Reviewerly/exHarmony huggingface dataset:
Chapter 5: The Generalization of Relations and the Dependence of Generalization on Analysis and Abstraction

Thinking is, by necessity, associated with generalization; thinking is done in generalizations and leads to generalizations of a higher and higher order. The very transition from problem-solving by...
Retrospective Analysis of the Performance of Students from a Public University in Pre-COVID Time due to the Use of Information and Communication Technologies

The present research was carried out in order to determine the influence that the use of information and communication technologies has on the academic performance of students in the subject Image Theory of the Professional Academic School of Communication Sciences of the UNICA School of Communication Sciences, Tourism and Archeology during 2019. The research is of an applied or technological type of explanatory level and a non- equivalent control group design that established the causal relationship between the study variables, in a sample of 54 students. To collect the information, the verification test was applied, which consisted of the pre-test and post-test tests, the control card and the research card were also used. Between the variables use of tics and academic performance, it was determined that there is a highly significant dependency relationship according to the statistical test applied in the contrasting of the hypotheses between the experimental group.
Multi-Rate Data Fusion for Wireless Sensor Networks with Time-Delay Based on Improved Cubature Kalman Filter

Aiming at the problem of time-delay in wireless sensor networks (WSNs), a multi-rate fusion method is proposed from the perspective of data fusion. The sensor uses different sampling and transmission rates for data collection and transmission. Firstly, in order to enhance the adaptability of cubature Kalman filter (CKF), the filtering performance of CKF is further improved. In this paper, the covariance matrix is modified online by constructing adaptive factors. The improved cubature Kalman filter (ICKF) is used to process these data collected by sensor nodes to generate local estimation (LE), and then the LE is formed into fusion estimation (FE) in the fusion center. The simulation results show that, compared with the traditional CKF and unscented Kalman filter (UKF), the ICKF algorithm has higher estimation performance and better stability.
Analysis of Faculty Teaching Performance Based on Student Feedback Using Fuzzy Mamdani Inference System

In this research, an Inference System of Fuzzy Mamdani system has been designed for the analysis of teaching skills of faculties in academic or educational institutes. Nowadays, various academic institutions have started Web-based techniques to collect students’ feedback of Faculty Teaching Performance. The performance evaluation of faculty in teaching activities performance is a major key to build a fairer academic institute. Major purpose of faculty performance is to identify strength & weakness of professional development of a teacher. This soft computing technique by using Fuzzy Mamdani Inference System (FMIS) for evaluating Faculty Teaching Performance will be useful for management of organization for evaluate faculty abilities with student outcomes.
Cross-modal variable-length hashing based on hierarchy

Due to the emergence of the era of big data, cross-modal learning have been applied to many research fields. As an efficient retrieval method, hash learning is widely used frequently in many cross-modal retrieval scenarios. However, most of existing hashing methods use fixed-length hash codes, which increase the computational costs for large-size datasets. Furthermore, learning hash functions is an NP hard problem. To address these problems, we initially propose a novel method named Cross-modal Variable-length Hashing Based on Hierarchy (CVHH), which can learn the hash functions more accurately to improve retrieval performance, and also reduce the computational costs and training time. The main contributions of CVHH are: (1) We propose a variable-length hashing algorithm to improve the algorithm performance; (2) We apply the hierarchical architecture to effectively reduce the computational costs and training time. To validate the effectiveness of CVHH, our extensive experimental results show the superior performance compared with recent state-of-the-art cross-modal methods on three benchmark datasets, WIKI, NUS-WIDE and MIRFlickr.
Entanglement formation in continuous-variable random quantum networks

Entanglement is not only important for understanding the fundamental properties of many-body systems, but also the crucial resource enabling quantum advantages in practical information processing tasks. Although previous works on quantum networks focus on discrete-variable systems, light—as the only traveling carrier of quantum information in a network—is bosonic and thus requires a continuous-variable description. We extend the study to continuous-variable quantum networks. By mapping the ensemble-averaged entanglement dynamics on an arbitrary network to a random-walk process on a graph, we are able to exactly solve the entanglement dynamics. We identify squeezing as the source of entanglement generation, which triggers a diffusive spread of entanglement with a parabolic light cone”. A surprising linear superposition law in the entanglement growth is predicted by the theory and numerically verified, despite the nonlinear nature of the entanglement dynamics. The equilibrium entanglement distribution (Page curves) is exactly solved and has various shapes depending on the average squeezing density and strength.
Private returns to investment in education in MENA countries

In Chapter 5, Aysit Tansel picks up an important theme central to the HDI by examining private returns to investment in education across the region. Here, she reviews the trends and patterns of the estimates of the private returns to investment in education in MENA countries. The overall regional average for MENA is observed to be rather low compared to the other regions of the world. Explanations for this observation are provided. Where possible, private returns to education by gender and by levels of schooling over time are also presented and discussed.
Speed Control of a DC Motor by Recurrent Fuzzy Logic Control Technique

In the present paper, we deal with controlling of the angular speed of a direct current (DC) motor under some nonlinear loads constant, signal and step, performing with closed loop control system, FLC, Recurrent FLC (RFLC) and Enhanced Recurrent FLC (ERFLC) Techniques in MATLAB / SIMULINK FUNCTIONS via Gradient Descent Algorithm. We obtain transfer function satisfied by DC motor. Drawback of the literature Fuzzy Logic Control membership functions is not to regulate the voltage values directly in dynamic systems and so the performance of the microcontroller is too low. That is the reason why we use some MATLAB FUNCTIONS with recurrent algorithms. RFLC Technique appears to have an angular speed stabilizer feature on the DC motor after the first two seconds with much less oscillation under the entire load change, but FLC values are discrete. In the ERFLC Technique, the angular speed of a DC motor under nonlinear loads is achieved by reaching the reference angular speed value with much less oscillation when the total load changes completely and clearer results are obtained. Thus, RFLC and ERFLC Techniques can be used for precise positioning system, global trajectory planning system of an electrical autonomous vehicles. In decision making system of an electrical autonomous vehicles, RFLC and ERFLC can be used in the anti-lock braking system to downhill speed control in level three and as an electronic stability control in the level four with foggy airs such that the visibility is extremely low and provide safe driving on slippery and icy surfaces.
Trajectory-Aware Buffering Strategy for HTTP adaptive streaming QoE enhancement for on-the-go users

The rise in popularity and the increasing use of HTTP video streaming in people's lives caused increased research on adaptive strategies. Devices and applications that support HTTP video streaming are used daily by on-the-go users. Unpredictability and variability of the environmental parameters, such as network link quality or bandwidth, are caused by traveling at vehicular speed through the real environment. Environmental parameters present the biggest challenge for adaptive streaming for on-the-go users, as they impact video playback. Video quality selection, as well as various factors of video playback, have an impact on the Quality of Experience (QoE) for on-the-go users. In this paper, we propose an HTTP adaptive streaming strategy that enhances QoE for on-the-go users, utilizing trajectory-awareness. The mentioned strategy uses different mechanisms to achieve trajectory-aware video playback. As a result, playback is robust, adaptive, and prepared in advance for the real environment parameter variations and unpredictability.
RDTCheck: A Smartphone App for Monitoring Rapid Diagnostic Test Administration

Rapid diagnostic tests are point-of-care medical tests that are used by clinicians and community healthcare workers to get quicker results at a better cost compared to traditional diagnostic tests. Distributing rapid diagnostic tests to people outside of the healthcare industry would significantly improve access to diagnostic testing; however, there are concerns that novices may administer rapid diagnostic tests incorrectly and thus be left with invalid results. In response to this concern, we propose RDTCheck — a mobile application that guides users through the instructions of Quidel’s QuickVue Influenza A+B test and ensures adherence to the procedure using computer vision. RDTCheck provides users with real-time feedback so that they may either correct their mistakes or re-administer their test. In this work, we conducted findings from a pilot study that demonstrates how well RDTCheck is able to detect common mistakes and successes during the various steps of the QuickVue test. For the 7 participants we recruited, RDTCheck had an average success rate of 91.1% at giving the correct feedback during the RDT administration procedure.
Attention Transition Prediction Based on Multi-Scale Spatiotemporal Features

The human visual system has powerful information processing capabilities, and humans can find their areas of interest in a very short time. The traditional salient object detection method is dedicated to detecting the area where the salient object is located. But when people are concentrating, they often only focus on a part of an object, not the whole object. Therefore, detecting the gaze position by detecting the eye movement can better simulate the attention transition. In order to achieve a more accurate simulation of human visual attention mechanism, we propose a model to predict attention transition of consecutive frames of video, which use multi-scale spatiotemporal features to extract static and dynamic saliency maps separately.
Classification of Cybercrime Indicators in Open Social Data

Posting information on social media platforms is a popular activity through which personal and confidential information can leak into the public domain. Consequently, social media can contain information that provides an indication that an organization has been compromised or suffered a data breach. This paper describes a technique for inferring if an organization has been compromised from information posted on social media. The proposed strategy forms the basis of an alarm system which generates an alert for possible unreported cybercrime incidents. The proposed strategy used two social media cybercrime related datasets that were collected from the Irish and New York regions from financial organizations’ Twitter accounts. The Tweets are labelled as either containing cybercrime indicators or not, and then the cybercrime Tweets were labelled further into crime categories. A deep dense pyramidal Neural Network model is used to classify the Tweets. This approach achieves an AUC of \(~0.85 \pm 0.03\) which outperforms the baseline of deep convolutional neural networks.
Sample Ratio Mismatch and Other Trust-Related Guardrail Metrics

Why you care: Guardrail metrics are critical metrics designed to alert experimenters about violated assumptions. There are two types of guardrail metrics: organizational and trust-related. Chapter 7 discusses organizational guardrails that are used to protect the business, and this chapter describes the Sample Ratio Mismatch (SRM) in detail, which is a trust-related guardrail. The SRM guardrail should be included for every experiment, as it is used to ensure the internal validity and trustworthiness of the experiment results. A few other trust-related guardrail metrics are also described here.
Barrister-Processing and Summarization of Terms &amp; Conditions / Privacy Policies

“You're confused into thinking these are there to inform users, as opposed to protect companies,” said Albert Gidari, the consulting director of privacy at the Stanford Centre for Internet and Society. Terms of service and privacy agreements are verbose and full of legal jargon, and the justifications for the processing and sale of your data are opaquely defined by corporations. The data market has become the powerhouse of the internet, and these terms and privacy rules that we accept without understanding or sometimes even reading help fuel the age of surveillance capitalism. It is common to access a wealth of services and applications, each with a long terms and conditions declaration and a one-click “allow” option. Often users accept these stipulations usually without ever reading the contractual obligations, let alone understanding their particulars. This work proposes a framework which is an automated text summarization application that can convert long, legalese, difficult-to-comprehend, terms and conditions / privacy policies to summarized, simplified simple English speech; using machine learning and natural language processing, which will dramatically decrease the reading time of these legalese documents and shed special attention on class action waivers. It is probably better to behave with one rule in mind until we reshape privacy policy to suit our needs, or we find an acceptable substitute. To be straightforward and concise: there is always someone watching.
Mining Deep Part Features for Pedestrian Search

In order to solve the problem of pedestrian's side or occlusions in pedestrian search task, which leads to that neural network can't learn some local features and slow search speed. BatchDrop module for pedestrian re-identification is proposed, which is applied to re-identification stage of two-stage pedestrian search. In the detection stage, faster R-CNN uses ASPP to detect targets. The pedestrian re-identification network is composed of two branches. The global branch extracts the significant global characteristics of pedestrians. Local branch uses BatchDrop module, which randomly discards the same semantically similar part of the feature to enhance the learning of the rest of the features in a training batch. Finally, the feature vectors of the two branches are fused as the final feature vector. The experimental results show that the algorithm has high search accuracy and the search speed is 6-25 times faster than other similar accuracy algorithms.
Ambient access control for smart spaces: dynamic guidance and zone configuration

Buildings and other facilities must protect their assets and occupants from hazards and unauthorised intrusion, with access control systems a critical component of the management of the facilities. The advent of pervasive systems and cyber-physical systems means that access control can become dynamic, responding to the observed locations of people and objects, and meeting the stated needs of the occupants. We present three components of a smart ambient access control system for safety and security. We describe dynamic routing around mobile assets or hazards, dynamic security classification which changes the security state of rooms and zones based on observed presence of items, and dynamic zone configuration, which adapts the access states of controlled doorways to construct larger virtual zones, to provide for free movement within newly constructed zones, and to provide secure zones around assets and people as required. The components are demonstrated in an architecture integrating GPS-enabled devices, webservers, management software and optimisation solvers.
Security Enhancement in Image Steganography using Generative Adversarial Networks

The technique of concealing information within photos, videos, music etc. is called image steganography. Steganography excels in that it does not disclose the presence of any data as opposed to other methods such as cryptology which only try to prevent reading the said data. A major factor in assessing the steganography algorithms is its security. Steganography has subsequently earned remarkable ground in the drawn-out battle with steganalysis. Steganography must be able to resist identification by steganalysis algorithms in order to boost the reliability of image steganography. Conventional steganography methods embed secret data into an image, which ultimately leaves a mark of alteration that can be detected by increasingly advanced AI-based steganalysis algorithms. Within this study, the authorscompare a method of concealing secret information within photos utilizing generative adversarial networks with conventional cryptography techniques. It is hypothesized that this technique has a clear potential to challenge best-in-class steganalysis measurements by producing high performing and increasingly stable payloads.
Authentic Leadership and Openness to Change in Pakistani Service Industry: The Mediating Role of Trust and Transparent Communication

Organizations to engage in strategic change initiatives to remain competitive. Leadership is the top determinant of successful change. This study investigated the factor affecting the employee’s openness to change during the process of change and how leadership affects that. We proposed a theoretical framework, modifying Yue et. al. (2019) by incorporating Authentic in place of transformational Leadership. We hypothesized that authentic leadership affects organizational trust during change both directly, as well as by inducing transparent communication. Trust would in turn positively affect openness to change. Authentic leadership included 1. Self-Awareness, 2. Relational Transparency, 3. Balanced Processing, and 4. Internalized Moral Perspective, dimensions. Whereas, transparent communication consisted of 1. Participation, 2. Substantiality, 3. Accountability, Factors. Empirical validity was established by conducting a survey using a close-ended questionnaire. Data was collected from 310 employees working across different service industries in Karachi and analyzed using confirmatory factor analysis and structured equation modeling. The finding revealed that transparent communication and authentic leadership have significant direct and indirect relationships with employees’ openness to change. Organizational trust has a significant relationship with employees’ openness to change during the change process. However Authentic leader has a weak direct relation with transparent communication and there is no significant relationship between authentic leadership and trust with the mediating role of transparent communication. However, trust significantly mediate all the factors of authentic leadership except Self-Awareness, and Openness to Change. Similarly, trust also mediated Substantiality, and Accountability factors of communication, and openness. Lastly, substantiality also offered a partial mediatory role between authentic leadership and trust.
Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning

Tame-map is a smartphone application that allows users to easily share regional events information within their daily lives by uploading digital flyer images on the web. Although there are a lot of event flyer images uploaded daily, these data have been organized manually. This manual work is hard and takes a huge time, so a system to automate the manual work has been required. An automatic processing method will support the organization, such as categorization, assigning keywords to each image. To develop a basis of automatic flyer images processing, this paper examines detection character region in event flyer images based on machine learning. Concretely, we compared the two features which are obtained vectors based on unsupervised/supervised leanings, such as k-means and SVM respectively.
The Practical Application of Artificial Intelligence Technology with the Development of Internet

Abstract Artificial intelligence is closely related to people’s life and production. With the rapid development of science and technology, people tend to quest for higher requirements for material life. The Internet and related information technology industries provide possibilities for artificial intelligence and have stepped toward the field of intelligence. This paper analyzes the characteristics of network engineering and discusses the application and innovation of artificial intelligence technology.
Minits-AllOcc: An Efficient Algorithm for Mining Timed Sequential Patterns

Sequential pattern mining aims to find the subsequences in a sequence database that appear together in the order of timestamps. Although there exist sequential pattern mining techniques, they ignore the temporal relationship information between the itemsets in the subsequences. This information is important in many real-world applications. For example, even if healthcare providers know that symptom Y frequently occurs after symptom X, it is also valuable for them to be able to estimate when Y will occur after X so that they can provide treatment at the right time. Considering temporal relationship information for sequential pattern mining raises new issues to be solved, such as designing a new data structure to save this information and traversing this structure efficiently to discover patterns without re-scanning the database. In this paper, we propose an algorithm called Minits-AllOcc (MINIng Timed Sequential Pattern for All-time Occurrences) to find sequential patterns and the transition time between itemsets based on all possible occurrences of a pattern in the database. We also propose a parallel multicore CPU version of this algorithm, called MMinits-AllOcc (Multicore Minits-AllOcc), to deal with Big Data. Extensive experiments on real and synthetic datasets show the advantages of this approach over the brute-force method. Also, the multicore CPU version of the algorithm is shown to outperform the single-core version on Big Data by 2.5X.
Communicative and Social-Psychological Support of Personalization of Higher Professional Education in the Context of Digital Challenges

The goal of research is to find socio-psychological methods, information technologies and communicative means to build a continuous model of socio-psychological and communicative support of professional education personalization. The goals was achieved in the methodological framework, set by traditions of positive psychology by Martin Seligman, differential psychology by B.M. Teplov, differential diagnosis method by E.A. Klimov, as well as vibraimage technologies for psychological profiling. The following results have been reached: firstly, the system of professional education selection has been elaborated; secondly, all chosen methods are based on information technologies, some of them using a highly sensitive indicator data reception; thirdly, recommendations on communicative support of each developed diagnostic procedures in the field of professional education selection were given.