Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Two Stage and Single Stage Detectors for Anomaly Detection

Detection of traffic anomalies involves detecting stalled or crashed vehicles. This vehicle becomes stationary and hence becomes a part of the background. The performance of anomaly detectors depends on the performance of object detectors on background images. Usually, the object detectors are compared for the performance on original frames. In this paper, we compare the performance of the two-stage detector and single-stage detector on background images. Faster Region-based Convolutional Neural Network(FRCNN) with Inception and ResNet network are compared with single-stage detector YOLO V3. The performance analysis shows that YOLO has a higher F1 score compared to FRCNN.
Remote Work as a Societal Incentive for Creativity: Phygital Initiative for Self-actualization

The concept of the basic guaranteed income for citizens is widely discussed by scientists and politicians. The pandemic forced authorities around the world to implement punctual payments to the whole population or some categories (such as families with children, compensation of losses for businesses at lockdown, etc.). These subsidies raised again the question of the future of labor: if routine work is accomplished by robots and bots, computers and neuron-networks, what activities stay for humans? The corporate loyalty of employees was highly appreciated during last centuries. But the pandemic introduced remote regime of work for the masses of specialists, the lockdown for services sector business and the unemployment for the professionals who lost their stable jobs, showed the necessity to discuss the content of the labor contract. The article represents the results of the exploratory interview with 43 employers (mainly from small and medium enterprises) to identify the core symptoms of the evolution of labor. The answers on labor contract’ essence, given by employers, allowed researchers to raise hypotheses about the new features of the labor market: if the most appreciated content of labor is the creative contribution, how the interaction between business and labor should be organized? During the pandemic, the rupture widened rapidly between the successful part of population who taken the functions of their own autonomous value creation process, and the other non-privileged part who lost their integration into societal and, particularly, economic system.
Ontology-based approach for analyzing nuclear overall I&amp;C architectures

Nuclear power plants have many different instrumentation and control (I&C) systems. Together, these systems (and their various dependencies) form the overall I&C architecture, which needs to fulfill the principle of defence-in-depth. The safety systems need to be sufficiently independent from the normal operation systems to avoid common cause failure.Semantic Web technologies use formal conceptual models— ontologies—to associate meaning with unstructured data. The knowledge base is built on named graphs, allowing complex queries with reasoning. The results are based on more than just statistical patterns.In this paper, we demonstrate the use of an OWL ontology to represent engineering knowledge about overall nuclear I&C architectures. We show how, using flexible SPARQL queries, we can then analyse the different dependencies between the I&C systems. We have built a public case study based on a proposed pressurised water reactor type. We detected several potential design issues, which suggests the approach could improve nuclear safety and support design work.
Random Walk Erasing with Attention Calibration for Action Recognition

Action recognition in videos has attracted growing research interests because of the explosive surveillance data in social security applications. In this process, due to the distraction and deviation of the network caused by occlusions, human action features usually suffer different degrees of performance degradation. Considering the occlusion scene in the wild, we find that the occluded objects usually move unpredictably but continuously. Thus, we propose a random walk erasing with attention calibration (RWEAC) for action recognition. Specifically, we introduce the random walk erasing (RWE) module to simulate the unknown occluded real conditions in frame sequence, expanding the diversity of data samples. In the case of erasing (or occlusion), the attention area is sparse. We leverage the attention calibration (AC) module to force the attention to stay stable in other regions of interest. In short, our novel RWEAC network enhances the ability to learn comprehensive features in a complex environment and make the feature representation robust. Experiments are conducted on the challenging video action recognition UCF101 and HMDB51 datasets. The extensive comparison results and ablation studies demonstrate the effectiveness and strength of the proposed method.
Development of a Bangla Speech to Text Conversion System Using Deep Learning

Bangla Speech-To-Text (STT) conversion is a technology that provides a means of converting spoken Bangla language to a written Bangla text form. The standard of speech recognition in different languages is rising step by step however Bangla speech recognition has drawn exceptionally little attention. Building up an STT framework is a bulky procedure and it requires a few stages. Deep neural network-based architecture replaces the stages with neural network components which makes the task simpler and removes the dependency on hand-engineered rules. CNN-RNN networks with CTC criterion are utilized in this undertaking to construct a Bangla STT system that generates text from speech. The architecture is trained solely on speech samples and text transcripts. We used 215.53 hours of speech data set for training, which includes a wide variety of speech samples collected by people of different ages and genders. This paper shows a comparison between genuine text transcripts with produced text transcripts for a similar sound example. Comparison of results between implemented architecture and already existing Bangla STT has also been presented in this paper. The paper is concluded with a discussion about the word error rate and implementation challenges. The key contribution of this paper is to develop a gender and speaker-independent continuous speech-to-text conversion system for the Bangla language using deep learning.
Towards Virtuous Cloud Data Storage Using Access Policy Hiding in Ciphertext Policy Attribute-Based Encryption

Managing and controlling access to the tremendous data in Cloud storage is very challenging. Due to various entities engaged in the Cloud environment, there is a high possibility of data tampering. Cloud encryption is being employed to control data access while securing Cloud data. The encrypted data are sent to Cloud storage with an access policy defined by the data owner. Only authorized users can decrypt the encrypted data. However, the access policy of the encrypted data is in readable form, which results in privacy leakage. To address this issue, we proposed a reinforcement hiding in access policy over Cloud storage by enhancing the Ciphertext Policy Attribute-based Encryption (CP-ABE) algorithm. Besides the encryption process, the reinforced CP-ABE used logical connective operations to hide the attribute value of data in the access policy. These attributes were converted into scrambled data along with a ciphertext form that provides a better unreadability feature. It means that a two-level concealed tactic is employed to secure data from any unauthorized access during a data transaction. Experimental results revealed that our reinforced CP-ABE had a low computational overhead and consumed low storage costs. Furthermore, a case study on security analysis shows that our approach is secure against a passive attack such as traffic analysis.
Effect of surface weathering on portable X‐Ray fluorescence analysis of artefacts: Evidence from Tasmanian hornfels

Abstract Hornfels was commonly utilised by Aboriginal people in southeastern Tasmania for stone artefact manufacture. Sourcing of hornfels artefacts in this region using pXRF analysis is potentially compromised by the weathering patina generally present on this lithology. To understand the impact of weathering on hornfels composition, the mineralogy and chemical composition of weathered and unweathered hornfels were analysed, and weathering was simulated in column experiments. The results demonstrate that the cordierite and, to a lesser extent, plagioclase present in the hornfels transform very rapidly into kaolinite, which is the dominant component of the pale, porous weathering patina. Weathering simulation experiments and isocon comparisons of major and trace element analyses show that this alteration is accompanied by a substantial loss of Mg, Na and Ca (≥50%). For the trace elements, 25%–35% are removed (except Zr, which is relatively immobile), with the remainder retained in unaltered minerals or adsorbed to the neoformed kaolinite. Rb and Nb are more stable than Sr, which demonstrates the highest absolute mobility, but even this element shows relatively little loss, provided that the weathering patina is &lt;1 mm thick. For Rb, Sr, Y, Zn and Nb, the percentage loss during weathering is similar (25%–35%), so bivariate plots of these trace elements show a spread due to weathering, but with a more or less constant ratio. As a result, pXRF analyses of these elements can be used to successfully differentiate hornfels artefacts from different sites in southeastern Tasmania. This study showed that Rb, Sr, Y and Nb, which are commonly used to source igneous artefacts, are also effective in sourcing hornfels, even though they are mobile during weathering. This study also demonstrated the importance of isocon plots in identifying the trace elements best suited to artefact sourcing using pXRF, if the analyses are affected by chemical weathering.
Video-Based Background/Foreground Estimation for Vehicle Detection

The paper describes a new approach to background/foreground estimation in images from a video stream. The proposed method is based on the analysis of small gradients in images. The video stream is recorded with the camera mounted in the measuring station above the road and directed along the road. Input images are converted into binary form and divided into square blocks. For each block the sum of binary values is calculated. Blocks are assigned to the background or to the foreground of an input image. Background/foreground assignment depends on sums of binary values within individual blocks. Vehicle detection is carried out through the analysis of blocks assigned to the foreground. The proposed method of background/foreground estimation is intended for systems of traffic measurements and surveillance.
Sentiment Analysis in Online Learning Environment: A Systematic Review

The global pandemic of COVID-19, has impacted various sectors around the globe, including education sector. It has compelled the educators and learners to go for the teaching/learning activities in online mode, rather than traditional face to face teaching. The technology-enabled interactions can be effective only when the student teacher bonding is created and the sentiments of the learners are understood fully. To be prepared for such outbreaks in future is the need of hour. The study imbibes the role of sentiment analysis with the introduction of what it means and how it can help in such outbreaks in an online learning environment. Recently few studies are being contributed for covering the various aspects of this evolving area of sentiment analysis. The literature however is scattered and unorganized, therefore there is a need to conduct a systematic literature review to compile all the relevant studies together and to arrange it in a framework. This paper attempts towards this to provide better insight on the usage of sentiment analysis for education sector. The outcome of this paper is a step towards proposal of future areas of the research in this emerging field.
Multi-Sentence Argument Linking via An Event-Aware Hierarchical Encoder

Multi-sentence argument linking aims at detecting implicit event arguments across sentences, which is indispensable when textual events span across multiple sentences in a document. Previous studies suffer from the inherent limitations of error propagation and lack the explicit modeling of the local and non-local interactions in a textual event. In this paper, we propose an event-aware hierarchical encoder for multi-sentence argument linking. Specifically, we introduce a hierarchical encoder to explicitly capture the local and global interactions in a textual event. Furthermore, we introduce an auxiliary task to predict the event-relevant context in a manner of multi-task learning, which can implicitly benefit the argument linking model to be aware of the event-relevant context. The empirical results on the widely used argument linking dataset show that our model significantly outperforms the baselines, which demonstrates the effectiveness of our proposed method.
VIP-VP: Early Validation of SoCs Information Flow Policies using SystemC-based Virtual Prototypes

The emergence of Virtual Prototypes (VPs) at the Electronic System Level (ESL) has played a major role in modernizing the System-on-Chips (SoCs) design flow to raise design productivity and reduce time-to-market constraint. Leveraging VPs and extending their use-cases for early security validation are shown as a promising direction. As the cost of fixing any security flaws increases with the stage of development, VP-based security validation can significantly avoid costly iterations. In this paper, we propose VIP-VP, a novel VP-based dynamic information flow analysis approach at the ESL.VIP-VP enables designers to validate the information flow policies of a given VP-based SoC against security threat models, such as information leakage (confidentiality) and unauthorized access to data in a memory (integrity). Experimental results including a real-world VP-based SoC demonstrate the scalability and applicability of the proposed approach.
Tracking Semantic Evolutionary Changes in Large-Scale Ontological Knowledge Bases

This paper is concerned with the problem of computing the semantic difference between different versions of large-scale ontological knowledge bases using a uniform interpolation (UI) approach. The semantic difference between two versions of an ontology are the axioms entailed by one version but not the other version, reflecting the evolutionary changes of the content of the ontology. In general, computing such axioms is not computationally feasible, since there are infinitely many of them. UI is an advanced reasoning technique that seeks to create restricted views of ontologies; it provides an effective means for computing a finite representation of the difference between two ontologies. While existing UI methods are designed for languages that are either more expressive or less expressive than the description logic ELH, the underlying language of typical large-scale ontologies, in this paper, we introduce a practical UI method tailored for the task of computing the semantic difference in large-scale ELH-ontologies. The method is terminating, sound, and can always compute UI results possibly including fresh definer symbols. Two case studies on different versions of the SNOMED CT terminology show that the method has overcome major limitations of existing UI methods and can be used to reveal modeling changes that have occurred over successive releases of SNOMED CT.
Ladder of Loschmidt anomalies in the deep strong-coupling regime of a qubit-oscillator system

We uncover a remarkably regular array of singularitylike structures within the deep strong-coupling limit of qubit-oscillator (e.g., light-matter) systems described by the quantum Rabi model as a function of time and coupling strength. These nonanalytic anomalies in the Loschmidt amplitude (echoes) suggest the existence of new forms of dynamical phase transition within this deep strong-coupling regime. The key feature whereby the initial state collapses into orthogonal states at select values of the interaction strength and select times may be used to enhance---or attack---quantum information processing or computation schemes that rely on removing---or retaining---a given quantum state.
Adapting Smart Home Voice Assistants to Users’ Privacy Needs using a Raspberry-Pi based and Self-Adapting System

Through improvements of voice recognition smart voice assistants have become available in various forms. They offer users the possibility to control devices by natural voice interaction. To improve accuracy, user conversation data is automatically streamed to companies for machine learning. This is perceived as an act privacy invasion by many users. This is also an argument for potential buyers against acquiring a voice assistant device. Therefore, the central design element of smart home voice assistants should be local processing. For this purpose, the work was based on the Privacy by Design principles by Cavoukian. Based on the analysis of existing cloud and offline solutions a prototype was developed. Through complete offline modality features like self adapting commands and more natural forms of voice interaction were integrated. This was possible while protecting user privacy. Furthermore, fulfilment of the goals with focus on accuracy and quick response time was evaluated. This paper shows that it is possible to implement an offline voice assistant with similar response times and high accuracy. Self adaptation and expanded recognition show great potential for future improvements.
Flexible Majority Rules for Cryptocurrency Issuance

We explore democratic currency issuance with flexible majority rules. With flexible majority rules, the vote-share needed to approve a particular currency issuance growth is increasing with this growth rate. By choosing suitable flexible majority rules, socially optimal growth rates can be achieved in simple settings. By adding a communication stage, in which agents can reveal their preferences for currency growth, the voting processonly needs three rounds. With fewer rounds, agents can manipulate the voting outcome. Finally, we show that optimal money growth rates are realized if agents entering financial contracts anticipate ensuing inflation rates determined by these flexible majority rules.
What's in the box

Machine learning models are now widely deployed in real-world applications. However, the existence of adversarial examples has been long considered a real threat to such models. While numerous defenses aiming to improve the robustness have been proposed, many have been shown ineffective. As these vulnerabilities are still nowhere near being eliminated, we propose an alternative deployment-based defense paradigm that goes beyond the traditional white-box and black-box threat models. Instead of training and deploying a single partially-robust model, one could train a set of same-functionality, yet, adversarially-disjoint models with minimal in-between attack transferability. These models could then be randomly and individually deployed, such that accessing one of them minimally affects the others. Our experiments on CIFAR-10 and a wide range of attacks show that we achieve a significantly lower attack transferability across our disjoint models compared to a baseline of ensemble diversity. In addition, compared to an adversarially trained set, we achieve a higher average robust accuracy while maintaining the accuracy of clean examples.
Improving First-Order Threshold Implementations of SKINNY

Threshold Implementations have become a popular generic technique to construct circuits resilient against power analysis attacks. In this paper, we look to devise efficient threshold circuits for the lightweight block cipher family SKINNY. The only threshold circuits for this family are those proposed by its designers who decomposed the 8-bit S-box into four quadratic S-boxes, and constructed a 3-share byte-serial threshold circuit that executes the substitution layer over four cycles. In particular, we revisit the algebraic structure of the S-box and prove that it is possible to decompose it into (a) three quadratic S-boxes and (b) two cubic S-boxes. Such decompositions allow us to construct threshold circuits that require three shares and executes each round function in three cycles instead of four, and similarly circuits that use four shares requiring two cycles per round. Our constructions significantly reduce latency and energy consumption per encryption operation. Notably, to validate our designs, we synthesize our circuits on standard CMOS cell libraries to evaluate performance, and we conduct leakage detection via statistical tests on power traces on FPGA platforms to assess security. (For reproducibility's sake, we provide a public repository containing the source code to all proposed schemes together with a script to run the SILVER verification suite [].)
Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach

Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.