Reviewerly/exHarmony huggingface dataset:
Methodology of Researching Perception Identity of Regions of Users' Interests While Viewing Streaming Video Containing Various Content and Compression Artefacts

Over the past two decades, eye-tracking devices have become significant due to continuous research and development in non-invasive eye-tracking solutions. Since the data generated by eye tracking devices can be used for a better understanding of human behavior and cognitive processes, the technology is applicable to a wide range of research areas, including telecommunications. In the presented work, the dependence of the change in the user's perception areas when re-watching a video with different media content and compression artifacts is considered. Proposed work presented the comparative analysis of the user's perception areas repeatability finding of viewing streaming video content will be given. A methodology for researching and measuring the identity of the regions of user interest perception when re-viewing streaming videos containing different content and compression artefact will also be presented
DB-MVSNet: Unsupervised multi-view 3D reconstruction algorithm with two branches

Supervised Multi-view 3D reconstruction algorithm based on ground truth data has great limitations in data acquisition cost and method promotion. Several unsupervised 3D reconstruction algorithms have even achieved similar results with supervised MVSNet series algorithms. We continue the work on the basis of previous research. Our DB-MVSNet includes two main tasks: first, the model introduces two parallel branches including depth estimation branch and semantic cluster branch. Secondly, we introduce normal depth in depth estimation branch, and adopt NNet network to realize the consistent propagation. The experimental results show that DB-MVSNet improves the overall performance of unsupervised 3D reconstruction algorithm.
Interactive Latent Variable Evolution for the Generation of Minecraft Structures

The open-world sandbox game Minecraft is well-known for applying a wide array of procedural content generation techniques to create unique and expansive game environments. However, procedurally generated buildings are absent in the Minecraft world, thus players must build their own structures to flesh out their worlds. This build process can be extremely time-consuming and appeals to more creatively-inclined players. To aid players in this process, we introduce a tool combining interactive evolution with latent variable evolution to evolve procedurally generated Minecraft structures to a player's aesthetic choices. We employ two separate neural network models to generate structures: a 3D generative model for generating the structure design and an encoding model for applying Minecraft textures to the structure's voxels. We evaluate this tool with a user study incorporating an online interface that allows participants to select, evolve, and guide a population of these generated 3D structures towards a specific design goal.
StoryWorld: Procedural Quest Generation Rooted in Variety &amp; Believability

Procedural Content Generation (PCG) in games has become more common in recent years. Procedural narrative, however, is still tricky, as designers fear that applying PCG to narrative means that they lose control over the system. In this paper, we attempt to tackle procedural generation of quests, a specific type of narrative, like in the Role Playing Games genre. To this end, we present StoryWorld, an Event-driven simulation of Items, Locations, Characters, and their Memories and Desires. Its modularity makes it easy to add new items and events. Future work could see its memory system made more realistic.
Regl-Scatterplot: A Scalable Interactive JavaScript-based Scatter Plot Library

Scatter plots are one of the most popular visualization methods to surface correlations (like trends or clusters) in bivariate data.They are used across all scientific domains.With datasets ever increasing in size, it can become challenging to effectively render and explore scatter plots.Hence, there is a need for scalable and interactive scatter plot libraries.
Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method

Wireless sensor systems often fail to provide measurements with uniform time spacing. Measurements can be delayed or even miss completely. Resampling to uniform intervals is necessary to satisfy the requirements of subsequent signal processing. Common resampling algorithms, based on symmetric finite impulse response (FIR) filters, entail a group delay of 10 s of samples, which is not acceptable regarding the typical interval of wireless sensors of seconds or minutes. The purpose of this paper is to verify the feasibility of single-delay resampling, i.e., the algorithm resamples the data without waiting for future samples. A new method to parametrize Kriging interpolation is presented and compared with two variants of Lagrange interpolation in detailed simulations for the resulting prediction error. Kriging provided the most accurate resampling in the group-delay scenario. The single-delay scenario required almost double the OSR to achieve the same signal-to-noise ratio (SNR). An OSR between 1.8 and 3.1 was necessary for single-delay resampling, depending on the required SNR and signal distortions in terms of jitter, missing samples, and noise. Kriging was the least noise-sensitive method. Especially for signals with missing samples, Kriging provided the best accuracy. The simulations showed that single-delay resampling is feasible, but at the expense of higher OSR and limited SNR.
Excitement surfeited turns to errors: Deep learning testing framework based on excitable neurons

Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have become a growing concern among the public due to their frequently-appearing erroneous behaviors. As a result, it is urgent to test the security issues with DNNs systematically before they are deployed to the real world. Fine-grained metrics based on neuron coverage have been provided by existing testing approaches, and various approaches have been proposed to enhance these metrics. However, it has been gradually realized that a higher neuron coverage does not necessarily represent better capabilities in identifying defects that lead to errors. Besides, coverage-guided methods cannot hunt errors due to faulty training procedure. So the robustness improvement of DNNs via retraining by these testing examples are unsatisfactory. To tackle this challenge, we introduce the concept of excitable neurons based on Shapley value and design a white-box testing framework for DNNs, namely DeepSensor. It is motivated by our observation that neurons with larger responsibility towards model loss changes due to small perturbations are more likely related to incorrect corner cases due to potential defects. By maximizing the number of excitable neurons that correspond to various incorrect behaviors of models, DeepSensor can generate testing examples that effectively trigger more erroneous security issues caused by malicious inputs (both adversarial and polluted data) and incomplete training. Extensive experiments implemented on both image classification models and speaker recognition models have demonstrated the superiority of DeepSensor. Compared to the state-of-the-art testing methods, DeepSensor has the ability to find more wrong model behaviors due to malicious inputs (i.e., ∼×1.2 for adversarial and ∼×4.7 for polluted data) and incompletely-trained DNNs. Additionally, it can help DNNs build larger l2-norm robustness bound (∼×3) via retraining according to CLEVER's certification. Furthermore, we provide interpretable certifications for effectiveness of DeepSensor by identifying excitable neurons and visualizations via t-SNE. The open source code of DeepSensor can be downloaded at https://github.com/Allen-piexl/DeepSensor/.
GPMT: Generating practical malicious traffic based on adversarial attacks with little prior knowledge

Machine learning (ML) is increasingly used for malicious traffic detection and proven to be effective. However, ML-based detections are at risk of being deceived by adversarial examples. It is critical to carry out adversarial attacks to evaluate the robustness of detections. Some research papers have studied adversarial attacks on ML-based detections, while most of them are in unreal scenarios. It mainly includes two aspects: (i) adversarial attacks gain extra prior knowledge about ML-based models, such as the datasets and features used by the model, which are unlikely to be available in reality; (ii) adversarial attacks generate unpractical examples, which are traffic features or traffic that doesn’t compliance with communication protocol rules. In this paper, we propose an adversarial attack framework GPMT, which generates practical adversarial malicious traffic to deceive the ML-based detection. Compared with previous work, our work mainly has the following advantages: (i) little prior knowledge: we limit the possessed prior knowledge to simulate black-box attacks for real situations; (ii) more adversarial and practical examples: we employ Wasserstein GAN (WGAN) to execute adversarial attacks and design a novel loss function, which generates practical adversarial examples that are more likely to deceive detections. We attack nine ML-based models in the CTU-13 dataset to demonstrate the framework’s validity. Experimental results show that GPMT is more effective and versatile than other methods. For nine models, mean evasion increase rate (EIR) can reach 65.53%, which is 16.48% higher than the best of related methods, DIGFuPAS. In addition, we test other datasets to verify the generality of the framework. The experiment shows that our attack is equally applicable.
Sequential reattempt of telecloning

The task of a telecloning protocol is to send an arbitrary qubit possessed by a sender to multiple receivers. Instead of performing Bell measurement at the sender's node, if one applies unsharp measurement, we show that the shared state can be recycled for further telecloning protocol. Specifically, in case of a single sender and two receivers, the maximal attempting number, which is defined as the maximum number of rounds used by the channel to obtain quantum advantage in the fidelity, turns out to be three both for optimal and nonoptimal shared states for telecloning while the maximal number reduces to two in case of three receivers. Although the original telecloning with quantum advantage being possible for arbitrary numbers of receivers, we report that the recycling of resources is not possible in telecloning involving a single sender and more than three receivers, thereby demonstrating a no-go theorem. We also connect the maximal achievable fidelities in each round with the bipartite entanglement content of the reduced state between the sender and one of the receivers as well as with the monogamy score of entanglement.
<b><i>Komala</i>and<i>Kaṭhora</i>: A Novel Approach Towards Classification of Hindi Poetry</b>

Literary compositions are very often analyzed using various constituent units like words, phrases, sentences, and paragraphs. Unlike the conventional research that focuses on the aforementioned constituent units, our task is a statistical effort carried out on the most fundamental unit of any literary composition called varna , or character, followed by automated classification using learning algorithms. This article is a case study on the Hindi adaptations of two significant literary pieces, namely, Jana-Gaṇa-Mana and Vande-Mātaram , and acknowledging that the two songs being studied belong to different classes based on their bhava, i.e., the inherent emotion of the poem. The present task is the first of its kind that uses the concept of komala and kaṭhora varna to establish diversity between the two. The two-proportion Z-test is successfully applied to statistical data pertaining to the candidate songs, thereby reestablishing the theoretical assertions by investigating real pieces of literature. Taking the statistical verification as ground, a learning-based classification system is designed to yield the best accuracy of 85%, which further compliments the theory reestablished statistically.
A transformer-based multi-task framework for joint detection of aggression and hate on social media data

Abstract Moderators often face a double challenge regarding reducing offensive and harmful content in social media. Despite the need to prevent the free circulation of such content, strict censorship on social media cannot be implemented due to a tricky dilemma – preserving free speech on the Internet while limiting them and how not to overreact. Existing systems do not essentially exploit the correlatedness of hate-offensive content and aggressive posts; instead, they attend to the tasks individually. As a result, the need for cost-effective, sophisticated multi-task systems to effectively detect aggressive and offensive content on social media is highly critical in recent times. This work presents a novel multifaceted transformer-based framework to identify aggressive and hate posts on social media. Through an end-to-end transformer-based multi-task network, our proposed approach addresses the following array of tasks: (a) aggression identification, (b) misogynistic aggression identification, (c) identifying hate-offensive and non-hate-offensive content, (d) identifying hate, profane, and offensive posts, (e) type of offense. We further investigate the role of emotion in improving the system’s overall performance by learning the task of emotion detection jointly with the other tasks. We evaluate our approach on two popular benchmark datasets of aggression and hate speech, covering four languages, and compare the system performance with various state-of-the-art methods. Results indicate that our multi-task system performs significantly well for all the tasks across multiple languages, outperforming several benchmark methods. Moreover, the secondary task of emotion detection substantially improves the system performance for all the tasks, indicating strong correlatedness among the tasks of aggression, hate, and emotion, thus opening avenues for future research.
Cryptocurrencies

With the advent of cryptocurrency during 2009, a new regime of exchange consideration, as well as an investment avenue without physical existence and centralized regulatory authority, started attracting the investors. These extremely volatile neo currencies created regulatory issues among apex monitoring authorities and also raised various challenges in the front of financial reporting. The main purpose of financial reporting is to present the true and fair view of the financial statement of a company. Any violation in this regard creates serious issue of corporate governance. On acceptance of cryptocurrencies as a legal tender, disclosure of such transactions will be mandatory on the part of the company trading and investing on it. At the same time, International Accounting Standard Board (IASB), has not yet issued any standards of IFRS as a guideline for accounting cryptocurrency. In this regard, a detail analysis has been presented in this chapter comprising various issues and challenges in recognizing cryptocurrency in financial statements under IFRS.
NPR-LBN: next point of interest recommendation using large bipartite networks with edge and cloud computing

Abstract During the last decades, tourism has been augmented worldwide through which the diversity of tourists’ interests is increased and is challenging to tackle with the traditional management system. Such challenges can be overcome by LBSNs (Location-Based Social Networks) such as Yelp, Foursquare, and Facebook which help to collect more personalized information close to tourists’ preferences/interests like check-ins, comments, and reviews. In this regard, solutions have been proposed to exploit the POI (Point of Interest) recommendation, but they failed to overcome sparsity and cold-start problems. Existing methods are also not focusing on important aspects, including geographical context, dynamics preferences and social influence, which are essential factors in POI recommendation. Therefore, this work tried to incorporate these factors and present a unified model using bipartite networks to learn users and POI dynamics. For this purpose, we have represented all the factors using eleven networks and combined them into a single latent space. In addition, Edge Computing processes data at the network's edge, reducing latency and bandwidth usage and enabling real-time and personalized recommendations. Furthermore, cloud computing could be used to store and process the large amounts of data collected from LBSNs, to support the proposed model's computational requirements and make it more accessible and scalable, allowing it to be easily used by tourism management systems worldwide. Experimental results show that our model outperforms state-of-the-art methods using real-world dataset in terms of accuracy and perform better against sparsity and cold-start problems.
Extracting Mare-like Cryptomare Deposits in Cryptomare Regions Based on CE-2 MRM Data Using SVM Method

A new kind of surface material is found and defined in the Balmer–Kapteyn (B-K) cryptomare region, Mare-like cryptomare deposits (MCD), representing highland debris mixed by mare deposits with a certain fraction. This postulates the presence of surface materials in the cryptomare regions. In this study, to objectively verify the existence of the MCD in the cryptomare regions, based on the Chang’E-2 microwave radiometer (MRM) data, the support vector machine (SVM) method was adopted, where the K-means algorithm was used to optimize the training samples and the random forest algorithm was used to select the proper band features. Finally, the extracted MCD is identified with the datasets including Lunar Reconnaissance Orbiter Wide Angle Camera, Diviner, and Clementine UV–VIS. The main findings are as follows: (1) Compared to the range outlined via the TB counter, the range of the MCD is objectively extracted using the SVM method in the B-K cryptomare region, which is reasonably indicated by the FeO abundance, TiO2 abundance, and rock abundance distributions. (2) The MCDs were extracted in the Dewar, Lomonosov–Fleming (L-F), and Schiller–Schickard (S-S) regions, indicating that the MCDs are widely distributed in the cryptomaria. (3) The presence of MCDs is concentrated in a limited region, accounting for 64.9%, 52.3%, 76.4%, and 64%, respectively, in the range of Dewar, L-F, S-S, and B-K regions identified using the optical data. The occurrence of the MCD gives a new understanding of the surface evolution in the cryptomare regions.
Sequence labeling with MLTA: Multi-level topic-aware mechanism

Current sequence labeling systems are typically based on CNNs-BiLSTM-CRF, which has been viewed as a standard method. However, on one hand, the standard method often uses static word embedding to embed input words without considering the polysemy. On the other hand, BiLSTM has limitations in modeling the global context information, which further makes the standard method hard to correctly recognize the label of words that are ambiguous. In this paper, we propose a multi-level topic-aware mechanism to obtain word-level and corpus-level topic representations for sequence labeling tasks. The word-level topic representation can capture different word sense to some extent and enhance the discriminative of each word. The corpus-level topic representation is a kind of global semantic information which is helpful for the understanding of a word in different contexts. Both of the word-level and corpus-level topic representations are adaptively fused with sequential information extracted by BiLSTM via a proposed fusion gate. Experimental results validate the superior performance of the proposed model compared with state-of-the-art variants of the standard methods. In particular, the multi-level topic-aware mechanism is helpful for achieving better performances on words that are ambiguous.
Blockchain's Data Integrity and Reliability

Services for online storage have expanded quickly with the quick rise in cloud computing technology. The fact that online storage servers are insufficient to properly store and retain data has drawn specific attention in relation to such distant storage solutions, which significantly undermines consumers' trust in acquiring and using cloud storage solutions. The centralised nature of conventional information integrity auditing methodologies for cloud-based storage creates significant security concerns owing to only a few failure points and server flaws. Customers constantly have good standards for the integrity and protection of personal data. This crucial guarantee of data integrity is provided by blockchain technology thanks to its advanced security capabilities. Integrity of data, privacy protection, accessibility of the information, security, and effectiveness are all guaranteed by the blockchain solution's cryptography encryption, which shields data from intruders, thwarts fraudulent activity and exploitation, and lessens the likelihood that information will be stolen or damaged.
PCA and Binary <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math>-Means Clustering Based Collaborative Filtering Recommendation

Aiming at the problem of similarity calculation error caused by the extremely sparse data in collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on slope one matrix prefilling model, principal component dimension reduction, and binary <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>K</mi> </math> -means clustering is proposed in this paper. Firstly, the algorithm uses the slope one model based on item similarity to prefill the original scoring matrix. Secondly, principal component analysis is used to reduce the dimension of the filled matrix, retain the most representative dimension of user characteristics, and remove the dimension with less information. Finally, in order to solve the time-consuming problem of similarity calculation of collaborative filtering algorithm in the case of large-scale system, binary <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>K</mi> </math> -means clustering is carried out in the reduced dimension vector space to reduce the search range of the nearest neighbour of the target user. The algorithm ensures the efficiency and accuracy of recommendation while the scale of users is expanded. The experimental results on movielens dataset show that the algorithm proposed in this paper is superior to the traditional collaborative filtering algorithm and the collaborative filtering recommendation algorithm based on PCA (principal component analysis) and binary <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>K</mi> </math> -means clustering in recall rate, accuracy rate, average error, and running time.
Planning Implementation of Agile and Scrum techniques in medium-sized IT companies

One of the most important issues in large-scale agile development is achieving efficient team planning and coordination. Combining the overview and predictability of long-term planning on a team level with the flexibility and adaptability of agile development is the promise of hybrid approaches to traditional and agile development. Agile techniques have been promoted to reduce project risks, increase output and improve product quality. The research idea presented in this paper relates to scrum methods, further demonstrating how to focus on decision-making or information sharing while adapting agile approaches to large projects and software product development programs in medium-sized enterprises. By using certain modifications, this approach can also be applied to large multinational companies.