Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Secure Browser for Computer-Based Tests

- This work is concerned with the development of a browser for computer based examination platforms. It focused on trends and mostly used browsers for online computer based examination and carried out a critical review of the browsers used for current computer based test system employed in Nigeria Universities. An alternative application to provide solutions to the current challenges identified in the existing system was then developed. The new application was designed using the Extreme programming methodology and implemented using Microsoft Visual Basic on a Windows 7 system. We created user-friendly interfaces driven by the Visual Basic codes. The application has amongst other features, disabled access to control box of the browser window and the Alt + F4 button as shortcut in closing browsers
The word problem for double categories

We solve the word problem for free double categories without equations between generators by translating it to the word problem for 2-categories. This yields a quadratic algorithm deciding the equality of diagrams in a free double category. The translation is of interest in its own right since and can for instance be used to reason about double categories with the language of 2-categories, sidestepping the pinwheel problem. It also shows that although double categories are formally more general than 2-categories, they are not actually more expressive, explaining the rarity of applications of this notion.
Pemodelan Curah Hujan-Limpasan Pada Sub DAS Cikapundung Hulu

The Cikapundung River is one of the rivers that divides the city of Bandung and is often used as the main drainage center of the city, the flow of sewage or waste, tourism objects, and raw water sources. This causes the importance of observing reliable discharge values flowing on the Cikapundung River. In this study the authors analyzed the availability of minimum river discharge in Cikapundung Hulu Sub Das using the FJ Mock method. This study uses rainfall data and climatology data. The rainfall data used are rainfall data from Dago Pakar station and Meteo Lembang station, with a span of 10 years while the climatological data used is data from the Bandung Geophysical Station with a span of 10 years. Mainstay discharge calculated using the F.J Mock Method, where in the F.J Mock calculation there are a number of parameters assumed. This modeling test process is by calibrating the parameters assumed by Trial and Error. The average discharge magnitude for Q50, Q80, Q90, and Q95 in calculating the mock model in the period 2008 to 2014 resulted in a value of 3.87 m³ / sec; 2.98 m³ / sec; 2.46 m³ / sec; and 2.21 m³ /sec. While the discharge values of Q50, Q80, Q90, and Q95 in the observation debit produce successive values of 3.85 m³ / sec; 2.45 m³ / sec; 2.05 m³ / sec; and 1.91 m³ / sec. Even with the increase in the calculation period, reliable debit values have a tendency to rise.
Optimizing Energy Efficiencies in Cloud Data Center Resources with Availability Constraints

Cloud infrastructure Resources hosted in Data Centers, support the effective execution of Cloud computing applications. Given the increased adoption of the Cloud Computing Applications and the Businesses getting to be Data-driven, there is a huge increase in the number of Data Centers and the Size and amount of resources hosted in these Data Centers. These Data Center resources consume a significant amount of energy and this continuous scaling of the resources is leading to increased power consumption and a large carbon footprint. Given our fragile eco-system, optimization of the Data Center resources for energy conservation and thus the carbon footprint is the primary area of our focus. Businesses also need to satisfy QoS guarantees on Availability to their customers. Optimization towards Energy efficiencies may compromise on the Availability and thus may warrant a trade-off, and a need for them to be considered together. Although there have been numerous studies towards Energy efficiencies, most of them have been focused on only energy. In this paper, we initially segregate Optimization activities towards the Data Center resources like Compute, Network, and Storage. We then study the different control parameters or approaches which will lead to meeting the objectives of Energy Efficiencies, Availability and Energy Efficiency constrained with Availability. Thus, this will support the selection of approaches for the optimization of energy while meeting the QoS Availability requirement.
AN ANALYSIS ON THE ATAMBUA STUDENTS’ MULTILINGUAL BACKGROUND AND THEIR ENGLISH ORAL PROFICIENCY

Multilingualism is the ability of someone to operate more than one language for communication. Atambua people are multilingual since they have more than one language background, it make them difficult in speaking English. This study aims to Analyze the English Oral Proficiency (EOP) of the Atambua students with multilingual background. It is a descriptive qualitative research involving multilingual Atambua students who also speak English. 10 out of 200 as the researcher participants are selected through questionnaire develop by the researcher to know their multilingual background. EOP in form of monologue was employed to the students to know their level using scoring rubric by Brown (2001). Then, analysis was done to see the relation of their level of EOP with their number of language useand age of their English acquisition or learning. The result of this study showed that 2 students with 3 languages use have 3,0 and 3+ levels, (able to participate effectively in formal and informal situation, as well as the professional topics). 5 students with 4 languages use have various level, ranging 1+ (able to satisfy limited social demands) to the 3+ level. 1 student with 5 languages was in level 1+, and 2 students matering 6 languages were in level 1+ and level 2 (able to satisfy routine social demands). 2 students learned English before 10 year-old, and their EOP level was 3+. 8 other students learned English in their 11 up to 14 year-old. Interesting phenomenon found that only 1 out of 8 students with with 3 languages use get the highest level (3) even she learn English over golden age. It indicates that the result of the research is in line with the previous theory stated that multilingual person will face difficult in learning English, Bialystok( 2001).
THE USE OF THINK PAIR SHARE TECHNIQUE IN TEACHING SPEAKING

The aim of this research was to describe the use of Think Pair Share technique in teaching speaking at the eighth grade of a Junior High School in Bandung. Think Pair Share is a part of cooperative learning strategies that enables the students work together in a small group. The research was conducted using qualitative research method involving 25 students as the research subjects. The data were gathered from observation and questionnaire. The result of the data analysis indicated that the students’ experience after they were taught by using Think Pair Share has shown their excitement towards the teaching process as described. There are good implications in speaking performance after learning by Think Pair Share technique. Think Pair Share technique in this case can be one of the alternative teaching techniques used by the English teacher in teaching English, especially speaking. Keywords: Speaking, Think Pair Share
Distinguishing True and Fake News by Using Text Mining and Machine Learning Algorithm

With recent advancements in social media and technology as a whole, online news sources have increased.Therefore there has been a higher demand of people wanting a convenient way to find recent, relevant and updated online news articles and posts from social media platforms.In the current status quo, many people feel comfortable with their main source of news being social media articles.Unfortunately, receiving news via social media platforms and unverified online sites has aroused many problems, one of which being fake news (news which contain incorrect or biased facts and statements).Many individuals all around the world are vulnerable and subject to fake news and becoming victims of propaganda and/or being misinformed.To solve this world-wide complication, we used word preprocessing skills to digest the content of articles, and used several mathematical vectors to pinpoint the legitimacy of a news article.To establish an accurate system, words used in examples of fake news and real news were collected using Python.Verifying fake and real news is an important process that all news should go through as it can result in immense consequences.Data on real news and fake news were collected from Kaggle.We had the conclusion that the trained machine learning algorithms showed high accuracy of distinguishing which indicates our research was successful.
Dealing with Incompatibilities among Procedural Goals under Uncertainty

During the first step of practical reasoning, i.e. deliberation, an intelligent agent generates a set of pursuable goals and then selects which of them he commits to achieve. An intelligent agent may in general generate multiple pursuable goals, which may be incompatible among them. In this paper, we focus on the definition, identification and resolution of these incompatibilities. The suggested approach considers the three forms of incompatibility introduced by Castelfranchi and Paglieri, namely the terminal incompatibility, the instrumental or resources incompatibility and the superfluity. We characterise computationally these forms of incompatibility by means of arguments that represent the plans that allow an agent to achieve his goals. Thus, the incompatibility among goals is defined based on the conflicts among their plans, which are represented by means of attacks in an argumentation framework. We also work on the problem of goals selection; we propose to use abstract argumentation theory to deal with this problem, i.e. by applying argumentation semantics. We use a modified version of the "cleaner world" scenario in order to illustrate the performance of our proposal.
Dialogue state tracking accuracy improvement by distinguishing slot-value pairs and dialogue behaviour

&lt;div&gt;&lt;p&gt;Dialog state tracking (DST) plays a critical role in cycle life of a task-oriented dialogue system. DST represents the goals of the consumer at each step by dialogue and describes such objectives as a conceptual structure comprising slot-value pairs and dialogue actions that specifically improve the performance and effectiveness of dialogue systems. DST faces several challenges: diversity of linguistics, dynamic social context and the dissemination of the state of dialogue over candidate values both in slot values and in dialogue acts determined in ontology. In many turns during the dialogue, users indirectly refer to the previous utterances, and that produce a challenge to distinguishing and use of related dialogue history, Recent methods used and popular for that are ineffective. In this paper, we propose a dialogue historical context self-Attention framework for DST that recognizes relevant historical context by including previous user utterance beside current user utterances and previous system actions where specific slot-value piers variations and uses that together with weighted system utterance to outperform existing models by recognizing the related context and the relevance of a system utterance. For the evaluation of the proposed model the WoZ dataset was used. The implementation was attempted with the prior user utterance as a dialogue encoder and second by the additional score combined with all the candidate slot-value pairs in the context of previous user utterances and current utterances. The proposed model obtained 0.8 per cent better results than all state-of-the-art methods in the combined precision of the target, but this is not the turnaround challenge for the submission.&lt;/p&gt;&lt;/div&gt;
Capturing the dynamic target by the robot manipulator in high-dimensional configuration space map

In robotics, the tough problem about the dynamic target capturing consists of tracking the target by the robot manipulator and grasping the target by the robot finger. For the sake of space, this article deals with only the first problem, tracking the dynamic target by the robot manipulator. The traditional approaches of capturing the dynamic target may work well when they are employed in low-dimensional space by reinforcement learning or physical modeling. However, they fail to work well in high-dimensional space. The traditional approaches have four limitations with respect to Cartesian space, configuration space, reinforcement learning, and physical modeling. To overcome these limitations, this article implements improved dynamic A* algorithm in high-dimensional configuration space map to capture the target. First, a space injection model injects the collision detection and target position from the Cartesian space into the configuration space to construct a high-dimensional map. Then, the target capturing method including the improved dynamic A* algorithm is applied on the map to track and capture the target. Finally, the experiment performed in time-varying environment and the dynamic target achieves a reliable result. This article has proposed an approach that makes the robot manipulator motion planning more accurate in high-dimensional dynamic configuration space. This approach enables the multi-joint manipulator to avoid the obstacle while tracking the target in high-dimensional configuration space. It takes the advantages of heuristic algorithms in the process of target capturing method designing. It adds precision and speed to target tracking. The success of the approach may apply to any industrial robot tracking target, surgical operation, and space probes. And, it may lay a solid foundation for dynamics control with a scope for future investigations.
Projeto de uma Antena Planar Dipolo Curvado para Aplicação na Comunicação da Indústria 4.0

This article contemplates a qualitative analysis of the operation of a Curved Planar Dipole Antenna, of the line meander type and its applicability in the Internet of Things (IoT). Technology originated in the midst of the intelligent technological development of the current period, considered as the Fourth Industrial Revolution or Industry 4.0 and marked by the improvement of systems that promote automation of tasks, control and data sharing in wireless networks in real time. IoT is associated with facilitated connectivity between the physical and digital world through the internet or miniaturized elements, generating optimization in the manufacturing and routine processes. Particularly, planar antennas stand out in this regard, as they provide the transmission or reception of signals by means of electromagnetic waves, have low manufacturing cost, small volume and weight and are built according to the desired purpose. Thus, the proposed objective was to design, simulate and analyze a meander line antenna that operates in the 2.4GHz ISM (Industrial, Scientific and Medical) frequency range, verifying its effectiveness in the IoT. However, with the aid of the HFSS® software, satisfactory results regarding the resonance frequency and radiation diagram of the studied device are obtained in a computer simulation environment.
Similarity-preserving hash for content-based audio retrieval using unsupervised deep neural networks

Due to its efficiency in storage and search speed, binary hashing has become an attractive approach for a large audio database search. However, most existing hashing-based methods focus on data-independent scheme where random linear projections or some arithmetic expression are used to construct hash functions. Hence, the binary codes do not preserve the similarity and may degrade the search performance. In this paper, an unsupervised similarity-preserving hashing method for content-based audio retrieval is proposed. Different from data-independent hashing methods, we develop a deep network to learn compact binary codes from multiple hierarchical layers of nonlinear and linear transformations such that the similarity between samples is preserved. The independence and balance properties are included and optimized in the objective function to improve the codes. Experimental results on the Extended Ballroom dataset with 8 genres of 3,000 musical excerpts show that our proposed method significantly outperforms state-of-the-art data-independent method in both effectiveness and efficiency.
Sistem Pakar untuk Mendiagnosis Gangguan Mental pada Anak Menggunakan Metode Forward Chaining dan Certainty Factor

Mental health problems in children, such as stress, anxiety, or depression are real and these problems are as important as physical health problems, but many children do not get the care they need so an artificial intelligence technology such as an expert system can help diagnose the disorder mentally in children early. This research developed an expert system to diagnose mental disorders in children using the mobile-based Forward Chaining and Certainty Factor method that diagnoses 7 types of mental disorders in children from 46 symptoms obtained based on the knowledge of 3 experts. Based on the results of the black box testing states that the expert system that was built has had compatibility in terms of functionality. The results of the theoretical calculation test states that the calculation system of an expert diagnosis of a child's mental disorder is in accordance with the results of calculations done manually. In testing the accuracy of the system from 30 cases tested on 3 experts, the expert system that was built succeeded in producing system accuracy values ​​based on the final belief value of the three experts, based on the belief values ​​of experts 1, 2, and 3, respectively 91.11%, 96.67%, 96.67% and 80%. Furthermore, the results of MOS testing conducted on 35 respondents produce MOS of 4.12 from a scale of 5 where based on that value the expert system built is categorized into a good system.
CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models

Given access to accurate dynamical models, modern planning approaches are effective in computing feasible and optimal plans for repetitive robotic tasks. However, it is difficult to model the true dynamics of the real world before execution, especially for tasks requiring interactions with objects whose parameters are unknown. A recent planning approach, CMAX, tackles this problem by adapting the planner online during execution to bias the resulting plans away from inaccurately modeled regions. CMAX, while being provably guaranteed to reach the goal, requires strong assumptions on the accuracy of the model used for planning and fails to improve the quality of the solution over repetitions of the same task. In this paper we propose CMAX++, an approach that leverages real-world experience to improve the quality of resulting plans over successive repetitions of a robotic task. CMAX++ achieves this by integrating model-free learning using acquired experience with model-based planning using the potentially inaccurate model. We provide provable guarantees on the completeness and asymptotic convergence of CMAX++ to the optimal path cost as the number of repetitions increases. CMAX++ is also shown to outperform baselines in simulated robotic tasks including 3D mobile robot navigation where the track friction is incorrectly modeled, and a 7D pick-and-place task where the mass of the object is unknown leading to discrepancy between true and modeled dynamics.
A QUALITATIVE STUDY: TEACHER-STUDENT INTERACTIONS IN A JUNIOR HIGH SCHOOL CLASSROOM IN BATAM CITY

This study aims to elaborate on the classroom interactions between an English teacher and seventh-grade students SMP Theodore Batam in teaching-learning English. This study employed a descriptive qualitative study on teacher’s instructions and teacher-student interactions. To analyze the data, some data collection steps were carried out. The researchers used observation, fieldnote and, interview to obtain the data. The results illustrated that the teacher actively involved the students to engage in the learning process. The classroom activities were conducted without any significant problems because the teacher implemented a teaching approach to make the material easier to be learned and the students were cooperative in joining the lesson. However, it was found that most of the interactions were set up by the teacher. It is important to find a suitable approach and teaching method, so the students will be able to show more confidence in initiating interaction. Furthermore, the teacher should set good time management in applying a specific teaching approach.
Phototropism rapidly exploring random tree: An efficient rapidly exploring random tree approach based on the phototropism of plants

Inspired by the phototropism of plants, a novel variant of the rapidly exploring random tree algorithm as called phototropism rapidly exploring random tree is proposed. The phototropism rapidly exploring random tree algorithm expands less tree nodes during the exploration period and has shorter path length than the original rapidly exploring random tree algorithm. In the algorithm, a virtual light source is set up at the goal point, and a light beam propagation method is adopted on the map to generate a map of light intensity distribution. The phototropism rapidly exploring random tree expands its node toward the space where the light intensity is higher, while the original rapidly exploring random tree expands its node based on the uniform sampling strategy. The performance of the phototropism rapidly exploring random tree is tested in three scenarios which include the simulation environment and the real-world environment. The experimental results show that the proposed phototropism rapidly exploring random tree algorithm has a higher sampling efficiency than the original rapidly exploring random tree, and the path length is close to the optimal solution of the rapidly exploring random tree* algorithm without considering the non-holonomic constraint.
Named Entity Recognition Based on Character-level Language Models and Attention Mechanism

As a basic task in the field of natural language processing, named entity recognition plays an important role in text data processing tasks. Extracting features from the original text can be considered as the first step in the identification of named entities, but on this basic issue, traditional research still stays at the coarser granularity of words. Unlike traditional research, this paper focuses on finer granularity-character-level named entity recognition research. In order to fully extract the character-level feature representation from the character-level language model, this paper uses CNN and BiLSTM to perform feature extraction together, and introduces the attention mechanism to achieve more effective combination of character features and word features, then combines with BiLSTM-CRF to construct a complete end-to-end deep learning model (At- BiLSTM-CNNs-CRF). The experimental results show that its recognition ability exceeds most deep learning models.