Reviewerly/exHarmony huggingface dataset:
Optimization of Sports Event Management System Based on Wireless Sensor Network

In recent years, wireless sensor network technology has developed rapidly and its role in managing systems for sports events has been widely used. Wireless sensor networks not only have low wiring cost, high monitoring accuracy, and good fault tolerance but also can be monitored remotely and have outstanding advantages in fault diagnosis and safety monitoring. In this paper, firstly, the wireless sensor network hierarchical routing protocol is studied and its network model and workflow are analyzed; according to the energy consumption of the wireless sensor network, the selection method of the optimal number of cluster heads is proposed to analyze the advantages and disadvantages existing in the protocol. Secondly, the improvement of the routing protocol is proposed to address the problems of uneven distribution of cluster heads and cluster head election without considering the residual energy of nodes in the protocol. When dividing clusters, the number of neighboring nodes is considered so that cluster heads are distributed more evenly in the network; when electing cluster heads, the residual energy of nodes in the cluster is considered to balance the whole network load, and when electing cluster heads, the residual energy of nodes in the cluster is considered to balance the whole network load. Finally, simulation experiments are conducted in this paper using simulation software, and the simulation results show that the data fusion algorithm is more effective than the protocol in reducing the average energy consumption of nodes and extending the network lifetime; these features make wireless sensors more beneficial for better management of sporting events as well as better optimization.
MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph

To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and gives an explanation for recommendation results. In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs. MNI explores not only the user-item interaction but also the neighbor-neighbor interactions, capturing a more sophisticated local structure. Besides, the entities and relations are also semantically embedded. And with the cross&amp;compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated. Through extensive experiments on real-world datasets, we demonstrate that MNI outperforms some of the state-of-the-art baselines both for CTR prediction and top-N recommendation.
A Critical Inquiry on Data Visualization Based Interactive Documentary: “The Fallen of World War II” as an example

In its historical development, documentary cinema has always utilized new mediums and produced new modes of representation. In recent times, especially with the big data surrounding different realms of our life, the production of interactive documentary based on data visualization has appeared as a new mode. In this context, “The Fallen of World War II” &lt;http://www.fallen.io/ww2/&gt; is one of the best examples of this mode of interactive documentary. The Fallen interactive documentary exposes mainly “the human cost of the Second World War.” The visual channel of the documentary operates mainly with data visualization of the number of dead soldiers and civilians of all countries attending the World War II, with their statistical distributions in the form of charts and archival photographs used on dramatic moments of the War. The audio channel is structured by an interpretive voice-over that is strengthened by affective sound design and musical score. The visual and audio channels are tightly interwoven to support each other in an entangled form. Within its linear cinematographic structure, viewers can pause the flow at two key moments of the data visualization narrative and interact with the charts and make their own interrogation in the data. In this paper, my aim is to make a critical analysis of the dynamic data visualization and archival photograph use in this interactive documentary. I will elaborate how the data sets are represented with visual(ization) and aural methods to call the viewer into interaction and encourage her to become an agent of interrogation.
Post-Pandemic Digital Education: Investigating Smart Workspaces within the Higher Education Sector

Human collaborations have been challenging with increased dependence on traditional human computer interaction technologies during the pandemic, especially in the education sector.Where conventional learning spaces have not been effective, opportunities have arisen for novel digital innovations to develop a future-proof more realistic learning environment.In the era of postpandemic digital living, a hybrid approach incorporating Internet of Things and augmented reality will transform future classrooms into highly immersive and collaborative learning spaces.This paper examines smart workspaces within the Higher Education sector and presents an energy efficient, future proof, and collaborative framework for smart learning environments.
Pixel Value Ordering Based Reversible Data Hiding with Novel MPBS Strategy

Recently, Reversible Data Hiding (RDH) techniques has gained much attention in many sensitive fields such as remote sensing, archive management, military and medical image processing systems. This is due to the lossless data extraction ability of RDH schemes. The primary goal of RDH schemes is to achieve high embedding rates while maintaining the quality of cover objects. For achieving better performance, Pixel Value Ordering (PVO) based reversible data hiding schemes have been proposed. PVO refers to the process of ranking the pixels in blocks and then modifying the pixels according to some embedding rules/conditions. So far, the existing PVO techniques have considered neighborhood pixels at unit distance. In this paper, an improved RDH using block based PVO scheme is proposed which exploits the pixel correlation efficiently by increasing the block size and applying a novel Median Pixel based Block Selection Strategy (MPBS). When block size is increased, the ordering of pixels is changed after embedding. So, to extract the secret data in a lossless manner, the secret bits are swapped in accordance with their corresponding Stego pixels’ index order. Also, the overflow and underflow conditions are effectively handled using Location Map. Experimental results show the better performance of the proposed RDH technique with the existing technique.
Cultural Capital and Argumentative Writing in English: Challenges and Strategies Used by EFL Student Teachers

Writing is one of the most difficult skills in language learning, particularly in learning English as a foreign language. The purposes of this qualitative study were to describe student teachers’ challenges in composing argumentative writing and to explain how they encountered those challenges at one public university teacher-training program in Jambi, Indonesia. The theoretical framework of cultural capital was used to guide the study while the data were collected through demographic profiles and semi-structured in-depth interviews with student teachers. Due to the Covid-19 pandemic, we only had access to invite 12 students to participate in our study—they were six male and six female student teachers. All participants in this study had taken English academic writing subjects and had been familiar with argumentative English writing. To analyze the data, within-case and cross-case analyses were used. The results of the data analysis produced eight salient themes including insufficient vocabulary and grammar, giving arguments, writing habits, parents’ occupation, the relationship between students, cognitive strategies, metacognitive strategies, and social strategies. The implications of the study are also discussed.
Security Enhancement of IoT and Fog Computing Via Blockchain Applications

Blockchain technology is now becoming highly appealing to the next generation because it is better tailored to the information age. Blockchain technologies can also be used in the Internet of Things (IoT) and fog computing. The development of IoT and Fog Computing technologies in different fields has resulted in a major improvement in distributed networks. Blockchain technology is now becoming highly appealing to the next generation because it is better tailored to the information age. Blockchain technologies can also be used in IoT and fog computing. The blockchain principle necessitates a transparent data storage mechanism for storing and exchanging data and transactions throughout the network. In this paper, first, we explained blockchain, its architecture, and its security. Then we view Blockchain application in IoT security. Then we explained Fog computing, Generic Security Requirements for Fog Computing, and we also discussed Blockchain applications that enhance Fog Computing Security. Finally, we conduct a review of some recent literature on using blockchain applications to improve IoT and fog computing security and compare the methods proposed in the literature.
Image Processing Method Based on Chaotic Encryption and Wavelet Transform for Planar Design

This paper provides an in-depth study and analysis of image processing for graphic design through chaotic encryption combined with a wavelet transform algorithm. Firstly, the traditional Mallat algorithm is optimized; since the mean value of the transform coefficients generated after the wavelet transform of the image is used as the initial value of the chaotic system to iterate, when the image is modified, then the mean value of the wavelet coefficients will also change, and the final iteration comes out as two different sequences using the property that the chaotic system is extremely sensitive to the initial conditions, so the algorithm has a certain sensitivity to tampering and localization effect. The image of the encrypted graphic design is decrypted by the chaos decryption system, and the final image information of the graphic design is obtained. In terms of the security of the graphic design image itself, the complex dynamical properties of chaos are fully utilized to encrypt it, and the algorithm has a good encryption effect after statistical characteristic analysis, attack complexity, difference analysis, adjacent pixel correlation analysis, and key sensitivity analysis. The plaintext image is decomposed in odd-even sequence using the boosting algorithm to get the sequence with an even index and the sequence with an odd index; then, the diffusion algorithm is applied to the two sequences by the prediction and update algorithm, and this process is repeated many times to get the two ciphertext sequences after scrambling, merging these two sequences, and matrixing them to get the ciphertext image. Finally, the testing of the embedded planar designed image revolves around four aspects, namely, image processing, tamper sensitivity, robustness, and imperceptibility, to examine the designed planar designed image system. In the diffusion manipulation, the key matrix for diffusion is obtained using the threshold processing method of the Local Binary Pattern (LBP) algorithm for the matrices generated by the Logistic Chaos system, which improves the randomness of the algorithm. The experimental results verify the effectiveness and security of the algorithm.
Entanglement in bipartite quantum systems: Euclidean volume ratios and detectability by Bell inequalities

Abstract Euclidean volume ratios between quantum states with positive partial transpose and all quantum states in bipartite systems are investigated. These ratios allow a quantitative exploration of the typicality of entanglement and of its detectability by Bell inequalities. For this purpose a new numerical approach is developed. It is based on the Peres–Horodecki criterion, on a characterization of the convex set of quantum states by inequalities resulting from Newton identities and from Descartes’ rule of signs, and on a numerical approach involving the multiphase Monte Carlo method and the hit-and-run algorithm. This approach confirms not only recent analytical and numerical results on two-qubit, qubit-qutrit, and qubit-four-level qudit states but also allows for a numerically reliable numerical treatment of so far unexplored qutrit–qutrit states. Based on this numerical approach with the help of the Clauser–Horne–Shimony–Holt inequality and the Collins–Gisin inequality the degree of detectability of entanglement is investigated for two-qubit quantum states. It is investigated quantitatively to which extent a combined test of both Bell inequalities can increase the detectability of entanglement beyond what is achievable by each of these inequalities separately.
Federated Learning for Privacy-Preserving Speaker Recognition

The state-of-the-art speaker recognition systems are usually trained on a single computer using speech data collected from multiple users.However, these speech samples may contain private information which users may not be willing to share.To overcome potential breaches of privacy, we investigate the use of federated learning with and without secure aggregators both for supervised and unsupervised speaker recognition systems.Federated learning enables training of a shared model without sharing private data by training the models on edge devices where the data resides.In the proposed system, each edge device trains an individual model which is subsequently sent to a secure aggregator or directly to the main server.To provide contrasting data without the need for transmitting data, we use a generative adversarial network to generate imposter data at the edge.Afterwards, the secure aggregator or the main server merges the individual models, builds a global model and transmits the global model to the edge devices.Experimental results on Voxceleb-1 dataset show that the use of federated learning both for supervised and unsupervised speaker recognition systems provides two advantages.Firstly, it retains privacy since the raw data does not leave the edge devices.Secondly, experimental results show that the aggregated model provides a better average equal error rate than the individual models when the federated model does not use a secure aggregator.Thus, our results quantify the challenges in practical application of privacypreserving training of speaker training, in particular in terms of the trade-off between privacy and accuracy.
Juvenile Delinquency and Crime Nairobi Slum Areas, Kenya

Juvenile delinquency has become an urgent concern for sociologists across the globe because it is a significant predictor of a possible increase in criminal activity among our growing population. A juvenile is a person who is under the age of ordinary criminal prosecution, which is normally 17 years. The increase in the incidence of juvenile delinquency implies that if measures are not taken in place, there are possibilities of the situation running out of hand. Juvenile delinquency can be considered as social maladjustment on the part of individuals to some difficult circumstances or conditions. The descriptive research design was adopted for this study since the researcher was interested in describing the salient traits of young offenders in the slum areas in Nairobi County and more specifically the predictors of their criminal behaviour and its effects. A sample of 200 respondents aged 18-45 years was generated using simple random sampling. A questionnaire was distributed amongst the respondents. Some questionnaires were distributed virtually using the Survey Monkey platform, which enabled the respondents to access and respond to the questions using their smartphones. The descriptive statistical analysis method was used to analyse the data, which was subsequently presented in table format.
RuNormAS-2021: a Shared Task on Russian Normalization of Annotated Spans

The paper presents result of the shared task on normalization of spans.The goal of the shared task is introduce datasets and to establish baseline for span normalization in Russian language on different data domains.The shared task introduces two datasets with annotated spans of different nature.The first consists of news articles from Vzglyad newspaper with marked up named entity spans to be normalized by participants.The second contains development strategies of Ministry of Economic Development with more generic spans to be normalized.On total 6 teams participated in the shared task with best results achieved on the generic track 0.980 exact match and on named entity track 0.981 exact match.Detailed competition description, the data, and evaluation scripts are available at: https://github.com/dialogue-evaluation/RuNormAS.
An optimized recognition algorithm for SSL VPN protocol encrypted traffic

With the widespread use of Virtual Private Networks (VPNs), the identification of Secure Sockets Layer (SSL) VPN encrypted traffic has become an important issue. This paper first introduced SSL VPN encrypted traffic and analyzed the flow of its handshake protocol. Then, an improved fingerprint recognition algorithm was designed to identify SSL streams. Capsule Neural Network (CapsNet), an optimized convolutional neural network, was used to recognize SSL VPN. An experimental analysis was carried out on the ISCXVPN2016 dataset. It was found that the recognition accuracy of the proposed method reached up to 99.98% for SSL streams, and the convergence speed was high; the recognition precision reached 98.16%, and the recall rate reached 99.98% for SSL VPNs, both of which were better than the algorithms such as random forest (RF) and C4.5. The experimental results verify the effectiveness of the optimized recognition algorithm for SSL VPN recognition and make some contributions to its application in practice.
A hybrid convolutional neural network approach for feature selection and disease classification

Many researchers have analyzed the high dimensional gene expression data for disease classification using several conventional and machine learning-based approaches, but still there exists some issues which make this task nontrivial.Due to the growing complexities of the unstructured data, the researchers focus on the deep learning approach, which is the latest form of machine learning algorithm.In the presented work, a kernel-based Fisher score (KFS) approach is implemented to extract the notable genes, and an improvised chaotic Jaya (CJaya) algorithm optimized convolutional neural network (CJaya-CNN) model is applied to classify high dimensional gene expression or microarray data.This model is tested on two binary class and two multi class standard microarray datasets.Here, the presented hybrid deep learning model (KFS based CJaya-CNN) has been compared with other standard machine learning classification models like CJaya hybridized multi-layer perceptron (CJaya-MLP), CJaya hybridized extreme learning machine (CJaya-ELM), and CJaya hybridized kernel extreme learning machine (CJaya-KELM).The suggested model is evaluated by classification accuracy percentage, number of significant genes selected, sensitivity and specificity values with receiver operating characteristic (ROC) curves.Eventually, the experimental outcomes obtained from the presented model has also been compared with the recent existing feature selection and classification models for a suitable research in analysing high dimensional microarray data.The presented model offered the classification accuracy percentage of 98.2, 99.96, 99.78, and 99.87 for colon cancer, leukemia, lymphoma-3, and small round blue cell tumor (SRBCT) datasets, respectively.All the experimental outcomes reveal that the KFS based CJaya-CNN model is outperforming.Hence, the presented method can be used as a dependable framework for disease classification.
Relation Extraction with Contextualized Relation Embedding (CRE)

Relation extraction (RE) is the task of identifying relation instance(s) between two entities given a corpus whereas Knowledge base (KB) modeling is the task of representing a knowledge base, in terms of relations between entities.This paper proposes an architecture for the relation extraction (RE) task that integrates semantic information with knowledge base (KB) modeling in a novel manner.Existing approaches for relation extraction either don't utilize knowledge base modelling or use separately trained KB models for the RE task.We present a model architecture that internalizes KB modeling in relation extraction.This model applies a novel approach to encode sentences into contextualized relation embeddings (CRE), which can then be used together with parameterized entity embeddings to score relation instances.The proposed CRE model achieves state of the art performance on datasets derived from The New York Times Annotated Corpus 1 and FreeBase 2 .The source code has been made available 3 to reproduce the results.
Case Investigation Technology Based on Artificial Intelligence Data Processing

Through data mining technology, the hidden information behind a large amount of data is discovered, which can help various management services and provide scientific basis for leadership decision-making. It is an important subject of current police information research. This paper conducts in-depth research on the investigation analysis and decision-making of public security cases and proposes a case-based reasoning model based on two case databases. Moreover, this paper discusses in detail the use of data mining technology to automatically establish a case database, which is a useful exploration and practice for the public security department to establish a new and efficient case investigation auxiliary decision-making system. In addition, this paper studies the method of using data mining technology to assist in the establishment of a case database, analyzes the characteristics of traditional case storage methods, and constructs a case investigation model based on artificial intelligence data processing. The research results show that the model constructed in this paper has certain practical effects.
E-learning in the Lebanese Higher Education Institutions: An Assessment of Factors Leading to Students’ Satisfaction

The Higher Education Institutions (HEIs) in Lebanon have been heavily impacted since the COVID-19 pandemic outbreak. The majority of the institutions had to react to the sudden decision to go online. In fact, they had to develop unplanned E-learning programs to assure the academic year’s survival. In Lebanon, the Ministry of Education &amp; Higher Education (MEHE) has ordered all educational institutions, public and private, to stop physical learning and start implementing E-learning through various online platforms. Because of Lebanon’s unprepared infrastructure, students in universities struggled to continue their studies and to keep pace with others who have better situations. The purpose of this study is to assess the factors that may impact the success of E-learning in Lebanese universities and that affect students’ satisfaction in adapting to this unplanned phase. The study is quantitative, explorative, descriptive and causal. A questionnaire was designed to collect primary data from 380 university students belonging to ten universities. Data analysis was carried out using the SPSS version 25 software. Several statistical techniques were performed including descriptive statistics and carrying out relational analysis via Factor Analysis (FA) and linear regression. Five constructs were defined namely, (1) Infrastructure, (2) Computer Skills, (3) E-Learning Content &amp; Autonomy, (4) Support from others, and (5) Satisfaction. Results show that students’ satisfaction is strongly influenced by the other four internal (2) and external (2) factors. Findings will support a set of recommendation directed to decision makers in HEI and the MEHE officials.