Reviewerly/exHarmony huggingface dataset:
“We found no violation!”: Twitter's Violent Threats Policy and Toxicity in Online Discourse

Threat moderation on social media has been subject to much public debate and criticism, especially for its broadly permissive approach. In this paper, we focus on Twitter's Violent Threats policy, highlighting its shortcomings by comparing it to linguistic and legal threat assessment frameworks. Specifically, we foreground the importance of accounting for the lived experiences of harassment—how people perceive and react to a tweet—a measure largely disregarded by Twitter's Violent Threats policy but a core part of linguistic and legal threat assessment frameworks. To illustrate this, we examine three tweets by drawing upon these frameworks. These tweets showcase the racist, sexist, and abusive language used in threats towards those who have been marginalized. Through our analysis, we highlight how content moderation policies, despite their stated goal of promoting free speech, in effect, work to inhibit it by fostering an online toxic environment that precipitates self-censorship in fear of violence and retaliation. In doing so, we make a case for technology designers and policy makers working in the sphere of content moderation to craft approaches that incorporate the various nuanced dimensions of threat assessment toward a more inclusive and open environment for online discourse. CONTENT WARNING: This paper contains strong and violent language. Please use discretion when reading, printing, or recommending this paper.
A Novel Robust Reversible Watermarking Method Against JPEG Compression

Robustness limits the application of reversible watermarking. To overcome this weakness, several robust reversible watermarking (RRW) techniques have been proposed so far. However, most existing RRW methods are unstable in terms of robustness and reversibility. Then, in this paper, to ameliorate these disadvantages, a new RRW algorithm is presented. A cover image is first divided into non-overlapping blocks, and a high pass filter is applied to each block to generate a histogram which is a Laplacien-like distribution. Then, the watermark is embedded into the blocks by shifting the generated histogram. Specifically, the histogram shifting is conducted by modifying each pixel in the block, and the embedding distortion is minimized based on a new modification mechanism. Moreover, for blind data extraction and image recovery, a strategy for determining the parameters used in histogram shifting is also proposed. In this way, more than 4,096 bits can be reversibly embedded into a cover image with a good visual quality and sufficient robustness against JPEG compression. The superiority of the proposed method is verified through extensive experiments.
A Privacy Preserving Hybrid Blockchain Based Announcement Scheme for Vehicular Energy Network

The vehicular announcement is an essential component of the Intelligent Transport System that enables vehicles to share important road information to reduce road congestion, traffic incidents, and environmental pollution. Due to the multiple security issues like single point of failure, data tampering, and false information dissemination, many researchers have proposed Blockchain (BC) based solutions to ensure data correctness and transparency in the vehicular networks. However, these schemes suffer from high computational cost and storage overhead due to the use of unsuitable BC on the vehicular layer, costly authentication schemes, and inefficient digital signature verification methods. Moreover, the privacy leakage can occur due to publicly available reputation values and lack of pseudonyms update mechanism. In this paper, we propose a privacy-preserving hybrid BC based vehicular announcement scheme to enable secure and efficient announcement dissemination. We use IOTA Tangle to enable the benefits of BC on vehicular layer while reducing the storage and computational cost. We employ Elliptic Curve Cryptography based pseudonym update mechanism for hiding the real identities of vehicles. To prevent false information dissemination in the network, we propose a reputation-based incentive mechanism for encouraging the users to provide honest ratings about the announcement messages. Furthermore, we use Cuckoo Filter to enable lightweight trustworthiness verification of the vehicles without revealing their reputation values. We also employ a batch verification mechanism to reduce the delays caused by digital signature verification. Moreover, we use InterPlanetary File System, and Ethereum BC for ensuring data availability and secure trust management. Finally, we evaluate the performance of our proposed scheme to prove its efficiency.
Comparative Study of Feature Reduction Techniques in Software Change Prediction

Software change prediction (SCP) is the process of identifying change-prone software classes using various structural and quality metrics by developing predictive techniques. The previous studies done in this field strongly confer the correlation between the quality of metrics and the performance of such SCP models. Past SCP studies have also applied different feature reduction (FR) techniques to address issues of high dimensionality, feature irrelevance, and feature repetition. Due to the vast variety of metric suites and FR techniques applied in SCP, there is a need to analyze and compare them. It will help in identifying the most crucial features and the most effective FR techniques. So, in this research, we conduct experiments to compare and contrast 60 Object-Oriented plus 26 Graph-based metrics and 11 state-of-the-art FR techniques previously employed for SCP over a range of 6 Java projects and 3 diverse classifiers. The AUC-ROC measures and statistical tests over experimental SCP models indicate that FR techniques are effective in SCP. Also, there exist significant differences in the performance of the different FR techniques. Furthermore, from this extensive experimentation, we were able to identify a set of the most effective FR techniques and the most crucial metrics which can be used to build effective SCP models.
Mutually Private Verifiable Machine Learning As-a-service: A Distributed Approach

Reliability is a crucial component to machine-learning-as-a-service platforms, as more and more critical applications depend on them. Thus, mechanisms employed to assure the integrity of computations performed on such platforms are pivotal to their robust functioning. Moreover, privacy protection, and performance guarantee at scale, are other major challenges surrounding these platforms that are by no means straightforward to overcome at the same time. In this paper, we have proposed a novel distributed approach, which uses specialized composable proof systems at its core, to respond to these challenges. At a high level, we adopt a divide-and-conquer approach to build efficient proof systems for machine-learning-based services in order to ensure the correctness of results. More precisely, the mathematical formulation of the machine learning task is divided into multiple parts, each of which is handled by a different specialized proof system; these proof systems are then combined with the commit-and-prove methodology to guarantee correctness as a whole. With privacy safeguards built into the design, our approach also assures that neither user data nor model parameters, which constitute the intellectual property of service providers are exposed in the process. We have showcased the usability of our approach within a machine learning service provider that offers classification services through a linear support vector machine (SVM) model. Our complexity analysis indicates that our system could be used in practical settings.
GAN models in natural language processing and image translation

Recently, generative adversarial networks (GANs) have actively been used in various research fields such as natural language processing, image generation, translation, cyber security, medicine, geo-informatics, etc. But in the field of Natural language processing and Image generation and translation, GANs have shown their remarkable performance when compared to other fields. This chapter provides insights on various GAN models classified based on their learning mechanism. The comparison of GAN models based on activation function, optimization function, and loss functions has been outlined. Further, how these GAN models are adapted for various applications of natural language processing, image generation, and translation are also discussed. This chapter also discusses the comparison of GAN models among NLP, image generation, and translation. This chapter outlines the various NLP and image datasets available for research. The evaluation metrics to evaluate the GAN performance, tools, and the languages used for GAN research are also discussed in this chapter. Finally, it discusses the open challenges for further research.
Releasing ARP Data with Differential Privacy Guarantees For LAN Anomaly Detection

Anomaly detection has emerged as a popular technique for detecting malicious behaviors in local area networks (LANs). Various aspects of LAN anomaly detection have been widely studied. Nonetheless, the privacy concern about individual users or their relationship in LAN has not been thoroughly explored in the prior work. In some realistic cases, the anomaly detection analysis needs to be carried out by an external party, located outside the LAN. Thus, it is important for the LAN admin to release LAN data to this party in a private way in order to reveal no information about LAN users; at the same time, the released data must also preserve the utility of being able to detect anomalies. This paper investigates the possibility of privately releasing ARP data that can later be used to identify anomalies in LAN. We present two approaches and show that they satisfy different levels of differential privacy - a rigorous and provable notion for quantifying privacy loss in a system. Our real-world experimental results confirm practical feasibility of our approaches. With a proper privacy budget, both of our approaches preserve more than 90% utility of the released data.
DeepDepict

In e-commerce platforms, the online descriptive information of products shows significant impacts on the purchase behaviors. To attract potential buyers for product promotion, numerous workers are employed to write the impressive product descriptions. The hand-crafted product descriptions are less-efficient with great labor costs and huge time consumption. Meanwhile, the generated product descriptions do not take consideration into the customization and the diversity to meet users’ interests. To address these problems, we propose one generic framework, namely DeepDepict, to automatically generate the information-rich and personalized product descriptive information. Specifically, DeepDepict leverages the graph attention to retrieve the product-related knowledge from external knowledge base to enrich the diversity of products, constructs the personalized lexicon to capture the linguistic traits of individuals for the personalization of product descriptions, and utilizes multiple pointer-generator network to fuse heterogeneous data from multi-sources to generate informative and personalized product descriptions. We conduct intensive experiments on one public dataset. The experimental results show that DeepDepict outperforms existing solutions in terms of description diversity, BLEU, and personalized degree with significant margin gain, and is able to generate product descriptions with comprehensive knowledge and personalized linguistic traits.
Tell me: Am I going to Heaven? A Diagnosis Instrument of Continuous Software Engineering Practices Adoption

Context: Continuous Software Engineering (CSE) involves a set of practices that aims at making software development continuous and integrated to business. However, moving from traditional to integrated, agile and data-driven software development requires changes in the organization´s culture, practices and structure, which may not be easy. Objective: Our focus is to help organizations get an overall view of the CSE practices they perform, identify where they are in the CSE evolutionary path and which areas should be improved. Method: We created a diagnosis instrument, called Zeppelin, to evaluate the adoption of CSE practices and applied it in five Brazilian software organizations. Results: Zeppelin was recognized as a useful tool to help organizations have a more comprehensive view of their CSE practices and envision the evolution and improvement path they can follow. Conclusion: Zeppelin supports software development organizations to get a big picture of CSE practices and identify their position in the CSE evolution path. Moreover, it contributes to identify strategies to advance software development towards a CSE environment.
Fostering Energy Transition in Smart Cities: DLTs for Peer-to-Peer Electricity Trading

This paper explores the applications of Distributed Ledger Technologies (DLTs) in Peer-to-Peer (P2P) electricity trading. It highlights the challenges and trade-offs of applying three different DLTs: Blockchain, Directed Acyclic Graph (DAG), and Holochain. First, the study introduces the energy transition concept in the smart city context. Second, P2P electricity trading and its supporting trading mechanisms are introduced. Third, DLTs are defined and three different types of DLTs are introduced. Forth, possibilities, challenges, and consequences of applying DLTs in electricity trading are explained. Last, applying DLTs for P2P electricity trading from different aspects are discussed. This paper provides a benchmark for applying DLTs to foster energy transition in smart cities. It highlights how this type of technology can serve smart circular economy.
Students' perceptions of computer-based testing using ExamSoft

In fall 2017, West Coast University School of Pharmacy implemented ExamSoft for testing. Three courses in each didactic year employed ExamSoft. Prior to this, courses had Scantron-based exams. We surveyed the students to assess their perception of ExamSoft. We hypothesized that students' inherent bias towards technology affected their perception of ExamSoft.To assess this hypothesis, we conducted a survey of all students. The survey contained questions about comfort with technology and nine questions on students' perceptions of ExamSoft and its usefulness.The survey responses were stratified according to the preference of respondents towards technology and its use in exams. Respondents were stratified into three groups: tech-embracers, tech-skeptics, and neutral. Our results showed that respondents classified as tech-skeptics tended to have a more negative view of ExamSoft and its perceived impact on their grades than students stratified as tech-embracers or neutral.Our study suggests that students' inherent bias towards technology plays an important role in their perception of computer-based testing. Assessing incoming students' comfort with technology and student orientation activities to help acquaint students with new technology could help improve their acceptance of educational technology used for testing.
IOTA-Based Mobile Application for Environmental Sensor Data Visualization

Over the last few years, Distributed Ledger Technology (DLT) emerged as a trending opportunity for innovation in several sectors, and a wide range of implementations, sometimes very different from each other, appeared on the market. In particular, the intrinsic features of DLT can represent enabling factors for the IoT sector. The huge number of IoT applications and the exponential growth of connected smart devices are shaping the present and the near future of our lives and the way we understand and see the world. The application of IoT to all sectors, spanning from smart home to industry, is conceived as the way to improve and optimize processes, consumption and efficiency. The large amount of data produced, the need of certifying exchanges of goods, the possibility to track assets, and so on, are challenges that can be addressed through DLT. This paper aims to tackle some of these challenges by presenting IoT devices and a mobile application that are able to exchange data through the IOTA Tangle. IOTA is an implementation of DLT, based on the Directed Acyclic Graph (DAG) structure. This paper presents the functionalities and the current implementation of the mobile application, its features, challenges and future developments.
Tire Pattern Classification Based On Few-Shot Learning

Learning from a limited number of samples is challenging since the learned model can easily become overfitted based on the biased distribution formed by only a few training examples. In this paper, we design a novel few-shot learning method to solve the problem of tire pattern classification proposed by Xi’an University of Posts and Telecommunications laboratory. The proposed method consists of two steps. On the one hand, we calibrate the distribution of these fewsample classes by transferring statistics from the classes with sufficient features (FD). On the other hand, an adequate number of examples can be sampled from the feature distribution to expand the inputs to the classifier (SVM). Experimental results on the Tire pattern dataset demonstrate that, compared with the existing few-shot learning models, the proposed FT with SVM provides noticeably more robust and higher performance, thus making it a useful tool for practical application scenarios.
Study on Deterministic and Probabilistic Computation of Primality Test

Every natural number greater than 1 is either prime or composite. Those numbers which have only two factors i.e., 1 and itself are considered as prime and rest are considered as composite numbers. The property of a number for being prime is called primality. In this survey paper we will deal with the algorithms that can check whether the number is prime or not i.e., primality test. This study is the detailed survey of probabilistic and deterministic algorithms like Fermat’s theorem of primality test, AKS theorem, Miller Rabin’s test, Solvay Strassen’s theorem etc. We will discuss different parameters regarding algorithms which are best for testing large prime numbers. Many aspects will be considered while discussing these algorithms.
Research on System of Data Mining Technology Based on Computer

Abstract The existing implicit feedback collaborative algorithm directly uses sparse binary social trust information to assist recommendation, which has serious data sparse problems and does not have the influence of deep integration of social trust information. In response to the above problems, the paper proposes an algorithm that uses a denoising autoencoder to deeply integrate user implicit feedback data and social information. At the same time, a cloud computing-based social trust information data mining system architecture is constructed, and its modules are described. This framework is conducive to solving the problems of high data complexity and large amount of data encountered in current data mining, and can significantly improve the performance of data mining.
Chemical Weathering in Jhelum River and its Tributaries, Kashmir Basin, Western Himalaya

The study describes the chemical analysis of water and sediment to understand the hydrogeochemical processes, chemical weathering rates and its intensities in the upper river Jhelum and its major tributaries in Kashmir Basin, Western Himalaya. A total of 50 water samples and 15 riverbed sediment samples were analyzed. It was found that Ca and Mg contribute 82% of the major cations and HCO3− contributes 92% of the anion budget with only 4% and 3% contributions from SO4 and Cl, respectively. The Chemical Weathering Rates (CWR) varied spatially among tributaries with Sukhnag showing the lowest and Sindh, the highest CWR during High Flow Period (HFP). However, during the Low Flow Period (LFP), Sukhnag recorded the lowest and Romush recorded the highest CWR thereby reflecting the influence of varied precipitation, discharge, basin lithology, topography and probably active tectonics. The CWR in the main Jhelum river ranged from 31.4 × 102 t/km2/month (LFP) to 107.0 × 102 t/km2/month (HFP) with an annual CWR of 11.1 × 102 t/km2/year. The Left Bank Tributaries (LBT) draining the tectonically active Pir-Panjal range showed higher CWR than the Right Bank Tributaries (RBT) draining the Great Himalayan range (except Sindh). However, in comparison to other Himalayan rivers and World averages (0.36 × 102 t/km2/year), the observed CWR in the main Jhelum river and its tributaries were also found to be higher. Furthermore, the main Jhelum river showed an average annual Silicate Weathering Rate (SWR) of 0.10 t/km2/year and Carbonate Weathering Rate (CrWR) of 0.35 t/km2/year. The Pohru tributary showed the lowest average SWR and CrWR of 0.04 t/km2/year, whereas the Sukhnag showed the highest SWR and CrWR of 0.41 t/km2/year and 0.48 t/km2/year respectively. The Chemical Index of Alteration (CIA) and Chemical Index of Weathering (CIW) support these findings and revealed a moderate weathering in the basin quite usual of cold regions.
Combine-Net: An Improved Filter Pruning Algorithm

The powerful performance of deep learning is evident to all. With the deepening of research, neural networks have become more complex and not easily generalized to resource-constrained devices. The emergence of a series of model compression algorithms makes artificial intelligence on edge possible. Among them, structured model pruning is widely utilized because of its versatility. Structured pruning prunes the neural network itself and discards some relatively unimportant structures to compress the model’s size. However, in the previous pruning work, problems such as evaluation errors of networks, empirical determination of pruning rate, and low retraining efficiency remain. Therefore, we propose an accurate, objective, and efficient pruning algorithm—Combine-Net, introducing Adaptive BN to eliminate evaluation errors, the Kneedle algorithm to determine the pruning rate objectively, and knowledge distillation to improve the efficiency of retraining. Results show that, without precision loss, Combine-Net achieves 95% parameter compression and 83% computation compression on VGG16 on CIFAR10, 71% of parameter compression and 41% computation compression on ResNet50 on CIFAR100. Experiments on different datasets and models have proved that Combine-Net can efficiently compress the neural network’s parameters and computation.
Classification of Facial Expression Recognition using Machine Learning Algorithms

Abstract Face is the reflection of brain facial expressions are the discernible consequences of moving a facial muscle. In the perception of human articulations, the facial expression like Sad, Happy, Anger, Disgust, Fear is assumes a fundamental part. The objective of this research focuses on facial expression recognition which related to machine learning and optimization algorithms. Here, we proposed Hybrid Adaptive Kernel based Extreme Learning Machine (HAKELM) algorithm to identify the human facial expression based on certain image processing technique. Thus, the obtained results shown the proposed HAKELM scheme achieved 10% more accuracy, sensitivity and specificity than existing algorithms like Principal Component Analysis (PCA) and Support Vector Machine (SVM).