Reviewerly/exHarmony huggingface dataset:
Social Media and Cybercrimes

Users can communicate with each other by posting multimedia materials such as audio, text, photographs, video, animation, and graphics via a forum known as social media networking through a website or application medium. Despite its benefits, Social media has posed a huge risk in various fields such as strategic interests and national security that leads to adverse impacts. Cybercriminals have turned to social media as an outlet for carrying out their criminal actions. Computers and the internet are used in carrying out cybercrimes by stealing other people’s identities as well as tracking down personal information and victims. There are several types of cyber-crimes. It is necessary to incorporate security measures and methods to control cyber-crimes. In this paper, we provide an overview of cybercrimes on social media. We will discuss common cybercrimes, their mitigation techniques, and protection in social media.
A Regulatable Blockchain Transaction Model with Privacy Protection

Blockchain is a decentralized distributed ledger technology.The public chain represented by Bitcoin and Ethereum only realizes the limited anonymity of user identity, and the transaction amount is open to the whole network, resulting in user privacy leakage.Based on the existing anonymous technology, the concealment of the sender, receiver, amount of the transaction, and does not disclose any information, which makes the supervision difficult.Therefore, the design of blockchain scheme with privacy protection and supervision functions is of great significance.In this paper, a blockchain transaction model with both privacy and supervision function is proposed.It uses probability encryption to realize the hiding of the true identity of the blockchain transaction, and uses the commitment scheme and zero-knowledge proof technology to realize the privacy protection and guarantee legitimacy verification of the transaction.With the use of encryption technology, the regulators can supervise blockchain transactions without storing the users' information, which greatly reduces the pressure on storage, computing and key management.In addition, it does not rely on specific consensus mechanism and can be used as an independent module.The security performance analysis shows that the proposed scheme has great practicability and has potential application in many fields.
Brainstorming With a Social Robot Facilitator: Better Than Human Facilitation Due to Reduced Evaluation Apprehension?

Brainstorming is a creative technique used to support productivity and creativity during the idea generation phase of an innovation process. In professional practice, a facilitator structures, regulates, and motivates those behaviors of participants that help maintain productivity and creativity during a brainstorm. Emerging technologies, such as social robots, are being developed to support or even automate the facilitator’s role. However, little is known about whether and how brainstorming with a social robot influences productivity. To take a first look, we conducted a between-subjects experiment ( N = 54) that explored 1) whether brainstorming with a Wizard-of-Oz operated robot facilitator, compared to with a human facilitator, influences productivity; and 2) whether any effects on productivity might be explained by the robot’s negative effects on social anxiety and evaluation apprehension. The results showed no evidence for an effect of brainstorming with a teleoperated robot facilitator, compared to brainstorming directly with a human facilitator, on productivity. Although the results did suggest that overall, social anxiety caused evaluation apprehension, and evaluation apprehension negatively affected productivity, there was no effect of brainstorming with a robot facilitator on this relationship. Herewith, the present study contributes to an emerging body of work on the efficacy and mechanisms of the facilitation of creative work by social robots.
Logic Guided Genetic Algorithms (Student Abstract)

We present a novel Auxiliary Truth enhanced Genetic Algorithm (GA) that uses logical or mathematical constraints as a means of data augmentation as well as to compute loss (in conjunction with the traditional MSE), with the aim of increasing both data efficiency and accuracy of symbolic regression (SR) algorithms. Our method, logic-guided genetic algorithm (LGGA), takes as input a set of labelled data points and auxiliary truths (AT) (mathematical facts known a priori about the unknown function the regressor aims to learn) and outputs a specially generated and curated dataset that can be used with any SR method. We evaluate LGGA against state-of-the-art SR tools, namely, Eureqa and TuringBot and find that using these SR tools in conjunction with LGGA results in them solving up to 30% more equations, needing only a fraction of the amount of data compared to the same tool without LGGA, i.e., resulting in up to a 61.9% improvement in data efficiency.
Neighborhood Consensus Networks for Unsupervised Multi-view Outlier Detection

Multi-view outlier detection recently attracted rapidly growing attention with the development of multi-view learning. Although promising performance demonstrated, we observe that identifying outliers in multi-view data is still a challenging task due to the complicated characteristics of multi-view data. Specifically, an effective multi-view outlier detection method should be able to handle (1) different types of outliers; (2) two or more views; (3) samples without clusters; (4) high dimensional data. Unfortunately, little is known about how these four issues can be handled simultaneously. In this paper, we propose an unsupervised multi-view outlier detection method to address these issues. Our method is based on the proposed novel neighborhood consensus networks termed NC-Nets, which automatically encodes intrinsic information into a comprehensive latent space for each view (for issue (4)) and uniforms the neighborhood structures among different views (for issue (2)). Accordingly, we propose an outlier score measurement which consists of two parts: the within-view reconstruction score and the cross-view neighborhood consensus score. The measurement is designed based on the characteristics of the different outlier types (for issue (1)) and no cluster assumption is needed (for issue (3)). Experimental results show that our method significantly outperforms state-of-the-art methods. On average, our method achieves 11.2% ~ 96.2% improvement in term of AUC and 33.5% ~ 352.7% improvement in term of F1-Score.
Design of a Kitchen-Monitoring and Decision-Making System to Support AAL Applications

Numerous researchers are working on Ambient Assisted Living systems to enable more comfortable and safer living for senior people in their homes. Due to modern lifestyles and an aging population, this has become a very important issue. According to the available literature, it is obvious that the kitchen is one of the most important and most dangerous rooms in the home. However, there is still evident lack of monitoring systems suitable for specific kitchen activities. In this paper, we propose a monitoring system capable of identifying activities related to the cooking process, and a decision-making system capable of identifying some unwanted and possibly critical conditions. The proposed systems are designed to satisfy the requirements of the modern Ambient Assisted Living systems dedicated to older adults. The proposed monitoring system consists of ultrasound, temperature, and humidity sensors. The acquired results from these sensors are the inputs for the decision-making system, which generate warnings or alarms intended for the senior users and/or formal or informal caregivers. This system is designed to improve home safety related to kitchen activities, as well as to provide information about the lifestyle and daily activities of senior users. Experimental validation of the proposed system confirms its functionality and accurate design approach.
Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Realizing accurate recognition of Chinese and English information is a major difficulty in English feature recognition. Based on this difficulty, this paper studies the English feature recognition model based on deep belief network classification algorithm and Big Data analysis. First, the basic framework based on deep belief network classification algorithm and Big Data analysis is proposed. Combined with the Big Data analysis training model, the English feature information is processed. Through the recognition of different English text features, the recognition and matching of English features are realized. Then the errors of deep belief network classification algorithm and Big Data analysis are evaluated. Second, this paper describes the quantitative evaluation of deep belief network classification algorithm and Big Data analysis in this system. In the evaluation, the language feature evaluation method is used to improve the evaluation function. At the same time, the deep belief network classification algorithm and Big Data analysis are used to self-study the model, and the English feature recognition method with strong applicability is established. Finally, the effectiveness of the recognition system is verified by the experiment.
Application of chaotic encryption algorithm based on variable parameters in RFID security

Abstract In the development of the Internet of Things technology, RFID technology plays a very important role in the application of the Internet of Things. However, due to the safety problems caused by the non-contact sensing of the RFID system, the further development of RFID technology in the application is largely hindered. In recent years, chaotic encryption has been applied in the field of cryptography by virtue of its unique characteristics, and it has received more and more attention in the security of RFID data transmission. Using the same key for encryption and decryption operations is a lightweight encryption algorithm. However, there are various problems in the application process of chaotic encryption: (1) nonlinear dynamic characteristics degradation and short-cycle cycle problems will occur under the influence of computer limited accuracy; (2) numerical conversion operations are required during application, to a certain extent It will affect the randomness of the iterative sequence; (3) During the iterative process, the iterative sequence cannot be spread over the entire value interval, and the randomness is poor. This paper proposes an improved segmented Logistic mapping encryption algorithm, uses the m-sequence to perturb initial value and sets a fixed step to change the control parameter value to generate a chaotic key stream sequence, and applies it to the RFID system data transmission security mechanism to encrypt the data. Experimental simulation and performance analysis show that the iterated chaotic sequence has good random distribution characteristics, unpredictability, and traversability. Compared to the previous improvement, the key space is increased to reach the size of 10 24 space and can meet the security needs, which improve RFID data security and can effectively avoid various security problems.
Investigating the Attitudes of First-Year Students of the Faculty of Physical Education and Sports of Galati towards Online Teaching Activities during the COVID-19 Pandemic

The paper identifies the perceptions of first-year undergraduate students from the Faculty of Physical Education and Sports in Galati on online teaching activities, dominant and needful in the current pandemic context. The questionnaire used contains 23 items and was structured based on four distinctive factors, namely attractiveness, accessibility, motivation and efficiency; it was applied after the winter session of the academic year 2020–2021. The values of the internal consistency coefficient Cronbach’s alpha indicate for the four mentioned factors a high fidelity for the measurements of the investigated features. The results of the 147 completed questionnaires allowed the definition of the independent variables sex (boys and girls) and environment of origin (rural and urban) the identification of their influence on the scores of each item (dependent variables) by using the statistical technique MANOVA (multivariate and univariate analysis), besides the values of F and the corresponding significance thresholds; the magnitude of the effect, expressed by partial eta squared (η2p), was also calculated. Even if the averages of item scores differ between sexes and backgrounds, the differences noted are in few cases significant: attractiveness and socialization for those in urban areas; participation in activities and effective involvement for girls; technical deficiencies, platform logging and weak computer skills for those in rural areas; and an increase in free time for girls and students in urban areas. The study undertaken allows the identification of the favorable aspects and the shortcomings of online teaching activities, these being the premises for optimizing the teaching process in the following stages.
Severity Prediction for Bug Reports Using Multi-Aspect Features: A Deep Learning Approach

The severity of software bug reports plays an important role in maintaining software quality. Many approaches have been proposed to predict the severity of bug reports using textual information. In this research, we propose a deep learning framework called MASP that uses convolutional neural networks (CNN) and the content-aspect, sentiment-aspect, quality-aspect, and reporter-aspect features of bug reports to improve prediction performance. We have performed experiments on datasets collected from Eclipse and Mozilla. The results show that the MASP model outperforms the state-of-the-art CNN model in terms of average Accuracy, Precision, Recall, F1-measure, and the Matthews Correlation Coefficient (MCC) by 1.83%, 0.46%, 3.23%, 1.72%, and 6.61%, respectively.
ILLUSTRATING THE PERCEPTION OF STUDENTS TOWARDS AUTONOMOUS SERVICE ROBOTS IN THE TOURISM INDUSTRY: AN EXPLORATORY STUDY

Research purpose – The purpose of the study was to explore the students’ perceptions towards autonomous service robots, using interviews and, in a novel manner, also their drawings, to generate data. The paper contributes to current knowledge claims around the Techno-Economic Paradigm by providing a greater understanding of, and student response to, autonomous service robots. Additional insights were gained on transformational skills set for tourism practitioners. Design/Methodology/Approach – A qualitative approach followed a participatory research design which was set in a public higher education institution. Undergraduate tourism and hospitality students who had previous exposure and engagement with robots were the purposive sample. The paper draws on the Techno-Economic Paradigm to illuminate how, according to students’ experiences and perceptions, autonomous robots interact in, and disrupt, the tourism industry. Findings – Four main drawings essentially expressed tourism and hospitality students’ perceptions of autonomous service robots in the tourism industry. Centrally there are opportunities for the absorption of robots in certain sectors of the tourism industry. Despite greater use of robots in service-driven industries like tourism, it remains challenging to establish the right balance between humans and robots, and up- and re-skilling transformation would be required of those working in the tourism industry and those studying towards a tourism qualification. This study advances that additional research is still required, including longitudinal studies on the effects of autonomous services in the tourism industry, as well as students’ perception on the use of robots, re-skilling as well as ethical risks to customers, the greater value to the economy and those working in the tourism industry. Originality of the research – The article contributes to the use of visual methodology as part of data generation, specifically how students’ perceptions regarding autonomous robots in the tourism industry were graphically distilled using this methodology.
Semisupervised Training of Deep Generative Models for High-Dimensional Anomaly Detection

Abnormal behaviors in industrial systems may be early warnings on critical events that may cause severe damages to facilities and security. Thus, it is important to detect abnormal behaviors accurately and timely. However, the anomaly detection problem is hard to solve in practice, mainly due to the rareness and the expensive cost to get the labels of the anomalies. Deep generative models parameterized by neural networks have achieved state-of-the-art performance in practice for many unsupervised and semisupervised learning tasks. We present a new deep generative model, Latent Enhanced regression/classification Deep Generative Model (LEDGM), for the anomaly detection problem with multidimensional data. Instead of using two-stage decoupled models, we adopt an end-to-end learning paradigm. Instead of conditioning the latent on the class label, LEDGM conditions the label prediction on the learned latent so that the optimization goal is more in favor of better anomaly detection than better reconstruction that the previously proposed deep generative models have been trained for. Experimental results on several synthetic and real-world small- and large-scale datasets demonstrate that LEDGM can achieve improved anomaly detection performance on multidimensional data with very sparse labels. The results also suggest that both labeled anomalies and labeled normal are valuable for semisupervised learning. Generally, our results show that better performance can be achieved with more labeled data. The ablation experiments show that both the original input and the learned latent provide meaningful information for LEDGM to achieve high performance.
A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica

The implementation of deep learning-based computer-aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a considerable amount of labelled images, which can be expensive to obtain as time and effort from clinical practitioners are required. To address this, a number of publicly available datasets have been built with data from different hospitals and clinics, which can be used to pre-train the model. However, using models trained on these datasets for later transfer learning and model fine-tuning with images sampled from a different hospital or clinic might result in lower performance. This is due to the distribution mismatch of the datasets, which include different patient populations and image acquisition protocols. In this work, a real-world scenario is evaluated where a novel target dataset sampled from a private Costa Rican clinic is used, with few labels and heavily imbalanced data. The use of two popular and publicly available datasets (INbreast and CBIS-DDSM) as source data, to train and test the models on the novel target dataset, is evaluated. A common approach to further improve the model's performance under such small labelled target dataset setting is data augmentation. However, often cheaper unlabelled data is available from the target clinic. Therefore, semi-supervised deep learning, which leverages both labelled and unlabelled data, can be used in such conditions. In this work, we evaluate the semi-supervised deep learning approach known as MixMatch, to take advantage of unlabelled data from the target dataset, for whole mammogram image classification. We compare the usage of semi-supervised learning on its own, and combined with transfer learning (from a source mammogram dataset) with data augmentation, as also against regular supervised learning with transfer learning and data augmentation from source datasets. It is shown that the use of a semi-supervised deep learning combined with transfer learning and data augmentation can provide a meaningful advantage when using scarce labelled observations. Also, we found a strong influence of the source dataset, which suggests a more data-centric approach needed to tackle the challenge of scarcely labelled data. We used several different metrics to assess the performance gain of using semi-supervised learning, when dealing with very imbalanced test datasets (such as the G-mean and the F2-score), as mammogram datasets are often very imbalanced. Graphical Abstract Description of the test-bed implemented in this work. Two different source data distributions were used to fine-tune the different models tested in this work. The target dataset is the in-house CR-Chavarria-2020 dataset.
A Novel BiGRUBiLSTM Model for Multilevel Sentiment Analysis Using Deep Neural Network with BiGRU-BiLSTM

In multilevel sentiment classification task, there is a challenging task of limited coherence, contextual and semantic information. This paper proposes a new hybrid deep learning architecture for multilevel text sentiment classification with less training and simple network structure for better performance and can handle the implicit semantic information and contextual meaning of text. In this research the proposed hybrid deep neural network architecture made with Bidirectional Gated Recurrent Unit (BiGRU) and Bi-Directional Long Term Short Memory(BiLSTM) of Recurrent Neural Network (RNN) for multilevel text sentiment classification and this performs better with higher accuracy than other methods compared. This proposed method BiGRUBiLSTM model outperformed the traditional machine learning methods and the compared deep learning models with about average of 1% margin accuracy on different datasets.
Post-Quantum Cryptography: Current state and quantum mitigation

This study provides an overview of the current state of affairs on the standardization process of Post-Quantum Cryptography (PQC). It presents the 5 main families of PQ algorithms; viz. code-based, isogeny-based, hash-based, lattice-based and multivariate-based. It also describes the NIST Round 3 finalists for encryption and signature schemes, as well as the alternative candidate schemes. Given that the NIST process will still run for a few years, the last chapter offers 2 proposals that system owners can implement now in order to protect the confidentiality of their data against a quantum capable attacker; namely hybrid implementations that use a combination of pre-quantum and post-quantum schemes, and the mixing of pre-shared keys into all keys established via public-key cryptography.