Reviewerly/exHarmony huggingface dataset:
Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs

The system of counteracting money laundering and financing of terrorism (ML/FT) is built mainly on analysis of data of obliged entities, risk assessment and application of financial security measures.The growing amounts of data connected with their processing for the purpose of execution of subject instruments requirements improvement of methods in the scope of their acquisition, analysis and management.Thus, this system is also a basis for support of the human factor by means of state-of-the-art technical solutions.Therefore, meta data analyses, machine learning, predictive modelling or semantic modelling of natural language are incorporated in the assessment of ML/FT threats.The assumption is that each of these support methods must simplify and accelerate as well as reduce the costs of the processes of identification of ML/FT threats.The data analysis techniques used are aimed at -in the initial phase, before establishing a relationship with the obliged entity -the search for primary data, their verification and determination of the purpose of the client's activity, which may generate a threat.In the advanced phase -ongoing relations with the obliged entity -with the control of its behavior in the profit / risk relationship for safety and the introduction of "drivers" [controller] or "security bells" to the offered products -which is associated with the need to counteract the threat.
Test environment code and test-case generators

In this paper, a new implementation of code and test-case generators within the existing test environment generator (TEG) is described. In particular, source code, CAPL code and test-case generators are implemented using C++ programing language. The execution time of the implemented solution is compared with the existing solution which is written in Python programming language. The comparison results show a significant improvement of the C++ solution in the comparison with the Python version.
Optimizing Extreme Learning Machine by Animal Migration Optimization

Extreme learning machine (ELM) is a promising machine learning model for both classification and regression challenges. The ELM is very fast because it does not require training, while weights between the input layer and single hidden layer along with biases for the hidden layer are initialized randomly at the begging of execution. However, at the same time this represents a problem because for each practical classification and regression challenge, near-optimum set of weights needs to be found for satisfying performance of the model. Finding optimal (near-optimal) set of weights and biases for each practical problem is NP-hard tasks and it is known that swarm intelligence metaheuristics are able to render good results in solving these kinds of challenges. In this paper, a recently proposed animal migration optimization (AMO) algorithm is applied for tuning the ELM. Performance of this meataheuristics in this area has not been evaluated before. The ELM tuned with the AMO is validated against 4 well-known classification datasets and obtained results were compared with those generated by other machine learning models, as well as with other metaheuristics which were also used for ELM optimization. Experimental results prove that the AMO can be successfully applied in this domain.
Firefly-Xgboost Approach for Pedestrian Detection

The area of machine learning, particularly computer vision, has many applications in various fields, including defect detection, metrology, intrusion detection, autonomous vehicles, etc. However, one of the most important challenge from this domain is that each model should be adapted for specific dataset. This problem is known as the hyperparameters optimization and refers to tuning untrainable model parameters for the specific task. Research proposed in this paper introduces hybrid framework between machine learning and swarm intelligence for pedestrian detection, which is very important for autonomous vehicles. With the goal of improving classification accuracy, the histogram of oriented gradients and local binary pattern features are first used to describe pedestrians and further these features are used as the input for the extreme gradient boosting classifier. During the optimization process, the eXtreme gradient boosting hyperparameters were tuned by using well-known firefly algorithm swarm intelligence metaheuristics. Proposed hybrid framework was validated against INRIA person datasets and achieved results were compared to those obtained by the support vector machine, as well as genetic algorithm method that was also utilized for eXtreme gradiend boosting tuning. Comparative analysis results show that the firefly algorithm has huge potential in this domain.
Performance of spatial digital image filtering on a real Advanced Driver Assistance System platform

In this paper, the implementation and optimization of different spatial filters on the realistic Advanced Driver Assistance Systems (ADAS) platform is performed. In particular, the moving average, high-pass, median and Gaussian spatial filters are implemented on the ADAS Alpha board. The optimization of the implemented filters is performed in the software and hardware domain on different Alpha board processors to improve the filter's execution times. After optimization, the performance of the implemented filters is measured and compared with the implementation on the personal computer. The given results show comparable execution times of the optimized solution.
User Interface for the Creation of Smart Home Automation Rules

Smart home solutions offer users the possibility to remotely monitor and control their households. One of the important features of modern smart home systems is the possibility for users to set automation rules. Smart home rules represent user-defined behaviors of the smart home system which are designed to provide a user-friendly home automation setup for various use-cases. Rules allow users to define triggers and action sets. Triggers define conditions under which the rule is activated, and action sets define which actuators, such as devices or smarts, will be utilized and how once the trigger action conditions are fulfilled. This paper presents one possible implementation of the user interface for the creation of smart home rules.
Higher education and creative economy in East Asia: Co(labor)ation and knowledge socialism in the creative university

This paper is a complete student-led, student-edited collective writing project (CWP) conducted virtually in Spring 2022 throughout the course Knowledge Socialism taught by professor Michael Peters for the Faculty of Education, Beijing Normal university. The CWP involves 4 international, 5 domestic Ph.D. students, and 2 senior Western scholars as reviewers, revealing their thoughts, arguments, understanding, and criticisms towards the creative economy status in East Asian countries (Japan and China mostly) higher education as reflected in the knowledge socialism narratives. Xiyuan as the lead author, co-editing with Woraport and Stefan, assigned each section to other authors and successfully devoted themself to organizing, proofreading, and revising the paper. Through a month of collective work, the final version of the paper elaborates on the contribution of East Asian creative universities and the economy as a whole to the knowledge socialism through co(labor)ation, with implications to the creative labors cultivation and shifted roles of students.
Nonparametric locality reforming projection for linear discriminative dimensionality reduction

Discriminative dimensionality reduction (DDR) makes significant sense in high-dimensional data analysis. Over the decades, research has demonstrated that modeling the intrinsic local structure can well represent the data. Using pairwise neighboring data points to represent the between-class and within-class differences, local structure-based DDR methods are effective. However, how to set the parameters, e.g., the parameter of k-nearest neighbors, is still an open question, which makes the algorithms perform unstably. To address this issue, we propose a nonparametric local structure-based DDR method, named nonparametric locality reforming projection (NLRP). Also, we propose an effective analytical method for effectiveness analysis in both cases of sufficient and insufficient training samples for DDR methods. The experiment results on the AR, Carnegie Mellon University pose, illumination, and expression, and ORL databases are consistent with the theoretical analysis and verified the effectiveness of the proposed NLRP method. Especially, NLRP performs well in the case of very insufficient samples.
Explore the weakness: Instructive exploration adversarial robust reinforcement learning

Although reinforcement learning has been proved to be effective in many simulated platforms, it may still fail in environments due to the difference between simulation environment and real world environment, as well as being subjected to unexcepted attacks that objectively exist. Therefore, it calls for improving the robustness of the agent to increase its stability. To address the problem, an algorithm that uses the curiosity mechanism to improve the model exploration, referred to as instructive exploration adversarial robust reinforcement learning(Iearrl), is proposed, which enhances the adaption ability of agents through adversary learning, ensuring that the agent chooses a better action in practical environments with different settings from the training environment. At the same time, in order to increase the efficiency of exploration and reduce the cost, a model used to evaluate the competency of the agent is built for mentoring internal rewards determining whether further exploration is needed by analyzing the agent’s action in the current state space. The experiments in MuJoCo platforms verified the effectiveness of the proposed method.
Cubicle: A family of space‐hard ciphers for IoT

As IoT has increasingly evolved in recent years, it has become more important to ensure security on IoT devices. Many of such devices are under the threat of attacks in the beyond black-box model. To protect from the threat, the cryptographic implementation that can offer secure execution in the grey-/white-box model is important. However, such cryptographic implementations require a large number of clock cycles to execute and cannot fully cover resistance against various types of side-channel attacks. In this paper, a new family of table-based cipher dubbed Cubicle is proposed, which can offer efficient execution and sufficient security against side-channel attacks on IoT devices powered by ARM Cortex-M processors, which are widely deployed in IoT applications. To evaluate the security of Cubicle in the grey-box model, the authors derive the bound of table leakage in the grey-box model by applying space hardness, which is the notion to evaluate the security against code lifting attacks in the white-box. The security of Cubicle in the grey-box model is shown by using this bound. In addition, the security of Cubicle is also shown in the black-box and white-box models. Finally, the performance of Cubicle and other ciphers in some devices powered by ARM Cortex-M3, -M4, and -M7 processors is evaluated. The authors show that Cubicle is significantly efficient compared to other grey-/white-box- model-secure ciphers in target experiments for IoT applications.
Status and Trends of Teacher Education Board Performance of Pangasinan State University Alaminos City Campus

Performance of a higher education institution in licensure examinations is also a determinant factor of its effectiveness, especially in the context of relevant curriculum. This study assessed the Licensure Examination for Teachers (LET) performance of the teacher education programs of the Pangasinan State University Alaminos City Campus (PSU-ACC) from 2014 to 2019. The study is quantitative, employing the descriptive-correlational research design. Findings disclosed that PSU-ACC performed higher than the National Passing Rate (NPR) of LET examinees nationwide, based on the yearly Institutional Passing Rate (IPR). However, the trend line equations of the IPR performances of BEEd and BSEd imply that there is a possibility that the IPR of both programs would lie below the NPR, which must be avoided. Further, greater number of teacher education LET examinees could produce higher number of LET passers. It was also found out that the IPR of PSU-ACC increases along with the NPR. Moreover, the BEEd program performed significantly higher than the BSEd program in terms of the percentage above NPR and the IPR. The conduct of thorough in-house LET reviews for graduating students and the infusion of such activity in the curriculum were recommended to improve the LET performance of PSU ACC.
Risky-Driving-Image Recognition Based on Visual Attention Mechanism and Deep Learning

Risky driving behavior seriously affects the driver's ability to react, execute and judge, which is one of the major causes of traffic accidents. The timely and accurate identification of the driving status of drivers is particularly important, since drivers can quickly adjust their driving status to avoid safety accidents. In order to further improve the identification accuracy, this paper proposes a risky-driving image-recognition system based on the visual attention mechanism and deep-learning technology to identify four types of driving status images including normal driving, driving while smoking, driving while drinking and driving while talking. With reference to ResNet, we build four deep-learning models with different depths and embed the proposed visual attention blocks into the image-classification model. The experimental results indicate that the classification accuracy of the ResNet models with lower depth can exceed the ResNet models with higher depth by embedding the visual attention modules, while there is no significant change in model complexity, which could improve the model recognition accuracy without reducing the recognition efficiency.
Flattening the Hate Speech Curve in the Digital Age: An Appraisal of Regulatory Frameworks in Nigeria

The OUSL Journal is a peer-reviewed open access journal published by the Open University of Sri Lanka (OUSL). It is the first scholarly journal in Sri Lanka focusing on research in the field of Open and Distance Learning (ODL), and is being published since 1997. The journal provides a platform to publish research and pedagogy relating to teaching and learning at the University level, and in particular to Open and Distance Learning (ODL). It also provides a forum for research and review articles related in the areas of Agriculture, Natural Sciences, Education, Engineering, Humanities and Social Sciences and Health Sciences.
Considering ‘Machine Testimony’: The Impact of Facial Recognition Software on Eyewitness Identifications

Andrea Roth’s seminal work in Machine Testimony and Trial by Machine presented a problem that is now upon us: addressing biased algorithms and the rampant reliance on technology by prosecutors and law enforcement. That reliance, however, is no longer unquestioning. Roth’s work came at a crucial moment in time, when other articles were embracing the apparent impartiality of technology and algorithms for use in the criminal legal system. Her scholarship steered us away from that blind acceptance and dove deep, not only questioning technology itself, but also how to frame those questions of technology in the courtroom.Research has widely demonstrated the decisional inaccuracy that results from unreliable identifications, either by human eyewitnesses, or facial recognition software. Wrongful convictions can occur when police use either of these identification methods without precautions. Contextual information is vital to whether a factfinder correctly interprets either type of evidence. This Article uses a wrongful conviction lens to compare identifications by machines, notably facial recognition software, with identifications by humans. The Article advocates for greater reliability checks on both, before use against a criminal defendant. It examines the cascading influence of facial recognition software on eyewitness identifications themselves and the related potential for greater errors. As a solution, the Article advocates the inclusion of eyewitness identification in the Organization of Scientific Area Committees’ (“OSAC”) review of facial recognition software for a more robust examination and consideration of software and its usage. The Article also encourages police departments to adopt double-blind procedures for eyewitness identifications, including when “matching” photos from facial recognition software are included. Finally, the Article concludes with a prediction of where these two fields will be ten years from now, in 2032.
Robust Web Annotations in Support of Knowledge Co-Creation

The Web today, through its plethora of technologies and applications, empowers its users by enabling interactions, communications, and collaborations on a global scale. One of these applications, that has been of particular importance for knowledge sharing and learning, and has existed since the inception of the web, is web annotation. Unfortunately, although web annotation systems have enabled users to exchange and critique pieces of web content; challenges being faced on the web, namely: difficulty in identifying, finding, evaluating, and using information effectively, volatility of web contents, and the lack of innovation in web annotation technologies have together prevented these systems from becoming a primary tool for online learning and knowledge sharing. To deal with these challenges, the work presented in this dissertation proposes a novel strategy for the detection and reattachment of web annotations. The proposed work is designed into a novel browser-based web annotation platform that comes with its own set of system features and is empirically validated against the current state-of-the-art web annotation systems through a first-of-its-kind annotation test bench.
Penerapan Particle Swarm Optimization Pada Algoritma Naïve Bayes Untuk Klasifikasi Hasil Belajar

Advances in industrial technology 4.0 provide many changes in today's life. One example attached to the advancement of industrial technology 4.0 is the use of communication, transactions, even to the level of education using the acceleration of information technology. Some sectors have used advances in information technology such as the government sector, the industrial sector and even the world of education. This is because the greater the influence of the use of information technology to accelerate the transformation of each sector that uses it. But on the other hand, advances in information technology, apart from having a positive impact, also have a negative impact. As a real example of the positive impact, namely in the education sector, during the COVID-19 pandemic, the use of technology could be felt, as far as learning could be close to existing learning videos. However, one of the challenges is that advances in information technology also have a negative impact in the field of education, namely the dependence of students or students to spend more time playing online games (e-sports). So that it can affect the results of student learning achievement. Therefore, the research method that will be used is to use the classification of the influence of e-sports on student learning outcomes using the Nave Bayes algorithm which is optimized using particle swarm optimization. In its implementation, it was found that some students experienced decreased learning outcomes and some increased, of course this was influenced by several factors, both internal and external to the students themselves. The implementation of the algorithm used in this study obtained a sufficient level of classification with an AUC (area under classification) value of 0.792 and an accuracy value of 75.95%
Modeling an efficient authentic provable data possession model using legacy filter model for IOT and cloud environment

Internet of Things (IoT) is extensively adopted to manage everyday life. Data is gathered from IoT devices cloud computing is inevitable to store and analyze the data. The storage over the cloud is also not owned by the end-user. Thus, it is not so feasible. Therefore, two diverse issues are directly connected with the verification of data integrity, i.e. the incoming data should be verified and the verification process is performed. Various prevailing approaches are used for performing data integrity verification over the trusted party and nodes with proper resources. Moreover, it is highly complex to apply different prevalent research methods to IoT devices with constrained resources. This work concentrates on performing secure cloud-based storage over an IoT environment using authentic provable data possession (APDP) using the Legacy filtering model (APDP-LFM). The major contribution is the data possession and filter process to reduce the computational complexity. The experimentation is performed using a MATLAB environment, and the outcomes demonstrate that the proposed model preserves computational time and no complexity over the verification process. This model helps avoid False Positives and efficiently works for the enormous amount of incoming data over the IoT environment.
An Efficient Multiscale Pyramid Attention Network for Face Detection in Surveillance Images

Although a large number of works have been done to explore efficient face detection in various scenes, practical face detection in unconstrained condition of varying lighting, pose, scale, and occlusion remains a challenging task. The primary limitation of existing solutions is that they are vulnerable to influence from the wild environment. Practical features extraction plays a crucial part in the face detection of low-quality images. Based on the EfficientNet, this paper builds a novel pyramid attention network to integrate multilevel features with rich context messages. Firstly, a context model is exploited to increase the receptive fields at the beginning of the network. Secondly, stacked pyramid feature attention modules and feature fusion simultaneously selectively integrate the contextual information and enable spatial details, thus enhancing the capacity to detect faces on hard images. In addition, hard samples augmentation of the training sets is conducted, which is beneficial for improving the accuracy. A thorough study on ablation verifies the effect of the proposed strategies. Moreover, extensive experiments on Wider Face and FDDB datasets, remarkably pushing the accuracy both of 96.3% (2%↑), demonstrate the performance of the proposed deep face detector which is superior and outperforms most of the preexisting methods. The method presented in this paper can perform the task of face detection in surveillance images well.