Reviewerly/exHarmony huggingface dataset:
Minimizing FLOPs to Learn Efficient Sparse Representations

Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification. Retrieval of such representations from a large database is however computationally challenging. Approximate methods based on learning compact representations, have been widely explored for this problem, such as locality sensitive hashing, product quantization, and PCA. In this work, in contrast to learning compact representations, we propose to learn high dimensional and sparse representations that have similar representational capacity as dense embeddings while being more efficient due to sparse matrix multiplication operations which can be much faster than dense multiplication. Following the key insight that the number of operations decreases quadratically with the sparsity of embeddings provided the non-zero entries are distributed uniformly across dimensions, we propose a novel approach to learn such distributed sparse embeddings via the use of a carefully constructed regularization function that directly minimizes a continuous relaxation of the number of floating-point operations (FLOPs) incurred during retrieval. Our experiments show that our approach is competitive to the other baselines and yields a similar or better speed-vs-accuracy tradeoff on practical datasets.
Kontribusi Perilaku Game Online Terhadap Motivasi Belajar siswa SMA

The online gaming behaviour referred to in this study is an individual response or reaction to the stimulation of online games or games connected via the Internet network. One factor that can influence the motivation to learn is the external factor or environmental factors that are parents, with the social support of parents then students should students have a high motivation to learn. The purpose of this research is to know the contributions of the behavior of online gaming to the learning motivation of high school students. This research is quantitative research with correlational design. The subject of this research was Shalahuddin high school student 94 students. The techniques used in sampling are total sampling techniques or saturated samples. The measuring instrument used in this study uses two scales of the scale of online gaming behaviour and learning motivation. The results showed that there was a link between the behavior of online gaming with the motivation of learning students (P = 0.000 > 0.05, r =-0.432) that is, there is a negative relationship between online gaming behaviour and the students ' learning motivation so that more students Spend time playing games online then the motivation of learning is getting lower.
A communicative robot to learn about us and the world

We describe a model for a robot that learns about the world and her com-panions through natural language communication. The model supports open-domain learning, where the robot has a drive to learn about new con-cepts, new friends, and new properties of friends and concept instances. The robot tries to fill gaps, resolve uncertainties and resolve conflicts. The absorbed knowledge consists of everything people tell her, the situations and objects she perceives and whatever she finds on the web. The results of her interactions and perceptions are kept in an RDF triple store to enable reasoning over her knowledge and experiences. The robot uses a theory of mind to keep track of who said what, when and where. Accumulating knowledge results in complex states to which the robot needs to respond. In this paper, we look into two specific aspects of such complex knowl-edge states: 1) reflecting on the status of the knowledge acquired through a new notion of thoughts and 2) defining the context during which knowl-edge is acquired. Thoughts form the basis for drives on which the robot communicates. We capture episodic contexts to keep instances of objects apart across different locations, which results in differentiating the acquired knowledge over specific encounters. Both aspects make the communica-tion more dynamic and result in more initiatives by the robot
A cashless payment transaction (CPaT) using RFID technology

&lt;span&gt;Virtual money is an actual money that a person own but in a form of a plastic card, an online account and a token or a ticket. Referring to the cash-base system, it is full of fraud and money scam activity. It even happens in a smaller division of a human population which are among family members. For example, when a child decided to do a money scam towards their parents by asking for extra money for what they actually needed to pay for school internal fee and with today’s technology, they can easily recreate a receipt of the payment based on the amount of money they have scammed. In this Cashless Payment Transaction (CPaT) in a school based on Low Frequency (LF) RFID Technology system, the system offers a virtual money transaction in the plastic card form and allows the transaction to be monitored by parents with the help of Radio Frequency Identification (RFID) technology, MySQL database and Email notification system. The MySQL database is paired with the PHP prgramming language where the calculation for deduction credit will be done before being updated to the RFID card and MySQL database. As a result, the system will allow a cash-free transaction for school payment activity and allow parents to monitor their child spending activity and nutrition intake via MySQL database that are designed based on Graphical User Interface (GUI) within one minute after the transaction is successfully done.&lt;/span&gt;
The estimation of efficiency of the university innovation activity: systems of indicators and bibliometric approach

The development of new forms of science and innovation at universities, as well as the interaction of higher education institutions with regional participants of the innovation system raises the question of evaluating the effectiveness of innovation as an independent phenomenon. Correct assessment of innovation activity helps the university to determine its strategic advantages, which is necessary in today’s conditions of tough competition, uncertainty of the external environment, the lack of all types of resources, globalization of education. The authors examined the differences in interpretations of the concepts of “effect” and “efficiency” in relation to the innovation activities of the university. The authors analyzed the methodological approaches to the integrated assessment of the innovation activities of the university, presented in modern studies on this topic. The authors paid attention to approaches based on the integral index of innovation activity, as well as measurements of various types of actions within the framework of innovation activity. Among the main groups of indicators, research productivity, research quality, academic results, etc. are highlighted. The authors also pay attention to alternative approaches to assessing innovation, including the bibliometric (scientometric) approach. This approach has been rapidly developing in recent decades and is actively used in the compilation of various university rankings. The authors have shown the main advantages of the bibliometric approach, as well as the possibilities of its use for evaluating the scientific activities of both the individual scientist and the university as a whole.
Traceable Private Set Intersection in Cloud Computing

Private set intersection (PSI) is a fundamental primitive of many secure applications, which is broadly attracting researchers' interests. The most of existing private set intersection solutions are constructed based on the designed garbled Bloom filter to prevent against the semi-honest adversary. However, the security attributes can no longer be hold in the presence of the malicious adversary. In this paper, we present a novel traceable PSI protocol to protect the confidentiality of the participants in the presence of the malicious adversary, when operating on the large-scale data set. Specifically, our scheme relies on the committed Bloom filter, committed random selection and ownership proof, which enables clients and the cloud server to conduct secure and fine-grained verification through randomly checking a small fraction of clients' data items. To further reduce the communication and computation overhead, a probabilistic approach is developed to provide efficient verification. In the end, we conduct security analysis of our scheme and prove that it is secure under the full universe attack.
Comparative Analysis of Selected Variant of Spider Monkey Optimization Algorithm

Spider Monkey Optimization (SMO) algorithmSharma, Basudev is recently developed optimiser that isKumar Sharma, Vivek stimulated by the extraordinary social activities ofKumar, Sandeep spider monkeys known as fission–fusion social structure. The SMO is developed to find the solution of difficult optimization problems in real world, which are difficult to solve by the available deterministic strategies. Here, three modifications in SMO algorithm are selected for the purpose of comparison, namely, exponential SMO, chaotic SMO and sigmoidal SMO. These modifications suggested new strategies for selecting perturbation rate in local leader and local leader decision phase. These strategies replaced linear approach of perturbation by nonlinear functions. The proposed strategies are tested over a set of benchmark functions.
Quantum speedup of training radial basis function networks

Radial basis function (RBF) network is a simple but useful neural network model that contains wide applications in machine learning. The training of an RBF network reduces to solve a linear system,...
Development of Multimodal Interaction-based Interactive Educational Content-Authoring Tool

With the growth of movie industry and spread of, many people have watched realistic and imaginative movies in history, science and disaster. In these movies, reality and imagination can be utilized in diverse fields if combined with interactive education which can deliver knowledge through interactions with users. This study developed an authoring tool with which multimodal interaction-based interactive educational content can be produced to enhance user immersion based on movies. With such authoring tool, interactive educational content producers are able to produce multimodal interaction-based educational content, using movies. This authoring tool has the advantage of provoking user immersion and interest, using the movies which are already familiar with users without making a separate video. In addition, user-media interactions offer diverse user interactions, using smartphone and low-prices leap motion devices.
Modernization Of The Flagship University - Contribution To The Region Development

Relevance. The relevance of the study is nessecciated by the need to modernize the development model of a flagship university in order to ensure a balanced economy of the region and meet the needs of its promising economic structure, as well as creating a mechanism for the effective use of a flagship university in regional development. Scientific significance of the work. Scientific novelty lies in the development of a concept and justification of the criteria for the modernization of the educational model of a flagship university, which ensure the growth of the universitys contribution to regional development. Methodology. System analysis, methodology for assessing the index of happiness, analysis of strengths and weaknesses. Results. The factors and conditions for the modernization of the educational model of the flagship university are identified and systematized, the criteria are defined and the concept of the modernization of the educational model is developed, based, unlike existing ones, on trends and prospects for the development of the labor market, new technologies, digitalization, innovation development trends in the country and the world. Findings. The practical significance of the study lies in the possibility of applying the results of work in the activities of federal authorities in the development of regional policies and programs for the development of flagship universities, as well as in the strategic management of flagship universities.
Incorporating HowNet-Based Semantic Relatedness Into Chinese Word Sense Disambiguation

This paper presents a semi-supervised learning method that incorporates sense knowledge into a Chinese word sense disambiguation (WSD) model. This research also effectively exploits HowNet-based semantic relatedness in order to leverage system performance. The proposed method includes Sense Colony task for improving context expansion and semantic relatedness calculating for sense feature representation. To incorporate sense knowledge into WSD, this paper employs the Semantic relatedness in a semi-supervised label propagation classifier. This research demonstrates state-of-the-art results on word sense disambiguation tasks.
An Interactive Teaching Model for Wisdom Classroom Based on Learning Situation Analysis

Due to the advancement of the information industry, the wisdom classroom has become a hot topic in current research. For a better promotion and application of wisdom classroom, problems like the lack of interaction types and the lack of interactive methods truly need to be solved. Aiming at the background of wisdom classroom, firstly, this paper analyzes and contrasts the existing wisdom classroom teaching mode. Secondly, it analyzes the students learning situation based on the existing teaching system of wisdom classroom. The deficiency in interaction type and interaction method is pointed out. Finally, the interactive data collection model for wisdom classroom is proposed. This paper proposes an interaction mode based on knowledge type and designs a wisdom classroom teaching model based on the interaction mode. Through the interaction design based on the analysis of learning situation, it is hoped that this paper can bring new methods and certain reference meanings for teaching interaction.
An Extended Sequence Labeling Approach For Relation Extraction

A relation extraction approach based on sequence labeling has been proposed to extract entities and relation triples jointly. That approach does not take triple overlapping into consideration. In this paper, the approach is improved to become more friendly to overlapped triples. First, the sequence labeling model is extended to make it possible to predict more than one tags for a token. And all gold tags of a token are used for supervision. Then a more effective algorithm is designed to construct triples. Experiments on CoNLL04 dataset show that our approach achieves a much better overall performance than our baselines.
Research on Classification Model of Natural Driving Scenario Based on LightGBM

With the rapid development of Artificial Intelligence and big data analysis, the application of driving scenarios has become a new hot spot in the research of smart connected vehicles. This study uses the collected Controller Area Network (CAN) bus and CARTAC driving scenario standard marker data implemented Discrete Wavelet Transform (DWT) and LightGBM algorithm, builded a high-speed driving scenario automation classification model to achieve follow-up driving, patrol Line driving, lane changing, adjacent car cutting in and front car cutting out five typical scenarios automatic classification. The accuracy of classification of all samples is 83%, and the accuracy rate of sub-scenario classification is over 78%. Compared with other supervised learning models, it has better automatic classification effect, effectively solving the problem of automatic processing of scenario data facing the industry. Industrial application value.
Comparative Analysis of Video Stabilization using SIFT Flow and Optical Flow

In any domain of computer vision it is good to have a high quality stabilized video but in many practical applications such as UAVs and Mobile Videos, it is difficult to get such videos. To work in these scenarios, it is best to stabilize the video for further processing. Video stabilization is the post processing method to eliminate the undesirable camera motion in a video sequence. In this paper, we are proposing a useful strategy to eliminate the accidental jitter and produce the high quality stabilized video. In this context we are using Scale Invariant Feature transform (SIFT) Flow to calculate the inter- frame motion. For better estimation of model between two frames, we use Random Sample Consensus (RANSAC) to reject the outlier and only use inlier for model estimation. We use projective transformation to estimate the model between two frames. In the end, we compare our results with conventional optical flow based video stabilization. It is demonstrated that our method effectively produce better stabilized video. The effectiveness of proposed frame work has been verified over a widely variety of videos.
Performance Evaluation and Cost Optimal Service Quality Prediction of Cloud Computing Systems in Unreliable Environments

Cloud computing, is a hot spot in current research and a new information service model. It provides reliable and cheap computing resources to users. The cloud system is built by the Service-Level-Agreement (SLA). Cloud providers must meet SLA performance commitment to their clients. Thus, researchers are concerned about the performance issues of cloud infrastructure. Based on the in-depth analysis of the essential attributes of cloud computing systems, this study introduces a method of service cloud performance analysis by using stochastic processes, queuing networks, time series prediction and other methods and methods. Fine-grained modeling of cloud computing systems in low-reliability environments focuses on describing the probabilistic characteristics of cloud system state transitions and transitions under different task complexity and fault frequency, and establishes a performance model for optimal capacity decision problems. The implementation cost is minimized and the optimization problem is solved numerically by simulated annealing.
Implementation of Real-time Computer-generated Integral Imaging System for Head Mounted Display using OpenGL Shading Language

In this paper, the real-time computer-generated integral imaging(CGII) system for the head mounted displays(HMDs) is implemented based on the open graphics library shading language, where the integral imaging system is composed of micro display and micro lens array applicable for the HMD application. To verify the performance of the implemented CGII system, the rendering speed of the virtual objects consisting of a large number of vertices is measured. The frame rate of virtual objects with over 100,000 vertices is 30 fps, which validates the real-time operation of proposed system. Also, displaying experiments for presenting 3D virtual images are performed with the implemented CGII system with the micro display and micro lens array.
Normal Mixture Model-Based Clustering of Data Using Genetic Algorithm

In this study, a new algorithm was developed for clustering multivariate big data. Normal mixture distributions are used to determine the partitions of variables. Normal mixture models obtained from the partitions of variables are generated using Genetic Algorithms (GA). Each partition in the variables corresponds to a clustering center in the normal mixture model. The best model that fits the data structure from normal mixture models is obtained by using the information criteria obtained from normal mixture distributions.
A Self-Healing Mechanism for NFV By Leveraging Resource Information Indexing Technique

Network Functions Virtualization (NFV) enables network operators to develop and deploy services to the market in a flexible and timely manner which has never been achieved before. Though NFV has revolutionized the telco industry with many advantages of virtualization technologies, there are challenges need to be taken into account especially the fault recovery. Current implementation of NFV systems offers limited self-healing features such as ping for health check and the recovery procedure is expensive by taking down the whole virtual machine and replacing with a new one. This article proposes a new self-healing mechanism for NFV by leveraging the resource information indexing technique.
Rank-wise Weighted Mean Filter for Gaussian Noise Removal

This paper presents a rank-wise weighted mean filter for restoring an image corrupted by Gaussian noise. The method is two stages: ranking of image pixels and noise removal. First, the rank of each pixel is found using a threshold value. Finally, the weighted mean is calculated for each pixel using the rank of the surrounding pixels as the weights. Experiments and comparisons involving different sample images with varying noise variances show that our method yields better results than the other state of art methods, even at high standard deviation of Gaussian noise.
Increasing the Efficiency of Rule-Based Expert Systems Applied on Heterogeneous Data Sources

Nowadays, the proliferation of heterogeneous data sources provided by different research and innovation projects and initiatives is proliferating more and more and presents huge opportunities. These developments create an increase in the number of different data sources, which could be involved in the process of decision-making for a specific purpose, but this huge heterogeneity makes this task difficult. Traditionally, the expert systems try to integrate all information into a main database, but, sometimes, this information is not easily available, or its integration with other databases is very problematic. In this case, it is essential to establish procedures that make a metadata distributed integration for them. This process provides a “mapping” of available information, but it is only at logic level. Thus, on a physical level, the data is still distributed into several resources. In this sense, this chapter proposes a distributed rule engine extension (DREE) based on edge computing that makes an integration of metadata provided by different heterogeneous data sources, applying then a mathematical decomposition over the antecedent of rules. The use of the proposed rule engine increases the efficiency and the capability of rule-based expert systems, providing the possibility of applying these rules over distributed and heterogeneous data sources, increasing the size of data sets that could be involved in the decision-making process.
Designing Combinational Circuits Using a Multi-objective Cartesian Genetic Programming with Adaptive Population Size

This paper proposes a multiobjective Cartesian Genetic Programming with an adaptive population size to design approximate digital circuits via evolutionary algorithms, analyzing the trade-off between the most often used objectives: error, area, power dissipation, and delay. Combinational digital circuits such as adders, multipliers, and arithmetic logic units (ALUs) with up to 16 inputs and 370 logic gates are considered in the computational experiments. The proposed method was able to produce approximate circuits with good operational characteristics when compared with other methods from the literature.