Reviewerly/exHarmony huggingface dataset:
Evaluation of Multimedia Micro-Courses Based on Augmented Reality Technology Bayesian Network

The multimedia micro-course is a professional basic course, having a very strong practicality and requiring the teacher to combine the theory with the practice to give lessons, this paper proposed one kind of evaluation of multimedia micro-Course based on the augmented reality technology Bayesian network. In this paper, the concept of the multi-branches tree of attribute in DDMB and the idea of reflecting the general characteristics of the data sets in the distributed envi-ronment through the multi-branches tree of attribute are expounded. This paper has introduced the method of constructing Multi-branches tree of attribute based on mobile agent access distribution Data Set, and specifically described the algo-rithm of generating integrated Bayesian network through multibranches tree of attribute The structure Learning and parameter learning of Bayesian networks oriented to multibranches tree of attribute and the method of determining the minimum threshold of dependent coefficient between attributes have been expounded. The experimental results show that, the model can effectively solve the problem of heavy learning burden, high storage cost and low executive efficiency of the original distributional environment Bayesian network, and has realized a more effective multimedia micro-course evaluation.
Gradient based Identification Algorithm for Separable Nonlinear Models

Separable nonlinear models are a class of models that can separately represent the linear and nonlinear parameters, which have a wide range of applications in many fields. This paper attempts to explore the performance of gradient based algorithms in identifying separable nonlinear models. We choose the RBF-AR model as the research objection, which is a classical separable nonlinear model and has a wide range of applications in time series modelling. Three different gradient based optimization strategies are used to estimate the parameters of the RBF-AR model, including the classical gradient descent (GD) method, the alternative gradient descent (GD-L) method, and the gradient based variable projection(GD-VP) method. The experimental results show that the gradient descent method is sensitive to the learning rate and is prone to gradient explosion problems, so the convergence speed is slow; while the VP algorithm can alleviate the impact of the learning rate, and improve the efficiency and stability of the gradient descent alaorithm.
Weighted Fusion Incremental Kalman Estimator for Descriptor Systems under Poor Observation Condition

Under certain environmental conditions, the unknown system errors often occur and yield to larger filtering errors when the unverified or uncalibrated measurement equation is used. Incremental equation can be introduced, which will effectively solve the problem of state estimation for the systems under poor observation condition. For the linear discrete descriptor systems under poor observation condition, a descriptor incremental Kalman estimation algorithm is firstly presented under the canonical form. It solves the state estimation problem for the descriptor systems with unknown measurement errors, which doesn't meet the requirements of classical Kalman filter. Furthermore, an optimal weighted fusion descriptor incremental Kalman estimator is given based on the linear minimum variance optimal fusion criterion. Compared with the existing augmented dimension-enlarged processing methods, the proposed algorithms are simple in form and small in computational burden so as to be easily applied in engineering practice. A simulation example shows its effectiveness and feasibility.
SISTEM INFORMASI PENJADWALAN OTOMATIS MEDIA SOSIAL INSTAGRAM UNTUK MENDUKUNG PROMOSI PROGRAM STUDI DI UNIVERSITAS DHARMA ANDALAS

Instagram's social media automatic scheduling system is one of the innovations in the field of promotion to introduce study programs to many people through social media. With this scheduling application, it will be easy for the university to schedule content to be published on Instagram social media. This application works on the desktop made with the PHP programming language with MySQL database support. This research was conducted to obtain data directly from the Social Media Manager of the Study Program by conducting observations and direct interviews to the management team and related parties so that the data contained in this report is real. The system testing that is carried out is how the application works as a whole, namely where the system can send scheduling automatically to Instagram which is contained in the database, there is an input update from a user (in this case a lecturer user) through the existing application.
Liquid 45 modeling based on Markov chain and fuzzy linguistic summary using fuzzy stochastic

LQ45 is one of many Indeks Harga Saham (IHS) which is included in Bursa Efek Indonesia (BEI). IHS is used by investors to predict the stock prices. The motion of IHS is unpredictable causes the investors in quandary situation. Stochastic model is probabilistic model based on an event which is characterized with parameters value and time-variant. IHS fluctuate causes unpredictable effect that is also the feature of stochastic process. The uncertainty can be measured by fuzzy then the composite of both is fuzzy stochastic. On fuzzy, membership value used as parameter approach is fuzzy linguistic summary (FLS). FLS has not examined chance of fluctuation the stock prices motion, so that Markov chain’s combination is used as the approach of parameter r. The r parameter has five state, those are the steady, rising, most rising, falling, and most falling. There are three kinds of stocks return, which are the positive return, negative return, and no return. The clustering process uses fuzzy c-means results the positive return clusters and negative return clusters. This research results interval on five sorted interval clusters −5.17 ≤ x ≤ 1.17 when IHS is most falling, −1.17 < x < 0 when IHS is falling, 0 when IHS is steady, 0 < x < 1.2 when IHS is rising, dan 1.2 ≤ x ≤ 3.73 when IHS is most rising and stochastic model obtained is X (n + 1) = X (n)er along r is the parameter of five states, rsteady = 0, 0001, rrising = 0, 0106, rmostrising = 0, 0135, rfalling = 0, 0072 and rmost falling = 0, 0104. The fuzzy stochastic model with those parameters on five states has MAPE value 0,49% therefore the model has great forecasting capability.
Detecting Credential Stuffing Between Servers

The goal of Detecting Credential Stuffing is to detect the Credential Stuffing attack in time and effectively. Based on OT and Cuckoo Filter, an effective credential stuffing detection protocol is proposed in this paper. The protocol involves two servers holding a collection of suspect credentials, get the result by private set intersection. By executing this protocol, the inquirer gets the same elements of both sets without knowing anything about the other elements in the other set, and the responder does not get any additional information. The validity, correctness and safety of the scheme under the semi-honesty model are verified by theoretical analysis.
Integrating Mental Models into Intelligent Tutoring Systems for Solving Random Sampling Word Problems

Mental models have been deeply concerned by researchers and practitioners since it plays a crucial role in problem-solving. In this study, we proposed mental models for random sampling word problems. On basis of the proposed mental models, we further performed a mental simulation with a rule-based mental reasoning method and a template-based approach to generate human-style explanations. And then we established an experiment on a prototype intelligent tutoring system. Experimental results showed that: the proposed mental models were available for most random sampling word problems; and a solution with human-style explanations to the problem encoded in mental models was produced effectively by means of mental reasoning. Based on these results, we concluded that our mental models can be used to encode essential information for solving random sampling word problems effectively, and it is profitable to take mental models into account when developing intelligent tutoring systems.
EmotionC: A Novel Framework for Emotion Detection Using Personalized Search Engine

In the days of the Modern era, searching or browsing through the Internet has been a part of our daily life. For example, if a person feels happy then he searches for a good movie or a song, if a person wants to commit suicide he searches the easy ways to commit suicide. The average person uses a browser for three to four times a day. So, detecting emotion by a person's search history could yield us the Emotional condition of a person. The existing search engines give us better results when the complete query is entered, hence it requires personalization to obtain better results with less input. In this paper, using ontology the users are segregated into different categories based on the emotion yielded. Also, each query is segregated into their respective category of emotion using clustering. Moreover, the privacy issue is addressed here by restricting the information only to the user profile. Finally, the user search history is tracked and based on the search history the emotion of a user will be detected.
Template matching based on deformation diversity similarity and scale filtering

To solve the problem that deformation diversity similarity (DDIS) has poor matching effects on rotation and multi-scale objects, a more robust template matching algorithm is proposed to deal with such conditions. The location distance of the nearest neighbor pairs is calculated by strengthening the constraint of the apparent features of the object on the location distance in polar coordinate system. The new confidence map composed of the DDIS response values is obtained and take the position with the highest values as the object center. The scale image pyramid is built according to the template to train the scale filter, and the scale filter is carried out on target image to determine the object scale. Experimental results show that the template matching effect of the proposed algorithm is better than DDIS, the new calculation method of location distance can effectively improve the robustness for rotating object and the scale filter can achieve the adaptive scale of object. Combining those two factors can further improve the matching accuracy.
Machine Intelligence-Based Approach for Effective Terrorism Monitoring

Terror incidents are accumulating because of novel methodologies, practices, and weapons. National security agencies track rebellious terror incidents by keeping records of persons of interests intended for their tracing. An individual's data can be taken from numerous sources in several formats — examining it with conventional approaches is a huge task; inspecting links between entities becomes tedious. This chapter presents a machine learning and computer vision-driven solution that will aid security agencies in checking terrorism. This answer will not only shape a 360-degree individual's profile but also display their connections with numerous incidents and entities. The solution projected is useful in performance and speed because it leverages the power of dispersed computing and multi-threading. The need for machine learning in defense planning and strategies increases with breaches and decimations caused by terrorist forces. A myriad of military bases, temporary campaigns, base camps, etc. are being targeted and attacked by several terrorists. The common problem in warfare and tumultuous international borders is the frequent and violent intrusion and breaches upon the military and army bases. Though they are successful in their task to identify the safest or the active base, a combined location that embraces effectiveness and vulnerability is invalid using a present analyzing and classification technology. This problem is due to the presence of collinearity between the parameters that determine both efficacy and susceptibility. A military base location can both be practical and vulnerable at the same time; a site that does not provide sufficient effectiveness to perform the military operation. To combat this problem, we propose an algorithm that identifies two parameters — efficiency and vulnerability — by cross-correlating them one by one to check for collinearity.
Game by Someone Else's Rules: a Quest Scenario as an Object of the Authorship Attribution

The authorship attribution of quest scenarios is discussed as an expertcase study. The expert has been assigned a task which involved tracing the signs of copying and revising of one text in another. The genre approach is employed: a description of important structure elements and linguistics characteristics of the quest scenario is given. The expertise is performed as per procedure of authorship examination: separatestudy of the texts, comparative study and the conclusion. The research has arrived at the conclusion,that the authorship attribution of the quest scenario, focused on the determination of the signs of copying and revising of one text in another,is similar in methods and features to a trademark expertise, because it also involves identifying similarity or difference of structures and semantic elements of the texts.
Analysis of Gaze Time Spent at the Gazing Point that is Required During Reading

This paper aims to clarify the lower limit of the gaze time required during reading by investigating at a display time of 98 msec or less using the visual information processing analyzer developed in the previous research.The image display range and display time at the point of gaze was controlled by moving the window in conjunction with the eye movement.As a result, it was found that normal reading, as if there were no window or visual field restriction, could not be performed with a display time of 42 msec or less, but that normal reading could be performed when sentences were displayed in a window of 7 characters at 56 msec.In this way, we believe we can obtain useful knowledge that is necessary for studying more effective methods of displaying text on electronic device.
Towards a Theory of Intentions for Human-Robot Collaboration

The architecture described in this paper encodes a theory of intentions based on the principles of non-procrastination, persistence, and relevance. The architecture reasons with transition diagrams at two different resolutions, with the fine-resolution description defined as a refinement of, and hence tightly-coupled with, a coarse-resolution description. For any given goal, non-monotonic logical reasoning with the coarse-resolution description computes an activity, i.e., a plan, comprising a sequence of abstract actions to be executed to achieve the goal. Each abstract action is implemented as a sequence of concrete actions by automatically zooming to and reasoning with the part of the fine-resolution transition diagram relevant to the coarse-resolution transition and the goal. Each concrete action is executed using probabilistic models of the uncertainty in sensing and actuation, and the corresponding coarse-resolution observations are added to the coarse-resolution history. Experimental results in the context of simulated and physical robots indicate improvements in reliability and efficiency compared with an architecture that does not include the theory of intentions, and an architecture that does not include zooming for fine-resolution reasoning.
Determination of Rice Quality Using the K-Means Clustering Method

Rice is a staple and food that has a source of energy for people that has high carbohydrate content but low protein. Buyers are very fond of determining the quality of good rice. One of the indicators in determining the quality is rice which is often bought by buyers. This study aims to classify rice based on clusters, making it easier for buyers to know the quality of rice, and sellers can determine the right price for rice based on quality. The method used in this research is the K-Means Clustering of 30 rice data brands. The test results obtained 3 clusters, namely cluster 0 with very good quality as many as 12 brands of rice, cluster 1 with good quality as many as 12 brands of rice, cluster 2 with poor quality as many as six brands of rice. With this research, Bulog can find out the results of the quality of rice and can provide preventive action to reduce problems in determining the quality of rice.
A Novel Change Detecting Method for Monitoring Data Streams in Data Centers

With the rapid expansion of data centers, there are a large number of sensors in data centers to collect real-time monitoring data of various electrical equipment. It has been widely accepted that a change may indicate the fault in machine, so the changes detecting is very critical to grasp the operating status of equipment and also useful modeling and prediction of equipment operations. However, there are a great challenge to perform an online changes detecting on this kind of data stream with various patterns. In this paper, we propose a novel method to automatically implement the online changes detecting for stream data which includes three main steps: Extracting time-frequency features, Clustering them based on an improved distance metric, and Evaluating clustering results to detect change points. We applied experiments on artificial datasets and real-world datasets collected from a real large data center and proved that the proposed method can effectively solve the problem of online change detecting.
A FIPA-ACL based communication utility for Unity

Multi-agent systems (MAS) allow facing complex, heterogeneous, distributed problems difficult to solve by only one software agent. Games often provide very adequate problems for the use of MAS. In the field of Games, Unity is one of the most used video game engines and allows the development of intelligent agents in 2D/3D virtual environments. However, although Unity allows working in multi-agent environments it does not provide functionalities to facilitate the development of genuine MAS. One crucial functionality is to allow agents to communicate and understand each other through an agent communication language (ACL). The objective of this work is to supply Unity with this capacity, adding support for the creation, sending and reception of messages. As a result, a communication system and an ACL are provided that facilitate the development of MAS in Unity, allowing to solve problems that require collaborative solutions among agents.
Improvement of control algorithm for mobile robot using multi-layer sensor fusion

Mobile robots have received much of attention in the last three decades due to their very high potential of applications such as smart logistics, exploration, and intelligent services. One of important functions of mobile robots is the navigation in which robot must know their location, the maps of environment and perform path planning with obstacle avoidance. In this work, we introduce an improvement of control algorithm for mobile robot using multi-layer sensor fusion toward the target of efficient obstacle avoidance. Based on our method, we used three layers of sensors arranging in three height-different planes of robot’s housing for sensor fusion. A control algorithm, which is extended from the so-called bubble rebound algorithm and uses signal from sensor system, was proposed. Experimental implementation on a mobile robot, named EAI, shows that our algorithm can control the robot to navigate and avoid obstacles much efficiently, in which obstacles in forms of different shapes and height can also be avoided. A high repeatability and stability of the algorithm is obtained.
The sounds of Tawrã (Digaru-Mishmi), a Tibeto-Burman language

Abstract The present study is a phonological analysis of the segments and tones of the Tawrã language (ISO 69-3: mhu; Glottolog: Diga1241), a Sino-Tibetan language spoken in Arunachal Pradesh, India, and in Tibet, China. This paper, the first collaboration between a Tawrã-speaking non-linguist and a non-Tawrã-speaking linguist, attempts to clear up some confusion in the existing literature. For example, previous studies did not note that stop codas /-p, -k/ are in free variation with glides [-w, -j, ɰ], and that the morpheme, rather than the syllable, is the tone-bearing unit. Acoustic analyses provide justification for the phonemic representation of the vowels and the tones. Finally, the paper is designed to introduce Tawrã speakers to the recently standardized (2020) orthography, and to show how the letters and letter combinations function together as a system.
Concept of development of professional pedagogical and legal competence of a future elementary school teacher

Випуск 77'2020Серія 5. Педагогічні науки: реалії та перспективи of determining the nature, content and structural components of professional readiness of future physical education teachers to formation patriotic values of student youth.The author's definition of the concept «patriotic values of student youth» is given.The author's definition of the concept «professional readiness of future physical education teachers for the formation of patriotic values of student youth» as a stable personal quality, characterized by the presence of relevant theoretical knowledge and practical skills (gnostic, projective, constructive, organizational, communicative), and also the presence of students' strong motivation to form patriotic values of student youth in the process of physical culture, high degree of psychological stability, conviction in the need for professional self-improvement and self-realization is proposed.The structure of professional readiness of future physical education teachers for the formation of patriotic values of student youth through a set of relevant components -motivational-volitional, cognitive, procedural is characterized.It is noted that: motivational and volitional component of professional readiness is characterized by the formation of future teachers of physical culture of a high level of patriotism, deep awareness of the importance of forming patriotic values of student youth on the basis of modern methodological, conceptual and methodological and practical developments, based on which an important aspect is the opposition to the aggressor country (namely, all manifestations of the hybrid war unleashed by the Russian Federation against Ukraine); the cognitive component presupposes that future physical education teachers have a set of knowledge, in particular about the purpose, tasks, principles, content and features of the formation of patriotic values of student youth, etc.; procedural component is characterized by the presence of future teachers of physical culture a set of practical skills on the maximum use of physical culture opportunities for the formation of patriotic values of student youth.