Reviewerly/exHarmony huggingface dataset:
EAMSA 512: New 512 Bits Encryption Al-gorithm Based on Modified SALSA20

This paper presents the 512-bit encryption algorithm based on modified SALSA20 using an 11-dimensional chaotic system used for generating the keys for the proposed encryption algorithm. Chaotic keys are derived from a combination of two systems, one with six dimensions and the other with five. These keys are used in different operations like shifting, Xoring, and encryption steps in the new proposed algorithm. The proposed algorithm consists of a combination of two parallel parts: Salsa20 and S boxes with chaos keys. The proposed system takes 512 bits of the plaintext, which will be split into two 256-bit parts, the left part encrypted with modified salas20 and the chaos keys, and the right 256-bit part encrypted using eight 8x8 s-boxes and the chaos keys. The results are swapped and combined, and this operation is repeated for 16 rounds to get the cipher text. The testing of EAMSA 512bit using various tests demonstrates the algorithm's strength and security, as well as its ability to avoid many attacks with lightweight processing
Chinese News Text Classification Method via Key Feature Enhancement

(1) Background: Chinese news text is a popular form of media communication, which can be seen everywhere in China. Chinese news text classification is an important direction in natural language processing (NLP). How to use high-quality text classification technology to help humans to efficiently organize and manage the massive amount of web news is an urgent problem to be solved. It is noted that the existing deep learning methods rely on a large-scale tagged corpus for news text classification tasks and this model is poorly interpretable because the size is large. (2) Methods: To solve the above problems, this paper proposes a Chinese news text classification method based on key feature enhancement named KFE-CNN. It can effectively expand the semantic information of key features to enhance sample data and then combine the zero–one binary vector representation to transform text features into binary vectors and input them into CNN model for training and implementation, thus improving the interpretability of the model and effectively compressing the size of the model. (3) Results: The experimental results show that our method can significantly improve the overall performance of the model and the average accuracy and F1-score of the THUCNews subset of the public dataset reached 97.84% and 98%. (4) Conclusions: this fully proved the effectiveness of the KFE-CNN method for the Chinese news text classification task and it also fully demonstrates that key feature enhancement can improve classification performance.
What is Hate Speech? The Case for a Corpus Approach

Abstract Contemporary public discourse is saturated with speech that vilifies and incites hatred or violence against vulnerable groups. The term “hate speech” has emerged in legal circles and in ordinary language to refer to these communicative acts. But legal theorists and philosophers disagree over how to define this term. This paper makes the case for, and subsequently develops, the first corpus-based analysis of the ordinary meaning of “hate speech.” We begin by demonstrating that key interpretive and moral disputes surrounding hate speech laws—in particular, surrounding their compatibility with the rule of law, democracy, and free speech—depend crucially on the ordinary meaning of “hate speech.” Next, we argue, drawing on recent developments in legal philosophy, that corpus linguistics constitutes a distinctively promising tool for ascertaining the ordinary meaning of “hate speech.” Finally, we offer a proof of concept, by outlining, and analyzing the interpretive and moral implications of, the first such study.
Contactless Real-Time Eye Gaze-Mapping System Based on Simple Siamese Networks

Human–computer interaction (HCI) is a multidisciplinary field that investigates the interactions between humans and computer systems. HCI has facilitated the development of various digital technologies that aim to deliver optimal user experiences. Gaze recognition is a critical aspect of HCI, as it can provide valuable insights into basic human behavior. The gaze-matching method is a reliable approach that can identify the area at which a user is looking. Early methods of gaze tracking required users to wear glasses with a tracking function and limited tracking to a small monitoring area. Additionally, gaze estimation was restricted to a fixed posture within a narrow range. In this study, we proposed a novel non-contact gaze-mapping system that could overcome the physical limitations of previous methods and be applied in real-world environments. Our experimental results demonstrated an average gaze-mapping accuracy of 92.9% across 9 different test environments. Moreover, we introduced the GIST gaze-mapping (GGM) dataset, which served as a valuable resource for learning and evaluating gaze-mapping techniques.
Loads prediction and consolidation of virtual machines in cloud

Summary Virtual machine (VM) consolidation is to assign a set of VMs requested by operators to the physical machines (PMs) in the data centers so that certain cost, profit or performance objective is optimized, subject to the PMs resource capacity constraints. It is an important mean to decrease the total power consumption by reducing the number of active physical machines (PM) in a cloud. In this context, most of the existing solutions rely on highly frequent live migration to reduce the number of active physical machines. However, live migration is a high resource consumption and time‐consuming operation, thus, frequent use of live migration not only increases energy consumption but also affects the stability of the physical machine, which in turn affects the services on the virtual machine. Reducing the number of active physical machines while reducing the number of live migrations is a major challenge in the face of massive fluctuating virtual machine loads. In order to solve this problem, in this paper, we present a VM consolidation algorithm for predictable loads (VCPL) to reduce the live migration operations. First, we present a cyclic usage prediction (CUP) method to predict the load in a whole cycle (a day) of a VM. Then, we separate the VMs with stable and cyclic load out from others and consolidate them to PMs by using VCPL to make sure each PM has a stable load. Thus, energy can be reduced by avoiding most of live migration operations, and the stability of the data center can be observably improved. We evaluate our solution through simulations on real‐world workloads, the results show that, 66% of long‐term VMs have stable and cyclic loads and are predictable, by using VCPL, the live migration operations occurring on the PMs which accommodate those VMs can be reduced significantly than other solutions.
Design and Implementation of Smart Contract in Supply Chain Management Using Blockchain and Internet of Things

In this paper, we have presented the design and implementation of a blockchain-based approach for ensuring reliable supply chain management for commodities transported through smart containers. To administer interactions between the sender and receiver, our developed system makes use of the Ethereum blockchain’s smart contract features. Smart containers equipped with Internet of Things (IoT)-enabled sensors are used to monitor shipping conditions to check predefined shipping requirements. Smart contracts on Ethereum are used to automate payments, validate receivers, and give refunds in the case of violation of predefined requirements. We have also implemented our designed front-end decentralized WebApp and wallet that allows the sender and receiver to communicate with Ethereum smart contracts.
Siri, you've changed! Acoustic properties and racialized judgments of voice assistants

As speech technology is increasingly integrated into modern American society, voice assistants are a more significant part of our everyday lives. According to Apple, Siri fulfills 25 billion requests each month. As part of a software update in April 2021, users in the U.S. were presented with a choice of 4 Siris. While in beta testing, users on Twitter began to comment that they felt that some of the voices had racial identities, noting in particular that Voice 2 and Voice 3 “sounded black.” This study tests whether listeners indeed hear the different Siri voices as sounding like speakers from different groups, as well as examines voice quality features that may trigger these judgments. In order to test evaluations of the four voices, 485 American English listeners heard each Siri voice reading the Rainbow Passage, via online survey conducted on Qualtrics. Following each clip, listeners responded to questions about the speaker's demographic characteristics and personal traits. An LMER model of normalized ratings assessed the interaction of voice and race judgment revealed that indeed, Voice 2 and Voice 3 were significantly more likely to be rated as belonging to a Black speaker than Voices 1 and 4 ( p &amp;lt; 0.001). Per-trait logistic regression models and chi-square tests examining ratings revealed Voice 3, the male voice rated as Black, was judged less competent ( X 2 = 108.99, x &amp;lt; 0.001), less professional ( X 2 = 90.97, p &amp;lt; 0.001), and funniest ( X 2 = 123.39, x &amp;lt; 0.001). Following analysis of listener judgments of voices, I conducted post-hoc analysis comparing voice quality (VQ) features to examine which may trigger the listener judgments of race. Using PraatSauce, I employed scripts to extract VQ measures previously hypothesized to pattern differently in African American English vs. Mainstream American English. VQ measures that significantly affected listener ratings of the voices are mean F0 and H1–A3c, which correlate with perceptions of pitch and breathiness. These results reveal listeners attribute human-like demographic and personal characteristics to synthesized voices. A more comprehensive understanding of social judgments of digitized voices may help us to understand how listeners evaluate human voices, with implications for speech perception and discrimination as well as recognition and synthesis.
CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics

Abstract Software development effort estimation (SDEE) is a critical activity in developing software. Accurate effort estimation in the early phases of software design life cycle has important effects on the success of software projects. COCOMO (Constructive Cost Model) is a parametric data‐driven SDEE model whose parameters must be calibrated with an organization's local data for accurate estimation. Such data are scarce for most organizations. On the other hand, CoBRA (Cost estimation, Benchmarking, and Risk Assessment) is one of the powerful hybrid methods that need a small number of local historical data for effort estimation. However, data gathering in CoBRA is time‐consuming and costly. To ease the use of CoBRA, in this paper, we design a methodology that extracts CoBRA‐required data from COCOMO datasets. By the proposed method, data collected for COCOMO would be used in CoBRA. Using CoBRA, a more accurate estimation of the required effort would be achieved with fewer number of historical data than what is required to calibrate the COCOMO model. We apply the proposed method on six well‐known public COCOMO datasets and use them in CoBRA. Obtained results depict an increase in the accuracy of estimations in comparison with other existing methods.
Already here: Metaverse in touch and sound

Abstract This commentary questions two common assumptions underlying discussions of how digital technology can enrich the consumer experience. First, broad conceptualizations tend to accommodate augmented reality and virtual reality under a single metaverse umbrella. This commentary draws attention to differences between the two and to the present‐day prevalence of augmented reality experiences. Second, discussions of the metaverse tend to focus on visual experiences. This commentary draws attention to the impact that augmented reality is having through other senses. I argue that augmented reality experiences are ubiquitous because smartphones and wearables enrich our everyday life with haptic and sonic information.
A Deep-Learning Neural Network Approach for Secure Wireless Communication in the Surveillance of Electronic Health Records

The electronic health record (EHR) surveillance process relies on wireless security administered in application technology, such as the Internet of Things (IoT). Automated supervision with cutting-edge data analysis methods may be a viable strategy to enhance treatment in light of the increasing accessibility of medical narratives in the electronic health record. EHR analysis structured data structure code was used to obtain data on initial fatality risk, infection rate, and hazard ratio of death from EHRs for prediction of unexpected deaths. Patients utilizing EHRs in general must keep in mind the significance of security. With the rise of the IoT and sensor-based Healthcare 4.0, cyber-resilience has emerged as a need for the safekeeping of patient information across all connected devices. Security for access, amendment, and storage is cumulatively managed using the common paradigm. For improving the security of surveillance in the aforementioned services, this article introduces an endorsed joint security scheme (EJSS). This scheme recognizes the EHR utilization based on the aforementioned processes. For each process, different security measures are administered for sustainable security. Access control and storage modification require relative security administered using mutual key sharing between the accessing user and the EHR database. In this process, the learning identifies the variations in different processes for reducing adversarial interruption. The federated learning paradigm employed in this scheme identifies concurrent adversaries in the different processes initiated at the same time. Differentiating the adversaries under each process strengthens mutual authentication using individual attributes. Therefore, individual surveillance efficiency through log inspection and adversary detection is improved for heterogeneous and large-scale EHR databases.
Improving Named Entity Recognition for Social Media with Data Augmentation

Social media is important for providing text information; however, due to its informal and unstructured nature, traditional named entity recognition (NER) methods face the challenge of achieving high accuracy when dealing with social media data. This paper proposes a new method for social media named entity recognition with data augmentation. First, we pre-train the language model by using a bi-directional encoder representation of the transformer (BERT) to obtain a semantic vector of the word based on the contextual information of the word. Then, we obtain similar entities via data augmentation methods and perform substitution or semantic transformation on these entities. After that, the input into the Bi-LSTM model is trained and then fused and fine-tuned to obtain the best label. In addition, our use of the self-attentive layer captures the essential information of the features and reduces the reliance on external information. Experimental results on the WNUT16, WNUT17, and OntoNotes 5.0 datasets confirm the effectiveness of our proposed model.
Personalized Travel Recommendation Systems: A Study of Machine Learning Approaches in Tourism

Recommender systems that utilize machine learning algorithms are a prominent tool in the design and implementation of personalized tourism experiences. These systems analyze user data to generate recommendations for destinations, attractions, accommodations, and activities based on user preferences, behavior, and similarity to other users. Collaborative filtering and content-based filtering are two widely used machine learning algorithms in recommender systems, and hybrid systems that combine both approaches have shown to be effective in producing more accurate recommendations. Tourism recommendation systems (TRS) provide several benefits, including personalization, convenience, improved user experience, and increased revenue for tourism businesses. These systems can suggest destinations, attractions, accommodations, and activities that match user preferences and past behaviors, ultimately simplifying the trip planning process. Machine learning algorithms can be trained on large datasets to generate personalized recommendations, and can continuously improve their effectiveness by incorporating new data and user feedback. This paper provides a state-of-the-art overview of various types of recommendation systems (RS), including those based on user preferences, behaviors, demographic profiles, and social network judgments. The paper also presents a comparison table for these approaches. Additionally, the paper discusses the different stages of the travel process and the sources of data that can be used to develop a recommender system. The concluding section of the paper highlights the importance of personalized recommendations in the tourism industry and the potential for future research in this area.
FoMO in the Bitcoin market: Revisiting and factors

We revisit the “fear of missing out” (FoMO) effect of Bitcoin by observing asymmetric volatility dynamics and further investigate its driving factors. Using a longer sample period covering the COVID-19 pandemic, our results show evidence of positive asymmetric volatility behavior in the Bitcoin market, confirming the presence of the FoMO effect. This effect also exists in some other major cryptocurrencies. Further analysis indicates that the happiness index, the ratio of short-term to long-term Bitcoin trading volume, and the geopolitical risk index contribute positively to the FoMO, while the volatility index and the Twitter-based uncertainty index exert an opposite effect.
ELEMENTS OF ARCHAIC MUSIC REFLECTED IN PETROGLYPHS AS A PHENOMENON OF CULTURAL HERITAGE, THE ORIGINAL SOURCE OF MATERIAL AND SPIRITUAL CULTURE

This article examines the process of forming a culture, the main characteristics of the emotional and spiritual attitude to the world, and the transition of emotions into the artistic and sensual imagery sphere. The necessity of researching petroglyphs, as the earliest sign systems, was revealed and substantiated; they reflect the initial stage of the formation of ancient art. For the first time, the model of the ancient ritual ceremonies was directly related to the religious beliefs found in the petroglyphs, with their semantic filling, which allowed to reveal the cosmogonic functions of ritual melodies in the process of perception of the world.
Data-Centric Artificial Intelligence, Preprocessing, and the Quest for Transformative Artificial Intelligence Systems Development

Some data-centric artificial intelligence (AI) practices often resemble the preprocessing used in traditional AI systems; therefore, there is a possible misconception about the two approaches. This article differentiates these two aspects to guide the AI community to unlock the hidden potential of the data-centric AI paradigm.
Trust and Voice Biometrics Authentication for Internet of Things

In recent years, IoT adoption has been higher, and this causes lots of security concerns. One of the fundamental security concerns in IoT adoption is the question, “Are you who you say you are?” Thus, authentication forms the gateway for a secure communication system with IoT. So far, the human voice is one of the most natural, non-intrusive, and convenient behavioural biometric factors compared to other biometric authentication methods. Despite the non-intrusive characteristics of voice as a biometric authentication factor when accessing IoT technologies, there is a concern of a general societal trust and distrust with IoT technology and the risk of theft of users' data and imitation. This study derived a realistic trust evaluation model that incorporates privacy, reliability, security, usability, safety, and availability factors into a trust vector for a flexible measurement of trust in the user accessing IoT technologies.
Quantizing YOLOv7: A Comprehensive Study

YOLO is a deep neural network (DNN) model presented for robust real-time object detection following the one-stage inference approach. It outperforms other real-time object detectors in terms of speed and accuracy by a wide margin. Nevertheless, since YOLO is developed upon a DNN backbone with numerous parameters, it will cause excessive memory load, thereby deploying it on memory-constrained devices is a severe challenge in practice. To overcome this limitation, model compression techniques, such as quantizing parameters to lower-precision values, can be adopted. As the most recent version of YOLO, YOLOv7 achieves such state-of-the-art performance in speed and accuracy in the range of 5 FPS to 160 FPS that it surpasses all former versions of YOLO and other existing models in this regard. So far, the robustness of several quantization schemes has been evaluated on older versions of YOLO. These methods may not necessarily yield similar results for YOLOv7 as it utilizes a different architecture. In this paper, we conduct in-depth research on the effectiveness of a variety of quantization schemes on the pre-trained weights of the state-of-the-art YOLOv7 model. Experimental results demonstrate that using 4-bit quantization coupled with the combination of different granularities results in ~3.92x and ~3.86x memory-saving for uniform and non-uniform quantization, respectively, with only 2.5% and 1% accuracy loss compared to the full-precision baseline model.
Dynamic knowledge graph completion through time-aware relational message passing

As the structure of knowledge graphs may vary over time, static knowledge graph completion methods do not deal with time-varying knowledge graphs. However, examining the paths between entities and entities' context information can lead to more accurate completion methods. This paper attempts to complete dynamic (time-varying) knowledge graphs by combining time-aware relational paths and relational context. The proposed model can improve dynamic knowledge graph completion methods by leveraging neural networks. Experimental results conducted on two standard datasets, ICEWS14 and ICEWS05-15, indicate our model's superiority in terms of Mean Reciprocal Rank (MRR) and Hit@k over its well-known counterparts, such as DE-TransE and DE-DistMult.
An election strategy for online streaming feature selection

Feature selection (FS) is one of the most effective methods in data preprocessing. In many real-world applications, such as social networks, getting all the features or even waiting for them is impossible. Hence, common feature selection methods are not applicable to such data. Thus, online streaming feature selection methods are provided to deal with such data where the entire feature space is not available from the beginning. On the other hand, ensemble methods have recently shown that they can effectively improve the performance of feature selection methods. In this paper, a new method is proposed based on the ensemble of multiple filter rankers to enhance the performance of feature selection methods in an online streaming space. This ensemble process is modeled as an election process, and the Weighted Borda Count (WBC) method is utilized to aggregate the votes. The proposed method showed better classification performance than the experiments' other methods.