Reviewerly/exHarmony huggingface dataset:
Modeling the Level of User Frustration for the Impaired Telemeeting Service Using User Frustration Susceptibility Index (UFSI)

Modern users are accustomed to always-accessible networks ready to serve all of their communication, entertainment, information, and other needs, at the touch of their devices. Spoiled with choices provided on the competitive markets, the risk of customer churn makes network and service providers sensitive to user Quality of Experience (QoE). Services that enable people to work and industries to function in these pandemic times, such as the telemeeting service, are becoming ever more critical, not just for the end-users but also for the providers. Nevertheless, the heterogeneity of end-users network environments and the uniqueness of the service (bidirectional video and audio transmissions and interactivity between the meeting peers) imposes specific QoE requirements. Hence, this paper focuses on understanding how different service quality degradations affect user perception and frustration with such impaired service. The impact of eight quality degradations was analyzed. Based on the conducted user study, we used the multiple regression analysis and developed three models capable of predicting user Level of Frustration (LoF) for the specific degradations that we have analyzed. The models work with the User Frustration Susceptibility Index (UFSI), which categorizes users into groups based on their tendency to become frustrated with the impaired service.
Big data and small beginnings – how people engage with data physicalizations

Data physicalization has emerged on the design scene as a way of making sense of big (quantitative) data.This study explores how bigdata physicalizations are designed, how people engage with them, and how that spurs innovation.Graduate student designers created 15 data physicalizations to engage bus planners and bus passengers at multi-stakeholder workshops in discussing bus services and bus designs.The physicalizations were based on passenger data from 9 city bus routes.We used dimensional analysis to scrutinize the data physicalizations as constructs and multimodal interaction analysis to understand how workshop participant interact with the physicalizations.Using the theories of Flow State and Play Moods as analytic perspectives we identified patterns of engagement that were stimulated by both material aspects of the data physicalizations and the designers' role in facilitating interaction.We contribute with a framework of how data physicalizations can scale big data insights to meaningful engagements, which in turn lead to Small Beginnings of innovation.
Anti-Islamic Arabic Text Categorization using Text Mining and Sentiment Analysis Techniques

The aim of this research is to detect and classify websites based on their content if it encourages spreading hate speech toward Islam and Muslims, or Islamophobia using sentiment analysis and web text mining techniques.In this research, a large dataset corpus has been collected, to identify and classify anti-Islamic online contents.Our target is to automatically detect the content of those websites that are hostile to Islam and transmitting extremist ideas against it.The main purpose is to reduce the spread of those webpages that give the wrong idea about Islam.The proper dataset is collected from different sources, and the two datasets for the Arabic language (balanced and unbalanced) have been produced.The framework of the proposed approach has been described.The approach used in this framework is based on supervised Machine Learning (ML) approach using Support Vector Machines (SVM) and Multinomial Naive Bayes (MNB) models as classifiers, and Term Frequency-Inverse Document Frequency (TF-IDF) as feature extraction.Different experiments including word level and trigram level on the two datasets have been conducted, and compared the obtained results.The experimental results shows that the supervised ML approach using word level is the finest approach for both datasets that produce high accuracy with 97% applied on the balanced Arabic dataset using SVM algorithm with TF-IDF as feature extraction.Finally, an interactive webapplication prototype has been developed and built in order to detect and classify toxic language such as anti-Islamic online textcontents.
Algorithms Of Deep Learning:Convolutional Neural Network Role With Colon Cancer Disease

The world's third most serious and lethal cancer rankings are colon cancer.Like cancer, the most important stage of early diagnosis is.Deep learning has become a leading learning tool for object detection and its successes in advancing the analysis of medical images have attracted attention.Convolutionary neural networks (CNNs), which play an indispensable role in the detection and potential early diagnose of colon cancer, are the most popular method of deep learning algorithms for this purpose.In this article we hope to take a look at the progress of colonic cancer analysis by studying profound learning practices.This study provides an overview of popular profound study algorithms used in analysis of colon cancer.All studies in the fields of colon cancer, including detection, classification as well as segmentation and survival prediction, will then be collected.Finally, we will conclude the work by summarizing the latest deep learning practices in analysis of colon cancer, a critical examination of the challenges and proposals for future research
Semi-supervised learning of cardiac MRI using image registration

In this work, we propose a method to aid the 2-D segmentation of short-axis cardiac MRI. In particular, the deformation fields obtained during the registration are used to propagate the labels to all time frames, resulting in a weakly supervised segmentation approach that benefits from the features in unlabelled volumes along with the annotated data. Experimental results over the M\&Ms datasets show that the addition of the synthetically obtained labels to the original dataset yields promising results in the performance and improves the capability of the network to generalise to scanners from different vendors.
ELT MATERIAL FOR BIOLOGY EDUCATION DEPARTMENT STUDENTS: WHAT ARE THE STUDENTS' NEEDS?

This research is aimed to analyze the need of Biology Education Department Students at UIN Alauddin Makassar. Based on the preliminary study, the researcher found that English subject on Biology Education does not have a specific syllabus and also the material is not through the need analysis based on priorities needed by students of Biology Education Department. The research design used in this study was survey method. This need analysis comprises of Target Situation Analysis, Present Situation Analysis, and Learning Needs. The procedures included analyzing need by spreading questionnaire and conduct an interview. The result of this research shows that students of Biology Education Department needs more specific English material and need English reading skills more than other skills. It implies that the lecturer should design material related to the students need to fulfill their needs.
Privacy-Aware Content-of-Interest Search and Recommendation in Internet of Things for Cross-Dressers

With the continuous development and gradual progress of Internet of Things (IoT) in human society, people are becoming increasingly diverse in terms of user preferences and things choices. In this situation, several new cultures or social phenomena have been emerging including the so-called Cross-dressing culture. As a special group of humans, cross-dressers are often very sensitive to their non-mainstream identities. Therefore, they are often confronted with more difficulties when using some modern information techniques such as Content-of-Interest (COI) search. Motivated by this fact, we introduce some advanced information retrieval and privacy protection techniques into the cross-dressing domain and further propose a privacy-aware COI search and recommendation solution for cross-dressers, named PCSR. First, PCSR uses fastText tool to transform the cross-dressers’ input keywords and the candidate webpages into corresponding vectors with less private content associated with cross-dressers. Afterwards, we use vector similarity calculation techniques to make privacy-preserving COI search and recommendation. At last, we validate the effectiveness of PCSR through a set of experiments. We believe that our proposed PCSR solution can benefit the cross-dressers significantly when performing COI search and recommendation in IoT while protecting sensitive information of cross-dressers.
An Approach to Efficient Dictionary Utilization and Improved Data Compression Technique for LZW Algorithm

This paper proposes an improved data compression technique compared to existing Lempel-Ziv-Welch (LZW) algorithm. LZW is a dictionary-updation based compression technique which stores elements from the data in the form of codes and uses them when those strings recur again. When the dictionary gets full, every element in the dictionary are removed in order to update dictionary with new entry. Therefore, the conventional method doesn’t consider frequently used strings and removes all the entry. This method is not an effective compression when the data to be compressed are large and when there are more frequently occurring string. This paper presents two new methods which are an improvement for the existing LZW compression algorithm. In this method, when the dictionary gets full, the elements that haven’t been used earlier are removed rather than removing every element of the dictionary which happens in the existing LZW algorithm. This is achieved by adding a flag to every element of the dictionary. Whenever an element is used the flag is set high. Thus, when the dictionary gets full, the dictionary entries where the flag was set high are kept and others are discarded. In the first method, the entries are discarded abruptly, whereas in the second method the unused elements are removed once at a time. Therefore, the second method gives enough time for the nascent elements of the dictionary. These techniques all fetch similar results when data set is small. This happens due to the fact that difference in the way they handle the dictionary when it’s full. Thus these improvements fetch better results only when a relatively large data is used. When all the three techniques' models were used to compare a data set with yields best case scenario, the compression ratios of conventional LZW is small compared to improved LZW method-1 and which in turn is small compared to improved LZW method-2.
Elements of form in folk art in producing innovative jewelry based on Egyptian heritage

Recognizing the patterns of users is necessary for every designer who wants to succeed in the field of design in general and the design of jewelry in particular, as the secret is to benefit from the solutions and innovations that help the user in developing the new jewelry product while studying the problems they face in the jewelry product to avoid them in modern products, and differ Patterns of users according to their different inclinations and attitudes according to the environment, culture, work, psychological aspects, the financial income of the user and many factors affecting the user and his ability to use and accept the product And by studying user patterns according to the theory of participation between the user and the designer, it is possible to find clear features for the designer in making a specific classification of user patterns to understand and familiarize with the user’s behavior and his various intellectual and psychological trends, and then achieve the broad demands of the audience of users in accordance with the design principles of the design elements to be used in the work Innovative and competitive design ideas with the participation of the user of various types in the design of jewelry, and to come up with innovative design results that satisfy all types of users.
A New Boolean Encoding for MAPF and its Performance with ASP and MaxSAT Solvers

Multi-agent pathfinding (MAPF) is an NP-hard problem. As such, dense maps may be very hard to solve optimally. In such scenarios, compilation-based approaches, via Boolean satisfiability (SAT) and answer set programming (ASP), have proven to be most effective. In this paper, we propose a new encoding for MAPF, which we implement and solve using both ASP and MaxSAT solvers. Our encoding builds on a recent ASP encoding for MAPF but changes the way agent moves are encoded. This allows to represent swap and follow conflicts with binary clauses, which are known to work well along with conflict-based clause learning. For MaxSAT, we study different ways in which we may combine the MSU3 and LSU algorithms for maximum performance. Our results, over grid and warehouse maps, show that the ASP solver scales better when the number of agents is increased on grids with few obstacles, while the MaxSAT solver performs better in scenarios with more obstacles and fewer agents.
Development prospects of primary education integration (on the example of exact and natural sciences)

This article discusses current state of science integration in primary education reflected in the literature and relevant sources review. In addition, integration of science content review given its meaning and function fully expresses the essence of education. The article also emphasizes importance of correctly established student - teacher relationships in the primary education process and their significant effect on education quality. The research work also puts forward the idea of designing the disciplines’ integration process on the basis of the opinion of teachers and argumentation of the result.
CNN-based Security Authentication for Wireless Multimedia Devices

Security is a major concern for wireless multimedia networks because of their role in providing various services.Traditional security techniques have inadequacies in identifying emerging security threats and also lacks in computing efficiency.Furthermore, conventional upper-layer authentication doesn't provide any protection for physical layer, thus leading to leakage of privacy data.Keep these issues in mind, the paper has envisioned an artificial intelligence-based security authentication system that is lightweight, adaptive and doesn't require any explicit programming.The neural network is built on convolutional filters which explore the data and learns the features or characteristic of the data.With this learned feature, the model will be able to recognize whether a wireless multimedia device present in a network is legitimate or not.Experimental analysis and validation have been performed on the trained model and ensure that the authentication of wireless multimedia devices can be achieved and also ensuring lightweight authentication system, which ensures less computation needs.The different neural model is also trained using gaussian noise of different standard deviation so that it can be used in a practical scenario like smart industry etc.
PRUDA: A Novel Measurement Attribute Set towards Robust Steganography in Social Networks

Cloud services have become an increasingly popular solution to provide different services to clients. More and more data are outsourced to the cloud for storage and computing. With this comes concern about the security of outsourced data. In recent years, homomorphic encryption, blockchain, steganography, and other technologies have been applied to the security and forensics of outsourced data. While encryption technologies such as homomorphic encryption and blockchain scramble data so that they cannot be understood, steganography hides the data so that they cannot be observed. Traditional steganography assumes that the environment is lossless. Robust steganography is grounded in traditional steganography and is proposed based on a real lossy social network environment. Thus, researchers, who study robust steganography, believe that the measurement should follow traditional steganography. However, the application scenario of robust steganography breaks through the traditional default lossless environment premise. It brings about changes in the focus of steganography algorithms. Simultaneously, the existing steganography methods miss the evaluation of applicability and ease of use. In this paper, “default parameters” are observed by comparing the process of robust image steganography with traditional image steganography. The idea of “perfecting default parameters” is proposed. Based on this, the attribute set of measuring robust image steganography is presented. We call it PRUDA (Payload, Robustness, ease of Use, antiDetection, and Applicability). PRUDA perfects default parameters observed in the process of traditional steganography algorithms. Statistics on image processing attacks in mobile social apps and analyses on existing algorithms have verified that PRUDA is reasonable and can better measure a robust steganography method in practical application scenarios.
Computer Aided Detection System using Entropy Based Segmentation as Decision Support for Detection of Neoplasms and Comparison of Feature Spaces for Classification

Objective: To design a computer aided detection system for the early detection of the breast cancer from the mammograms as it can assist the doctors in the diagnosis.Methodology: The proposed method used in the design of Computer-Aided Detection (CAD) is based the on using the textural differences of the abnormal and normal mammograms to detect the breast cancer.The Gabor Features and gray-level co-occurrence matrix (GLCM) features are extracted from the region of interest of the segmented mammograms using the Entropy based segmentation.The Support Vector Machine (SVM) classifier is used for classifying the mammograms into the cancerous and non-cancerous cases.The 35 number of normal and abnormal mammograms are taken from the Mammographic Image Analysis Society (MIAS) data set.The MIAS database is chosen as it carries more challenging data because it carries lot of unwanted tissues and flesh part included in it which has the intensity level more than the micro-calcifications. Findings: The classification accuracies obtained are 92.98% and 98.11% using Gabor and gray-level co-occurrence matrix features respectively.The sensitivity achieved with the gray-level co-occurrence matrix features is 100% which shows no missed cancerous case.The classification accuracy is higher using gray-level co-occurrence matrix features as compared to the Gabor features which shows superiority of these features in capturing the texture of the mammograms.Novelty: The removal of the pectoral muscle is an important pre-processing step.Only with the proper elimination of the pectoral muscle, the segmentation of the mammograms is possible.The method proposed to remove the pectoral muscle in this paper removed the pectoral muscles from all the mammograms used in the study.The entropy based segmentation and the technique of the removal of the non-contributing features outperforms other CAD systems in the literature available.These are very promising results for successful design of a Computer-aided detection system for early detection of the breast cancer that can be put to clinical trials or used for the double reading.
NISD2: A Common Framework for Information Sharing Among Network Defenders

Sharing information about vulnerabilities and attacks is essential to defend information systems against threats such as malware, phishing and unauthorised access.By identifying this information sharing as a legitimate interest of data controllers, and highlighting the public interests that it serves, the draft Network and Information Security Directive provides a framework to encourage European participation in global information sharing, benefitting all users of the Internet.
PRM navigation in trading drone and Gazebo simulation

Starting from a commercial drone AR Dron Parrot 2.0, an autonomous navigation process is developed with a PRM probabilistic route planner in real time, through a ROS network between the drone and the Gazebo simulation software. Using the robotics system toolbox from software Matlab that interacts with Gazebo, it is possible to study the desired trajectory planner, in addition, the creation and connection of the ROS network on the Linux operating system, where the navigation algorithm is analyzed from the practical vs., simulation points of views. The errors that are presented are minimal, taking into account the propagation delays and the control algorithm; this is in charge of receiving location information in order to correct and minimized the mean square error.
Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short-Term Power Prediction in Photovoltaic Power Generation System

When a photovoltaic (PV) system is connected to the electric power grid, the power system reliability may be exposed to a threat due to its inherent randomness and volatility. Consequently, predicting PV power generation becomes necessary for reasonable power distribution scheduling. A hybrid model based on an improved bird swarm algorithm (IBSA) with extreme learning machine (ELM) algorithm, i.e., IBSAELM, was developed in this study for better prediction of the short-term PV output power. The IBSA model was initially used to optimize the hidden layer threshold and input weight of the ELM model. Further, the obtained optimal parameters were input into the ELM model for predicting short-term PV power. The results revealed that the IBSAELM model is superior in terms of the prediction accuracy compared to existing methods, such as support vector machine (SVM), back propagation neural network (BP), Gaussian process regression (GPR), and bird swarm algorithm with extreme learning machine (BSAELM) models. Accordingly, it achieved great benefits in terms of the utilization efficiency of whole power generation. Furthermore, the stability of the power grid was well maintained, resulting in balanced power generation, transmission, and electricity consumption.
DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges

We summarize the experience of participating in two differential privacycompetitions organized by the National Institute of Standards and Technology (NIST). Inthis paper, we document our experiences in the competition, the approaches we have used,the lessons we have learned, and our call to the research community to further bridge thegap between theory and practice in DP research.
Theory of Photon Subtraction for Two-Mode Entangled Light Beams

Photon subtraction is useful to produce nonclassical states of light addressed to applications in photonic quantum technologies. After a very accelerated development, this technique makes possible obtaining either single photons or optical cats on demand. However, it lacks theoretical formulation enabling precise predictions for the produced fields. Based on the representation generated by the two-mode SU(2) coherent states, we introduce a model of entangled light beams leading to the subtraction of photons in one of the modes, conditioned to the detection of any photon in the other mode. We show that photon subtraction does not produce nonclassical fields from classical fields. It is also derived a compact expression for the output field from which the calculation of conditional probabilities is straightforward for any input state. Examples include the analysis of squeezed-vacuum and odd-squeezed states. We also show that injecting optical cats into a beam splitter gives rise to entangled states in the Bell representation.