Reviewerly/exHarmony huggingface dataset:
Legal Micro-Directive Amplification Via Artificial Intelligence

Legal micro-directives have been identified as a means of automating and in a sense enlivening the law. Their advent has great significance. When further combined with AI, a potent combination results, for which there are advantages and yet potential disadvantages too.
MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification

RGB–IR cross modality person re-identification (RGB–IR Re-ID) is an important task for video surveillance in poorly illuminated or dark environments. In addition to the common challenge of Re-ID, the large cross-modality variations between RGB and IR images must be considered. The existing RGB–IR Re-ID methods use different network structures to learn the global shared features associated with multi-modalities. However, most global shared feature learning methods are sensitive to background clutter, and contextual feature relationships are not considered among the mined features. To solve these problems, this paper proposes a dual-path attention network architecture MFCNet. SGA (Spatial-Global Attention) module embedded in MFCNet includes spatial attention and global attention branches to mine discriminative features. First, the SGA module proposed in this paper focuses on the key parts of the input image to obtain robust features. Next, the module mines the contextual relationships among features to obtain discriminative features and improve network performance. Finally, extensive experiments demonstrate that the performance of the network architecture proposed in this paper is better than that of state-of-the-art methods under various settings. In the all-search mode of the SYSU and RegDB data sets, the rank-1 accuracy reaches 51.64% and 69.76%, respectively.
Real-time anomaly detection in Deep Space Network operations using machine learning

The Deep Space Network (DSN) is NASA’s international array of antennas that support interplanetary spacecraft missions. DSN provides radar and radio astronomy observations that enhance our understanding of the solar system and the larger universe. A track is a block of multi-dimensional time series from the beginning to end of DSN communication with the target spacecraft, containing thousands of monitor data items lasting several hours at a frequency of 0.2-1Hz. Monitor data on each track reports on the performance of specific spacecraft operations and the DSN itself. DSN is receiving signals from 32 spacecraft across the solar system. DSN has pressure to reduce costs while maintaining the quality of support for DSN mission users. DSN Link Control Operators (LCOs) need to simultaneously monitor multiple tracks and identify anomalies in real time. DSN has seen that as the number of missions increases, the data that needs to be processed increases over time. In this project, we look at the last 8 years of data for analysis. Any anomaly in the track indicates a problem with either the spacecraft, DSN equipment, or weather conditions. DSN operators typically write Discrepancy Reports (DR) for further analysis. It is recognized that it would be quite helpful to identify 10 similar historical tracks out of the huge database to quickly find/match anomalies. This tool has three functions: (1) identification of the top 10 similar historical tracks, (2) detection of anomalies compared to the reference normal track, and (3) comparison of statistical differences between two given tracks. The requirements for these features were confirmed by survey responses from 21 DSN operators and engineers. The preliminary machine learning model has shown promising performance (AUC=0.92). We plan to increase the number of data sets and perform additional testing to improve performance further before its planned integration into the track visualizer interface to assist DSN field operators and engineers.
Estimating the Traffic Density from Traffic Cameras

Traffic congestion is a daily occurrence in most urban of Vietnam, because of many reasons: an increasing number of vehicles, the rush hours (time to go to school, to work, after school, after work). To partly avoid these situations, it is necessary for a support system to provides information of traffic flow on key roads with traffic cameras at junctions, crossroads, in front of hospital gates, markets ... Estimating the traffic density from the traffic cameras helps the functional forces divide and divert traffic in time. In this research, the Faster R-CNN and YOLOv4 models is used to detect vehicles in traffic on the road, thereby estimating the density and sending alerts to the user. Experimental data were taken in front of Kien Giang hospital’s gates, including 1260 images for training, 360 images for validation, and 3 sets of images for testing. The first set of 180 images has the same camera angle as the training set. The second set and the third set each contains 100 images with different camera angles and different camera angles from the training set. The results are about 92% of accuracy in detection of vehicles for the first set (2 models); 75% and 35% for the second set; 85% and above 20% for the third set and above 20% for the third set.
Preliminary Evaluation of the Moving Rubber Foot Illusion in a Sample of Female University Students

The Rubber Foot Illusion (RFI) is an illusion in which one is made to feel that a model foot is their own through synchronous visuo-tactile stimulation. Previous research suggests that the conditions the RFI can be elicited under are similar to those of the Rubber Hand Illusion (RHI). However, it was unknown if the RFI could be elicited by synchronous movement of a participant's foot and a model foot. To examine this, we developed the Moving Rubber Foot Illusion (mRFI) and compared participants' experience of it to the RFI. The results of this study suggests that the RFI can be elicited through synchronous movement, and results in more proprioceptive drift than a static variant of the RFI. More work is needed to better understand the mechanisms underlying the mRFI.
Implementing the EBDI model in an E-health system

Health-care systems (patient monitoring and diagnosis systems) have recently attracted the interest of researchers. Intelligent software has been used as an agent in this field where research addresses the areas of electronic health (E-health) to develop and improve health care systems and to reduce the effort and time on health users (physicians, nurses) and to follow up on the condition of patients, especially the elderly, and also Intelligent software agents are using a result when most hospitals are out of capacity.  The major goal of this study is to learn how we may use intelligent software for  E-Health to assist physicians, nurses, and other health practitioners in collecting and tracking patient data on a daily basis in order to enhance treatment decisions. How can we achieve this in real-time in a dynamic and non-deterministic environment? As a result, we have built a model-based system such as the Extensible Beliefs Desires and Intentions (EBDI) model as architecture for located autonomic software agents in run-time under dynamic and non-deterministic environments circumstances in this study. We have formalized the EBDI agent in E-Health as an online observer to monitor the state of a  patient linked to sensors (thermometer, glucose meter, oxygen meter, etc.). This agent filters the data (sensor readings) and determines which readings are normal or abnormal before sending the patient's information to the administrative agent, which is a different type of EBDI agent. In addition, the system proposes a treatment plan based on the sensor readings and necessitates the participation of a human doctor in emergency situations. This study proposes developing a monitoring system to follow up (monitor) the health status of patients based on a multi-agent-system (MAS) with the Clustering process (using the K-mean Algorithm), where the system is designed to support warnings and alerts in abnormal health conditions and to call for a doctor's intervention in emergencies (when it is necessary).
Real-Time Detection of Safety-Relevant Forklift Operating States using Acceleration Data with a Windowing Approach

In recent decades, many studies have been published on the topic of activity recognition. The research field is continuously evolving. This paper presents a framework to classify incoming acceleration data from a forklift fork to determine the current operating state of the forklift. The aim is to develop a system that reliably detects safety-relevant operating states, that can be reported to the driver as a warning signal or to a fleet management system as the basis for predictive maintenance of the fork. Thus, the fork wear can be reduced and the safety increased. For machine activity recognition, we train machine-learning models on a created data set. The algorithm focuses on fast and low-computation processing of incoming sensor data. Thus, a time-based windowing approach that aims at early classification of patterns is used. Therefore, we discuss an approach to future extraction and determine the optimal window size when classifying. With the chosen approaches, a robust realtime detection of the operating states with an accuracy up to 99% can be achieved. We provide the algorithms and data set on GitHub at https://github.com/tum-fml/dofos.
Efficient Feature Selection Method for Histopathological Images Using Modified Golden Eagle Optimization Algorithm

Automated medical analysis is growing rapidly for advanced clinical treatment and intervention in diagnosis of patients. Exploration and visualization in the field of histopathology examination are enhancing the decision-making process of pathologists. In this paper, a modified golden eagle optimization technique is proposed for selecting the best subset of features from histopathological images which improve the classification of different categories of histology images. The mean and the best optimal values of the proposed modified golden eagle optimization algorithm is tested on benchmark functions (CEC 2017) namely GEO, WOA, APSO, CSO and LOA. Moreover, the comparative analysis is performed against the recent state of the art algorithm. Furthermore, the experiment results validate that the proposed method outperforms the other considered methods. The accuracy, specificity, sensitivity, recall and F1 score are evaluated for each category of histopathological images using a support vector machine and artificial neural network.
Empowering Integrity Auditing Based on Identity and Information Sharing for Secure Cloud Storage Using Delicate Data Concealment

Users can store their Information in the cloud to reduce the cost of using regular storage by using the storage services provided by cloud and data management schemes that are provided. To ensure the integrity of information preserved in the Cloud, using several data integrity auditing. In cloud storage services, users can store their data remotely inside the cloud and can share it with any others. In this paper, an encryption method has been adapted to encrypt the information in accordance of conscious details of the documents and modifies these information file’s signatures into reasonable ones for the encrypted file. To confirm the honesty of encrypted report within the segment of integrity auditing the signatures are used. The outcome results in this approach make the document saved within the cloud capable of proportion and utilized by others at the limit that the touchy facts is hidden. Along with that, identity-based cryptography is the basis of the proposed scheme, where it simplifies the complex certificate management. The proposed scheme is more secure and efficient than the older one is proved by the evaluation of performance and security analysis.
PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKTIF BERBASIS POWERPOINT DAN ISPRING DI ANDROID UNTUK MENINGKATKAN EFEKTIFITAS PEMBELAJARAN IPA DI KELAS 3 SD

Abstrak&#x0D; Media interaktif berbasis PowerPoint adalah media pembelajaran kembangan, berisi materi pembelajaran IPA yang dirancang tersistem untuk peserta didik di kelas 3 Madrasah Ibtidaiyah melalui program dasar powerpoint dan dikembangkan dengan ispring, agar kegiatan belajar mengajar dapat lebih efektif dan maksimal meskipun pembelajaran di lakukan tanpa tatap muka atau pembelajaran jarak jauh. Penelitian ini termasuk penelitian dan pengembangan (Research and Development), dengan menggunakan model penelitian ADDIE dari tahap Analyze, Design, Develop, Implement, dan Evaluate (Branch, 2009). yg melalui beberapa tahap yaitu analisis, desain (perancangan), pengembangan, implementasi, dan evaluasi. Uji coba media pembelajaran interaktif didukung dengan penilaian kevalidan tim ahli, dan data angket kevalidan dari para responden yaitu guru guru Madrasah Ibtidaiyah, dan berdasarkan hasil penelitian 76% media pembelajaran ini layak untuk digunakan.&#x0D; Kata kunci : media interaktif, berbasis powerpoint, Android.&#x0D; Abstract &#x0D; PowerPoint-based interactive media is a developed learning media, containing science learning materials that are systematically designed for students in grade 3 Madrasah Ibtidaiyah through the basic powerpoint program and developed with ispring, so that teaching and learning activities can be more effective and maximal even though learning is done without face-to-face or distance learning. This research includes research and development (Research and Development), using the ADDIE research model from the Analyze, Design, Develop, Implement, and Evaluate stages (Branch, 2009). which goes through several stages, namely analysis, design (design), development, implementation, and evaluation. Interactive learning media trials are supported by a team of expert validity assessments, and validity questionnaire data from respondents, namely Madrasah Ibtidaiyah teachers, and based on research results 76% of this learning media is feasible to use.&#x0D; Keywords : interactive media, powerpoint based, Android.
Anonymizing Global Edge Weighted Social Network Graphs

Privacy protection of individual users in social networks is becoming more and more important, thus it requires effective anonymization techniques. In this paper, we use Kruskal and Prim algorithms to model the linear programming of the minimum spanning tree. Finally, we execute the experiments on the number of anonymity solutions and time with different edge weights to analyze the Kruskal algorithm and Prim algorithm to verify their anonymity feasibility.
Google classroom : An online platform for teaching English

In the development of the internet era, classroom learning changes from traditional teaching to online learning. The purpose of this research is to report action research that was carried out to investigate the effectiveness of google classroom on students’ learning performance in STKIP PGRI Bandar Lampung. The participants were 25 students studying English. The researchers conducted some exercises, discussions, quizzes, and activities on Google Classroom based on three lesson plans that were applied to teach the students in the writing course. Analysis of students’ second writing shows a significant improvement in students’ writing. The students have positive perceptions towards using Google Classroom in language learning based on a post-treatment questionnaire result.
Cognitive modeling of informatization influence on socio-economic indicators of the region

The article discusses topical issues of the influence of informatization on the development of the country’s regions in the conditions of the modern unstable world. The nature of the development of a region can be reflected and understood on the basis of qualitative and quantitative information about its socio-economic indicators, about their relationship and trends in their changes under the influence of internal and external factors. At the same time, information can most often be incomplete, difficult to access, untimely, contradictory, etc. Therefore, in this paper, it is proposed to use a cognitive approach and cognitive modeling of complex systems to overcome the problems of information deficiency by imitating cognitive modeling of the structure and behavior of a complex regional system. The simulation was carried out using the author’s CMCS (Cognitive Modeling Complex System) software system. The results of multi-stage cognitive modeling, consisting in the development of cognitive maps “Influence of ICT on the state of the region” and “Digitalization of the republic” (according to the socio-economic state of the Republic of Dagestan), analysis of structural properties and modeling scenarios for the development of situations on the model are presented. Scenarios make it possible to foresee the ways of possible development of the system under the influence of various factors, including the factor of informatization.
Revisiting Matplotlib Visualizations

In the previous chapter, you learned about the many routines for creating and manipulating Ndarrays in the NumPy library. You will need many of those routines throughout this book.
IMAGE FORGERY DETECTION USING ADAPTIVE OVER SEGMENTATION AND FEATURE POINT MATCHING

In this paper we propose a scheme to detect the copy-move forgery in an image, mainly by extracting the key points for comparison. The main difference to the traditional methods is that the proposed scheme first segments the test image into semantically independent patches prior to key point extraction. As a result, the copy-move regions can be detected by matching between these patches. The matching process consists of two stages. In the first stage, we find the suspicious pairs of patches that may contain copy-move forgery regions, and we roughly estimate an affine transform matrix. In the second stage, an EM-based algorithm is designed to refine the estimated matrix and to confirm the existence of copy-move forgery. Experimental results prove the good performance of the proposed scheme via comparing it with the state-of-the-art schemes on the public databases.
Humour of solidarity in Russian-speaking discourse on social networking groups in Lithuania

The chapter explores the solidarizing potential of humorous language behaviour in multi-ethnic Russian-speaking network communities in Lithuania. Observations of the communicative behaviour within these communities demonstrate rejection of harsh forms of the comic (irony, satire) and avoidance of potentially conflicting topics. Humour is used as “social glue” and contributes to the creation of symbolic solidarity among group members through comic interpretation of common social issues going beyond national, social, or other identification. The authors’ pragma-stylistic analysis reveals the prevalence of jokes prompting conflict-free laughter. Comic content sanctioned by the group undergoes three stages of censorship: (1) self-censorship of the author of the publication, (2) censorship of the moderator, and (3) collective censorship of group members who either react or do not respond to the content. This multistage collective sanctioning of comic content is indicative of specific ethical norms adopted within the intra-group social culture.
Newspapers and the Persisting Usefulness of Microfilm: A Matter of Context

Microform collections have become less utilized in recent decades. Specifically, the use of newspapers on microfilm has become seemingly superfluous as more and more newspapers become available electronically. This study examines graduate student opinions of and experiences with newspaper resources on microfilm to illustrate its perception among some academic library users. It also examines published opinions from researchers on the benefits of accessing newspapers on microfilm. Through an understanding of these benefits, libraries can weigh them against microforms’ physical footprint when making decisions about the fate of these collections.
PAINTING PERFORMANCES: METHODOLOGICAL TOOLS FOR FUTURE EDUCATORS TRAINING

The article discusses the issue of future educators’ professional training as a complex problem. The artistic component of future educators’ professional training is singled out. The mechanisms and methods of forming the creative potential of future educators are revealed, among which a special place is occupied by methods related to the increase of reflexive culture, with the stimulation of a special mental state of creative activity, with the use of techniques to intensify the search for ideas, the development of imaginative abilities, the search for internal motivating reasons for the intensification of creative activity. Effective means of future educators’ training are identified: communication on the materials of art, the basis of which is subject-subject interaction in the “teacher-student” system, which is retransmitted into the “educator-child” system. The tool of development of creative potential of future educators is given — acquaintance with works of art: direct acquaintance with art objects during art excursions, art walks, visits to theater, etc.; artistic reflection; art technologies, art techniques, and art methods. The results of approbation of painting performances in the format of offline and online as a tool for the development of creative potential and raising the level of general culture of future educators are described. The algorithm of creation of painting performances is characterized: presentation of idea; elaboration of literature and other sources; work by the method of creative structured improvisation to characterize the images of the characters; choice of props; modeling of painting performances; artistic reflection at the final stage (reflection exercises that motivate students to self-knowledge and determine their own emotions; develop the ability to analyze and evaluate their feelings, process and results of artistic activity). The presented methods of acquaintance with the art of painting as a means of developing the creative potential of future educators and preschool children are implemented in the direction of perception — reflection — attitude, allow to appropriate the experience, work on the formation of life competence, as related to the analogy of art and life.
Development of Autonomous Driving System based on Image Recognition using Programmable SoCs

We design and implement an autonomous driving system based on image recognition using programmable SoCs. The proposed system equips two FPGA boards and three cameras. One FPGA board implements a driving control system, and the other FPGA board implements object detection and recognition using machine learning algorithms. Driving control is performed based on road edge line detection and road marking recognition using the canny edge detection. On the other hand, image detection and recognition of traffic lights are implemented using the random forest method with HOG features. In the development framework of programmable SoC of Zynq 7000, we adopt a Hardware/Software co-design to balance the design period and system performance required for real-time processing.
Reversible and Robust Watermarking Technique

We propose a reversible and robust watermarking technique based on the prediction-error embedding of color images. Then, lossless or lossy watermark extraction is used. In the lossless algorithm, we use the parameters of the predicted model and the location map to improve the quality of the stego image and restore the cover image. During lossy watermark extraction, two-dimensional linear discriminant analysis and two-dimensional principal component analysis are applied. We combine reversible information hiding and watermarking schemes. The experimental results show that the embedded watermark can be correctly extracted and provide better robustness after various image attacks.