Reviewerly/exHarmony huggingface dataset:
Regulatory Challenges in Cryptocurrency: An India Perspective

Cryptocurrencies are emerging as a prominent investment avenue among investors, they have been in the limelight in recent times due to ongoing debate on the regulation of these assets. Governing bodies across the world are facing the dilemma of acknowledging and regulating these instruments. This paper traces the current regulatory framework across the world and identifies the India specific challenges in regulating cryptocurrency. Some of the key challenges that the regulatory authorities face concerning crypto assets are: classifying cryptos as a mere asset class due to their complex features and intended purpose of creating an alternate means for barter, financial transactions and investments; decentralised nature of the assets which complicates levying of laws; money laundering, terrorist financing is rampant with digital currencies; cryptocurrency has been able to radically democratize the ability of start-ups to raise funds, regulation should continue to empower small businesses to borrow from a wide variety of funding community but monitor and curb activities which are not conducive for the business ecosystem. This paper ascertains the key challenges in regulating cryptocurrency by studying various documents, reports, journals across nations, aligning the challenges with an Indian context and tracing the changes in regulatory norms concerning crypto assets over the past decade, to assess the future of cryptocurrencies in India. This study is particularly important due to the lower rate of financial literacy, the constraints within which regulatory authorities continue to operate and the unique financial ecosystem of India. This bird’s eye view of regulatory norms and the existing challenges will enable regulators to build a framework that is conducive for growth and aligns with the existing financial ecosystem.
EU and USA: Trans-Atlantic Data Protection Framework for Data Transfers between the EU and the USA

Article EU and USA: Trans-Atlantic Data Protection Framework for Data Transfers between the EU and the USA was published on April 1, 2022 in the journal Computer Law Review International (volume 23, issue 2).
Integration With Docker Container Technologies for Distributed and Microservices Applications

Docker containerization is a newly emerging technology, which brings virtualization to software applications. Specifically, lightweight-ness has brought higher advantage for the Docker containers. Often, the deployment of distributed net centric applications on cloud hosted Docker containers is a new approach for enterprise applications. This emerging approach has been discussed on many online forums, but currently there is no secondary research study to consolidate this approach. This research study aims to identify, systematically compare and taxonomically classify the existing research studies and their practical approaches. The systematic study was conducted of fifty-seven (57) selected research studies. Selected research studies were classified and compared based on the characterization framework. The study brings the results in a discussion of emerging concerns in the Docker based deployments. Furthermore, the study is moving closer to current concerns in distributed, microservices based net centric software applications
ShaTure: Shape and Texture Deformation for Human Pose and Attribute Transfer

In this paper, we present a novel end-to-end pose transfer framework to transform a source person image to an arbitrary pose with controllable attributes. Due to the spatial misalignment caused by occlusions and multi-viewpoints, maintaining high-quality shape and texture appearance is still a challenging problem for pose-guided person image synthesis. Without considering the deformation of shape and texture, existing solutions on controllable pose transfer still cannot generate high-fidelity texture for the target image. To solve this problem, we design a new image reconstruction decoder - ShaTure which formulates shape and texture in a braiding manner. It can interchange discriminative features in both feature-level space and pixel-level space so that the shape and texture can be mutually fine-tuned. In addition, we develop a new bottleneck module - Adaptive Style Selector (AdaSS) Module which can enhance the multi-scale feature extraction capability by self-recalibration of the feature map through channel-wise attention. Both quantitative and qualitative results show that the proposed framework has superiority compared with the state-of-the-art human pose and attribute transfer methods. Detailed ablation studies report the effectiveness of each contribution, which proves the robustness and efficacy of the proposed framework.
XAI: A Natural Application Domain for Fuzzy Set Theory

As digital systems cover all personal and professional activities, artificial intelligence is now everywhere. In this context, it is crucial for systems and decisions to be understandable for humans, in a human-in-the-loop process. This global objective is known as eXplainable Artificial Intelligence (XAI). In this chapter, we argue that fuzzy logic is a key concept for XAI as it offers a theoretical framework that is closer than many others to human cognition, human reasoning and human intuitions. We exemplify the many advantages fuzzy logic offers to the XAI domain.
Research on Acoustic Anomaly Detection in Public Scene Based on Multi-dimensional Feature Space

Aiming at the problem of detection and recognition of abnormal sound events in public scenes, this paper proposes an algorithm based on machine hearing algorithm to automatically complete the detection and classification of abnormal sound activities. Through real-time monitoring of the scene, template matching is carried out with the list of abnormal events to realize the judgment of abnormal sound activities in public scenes. The feature mapping of multi-dimensional vector space is completed for the speech segments of potential acoustic activity. The feature vector includes not only the own features, but also the features related to the event list template. The SVM algorithm based on Gaussian radial basis function is used to train and test the performance on the self-organized dataset. The results show that the algorithm has a good performance in detecting the accuracy of classification.
Exercising cyber resilience: The Finnish experience.

Together, the Finnish National Emergency Supply Agency and Digipool are developing exercises to improve the nation's cyber security. This paper provides examples of these exercises and explains how they contribute to companies' emergency response and business continuity plans. The paper also describes how the model is being developed to further improve continuity and emergency planning in the area of cyber resilience.
Exploration on the Construction of College Students’ Innovation Practice Base Under the Background of “Collaborative Education”

College education will have an important impact on social development and students’ personal development. As an &#x0D; important training base for social professionals, college education needs to think about how to effectively ensure the professionalism &#x0D; of talents. Through the development of college education, students can invest in social practice faster and better after receiving &#x0D; education, It is very necessary to reduce the time cost and energy cost for students to integrate into society after graduation. This &#x0D; article also carries out corresponding analysis and Research on the construction of College Students’ innovation practice base &#x0D; under the background of collaborative education, and analyzes the construction methods of College Students’ innovation practice &#x0D; base, from the construction objectives, construction schemes Operation mode, management mechanism and so on.
UFO RPN: A Region Proposal Network for Ultra Fast Object Detection

Deep learning enables high accuracy in object detection in comparison with alternative methods. However deep learning based algorithms are often computationally expensive. That limits the use in many real world scenarios. For decades, researchers have been working on speeding up object detection. One bottleneck in current state-of-the-art methods is the region proposal generation stage as hundreds and thousands of proposed regions need to be processed before detection. Most of the regions are background areas which do not contribute to the actual detection. To improve the efficiency, we propose a region proposal network that can significantly reduce background while maintaining high accuracy. The comparison with SOTA methods shows that our network can be up to 70 times faster, since it only contains 1/15 to 1/150 parameters relative to these methods. The class IoU for MS COCO subsets achieves 40% to 70% and the inference speed on GTX 1080Ti can achieve above 1000 FPS performance. In addition, our study shows that high resolution input is not a must for high accuracy. The use of down-sampled images can further reduce computation costs while retaining or even improving accuracy.
A Background Knowledge Revising and Incorporating Dialogue Model

Currently, dialogue systems have attracted increasing research interest. In particular, background knowledge is incorporated to improve the performance of dialogue systems. Existing dialogue systems mostly assume that the background knowledge is correct and comprehensive. However, low-quality background knowledge is common in real-world applications. On the other hand, dialogue datasets with manual labeled background knowledge are often insufficient. To tackle these challenges, this article presents an algorithm to revise low-quality background knowledge, named background knowledge revising transformer (BKR-Transformer). By innovatively formulating the knowledge revising task as a sequence-to-sequence (Seq2Seq) problem, BKR-Transformer generates the revised background knowledge based on the original background knowledge and dialogue history. More importantly, to alleviate the effect of insufficient training data, BKR-Transformer introduces the ideas of parameter sharing and tensor decomposition, which could significantly reduce the number of model parameters. Furthermore, this work presents a background knowledge revising and incorporating dialogue model that combines the background knowledge revision with response selection in a unified model. Empirical analyses on real-world applications demonstrate that the proposed background knowledge revising and incorporating dialogue system (BKRI) could revise most low-quality background knowledge and substantially outperforms previous dialogue models.
Running a Blog

We now have two fully functioning web servers, with a load balancer distributing incoming connections between each of them. The next step is making this useful with a content management system. In this chapter, we will work through installing and configuring our first blog using Wordpress in a setup that utilizes everything we have built so far.
Effect of Color Variations (Background and Letters) on Current Sources in Running Text P.10 Full Color

The development and advancement of technology are increasing rapidly, especially in the electronics field to convey information. Running text boards are one of the technologies that are considered efficient to attract the attention of the public in conveying information in public places. The purpose of this study was to determine how the influence of current sources on variations in bacground color and font color variations in running text P.10 full color. This research was conducted by varying 6 color (bacground and letter writing) on the flow. Based on the data obtained, it shows that the color combination shows the number of different currents. The highest current is generated by a white background, especially yellow writing of 4.4 amperes and the lowest current by a black background, especially blue writing of 0.44 amperes.
Consumer Bullying in Online Brand Communities—Quantifying a Dark Social Media Phenomenon

Whilst authors in the marketing literature have made some qualitative suggestions that aggressive consumer-to-consumer interactions (i.e., bullying) negatively affect users’ social media experience, there is no quantitative evidence to substantiate such propositions. Moreover, several studies emphasize that large brands such as Nike and Coca Cola are reluctant to get involved when consumer interactions turn hostile. Applying text mining techniques from computer science to a novel phenomenon in marketing, this paper aims to provide a quantitative overview of the prevalence and impact of bullying in online brand communities, and the potential effectiveness of brand responses. To this purpose, we scraped data from the official Facebook community of 14 retail brands (e.g., Lidl, Tesco) for a 3-month period, analyzed the content and sentiment of consumer comments that classified as bullying, and ran t-tests to measure the impact of a brand response. Analyzing a total of 49,866 comments, our findings offer detailed insights on the average bullying rate for retailers’ online brand communities and significant variations in bullying between these retailers. Furthermore, we show that bullying has a largely negative emotional impact on community users, and that a brand who responds to bullying can cause a substantial, positive sentiment shift. We thus offer theoretical and managerial contributions to the novel and under-researched ‘dark side of social media’ in the digital marketing literature.
Inference of Interactions between Players for Asynchronously Updated Evolutionary Game

The interactions between players of prisoner's dilemma (PD) game are reconstructed using observed game data. All participants play the game with their counterparts and gain corresponding rewards during each round of the game. The strategies of each player are updated asynchronously during the game. Two inference methods of the interactions between players are derived with naive mean-field (nMF) approximation and maximum log-likelihood estimation (MLE) respectively. The two methods are tested numerically also for fully connected asymmetric Sherrington-Kirkpatrick (SK) models, varying the data length, asymmetric degree, payoff and system noise (coupling strength). We find that the reconstruction mean square error (MSE) of MLE method is proportional to the inverse of data length and typically half (benefit from the extra information of update times) of that by nMF. Both methods are robust to the asymmetric degree but works better for large payoff. Compared with MLE, nMF is more sensitive to the couplings strength which prefers weak couplings.
The Contribution of EEG Rhythms’ Changes to the Audiovisual Recognition of Words in University Students with Dyslexia

Dyslexia is one of the most frequent specific learning disorders often associated with phonological awareness deficits mainly concerning auditory and visual inabilities to recognize and discriminate phonemes and graphemes within words.Neuroimaging techniques have been widely used to assess hemispheric differences in brain activation between students with dyslexia and their typical counterparts, albeit the research in adult population is rather limited.In this study, we examined the brain activation differences between 14 typical and 12 university students with dyslexia.The two tasks consisted of words having different degrees of auditory and visual distinctiveness.The whole procedure was recorded with a 14-sensor sophisticated EEG recording device (Emotiv EPOC+).The findings from the auditory task revealed statistically significant differences in the left temporal and occipital lobe and in the right prefrontal area.Concerning the visual task, differences were evident again in the left temporal and occipital lobe, in the parietal lobe and in the right occipital lobe.The findings indicate differences in the hemispheric brain activation of students with or without dyslexia in various rhythms in both experimental conditions, shedding light in the neurophysiological discrepancies between the two groups.They also lay great emphasis on the necessity of carrying out more studies in adult population with dyslexia.
Petrotectonic framework of Siwalik Group in Khairi Murat-Kauliar area, Potwar Sub-Basin, Pakistan

The Khairi Murat-Kauliar area lies between the Salt Range Thrust (SRT) in the south whereas Main Boundary Thrust (MBT) in the north which is the part of the Potwar Sub Basin. Eocene to Recent succession is exposed in the study area. The selected siliciclastic Siwalik Group display various facies in a fluviatile environment, with different lithologies, and sedimentary features. The petrographic studies of Siwalik Group delineate different microfacies including feldspathic litharenite, litharenite and sub arkose. The provenance study of sandstones depicted the terrane of dissected arc and recycled orogeny, indicating that the detritus was received from sedimentary, metamorphic, plutonic and volcanic rocks from the northern domain of Indian Plate, Karakoram Ranges and Kohistan Island arc. The Siwalik sandstone also contains heavy mineral assemblage such as tourmaline, garnet, epidote, hornblende and chlorite. This heavy detritus assemblage depicted that the deposits were received from metamorphic and igneous origin. A modified dynamic depositional stages of the Siwalik Group with respect to Himalayan orogeny is further built using outcrop data, microfacies and heavy minerals analysis. According to this concept, the group was deposited along Himalayan uprising and thrusting, on the depository in targeted area. The Siwalik Group depicted different phase of Himalayan tectonics and these molasse sequence were deposited in a subsiding foreland basin under the conditions of rapid erosion and quick deposition.
Fabrication of a Gel-Based Nanocomposite Through Persulfate Activated Bread Crumbs for Fulvic Acid Release and Pb(Ii) Removal

Bread making is one of the largest food industries in the world and hundreds of tons of bread are wasted in the world every year. In this work, a gel-based nanocomposite (GN/FABC) was fabricated through persulfate activated bread crumbs to release fulvic acid (FA) and remove Pb(II). The bread crumbs were treated by K 2 S 2 O 8 in the condition of alkaline to produce FA-based bread crumb (FABC). After that, FABC, biochar, and Fe 3 O 4 were mixed into sodium alginate (SA) aqueous solution uniformly, and then the mixed solution was dropwise added to FeCl 3 aqueous solution to fabricate GN/FABC. Sodium alginate therein could be cross-linked with FeCl 3 to form gel sphere. After 2 h, the content of FA in FABC could reach 50 mg/g and the highest temperature could reach 80 ◦ C during the activated process of bread crumbs. Additionally, the release equilibrium quantity of FA from GN/FABC could achieve 42 mg/g after 24 h in the water. Meanwhile, the remove efficiency of GN/FABC for Pb(II) could be 75% in the water after 36 h. Furthermore, the release mechanism of FA from GN/FABC was consist with the Ritger-peppas law and the removal kinetics of Pb(II) was in accord with pseudo second order model. Notably, pot experiment showed that GN/FABC was able to inhibit the bioavailability of Pb(II). Thus, this study provides a new method to deal with waste bread and achieve the resource utilization.
Straight-Line Recognition Using a Triangular Grid

Object detection is a classical problem in image processing that involves model definitions for digital objects and establishment of recognition algorithms. This paper deals with analytical straight line recognition in a two dimensional space based on the Hough Transform method. The main purpose is to reduce processing time in using a virtual triangular mesh. Standard Hough Transform is then applied to triangular cells generated by a virtual triangular grid throught various algorithms. Experimental analysis, with Opencv Library in python language presents effectively implementations of analytical straight line recognition in a triangular mesh. That shows possibilities of optimization on the execution time in taking into account the surface size of the triangle defined by its height and its base, issued by mesh generation.
Deep learning-based distributed denial-of-service detection

The nuisance of distributed denial-of-service (DDoS) attacks has extended unremittingly nowadays. Thus, guaranteeing system availability in this open-ended pandemic is a crucial task. In this work, we propose three different deep learning strategies as a network anomaly-based intrusion detection system (N-IDS) for a DDoS multi-classification task. We built a deep convolutional neural network (CNN), a stacked long short-term memory (S-LSTM) neural network which is a distinct artificial recurrent neural network (RNN), the third model is a hybridisation between CNN and LSTM. Then, we evaluated them on three up to date flow-based datasets: CICIDS2017, CICDDoS2019 and BoT-IoT benchmarks. The outcomes demonstrate that hybrid CNN-LSTM outperforms the existing state-of-the-art schemes in almost all the validation metrics.
Extension of Stochastic Point Location for Multimodal Problems

Stochastic point location (SPL) involves a learning mechanism (LM) determining an optimal point on the line when the only inputs LM receives are stochastic information about the direction in which LM should move. The complexity of SPL comes from the stochastic responses of the environment, which may lead LM completely astray. SPL is a fundamental problem in optimization and was studied by many researchers during the last two decades, including improvement of its solution and all-pervasive applications. However, all existing SPL studies assume that the whole search space contains only one optimal point. Since a multimodal optimization problem (MMOP) contains multiple optimal solutions, it is significant to develop SPL’s multimodal version. This article extends it from a unimodal problem to a multimodal one and proposes a parallel partition search (PPS) solution to address this issue. The heart of the proposed solution involves extracting the feature of the historical sampling information to distinguish the subintervals that contain the optimal points or not. Specifically, it divides the whole search space into multiple subintervals and samples them parallelly, then utilizes the feature of the historical sampling information to adjust the subintervals adaptively and to find the subintervals containing the optimal points. Finally, the optimal points are located within these subintervals according to their respective sampling statistics. The proof of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -optimal property for the proposed solution is presented. The numerical testing results demonstrate the power of the scheme.
Secure Data Transfer Through Internet Using Image Steganography

Data is one of the most relevant and important term from the ancient Greek age to modern science and business. The amount of data and use of data transformation for organizational work is increasing. So,for the sake of security and to avoid data loss and unauthorized access of data we have designed an image Steganographic algorithm implementing both Cryptography and Steganography. This algorithm imposed a cipher text within a cover image to conceal the existence of the cipher text and the stego-image is transferred from sender to intended receiver by invoking a distributed connection among them to achieve the data authenticity.
OSSpal Qualitative and Quantitative Comparison: Couchbase, CouchDB, and MongoDB

With the rapid increase in cloud computing, big data, and web applications in real time, there is a great need to turn to database platforms that can handle and process large volumes of data, with high performance and excellent scaling capabilities. Since its creation, this has been one of the main goals of NoSQL databases. This article will cover three of the most popular NoSQL databases, MongoDB, Couchbase, and CouchDB. We will take a more theoretical approach to each database and compare them using the OSSpal methodology.