In this paper, we provide an evaluation of Kubernetes in a WANWide context. Precisely, we present and discuss results we obtained during an experimental campaign to analyze the impact of WAN links on its behaviour. While there exist initiatives investigating Kubernetes revisions to deal with distribution aspects, there is, to the best of our knowledge, no rigorous performance evaluations to disqualify the vanilla code.
A Study of Objective Quality Metrics for HLG-Based HDR/WCG Image Coding

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In this work, we study the suitability of high dynamic range, wide color gamut (HDR/WCG) objective quality metrics to assess the perceived deterioration of compressed images encoded using the hybrid log-gamma (HLG) method, which is the standard for HDR television. Several image quality metrics have been developed to deal specifically with HDR content, although in previous work we showed that the best results (i.e., better matches to the opinion of human expert observers) are obtained by an HDR metric that consists simply in applying a given standard dynamic range metric, called visual information fidelity (VIF), directly to HLG-encoded images. However, all these HDR metrics ignore the chroma components for their calculations, that is, they consider only the luminance channel. For this reason, in the current work, we conduct subjective evaluation experiments in a professional setting using compressed HDR/WCG images encoded with HLG and analyze the ability of the best HDR metric to detect perceivable distortions in the chroma components, as well as the suitability of popular color metrics (including</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta I T P_{R}$ </tex-math></inline-formula> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">which supports parameters for HLG) to correlate with the opinion scores. Our first contribution is to show that there is a need to consider the chroma components in HDR metrics, as there are color distortions that subjects perceive but that the best HDR metric fails to detect. Our second contribution is the surprising result that VIF, which utilizes only the luminance channel, correlates much better with the subjective evaluation scores than the metrics investigated that do consider the color components</i> .
Comparative Evaluation of Cooperative Multi-Agent Deep Reinforcement Learning Algorithms

Multi-agent deep reinforcement learning (MARL) suffers from a lack ofcommonly-used evaluation tasks and criteria, making comparisons betweenapproaches difficult. In this work, we evaluate and compare three differentclasses of MARL algorithms (independent learning, centralised multi-agentpolicy gradient, and value decomposition) in a diverse range offully-cooperative multi-agent learning tasks. Our experiments can serve as areference for the expected performance of algorithms across different learningtasks. We also provide further insight about (1) when independent learningmight be surprisingly effective despite non-stationarity, (2) when centralisedtraining should (and shouldn't) be applied and (3) which benefits valuedecomposition can bring.
Copyright Infringement in AI-Generated Artworks

This Note examines potential copyright infringement issues arising from AI-generated artwork and argues that, under current copyright law, an engineer may use copyrighted works to train an AI program to generate artwork without incurring infringement liability.
Evolving Evaluation Functions for Collectible Card Game AI

In this work, we presented a study regarding two important aspects of evolving feature-based game evaluation functions: the choice of genome representation and the choice of opponent used to test the model. We compared three representations. One simpler and more limited, based on a vector of weights that are used in a linear combination of predefined game features. And two more complex, based on binary and n-ary trees. On top of this test, we also investigated the influence of fitness defined as a simulation-based function that: plays against a fixed weak opponent, plays against a fixed strong opponent, and plays against the best individual from the previous population. For a testbed, we have chosen a recently popular domain of digital collectible card games. We encoded our experiments in a programming game, Legends of Code and Magic, used in Strategy Card Game AI Competition. However, as the problems stated are of general nature we are convinced that our observations are applicable in the other domains as well.
Prediction-Based Cost-Efficient Resource Allocation Scheme for Big Data Streams in Cloud Systems

Resource allocation (RA) for cloud-based big data systems mainly consists of assigning the tasks to suitable resources. A dynamic unbalanced load in individual virtual machines (VMs) causes the complete inefficient utilization of resources. Apart from this, high execution cost and lower performance are the other issues of RA. A predictive cost-efficient resource allocation (PCRA) scheme for big data applications is developed. In this scheme, the user specifies the number of executors, cores, memory, etc., for the required application. Then, the task accomplishment period of a solicitation is predicted centered on the number of initiators and the specific properties of the application. Then, VMs are determined for the task requests, such that the number of executors is sufficient to minimize the task completion time and total cost. Some idle executors are released to return some resources to the hosts which could be revoked in the future if needed.
A note on the worst-case complexity of nonlinear stepsize control methods for convex smooth unconstrained optimization

In this paper, we analyse the worst-case complexity of nonlinear stepsize control (NSC) algorithms for solving convex smooth unconstrained optimization problems. We show that, to drive the norm of the gradient below some given positive ε, such methods take at most O(ϵ−1) iterations, which shows that the complexity bound for these methods is in parity with that of gradient descent methods for the same class of problems. As NSC algorithm is a generalization of several methods such as trust-region and adaptive cubic with regularization methods, such bound holds automatically for these methods as well.
Feature Disentanglement in generating three-dimensional structure from two-dimensional slice with sliceGAN.

Deep generative models are known to be able to model arbitrary probability distributions. Among these, a recent deep generative model, dubbed sliceGAN, proposed a new way of using the generative adversarial network (GAN) to capture the micro-structural characteristics of a two-dimensional (2D) slice and generate three-dimensional (3D) volumes with similar properties. While 3D micrographs are largely beneficial in simulating diverse material behavior, they are often much harder to obtain than their 2D counterparts. Hence, sliceGAN opens up many interesting directions of research by learning the representative distribution from 2D slices, and transferring the learned knowledge to generate arbitrary 3D volumes. However, one limitation of sliceGAN is that latent space steering is not possible. Hence, we combine sliceGAN with AdaIN to endow the model with the ability to disentangle the features and control the synthesis.
On the Adaptation of Bitcoin-Like Blockchains for the Secure Storage of Traffic-Related Events

This paper investigates Bitcoin-like blockchains enabled traffic efficiency applications in VANETs. Prior works do not provide a thorough study on the adaptation of the Proof-of-Work (PoW) mechanism in the context of traffic records management, nor a detailed study of performance. Therefore, in this work, we study a PoW based blockchain for secure traffic-related records in the VANETs’ context. We introduce a blockchain simulator to assess our protocol’s performance (i.e., throughput and latency). Results show that the proposed scheme ensures the integrity and traceability of the shared traffic-related records and point out trade-offs between security and performance.
Navigating the Web

Despite the importance of online information-seeking behaviour being recognised by many researchers (Enriquez Raido, 2011, 2014; Gough, 2015; Hvelplund, 2017, 2019), only a handful of studies have focused on them. This study therefore sets out to explore how professional translators navigate the web with respect to their query behaviour (i.e. primary action) and their browsing and clicking behaviour (i.e. secondary action) (White, 2016, pp. 21–37). Unlike previous studies, it adopts a qualitative eye-tracking methodology, i.e. eye-tracking stimulated retrospective think-aloud (RTA) with ten professional translators. A model of what is involved in translators’ primary and secondary actions and how these two actions interact with one another is presented as a result.
Gradient Local Binary Pattern Layer to Initialize the Convolutional Neural Networks

Deep neural network technology is a milestone achievement in the field of computer vision. It obtained the performance that the shallow network cannot achieve through the multi-layer network structure and the learning method of reverse adjustment parameters. However, the feature extraction algorithm of the shallow network is very effective and also is more beneficial for deep neural networks. In this paper, we combine the shallow network algorithm to proposes the gradient local binary pattern layer(GLBP layer) to replace the first layer of Convolutional Neural Networks(CNNs). The GLBP layer plays a role in initializing the CNNs and can improve network performance without increasing the number and complexity of network layers. In the experiment, using the extracted layer modified by the GLBP feature algorithm to replace other classic deep neural networks, 2.65% and 2.9% performance improvements were obtained in the WideResNet16-2 and ResNet-101 respectively when training on CIFAR-100 dataset.
Geochemistry of Negombo Lagoon sediments in Sri Lanka: implications for environmental monitoring

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2020 Impact Factor: 0.515The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
MorphoNet: A Deep Image Super Resolution Network Using Hierarchical and Morphological Feature Generating Residual Blocks

Morphological operations are nonlinear mathematical operations that are capable of performing signal processing tasks based on the structures and textures of the signals. With this motivation of the capability of morphological operations, in this paper, a novel residual block that can generate morphological features of images and fuse them with the conventional hierarchical features has been proposed. The proposed residual block is then used to design a light-weight deep neural network architecture in a residual framework for the task of image super resolution. It is shown that a fusion of morphological features of images with the conventional hierarchical features can improve the super resolution capability of a deep convolutional network. Experiments are performed to demonstrate the effectiveness of the proposed idea of using morphological operations and the superiority of the network designed based on this idea in super resolving low quality images.