Reviewerly/exHarmony huggingface dataset:
Tor forensics: Proposed workflow for client memory artefacts

The Internet is now part of everyday life, and plays a significant role in communication, online shopping, online banking, etc. However, one of the current issues with using the Internet is lack of security since it is still possible for an eavesdropper to be able to intercept transferred data. As a result, the number of incidents has increased, posing a real threat to the user while people have become more conscious about how applications treat their personal data. Therefore, some users have shifted to using The Onion Router (Tor) as it claims to preserve user's anonymity and privacy. However, while using or investigating the use of Tor, the question of how the memory residue of the client leaks anonymity during Tor's interaction arises. This question is addressed in this paper as it investigates how the client's memory residue leaks anonymity before, during, and after Tor's interaction. While there has been significant research on the topic of Tor, there is a gap in the literature concerning Tor forensics. One of the leading concepts to identify artefacts in digital investigation is digital forensics. Thus, this paper will address the question by an experimental method that uses memory forensics tactics on Tor clients to find artefacts related to Tor usage. Subsequently, an analysis of the findings can stand against Tor's claims about the user's privacy and anonymity since the Tor browser keeps a plethora of details about client activities, which could be gained during or even after closing the client session. This paper provides a workflow and a python shell script for analyzing the Tor client's memory residue, which will serve as a workflow and act as a starting point for broadening studies in a similar area. It also introduces a positive impact on the investigators. It aims to make the process easier and contributes to society as users will be aware of how Tor treats their data.
Superpixel-based Refinement for Object Proposal Generation

Precise segmentation of objects is an important problem in tasks like class-agnostic object proposal generation or instance segmentation. Deep learning-based systems usually generate segmentations of objects based on coarse feature maps, due to the inherent downsampling in CNNs. This leads to segmentation boundaries not adhering well to the object boundaries in the image. To tackle this problem, we introduce a new superpixel-based refinement approach <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> on top of the state-of-the-art object proposal system AttentionMask. The refinement utilizes superpixel pooling for feature extraction and a novel superpixel classifier to determine if a high precision superpixel belongs to an object or not. Our experiments show an improvement of up to 26.0% in terms of average recall compared to original AttentionMask. Furthermore, qualitative and quantitative analyses of the segmentations reveal significant improvements in terms of boundary adherence for the proposed refinement compared to various deep learning-based state-of-theart object proposal generation systems.
Discriminability of Single-Layer Graph Neural Networks

Network data can be conveniently modeled as a graph signal, where data values are assigned to the nodes of a graph describing the underlying network topology. Successful learning from network data requires methods that effectively exploit this graph structure. Graph neural networks (GNNs) provide one such method and have exhibited promising performance on a wide range of problems. Understanding why GNNs work is of paramount importance, particularly in applications involving physical networks. We focus on the property of discriminability and establish conditions under which the inclusion of pointwise nonlinearities to a stable graph filter bank leads to an increased discriminative capacity for high-eigenvalue content. We define a notion of discriminability tied to the stability of the architecture, show that GNNs are at least as discriminative as linear graph filter banks, and characterize the signals that cannot be discriminated by either.
CoV2eX: A COVID-19 Website with Region-wise Sentiment Classification using the Top Trending Social Media Keywords

The ongoing pandemic has caused several impacts on human life. Social media has become more popular during the pandemic, wherein people share all their thoughts and emotions. Due to the social distancing norms and other preventive measures, people are connecting with each other through this platform. This gave us an occasion to study the overall mental reaction of the public to this disease. To measure the region-wise sentiment value, we used the tweets associated with COVID-19 in this paper. This is then used for the model's training. Then, we generate the average emotion conveyed by each region. This can be beneficial in validating the psychological impact that COVID-19 had on the people, helping governments to take effective actions. The output as well as the COVID-19 guidelines and data are then represented in a website.
Galvanically Isolated, Power and Electromagnetic Side-Channel Attack Resilient Secure AES Core with Integrated Charge Pump based Power Management

A galvanic isolation (GI) technique for cryptographic cores is proposed to mitigate power and electromagnetic (EM) sidechannel analysis (SCA) attacks. The design uses deep N-well technology and an integrated charge pump-based power delivery and management to completely isolate V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CC</sub> , V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SS</sub> , and substrate nodes from the external supply and ground pins, improving the SCA resilience due to supply as well as ground bounce. Measured results from a 128-bit Advanced Encryption Standard (AES) core implemented in a 40nm CMOS show >600x and >t220x improvement against a correlation power analysis (CPA) and coarse-grained EM SCA attack, respectively, while operating at 20% lower frequency, consuming 2.3x more power, and occupying 0.0136 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> larger area.
Perceptive VM Allocation in Cloud Data Centers for Effective Resource Management

Virtual Machine allocation in cloud computing centers has become an important research area. Efficient VM allocation can reduce power consumption and average response time which can benefit both the end users as well as the cloud vendors. This work presents a perceptive priority aware VM allocation policy named P-PAVA algorithm, which takes into account the priority of an application along with its compute, memory and bandwidth requirement. The algorithm performs allocation of the applications based on the priority it gets using a machine learning based prediction model. Furthermore, to reduce the overhead of the allocation algorithm, parallelization is employed before assigning various workloads. To achieve this, the algorithm employs the First fit technique as a baseline for the requests allocation with a criteria as low priority. When compared to the state of the art algorithm for VM allocation for priority aware applications, P-PAVA performs better on several criteria such as average response time, execution time and power consumption.
Information Structures for Causally Explainable Decisions

For an AI agent to make trustworthy decision recommendations under uncertainty on behalf of human principals, it should be able to explain why its recommended decisions make preferred outcomes more likely and what risks they entail. Such rationales use causal models to link potential courses of action to resulting outcome probabilities. They reflect an understanding of possible actions, preferred outcomes, the effects of action on outcome probabilities, and acceptable risks and trade-offs—the standard ingredients of normative theories of decision-making under uncertainty, such as expected utility theory. Competent AI advisory systems should also notice changes that might affect a user’s plans and goals. In response, they should apply both learned patterns for quick response (analogous to fast, intuitive “System 1” decision-making in human psychology) and also slower causal inference and simulation, decision optimization, and planning algorithms (analogous to deliberative “System 2” decision-making in human psychology) to decide how best to respond to changing conditions. Concepts of conditional independence, conditional probability tables (CPTs) or models, causality, heuristic search for optimal plans, uncertainty reduction, and value of information (VoI) provide a rich, principled framework for recognizing and responding to relevant changes and features of decision problems via both learned and calculated responses. This paper reviews how these and related concepts can be used to identify probabilistic causal dependencies among variables, detect changes that matter for achieving goals, represent them efficiently to support responses on multiple time scales, and evaluate and update causal models and plans in light of new data. The resulting causally explainable decisions make efficient use of available information to achieve goals in uncertain environments.
Cross-Corpora Language Recognition: A Preliminary Investigation with Indian Languages

In this paper, we conduct one of the very first studies for cross-corpora performance evaluation in the spoken language identification (LID) problem. Cross-corpora evaluation was not explored much in LID research, especially for the Indian languages. We have selected three Indian spoken language corpora: IIITH-ILSC, LDC South Asian, and IITKGP-MLILSC. For each of the corpus, LID systems are trained on the state-of-the-art time-delay neural network (TDNN) based architecture with MFCC features. We observe that the LID performance degrades drastically for cross-corpora evaluation. For example, the system trained on the IIITH-ILSC corpus shows an average EER of 11.80 % and 43.34 % when evaluated with the same corpora and LDC South Asian corpora, respectively. Our preliminary analysis shows the significant differences among these corpora in terms of mismatch in the long-term average spectrum (LTAS) and signal-to-noise ratio (SNR). Subsequently, we apply different feature level compensation methods to reduce the cross-corpora acoustic mismatch. Our results indicate that these feature normalization schemes can help to achieve promising LID performance on cross-corpora experiments.
Scene Text Detection with Selected Anchors

Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall. It results in the significant improvement in accuracy but waste of computational searching, regression and classification. In this paper, we propose an anchor selection-based region proposal network (AS-RPN) using effective selected anchors instead of dense anchors to extract text proposals. The center, scales, aspect ratios and orientations of anchors are learnable instead of fixing, which leads to high recall and greatly reduced numbers of anchors. By replacing the anchor-based RPN in Faster RCNN, the AS-RPN-based Faster RCNN can achieve comparable performance with previous state-of-the-art text detecting approaches on standard benchmarks, including COCO-Text, ICDAR2013, ICDAR2015 and MSRA-TD500 when using single-scale and single model (ResNet50) testing only.
Dynamic Field of View Restriction in 360° Video: Aligning Optical Flow and Visual SLAM to Mitigate VIMS

Head-Mounted Display based Virtual Reality is proliferating. However, Visually Induced Motion Sickness (VIMS), which prevents many from using VR without discomfort, bars widespread adoption. Prior work has shown that limiting the Field of View (FoV) can reduce VIMS at a cost of also reducing presence. Systems that dynamically adjust a user’s FoV may be able to balance these concerns. To explore this idea, we present a technique for standard 360° video that shrinks FoVs only during VIMS inducing scenes. It uses Visual Simultaneous Localization and Mapping and peripheral optical flow to compute camera movements and reduces FoV during rapid motion or optical flow. A user study (N=23) comparing 360° video with unrestricted-FoVs (90°), reduced fixed-FoVs (40°) and dynamic-FoVs (40°-90°) revealed that dynamic-FoVs mitigate VIMS while maintaining presence. We close by discussing the user experience of dynamic-FoVs and recommendations for how they can help make VR comfortable and immersive for all.
Explorable Tone Mapping Operators

Tone-mapping plays an essential role in high dynamic range (HDR) imaging. It aims to preserve visual information of HDR images in a medium with a limited dynamic range. Although many works have been proposed to provide tone-mapped results from HDR images, most of them can only perform tone-mapping in a single pre-designed way. However, the subjectivity of tone-mapping quality varies from person to person, and the preference of tone-mapping style also differs from application to application. In this paper, a learning-based multimodal tone-mapping method is proposed, which not only achieves excellent visual quality but also explores the style diversity. Based on the framework of BicycleGAN [1], the proposed method can provide a variety of expert-level tone-mapped results by manipulating different latent codes. Finally, we show that the proposed method performs favorably against state-of-the-art tone-mapping algorithms both quantitatively and qualitatively.
Sentiment analysis on COVID tweets using COVID-Twitter-BERT with auxiliary sentence approach

Sentiment analysis is a fascinating area as a natural language understanding benchmark to evaluate customers' feedback and needs. Moreover, sentiment analysis can be applied to understand the people's reactions to public events such as the presidential elections and disease pandemics. Recent works in sentiment analysis on COVID-19 present a domain-targeted Bidirectional Encoder Representations from Transformer (BERT) language model, COVID-Twitter BERT (CT-BERT). However, there is little improvement in text classification using a BERT-based language model directly. Therefore, an auxiliary approach using BERT was proposed. This method converts single-sentence classification into pair-sentence classification, which solves the performance issue of BERT in text classification tasks. In this paper, we combine a pre-trained BERT model from COVID-related tweets and the auxiliary-sentence method to achieve better classification performance on COVID tweets sentiment analysis. We show that converting single-sentence classification into pair-sentence classification extends the dataset and obtains higher accuracies and F1 scores. However, we expect a domain-specific language model would perform better than a general language model. In our results, we show that the performance of CT-BERT does not necessarily outperform BERT specifically in understanding sentiments.
Two Layers Machine Learning Architecture for Animal Classification Using HOG and LBP

There is a social relation between animals and humans. Both are directly or indirectly dependent on each other. Therefore, it is our duty to maintain the healthy existence of both. To maintain this situation, we have to develop an efficient model that is able to protect the life of animals. To do the same, first it is necessary to identify different animals so that we can concentrate more on the endangered species (animal). But due to various varieties of animals, we need a system to recognize such type of animals automatically. In this paper, we propose a system based on two-layered machine learning architecture by using histogram of oriented gradients (HOG) method and local binary pattern (LBP) to perform the detection of animal. SVM and gradient boosting classifiers are used on different layers to classify the object efficiently. The result analysis shows that our proposed model works efficiently and effectively with an acceptable accuracy, i.e., 95.15%.
Cut and Compare: End-to-end Offline Signature Verification Network

Offline signature verification, to determine whether a handwritten signature image is genuine or forged for a claimed identity, is needed in many applications. How to extract salient features and how to calculate similarity scores are the major issues. In this paper, we propose a novel end-to-end cut-and-compare network for offline signature verification. Based on the Spatial Transformer Network (STN), discriminative regions are segmented from a pair of input signature images and are compared attentively with help of Attentive Recurrent Comparator (ARC). An adaptive distance fusion module is proposed to fuse the distances of these regions. To address the intra personal variability problem, we design a smoothed double-margin loss to train the network. The proposed network achieves state-of-the-art performance on CEDAR, GPDS Synthetic, BHSig-H and BHSig-B datasets of different languages. Furthermore, our network shows strong generalization ability on cross-language test.
Event-oriented linkable and traceable anonymous authentication and its application to voting

In the pursuit of anonymous authentication schemes that balance anonymity and accountability, various authentication schemes have been proposed. However, most of existing schemes cannot achieve linkability while holding public traceability. In this paper, we introduce a new variant of anonymous authentication called event-oriented linkable and traceable anonymous authentication (EOLTAA) and provide a generic construction. In an EOLTAA scheme, a message to be authenticated binds an event. If two different messages binding the same event are authenticated by an identical user, anyone can link the two authentications and further reveal the user’s identity. Then we formally define the security requirements of EOLTAA, including unforgeability, anonymity, linkability and public traceability. We give a generic construction satisfying the security requirements. With this new authentication scheme, we construct the first decentralized, anonymous, linkable and publicly traceable e-voting based on blockchain.
Mobile Robot Navigation Based on Deep Reinforcement Learning with 2D-LiDAR Sensor using Stochastic Approach

In recent years, there has been a significant progress in mobile robotics and their applications in different fields. Currently, mobile robots are employed for applications such as service robots for delivery, exploration, mapping, search and rescue, and warehouses. Recent advances in computing efficiency and machine learning algorithms have increased the variations of intelligent robots that can navigate autonomously using sensor data. Particularly, reinforcement learning has recently enjoyed a wide variety of success in controlling the robot motion in an unknown environment. However, most of the reinforcement learning-based navigation gets the path plan with a deterministic method, which results in some errors. Therefore, we present a navigation policy for a mobile robot equipped with a 2D range sensor based on the Proximal Policy Optimization of a stochastic approach. The tested algorithm also includes a stochastic operation, which simplifies the policy network model. We trained a differential drive robot in multiple training environments, and based on such stochastic learning, the training data accumulates faster than before. We tested our algorithm in a virtual environment and present the results of successful planning and navigation for mobile robots.
A Comparison of Discrete Latent Variable Models for Speech Representation Learning

Neural latent variable models enable the discovery of interesting structure in speech audio data. This paper presents a comparison of two different approaches which are broadly based on predicting future time-steps or auto-encoding the input signal. Our study compares the representations learned by vq-vae and vq-wav2vec in terms of sub-word unit discovery and phoneme recognition performance. Results show that future time-step prediction with vq-wav2vec achieves better performance. The best system achieves an error rate of 13.22 on the ZeroSpeech 2019 ABX phoneme discrimination challenge.
Toward a cost–benefit analysis of quality programs in digital forensic laboratories in the United States

Abstract There are few quantitative metrics to help evaluate the adoption of quality measures from a laboratory funder's standpoint. This article synthesizes existing data to create a simple prototype for a model to anticipate the costs or savings associated with implementing a quality program, including accreditation, in a digital forensic laboratory. Digital forensic laboratories were selected as an area of interest, as the field is largely unregulated in the United States, there are disparities in the qualifications of practitioners, and there is no empirical understanding of how frequently error occurs in the discipline. The model is used to determine under what conditions there may be anticipated overall financial savings, assuming an annual case throughput of n cases and a lowered frequency of miscarriages of justice, associated with properly implementing an accredited quality system, given by F . While this model does not account for the human cost associated with miscarriages of justice, it demonstrates that the cost of a robust quality system may prove cheaper than the risk and cost associated with the negative outcomes stemming from miscarriages of justice. Under the parameters used for testing, only under two realistic conditions will the implementation of a quality system never realize a benefit: when the cost of a wrongful conviction is relatively small and when forensic error leads to wrongful conviction on the order of 1 in 100,000 cases or less. In all other scenarios, it is possible that, under realistic conditions, laboratory funders may realize savings by implementing an accredited quality program. This article is categorized under: Jurisprudence and Regulatory Oversight &gt; Interdisciplinary Collaboration Digital and Multimedia Science &gt; Forensic Visualization
Tag-Enhanced Dynamic Compositional Neural Network over arbitrary tree structure for sentence representation

Learning the distributed representation of a sentence is a fundamental operation for a variety of natural language processing tasks, such as text classification, machine translation, and text semantic matching. Tree-structured dynamic compositional networks have achieved promising performance in sentence representation due to its ability in capturing the richness of compositionality. However, existing dynamic compositional networks are mostly based on binarized constituency trees which cannot represent the inherent structural information of sentences effectively. Moreover, syntactic tag information, which is demonstrated to be useful in sentence representation, has been rarely exploited in existing dynamic compositional models. In this paper, a novel LSTM structure, ARTree-LSTM, is proposed to handle general constituency trees in which each non-leaf node can have any number of child nodes. Based on ARTree-LSTM, a novel network model, Tag-Enhanced Dynamic Compositional Neural Network (TE-DCNN), is proposed for sentence representation learning, which contains two ARTree-LSTMs, i.e. tag-level ARTree-LSTM and word-level ARTree-LSTM. The tag-level ARTree-LSTM guides the word-level ARTree-LSTM in conducting dynamic composition. Extensive experiments demonstrate that the proposed TE-DCNN achieves state-of-the-art performance on text classification and text semantic matching tasks.