Reviewerly/exHarmony huggingface dataset:
Aplikasi Monitoring Data Imunisasi Berkala Untuk Meningkatkan Pelayanan Posyandu Menggunakan Metode RAD Berbasis Android

In an effort to reduce the mortality rate due to infection in infants, immunization activities are carried out. Implementation of immunization, namely through Integrated Service Posts. In the current system, child immunization data is recorded on the KMS (Kartu Menuju Sehat) that parents bring to the immunization program. The card is prone to missing, which causes parents to not have their child's immunization history data. The existence of a computerized application can help the health service process to be faster and provide valid and up to date data. Immunization data monitoring application is an application that was built to help organize the data into information needed to support routine monitoring activities at the public health center of Pembina. The design of this application uses the Unified Modeling Language (UML) and is developed using the Rapid Application Development (RAD) method that can produce systems quickly and directly involve the user in the application development process. This research resulted in an immunization data monitoring application intended for parents and officers of the Integrated Service Post at public health center.
An Indexing Scheme for Telerehabilitation Big Data

Reduced cost and increased proliferation of sensors has resulted in the generation of a large amount of data. To make use of this data effectively, efficient techniques for collecting, storing and managing data are needed. In this paper, we present a health big data indexing technique that allows for the efficient storage, querying and management of tele-rehabilitation related big data collected via health sensors connected to client machines accessing web-based e-Health frameworks. Our novel scheme is based on indexing of rehabilitation data based on the human body joint model. We further index the data based on other parameters that may be of interest to researchers such as ethnicity and geographical location. We have described all components of the system in brief.
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs

We study the problem of learning the causal relationships between a set of observed variables in the presence of latents, while minimizing the cost of interventions on the observed variables. We assume access to an undirected graph $G$ on the observed variables whose edges represent either all direct causal relationships or, less restrictively, a superset of causal relationships (identified, e.g., via conditional independence tests or a domain expert). Our goal is to recover the directions of all causal or ancestral relations in $G$, via a minimum cost set of interventions. It is known that constructing an exact minimum cost intervention set for an arbitrary graph $G$ is NP-hard. We further argue that, conditioned on the hardness of approximate graph coloring, no polynomial time algorithm can achieve an approximation factor better than $\Theta(\log n)$, where $n$ is the number of observed variables in $G$. To overcome this limitation, we introduce a bi-criteria approximation goal that lets us recover the directions of all but $\epsilon n^2$ edges in $G$, for some specified error parameter $\epsilon > 0$. Under this relaxed goal, we give polynomial time algorithms that achieve intervention cost within a small constant factor of the optimal. Our algorithms combine work on efficient intervention design and the design of low-cost separating set systems, with ideas from the literature on graph property testing.
Exploring Selected Game on English for Young Learner’s Vocabulary Mastery: The Word Clap Vs Quiz

This research was conducted to offer an alternative teaching strategy on raising vocabulary mastery using the Word Clap Game. The purpose of this study was to determine whether there was a difference in the results of the Word Clap Game strategy on EYL’s vocabulary mastery of the fifth-grade Narwastu elementary school in Pekanbaru, Indonesia. This quasi experiment research involved 60 students with two classes; The experimental class with 29 students (VB class) and the control class with 31 students (VA class). The results of this study highlighted the post-test scores in the experimental class as having increased compared with the pre-test scores, which can be seen from the average score of the experimental class post-test that was 76.03 while the pre-test was 62.75. The average post-test value in the control class is 74.19, which was lower than the experimental class. Therefore, this study believes that there was no significant difference in students who are taught using the Word Clap Game strategy in mastering vocabulary with students who were not taught using the Word Clap Game strategy both seem effective in raising the EYL vocabulary mastery.
The Design and Optimization of Swing Automatic Paper Folding Mechanism

In order to solve the multi-directional bending problem of paper cards with different specifications, a swing type automatic paper folding mechanism was designed. Through gear rack mechanism and rocker slider mechanism, the paper card can be folded vertically and horizontally for many times. The MATLAB optimization function library is used to optimize the rocker, connecting rod, swing angle and other size parameters of the mechanism, and the swing angle of the bending block is determined to be 15 degrees. The motion equation of paper folding process is solved by vector equation and coordinate transformation, and kinematics simulation of swing paper folding mechanism is carried out by motion plug-in of SolidWorks. The simulation results show that the mechanism can meet the motion requirements of the waist seal, and the simulation parameters are consistent with the calculation parameters.
“Forget” the Forget Gate: Estimating Anomalies in Videos Using Self-contained Long Short-Term Memory Networks

Abnormal event detection is a challenging task that requires effectively handling intricate features of appearance and motion. In this paper, we present an approach of detecting anomalies in videos by learning a novel LSTM based self-contained network on normal dense optical flow. Due to their sigmoid implementations, standard LSTM’s forget gate is susceptible to overlooking and dismissing relevant content in long sequence tasks. The forget gate mitigates participation of previous hidden state for computation of cell state prioritizing current input. Besides, the hyperbolic tangent activation of standard LSTMs sacrifices performance when a network gets deeper. To tackle these two limitations, we introduce a bi-gated, light LSTM cell by discarding the forget gate and introducing sigmoid activation. Specifically, the proposed LSTM architecture fully sustains content from previous hidden state thereby enabling the trained model to be robust and make context-independent decision during evaluation. Removing the forget gate results in a simplified and undemanding LSTM cell with improved performance and computational efficiency. Empirical evaluations show that the proposed bi-gated LSTM based network outperforms various LSTM based models for abnormality detection and generalization tasks on CUHK Avenue and UCSD datasets.
A Syntactic Model of Mutation and Aliasing

Traditionally, semantic models of imperative languages use an auxiliary structure which mimics memory. In this way, ownership and other encapsulation properties need to be reconstructed from the graph structure of such global memory. We present an alternative "syntactic" model where memory is encoded as part of the program rather than as a separate resource. This means that execution can be modelled by just rewriting source code terms, as in semantic models for functional programs. Formally, this is achieved by the block construct, introducing local variable declarations, which play the role of memory when their initializing expressions have been evaluated. In this way, we obtain a language semantics which directly represents at the syntactic level constraints on aliasing, allowing simpler reasoning about related properties. To illustrate this advantage, we consider the issue, widely studied in the literature, of characterizing an isolated portion of memory, which cannot be reached through external references. In the syntactic model, closed block values, called "capsules", provide a simple representation of isolated portions of memory, and capsules can be safely moved to another location in the memory, without introducing sharing, by means of "affine' variables. We prove that the syntactic model can be encoded in the conventional one, hence efficiently implemented.
A reconciliation model of agile C2 organization based on converged networks

With the promotion of information technology and big data technology, and the traction of new operational concepts such as Mosaic Warfare and Joint Global Operations, the research and practice of agile command & control(C2) organization has become a hot issue in the field of C2. One of the focal points of the debate is the contrast between the traditional hierarchy structure of C2 organizations and the edge structure. Is the traditional hierarchy structure of C2 necessarily in conflict with the edge structure? Can these two organizational structure models be reconciled to achieve organizational agility transformation? How to reconcile these two models of organizational structure? In order to solve the above problem, we put forward a kind of suitable reconciliation model of agile C2 organization considering our actual situation. On this basis, we use ontology modeling method to form characteristics of adaptive and intelligent cross-domain integrated solutions, through the establishment of agile ontology and reasoning mechanism of C2 organization.
A survey on the process of knowledge representation of the small banana farmers (Musa spp.) in mangaratiba, rj

The banana, the world's most widely produced and commercialized fruit, is grown in all tropical regions of the world, being strongly present in local businesses and subsistence crops serving as an important source of nutrients for the poorest populations. In the state of Rio de Janeiro it is commonly found in hillside and difficult access areas, where most other crops would not be able to settle and, because of this, is grown with inadequate management or insufficient, resulting in low productivity in the areas of Rio de Janeiro. The objective of the present work is to carry out a survey of smallholder information from the Vale do Rio Sahy Association in Mangaratiba, RJ, to enable the representation of knowledge in this domain. From the data collected in this research, it was realized that producers have been engaged in this activity for a long time. However, it was found that the knowledge used to production is extremely tacit, without systematization. The variety of banana species (Musa spp.) grown in the production area of the association's small farmers. The knowledge transfer process knowledge to the knowledge base of an expert system is called knowledge acquisition, where it involves extract all the knowledge from the source of the specialists to systematically represent in a coded form the domain information in an appropriate medium. It was observed, even if preliminarily, that this knowledge are not represented in a database for consultation. Thus, there is a need to define human expertise or producers capable of representing in a technological way data that can be conveniently accessed for Problem solving. In view of the evidence presented in the research, the use of representation of human knowledge (small local producers) to feed and train the system according to the domain presented. Thus, enabling the prototype to help understand climate and soil variables and collaborate in decision making.
An analysis of content-based image retrieval

Nowadays, working on digital images is gaining much popularity in multimedia systems, due to the rapid increase in the utilization of large image databases. Thus, the Content-Based Image Retrieval (CBIR) method has become the most valuable method for these databases. This study mainly focuses on content-based image retrieval; which uses image features like color, shape, texture, etc. by searching the user query image from a large image database based on user request. CBIR is the most widely used technique as its searching capability is faster than the other traditional methods, and it works well in retrieving images automatically. It is also a big alternative approach to traditional methods. The CBIR techniques are used in many applications like surveillance detection, crime avoidance, fingerprint identification, E-library, medical, historical monument and biodiversity information systems, and many more. A total of 38 CBIR articles were comparatively analyzed.
A Solution for Preventing the Rogue Certificate Attack

In today’s online world, internet security heavily relies on the trust in Certificate Authorities. Modern browsers and operating systems provide a comprehensive list to their users, which includes all the CAs they trust by default. This could turn into a serious problem when even one of the CAs is compromised and/or goes rogue. It is especially relevant for enterprise applications, as they are more likely to be targeted for this kind of attack. In this paper, we propose a solution which can mitigate this kind of attack against large organizations. We also discuss the security of the proposed method, offering acceptable security/performance tradeoff.
Coaching Clinic Pembuatan Media Evaluasi dengan Menggunakan I-Spring bagi Guru Bahasa Indonesia SMA di Kecamatan Abung Semuli dan Abung Selatan Kabupaten Lampung Utara

Community service activities are aimed at Senior High School (SMA) Indonesian Language Teachers in two sub-districts with the highest number of SLTAs in North Lampung Regency, namely Abung Semuli and South Abung Districts. The purpose of this community service is to provide assistance in making learning evaluation media using i-Spring media. The method of implementing activities is in the form of training, followed by discussion and question and answer. The training participants are all high school Indonesian teachers in Abung Semuli and South Abung Districts. Implementation of activities at SMA Negeri 1 Abung Semuli by inviting all members of the Indonesian Language MGMP in the two districts. The results of this activity indicated that the training participants were enthusiastic about participating in each stage of the activity. Based on the evaluation of learning, this coaching clinic can be said to have succeeded in improving the ability of teachers to make learning evaluations using the i-spring media.
Mechanisms for attracting foreign students to Russian universities with the aim of their subsequent employment in Russian and transnational companies abroad

The Yearbook contains articles of a general theoretical, historical and applied nature concerning Russian education and science.The activities of world-class scientific and educational centers and the sectoral system of international scientific and educational cooperation, various problems of Russian educational organizations, including those related to their optimization, trends in socialization and professional orientation of young people are covered.A number of articles are devoted to international education and training of foreign students in Russia, the use and study of the Russian language in schools and universities of foreign countries.The Yearbook is written for employees of Russian educational and scientific organizations, students and graduate students of sociological and pedagogical faculties, as well as to anyone interested in trends in the development of domestic and international education and science.
Robustness of Integrated Classification Algorithm on Offender Profiling

An integrated classification algorithm is a decision-making method that is not limited to a single classifier. It comprises multiple classifiers to maintain a high classification performance for various datasets. This study investigated the feasibility of an integrated classification algorithm for offender profiling. Offender profiling is the analysis of a crime scene using statistical and psychological methods to estimate information such as the age, job, and criminal record of the offender. In this study, the following 12 machine learning algorisms were used: decision tree (C5.0, CART by entropy or Gini), logistic regression analysis (LR), naïve bayes (NB), random forest (RF), bagging, boosting, support vector machine (SVM by radial basis function or polynomial), k-nearest neighbor (KNN), and neural network (NN). The results of the study showed that the classification performances of each algorithm varied for different objective variables of the dataset (e.g., criminal record, age, or job of offenders of residential burglar). However, the majority decisions made by a combination of three classifier algorithms (e.g., decision tree, LR, and NB) showed high classification performance regarding any dataset.
Video-aided Unsupervised Grammar Induction

We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video. Existing methods of multi-modal grammar induction focus on learning syntactic grammars from text-image pairs, with promising results showing that the information from static images is useful in induction. However, videos provide even richer information, including not only static objects but also actions and state changes useful for inducing verb phrases. In this paper, we explore rich features (e.g. action, object, scene, audio, face, OCR and speech) from videos, taking the recent Compound PCFG model as the baseline. We further propose a Multi-Modal Compound PCFG model (MMC-PCFG) to effectively aggregate these rich features from different modalities. Our proposed MMC-PCFG is trained end-to-end and outperforms each individual modality and previous state-of-the-art systems on three benchmarks, i.e. DiDeMo, YouCook2 and MSRVTT, confirming the effectiveness of leveraging video information for unsupervised grammar induction.
World-Class Science and Education Centers (Noc) as Socio-Organizational Innovation

The Yearbook contains articles of a general theoretical, historical and applied nature concerning Russian education and science.The activities of world-class scientific and educational centers and the sectoral system of international scientific and educational cooperation, various problems of Russian educational organizations, including those related to their optimization, trends in socialization and professional orientation of young people are covered.A number of articles are devoted to international education and training of foreign students in Russia, the use and study of the Russian language in schools and universities of foreign countries.The Yearbook is written for employees of Russian educational and scientific organizations, students and graduate students of sociological and pedagogical faculties, as well as to anyone interested in trends in the development of domestic and international education and science.
Exercise Recognition System using Facial Image Information from a Mobile Device

Daily exercise has played a significant role for people in staying healthy; however, some people cannot do moderate exercise continuously. In this paper, we proposed an exercise recognition system using facial image information to make daily exercise management convenient. The proposed system gets facial image information from a built-in camera on a mobile device and can recognize nine exercises by a support vector machine's classifier. When a user faces the camera during the exercise, the system gets time-series data consisting of 62 features on the face. Via leave-one-subject-out cross-validation, the average classification accuracy reached up to 88.2%.
What is the state’s role in a world driven by AI tools?

Artificial Intelligence (AI) is one of those digital innovations that can fundamentally change the society including the public sector and its public servants. It may even help shape a new role and give new legitimacy to the public sector and governments in general. AI solutions (e.g. chatbots, process automation, and image recognition software) transform public sector work and the public sector workforce. In our acceptance, AI can save lives and greatly enhance safety by predicting potential risks or looming structural failures. In our day-to-day life, AI can streamline customer experience and create new experiences never imagined.&#x0D; Even if we tend to develop, AI still remains an unknown world. We don’t know by sure what is AI, we don’t know if we have to monitor the AI’s use, we haven't established yet the responsible authority, we are not sure about the opportunities of AI and if these opportunities will be ethically used and proposed in a transparent manner.&#x0D; Information technologies are developed outside of the reaction of the state and faster than the law and legal provisions. Thus, by this article, we aim to identify legal and organizational constraints, which have to be settled in parallel with the development of AI systems.
Digital forensics and its role in promoting criminal prosecution

Digital forensics is essentially synonymous with computer forensics, but the term "digital forensics" is generally used for the technical review of all devices that have the ability to store data. Today, digital criminology is challenged in cloud computing. The first problem is to understand why and how criminal and social actions are so unique and complex. The second problem is the lack of accurate scientific tools for forensic medicine in cyberspace. So far, no complete tools or explanations for criminology have been provided in the virtual infrastructure, and no training for security researchers has been provided in detail. Therefore, the author of the present descriptive-analytical research is based on library resources and using fish taking tools. To investigate suspicious cases related to cyberspace, criminologists must be well-equipped with technical and legal issues to deal with. In this article, we analyze digital criminology and its role in judicial law. The benefit of computer forensic knowledge is not only an indispensable necessity for security and judicial institutions, but also professional users and owners of computer systems, systems and networks must be fully aware of and properly comply with its legal and technical requirements.