Reviewerly/exHarmony huggingface dataset:
Integration of didactic concepts in using of multi-level mobile computer books of problems in teaching the “Logic” section of school curricula

Problem and goal. The article actualizes the issues of searching for didactic-methodological bases and their integration in teaching of the “Logic” section within the framework of school curricula of computer science. The goal of the described research is an attempt to consider the content of the discipline and teaching using the multi-level mobile computer books of problems developed in the course of study as a didactic-methodological factor. Methodology. Implementation in the school educational process on computer science of mechanisms that contribute to the maximum immersion of students in the practice-oriented and professionalcontextual field through the use of multi-level mobile computer books of problems. Result. The research found that the use of multi-level mobile computer-based books of problems concerning the provisions of modern didactic concepts in the methodology of teaching computer science provides the opportunity to access education anytime and anywhere. In this, the approach based on the use of multi-level mobile computer books of problems serves as the most efficient means of developing students’ logical, analytical and critical thinking, since their content is based on multilevel basic knowledge of logic and computer science. Conclusion. The research shows that the use of multi-level mobile computer books of problems in teaching logic in the course of computer science at the stage of school education makes it possible to effectively implement didactic-methodological synthesis, making the educational process practiceoriented and professional-contextual.
Action Graphs for Performing Goal Recognition Design on Human-Inhabited Environments

Goal recognition is an important component of many context-aware and smart environment services; however, a person's goal often cannot be determined until their plan nears completion. Therefore, by modifying the state of the environment, our work aims to reduce the number of observations required to recognise a human's goal. These modifications result in either: Actions in the available plans being replaced with more distinctive actions; or removing the possibility of performing some actions, so humans are forced to take an alternative (more distinctive) plan. In our solution, a symbolic representation of actions and the world state is transformed into an Action Graph, which is then traversed to discover the non-distinctive plan prefixes. These prefixes are processed to determine which actions should be replaced or removed. For action replacement, we developed an exhaustive approach and an approach that shrinks the plans then reduces the non-distinctive plan prefixes, namely Shrink-Reduce. Exhaustive is guaranteed to find the minimal distinctiveness but is more computationally expensive than Shrink-Reduce. These approaches are compared using a test domain with varying amounts of goals, variables and values, and a realistic kitchen domain. Our action removal method is shown to increase the distinctiveness of various grid-based navigation problems, with a width/height ranging from 4 to 16 and between 2 and 14 randomly selected goals, by an average of 3.27 actions in an average time of 4.69 s, whereas a state-of-the-art approach often breaches a 10 min time limit.
Recognition and reconstruction of liquid crystal display monitor electromagnetic emanation signals

Computer electronic equipment will emit unintentional electromagnetic (EM) signals in the working state, especially liquid crystal display (LCD) monitors (desktop and laptop). The monitor emit unintentional EM signals across the Radio Frequency (RF) spectrum making it error-prone, time consuming, a nd difficult to find the relatively few emanations that obtain hidden information. In this paper, the characteristics of emitted signals which emanations by LCD monitor is verified by analyzing display mechanism, the EM emanations are received and reconstructed by the eavesdropping experiment. According to the principle of LCD, the study shows that refresh rate, horizontal frequency, and pixel frequency are confirmed as key factors to reconstruct image of LCD compromising emanations (CE). In addition, this study promotes the reconstitution quality of LCD CE based on wavelet transform spatial correlation filtering denoising method. Experimental results show that the method can effectively suppress noise and improve the reconstruction quality of weak leakage signal.
Efficiently Approximating Edit Distance Between Pseudorandom Strings

We present an algorithm for approximating the edit distance ed(x, y) between two strings x and y in time parameterized by the degree to which one of the strings x satisfies a natural pseudorandomness property. The pseudorandomness model is asymmetric in that no requirements are placed on the second string y, which may be constructed by an adversary with full knowledge of x.We say that x is (p, B)-pseudorandom if all pairs a and b of disjoint B-letter substrings of x satisfy ed(a, b) ≥ pB. Given parameters p and B, our algorithm computes the edit distance between a (p, B)-pseudorandom string x and an arbitrary string y within a factor of O(1/p) in time Õ(nB), with high probability. If x is generated at random, then with high probability it will be (Ω(1), O(log n))-pseudorandom, allowing us to compute ed(x, y) within a constant factor in near linear time. For strings x of varying degrees of pseudorandomness, our algorithm offers a continuum of runtimes.Our algorithm is robust in the sense that it can handle a small portion of x being adversarial (i.e., not satisfying the pseudorandomness property). In this case, the algorithm incurs an additive approximation error proportional to the fraction of x which behaves maliciously.The asymmetry of our pseudorandomness model has particular appeal for the case where x is a source string, meaning that ed(x, y) will be computed for many strings y. Suppose that one wishes to achieve an O(α)-approximation for each ed(x, y) computation, and that B is the smallest block-size for which the string x is (1/α, B)-pseudorandom. We show that without knowing B beforehand, x may be preprocessed in time , so that all future computations of the form ed(x, y) may be O(α)-approximated in time Õ(nB). Furthermore, for the special case where only a single ed(x, y) computation will be performed, we show how to achieve an O(α)-approximation in time Õ(n4/3 B2/3).
Research of Ant Colony Algorithm Based on Multi-objective Optimization in Cloud Platform Virtual Machine Initialization

Abstract In order to solve the problem of resources waste in the process of virtual machine deployment in the cloud platform, we propose an ant colony algorithm based on multi-objective optimization. Firstly, the multi-objective optimization combination model is established. Then, we apply the model to ant colony algorithm. Finally, we modify the ant colony algorithm to adapt to cloud platform virtual machine initialization. In order to verify the effectiveness of the algorithm, cloudsim cloud simulation platform was used to carry out simulation experiments. The experimental results showed that the algorithm proposed in this paper could reach a relatively good level in SLA violation rate, power consumption, and resource loss of cloud platform compared with other commonly used algorithms.
Towards automatic problem detection in web navigation based on client-side interaction data

Nowadays the importance of digital competences is unarguable, and specially for people with functional diversity. On the other hand, the website should adapt to the user necessities automatically. This work focuses on the latter, in detecting navigation problems automatically. Firstly, the device used by the user will be detected by proposing two level hierarchy of supervised classifiers that divides in different levels errors of different criticality. Afterwards, in order to detect problems automatically, clustering algorithms and anomaly detection have been used, finding for each device a set of potential problems' indicators automatically. Moreover, the effect of cursor's adaptions in different problems has been analyzed.
Rancang Bangun Sistem Informasi Manajemen Aset Pada Universitas Pamulang Berbasis Web

Pamulang University is a successful and growing private university located in South Tangerang City. Of course, University of Pamulang has many assets to support its activities. But currently the University of Pamulang does not yet have an internal information system to control the assets owned. There is no asset management information system that will certainly experience difficulties in tracking asset data. Solution to overcome existing problems, of course, it requires an asset management information system that can run asset management more efficiently and structured, and facilitate the data collection of assets, data collection of assets based on their conditions, grouping assets based on their types and making it easier to search asset data. The system development used is waterfall. This model consists of requirement analysis, system and software design, implementation and unit system, system testing, operation and maintenance. The tools used in this research are Windows 10 pro 64 bit operating system, sublime text 3, and the database used as data storage is XAMPP. The final results of this research resulted in an Asset Management Information System at the Web-Based University of Pamulang.
Real-time active underwater polarization descattering

Underwater imaging’s promising but challenging. This paper addresses a method to realize real-time underwater imaging by estimating DOP of backscatter automatically based upon polarization signatures of images. The results demonstrate the achievability of video processing.
Cross-Corpus Training with CNN to Classify Imbalanced Biomedical Relation Data

Information extraction from unstructured text is a challenging task which demands automation. Relation extraction is an important sub-task of information extraction and it is usually modeled as a classification problem. In the field of biomedicine, relation extraction helps in improving the health-care system and also in manufacturing safer drugs. But lack of a huge single annotated corpus for all biomedical relation types and the presence of class imbalance problem hinders most of the classifier's performance. For this reason, cross-corpus training of a deep-learning model namely the convolutional neural network (CNN) is carried out with annotated corpora developed for different biomedical relation types. Before modeling the CNN, SMOTE, an oversampling technique is used to balance the dataset. The input to the CNN is a concatenated feature embedding vector of a sentence obtained from six distinct features. From the six distinct features, word context and dependency context primarily contributed to the system's performance. Three types of biomedical relations namely drug-drug, drug-adverse effect and drug-disease are handled in this work. The experimental results showed that training an algorithm with a balanced dataset gives better results than using an imbalanced dataset. Additionally, use of cross-corpus training improves the relation classification task's performance for annotated datasets that are limited in size.
Simulating unmanned aerial vehicle flight control and collision detection

An unmanned aerial vehicle (UAV) is a small, fast aircraft with many useful features. It is widely used in military reconnaissance, aerial photography, searches, and other fields; it also has very good practical-application and development prospects. Since the UAV’s flight orientation is easily changeable, its orientation and flight path are difficult to control, leading to its high damage rate. Therefore, UAV flight-control technology has become the focus of attention. This study focuses on simulating a UAV’s flight and orientation control, and detecting collisions between a UAV and objects in a complex virtual environment. The proportional-integral-derivative control algorithm is used to control the orientation and position of the UAV in a virtual environment. A version of the bounding-box method that combines a grid with a k-dimensional tree is adopted in this paper, to improve the system performance and accelerate the collision-detection process. This provides a practical method for future studies on UAV flight position and orientation control, collision detection, etc.
An Extension of Newton Gravity Formula Considering the Universe Expansion Effects

The Universe expansion and gravity are perhaps the main factors that determine the Universe evolution at global scale.The expansion of the Universe seen as a sphere whose frontier is moving at the speed of light in vacuum ( V c ) increases the distances among bodies similarly to a dilatation process.If one studies the expansion effects on bodies in interaction, an interesting question is whether the configuration of these bodies is maintained such that the distances among them are proportionally modified.For example, it is known that the Solar planetary system has the same configuration for billion of years.Then, by applying the mechanical conservation laws, one may obtain important conclusions.Here we show that one of such important consequences is the dependence of the terms in Newton expression of the gravity force (including the universal coefficient of gravity) on the age of the Universe.An expression for the Hubble coefficient as a function of the age of Universe and a suggestion for the value of the actual age of Universe are also given.
Small Traffic Sign Detection and Recognition in High-Resolution Images

Traffic sign detection and recognition is a research hotspot in the computer vision and intelligent transportation systems fields. It plays an important role in driver-assistance systems and driverless operation. Detecting signs, especially small ones, remains challenging under a variety of road traffic conditions. In this manuscript, we propose an end-to-end deep learning model for detecting and recognizing traffic signs in high-resolution images. The model consists of basic feature extraction and multi-task learning. In the first part, a network with fewer parameters is proposed, and an effective feature fusion strategy is adopted to gain a more distinct representation. In the second part, multi-task learning is conducted on different hierarchical layers by considering the difference between the detection and classification tasks. The detection results on two newly published traffic sign benchmarks (Tsinghua-Tencent 100K and CTSD) demonstrate the robustness and superiority of our model.
Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks

We report our ongoing work about a new deep architecture working in tandem with a statistical test procedure for jointly training texts and their label descriptions for multi-label and multi-class classification tasks.A statistical hypothesis testing method is used to extract the most informative words for each given class.These words are used as a class description for more label-aware text classification.Intuition is to help the model to concentrate on more informative words rather than more frequent ones.The model leverages the use of label descriptions in addition to the input text to enhance text classification performance.Our method is entirely data-driven, has no dependency on other sources of information than the training data, and is adaptable to different classification problems by providing appropriate training data without major hyper-parameter tuning.We trained and tested our system on several publicly available datasets, where we managed to improve the state-of-the-art on one set with a high margin, and to obtain competitive results on all other ones.
CAUSATIVE CONSTRUCTION OF ACEH LANGUAGE: TYPOLOGY APPROACH

This study discusses causative construction in Acehnese language typologically. The problems studied were (1) the type of causative construction of the Aceh language typologically, (2) the relationship of the causative construction of Aceh language in typology, and (3) structures that establish the causative construction of Acehnese language. Research data, both oral and written, were obtained by speaking and listening methods. All data are examined by the identity and distributional methods. The results of the study showed that typologically the causative construction of Aceh language consists of morphological causative, lexical causative and analytic causative. Aceh language causative construction in this paper is investigated through parameters of semantics and formal. This construction was produced by combination of clause and conjunction ‘kerna’ or ‘seubap’; using analytical causative verb ‘peugot’ and ‘geuyu’; morphological causative affixes ‘meu-‘, ‘peu-‘ and ‘seu-‘; and using certain lexical causative in which have causative meaning. The relationship of the causative construction of the Aceh language in a syntactic typology, namely causative morphology and lexical causative was included in the causative construction of monoclauses, whereas analytic causative was included in the causative construction of biclauses. Structurally, testing is done by pinning syntactic operations, hence causative analytic was formed by biclauses structures, while lexical and morphological causative was formed by monoclausal structures.
A Comprehensive Collaborating Filtering Approach using Extended Matrix Factorization and Autoencoder in Recommender System

Recommender system is an approach where users get suggestions based on their previous preferences.Nowadays, people are overwhelmed by the huge amount of information that is being present in any system.Sometimes, it is difficult for a user to find an appropriate item by searching the desired content.Recommender system assists users by providing suggestions of required information or items based on the similar features among the users.Collaborative filtering is one of the most re-known process of recommender system where the recommendation is done by similar users or similar items.Matrix factorization is an approach which can be used to decompose a matrix into two or more matrix to generate features.Again, autoencoder is a deep learning based technique which is used to find hidden features of an object.In this paper, features are calculated using extended matrix factorization and autoencoder and then a new similarity metric has been introduced that can calculate the similarity efficiently between each pair of users.Then, an improvement of the prediction method is introduced to predict the rating accurately by using the proposed similarity measure.In the experimental section, it has been shown that our proposed method outperforms in terms of mean absolute error, precision, recall, f-measures, and average reciprocal hit rank.
An Advanced Mechanism for Software Size Estimation Using Combinational Artificial Intelligence

Software Size Estimation is the most essential and crucial calculations of the Software Development Life Cycle (SDLC) process.If done wisely, it may accumulate a large amount and time and if done badly, it may cost a lot of amounts.In this modern era of development, traditional methods of estimation do not stand a chance to provide output precisely.Constructive Cost Model (COCOMO)-2 is one of finest calculation methods for size and cost.This paper presents an automated early size estimation technique using Artificial Intelligence (AI).The categorization of the size estimation is into three parts, that is, UML diagrams/code folder mapping via COCOMO-2, COCOMO-2 metrics training, and the classification process to have an appropriate size.This paper focused on regression based training of AI which makes the estimation model more precise.For the evaluation, Mean Square Error (MSE) and Size estimation have been considered according to project samples.It has been seen that the proposed mechanism has attained a minimum MSE of 0.0115 and the difference in sizes is also not high.The comparison has also been done to depict the efficacy of the proposed work with A. B. Nassif et al. and S. Lohmor with B.B. Sagar for True positive rate and Mean square error.
Aplikasi Android Contruct 2 untuk Media E-Learning pada Materi Peluang

This study aims to produce an Android-based E-Learning learning media on opportunity material. This research method is Research and Development (R &amp; D) based on Sugiyono's development model. The material expert's assessment of the learning media is included in the "Good" category with an average percentage of 79.05%. The media expert's assessment of the learning media is included in the "Very Good" category with an average percentage of 87.67%, the assessment of education practitioners on this learning media is included in the "Very Good" category with a percentage of completeness of 82.57%. In a small scale trial which was attended by 10 students in class XI obtained a percentage of 81.60%. Based on the results of the questionnaire responses that have been filled in by students, these results place the learning media on the criteria of "Very Interesting". Based on the results of a large-scale trial which was attended by 30 students of class XI, the percentage of 88.78% was included in the criteria of "Very Interesting", and the results of the testing of the effectiveness of learning media were 88.33% included in the criteria of "Very Effective".
Probabilistic Rewriting and Asymptotic Behaviour: on Termination and Unique Normal Forms

While a mature body of work supports the study of rewriting systems, abstract tools for Probabilistic Rewriting are still limited. In this paper we study the question of uniqueness of the result (unique limit distribution), and develop a set of proof techniques to analyze and compare reduction strategies. The goal is to have tools to support the operational analysis of probabilistic calculi (such as probabilistic lambda-calculi) where evaluation allows for different reduction choices (hence different reduction paths).