Reviewerly/exHarmony huggingface dataset:
Pengembangan Modul Pada Pembelajaran Otomatisasi Dan Tata Kelola Kehumasan Dengan Teknik Problem Based Instruction Di SMK Negeri 1 Surakarta

Teaching and learning process is essentially a process of communication between educators and students, one of which is the delivery of subject matter. Therefore, it is necessary to have an interesting learning, easy to understand, making students active and not boring to achieve their learning goals, one of them by developing student practice modules with problem based instruction. The objectives of research are: 1) describing modules in learning Automation Public Relations and Protocol Management in SMK Negeri 1 Surakarta. 2) describe the development of modules with the Problem Based Instruction technique at SMK Negeri 1 Surakarta. 3) testing the feasibility of the module with the Problem Based Instruction technique can improve learning outcomes Automation of Public Relations and Protocol Automation in KD 4.5 for students of class XI OTKP SMK, in terms of learning aspects, material aspects and media aspects. 4) test the effectiveness of the module with the Problem Based Instruction technique of the results of the development of the mastery of student learning outcomes of SMK Negeri 1 Surakarta on KD 4.5. The research used is research and development (RD 2) literature study, class observations and research preparation; 3) carry out planning and research; 4) develop the initial product draft; 5) Carry out initial field trials; 6) conducting field trials; 7) improvement of the product of field test results; 8) conducting a field test. This development has been carried out trials, where each test carried out pretest and posttest. The effectiveness of the development of Module Development in the Automation and Protocol Management Automation Learning in SMK Negeri 1 Surakarta using the problem based instruction model can increase the average value in testing in class XI OTKP SMK. This shows that the Module in Automation and Protocol Management Automation Learning is proven to be feasible to use.Keyword : development, learning modules, problem based instruction techniques
Implementation and Area Optimization of LUT6 Based Convolution Structure on FPGA

To implement the parallel acceleration of convolution operation of Convolutional Neural Networks (CNNs) on field programmable gate array (FPGA), large quantities of the logic resources will be consumed, expecially DSP cores. Many previous researches fail to make a well balance between DSP and LUT6. For better resource efficiency, a typical convolution structure is implemented with LUT6s in this paper. Besides, a novel convolution structure is proposed to further reduce the LUT6 resource consumption by modifying the typical convolution structure. The equations to evaluate the LUT6 resource consumptions of both structures are presented and validated. The theoretical evaluation and experimental results show that the novel structure can save 3.5-8% of LUT6s compared with the typical structure.
On the Distribution of &lt;i&gt;p&lt;/i&gt;-Error Linear Complexity of &lt;i&gt;p&lt;/i&gt;-Ary Sequences with Period &lt;i&gt;p&lt;sup&gt;n&lt;/sup&gt;&lt;/i&gt;

Linear complexity and the k-error linear complexity of periodic sequences are the important security indices of stream cipher systems. This paper focuses on the distribution of p-error linear complexity of p-ary sequences with period pn. For p-ary sequences of period pn with linear complexity pn-p+1, n≥1, we present all possible values of the p-error linear complexity, and derive the exact formulas to count the number of the sequences with any given p-error linear complexity.
A Brief Analysis of the Types and Characteristics of Scientific Research Burnout of College Teachers

Nowadays in China, because many colleges and universities pay more attention to scientific research and take less account of teaching, teachers are under great pressure in scientific research, which easily leads to the scientific research burnout. It was suggested that the scientific research burnout belongs to the category of job burnout, but it is a new form which means college teachers can’t adapt to the current scientific research evaluation system, having difficulties in finishing research targets. According to observations and interviews, college teachers' scientific research burnout is divided into three types, corresponding to different groups, with different psychological characteristics and solutions.
A Book Recommendation Algorithm Considering User Development

There commonly exists the problem of unsatisfactory recommendations given by book recommendation systems.Aiming at the developing characteristic of library user group in university which reflects the consistency between the book borrowing sequence and the accumulation of knowledge, the paper introduces the book lists borrowed by similar users in user group with different development stage to the application of collaborative filtering algorithm in order to increase effective information contained by the whole recommendation process.Consequently, the consistency of the recommendation results and the actual needs of users is improved while the performance of recommendation system rises.The effectiveness of the introduction of user development stage is shown and proved by experiments.
An extensive research survey on data integrity and deduplication towards privacy in cloud storage

Owing to the highly distributed nature of the cloud storage system, it is one of the challenging tasks to incorporate a higher degree of security towards the vulnerable data. Apart from various security concerns, data privacy is still one of the unsolved problems in this regards. The prime reason is that existing approaches of data privacy doesn't offer data integrity and secure data deduplication process at the same time, which is highly essential to ensure a higher degree of resistance against all form of dynamic threats over cloud and internet systems. Therefore, data integrity, as well as data deduplication is such associated phenomena which influence data privacy. Therefore, this manuscript discusses the explicit research contribution toward data integrity, data privacy, and data deduplication. The manuscript also contributes towards highlighting the potential open research issues followed by a discussion of the possible future direction of work towards addressing the existing problems.
Bio-inspired route estimation in cognitive radio networks

Cognitive radio is a technique that was originally created for the proper use of the radio electric spectrum due its underuse. A few methods were used to predict the network traffic to determine the occupancy of the spectrum and then use the ‘holes’ between the transmissions of primary users. The goal is to guarantee a complete transmission for the second user while not interrupting the trans-mission of primary users. This study seeks the multifractal generation of traffic for a specific radio electric spectrum as well as a bio-inspired route estimation for secondary users. It uses the MFHW algorithm to generate multifractal traces and two bio-inspired algo-rithms: Ant Colony Optimization and Max Feeding to calculate the secondary user’s path. Multifractal characteristics offer a predic-tion, which is 10% lower in comparison with the original traffic values and a complete transmission for secondary users. In fact, a hybrid strategy combining both bio-inspired algorithms promise a reduction in handoff. The purpose of this research consists on deriving future investigation in the generation of multifractal traffic and a mobility spectrum using bio-inspired algorithms.
DeepSFM: Structure From Motion Via Deep Bundle Adjustment

Structure from motion (SfM) is an essential computer vision problem which has not been well handled by deep learning. One of the promising trends is to apply explicit structural constraint, e.g. 3D cost volume, into the network. However, existing methods usually assume accurate camera poses either from GT or other methods, which is unrealistic in practice. In this work, we design a physical driven architecture, namely DeepSFM, inspired by traditional Bundle Adjustment (BA), which consists of two cost volume based architectures for depth and pose estimation respectively, iteratively running to improve both. The explicit constraints on both depth (structure) and pose (motion), when combined with the learning components, bring the merit from both traditional BA and emerging deep learning technology. Extensive experiments on various datasets show that our model achieves the state-of-the-art performance on both depth and pose estimation with superior robustness against less number of inputs and the noise in initialization.
Achieving Wellness by Monitoring the Gait Pattern with Behavioral Intervention for Lifestyle Diseases

Obesity is becoming one of the prevalent lifestyle diseases across the globe; need to be deal with behavioral intervention through self-management and motivation in line with rigorous physical exercise. When it is a matter to handle obesity to regain the health, parameters like: self-motivation, self-control, guided treatment, counseling, monitored exercise, and medical assistance are essential in consideration list. Paper proposes the model encompassing three-dimensional care including nutritional intake, counseling, and gait monitoring during exercise. Considering the effect of obesity on biomechanics of foot, gait pattern analysis of obese person provides greater information regarding variations in spatio-temporal parameters. Ever-increasing contribution of behavioral intervention will maintain the line of action in the perfect direction. A selection of accelerometer, gyroscope, and electromyography sensors is appropriate for the cause to derive basic hardware. MSP430 processor and ZigBee module are used for processing information and establishing communication. Within close proximity and placement of nodes at a different level, nodes are able to achieve 90–94% packet delivery ratio in actual environment compare to the 100% packet delivery in simulation environment. Result suggests that with the adaption of accurate classification process, system could be useful for controlled exercise monitoring or for daily activity monitoring, which is working at low-power level with affordable wearable technology in achieving wellness.
Introduce Artificial Intelligence in Controlling a Solar Tracker

According to the report on the global status of renewable energies 2016 published by REN21 (Renewable Energy Policy Network For The 21st Century) the production of solar photovoltaic energy for the year 2015 reached 227 GW with more than 50 GW compared to the year 2014 that was 177 GW this rapid growth is due to research and dedicated scientific developments for this type of energies all these last have the same goal to improve the energy production capacity of these panels. In order to increase the efficiency of the solar panels, we have thought about the design and the realization of a two-axis solar tracker, which will allow the panels to follow the sun and to have the optimal position where there is the maximum of solar power that our panel can acquire. In principle, our system consists of three cards, the first one is the acquisition card or the sensors card it delivers the information on the position of the sun, the second is the control card where we have programmed our algorithm which is responsible for continuously regulating of the position of our tracker, the third is a power card that acts as the intermediary between the control board and the actuators (the two motors of the two axes). By using a PID algorithm and after a real test of our solar tracker it was found that the regulation is done in a correct way but we noticed that the actuators consume a lot of energy to keep the optimal position and we lose the information after the position change, this is why we introduced the notion of artificial intelligence through the development of an algorithm based on advanced fuzzy logic with adaptable rules. Our algorithm will replace the old algorithm (PID) to control the movement of the axes of our tracker as well to find the optimal point where there is the maximum of solar irradiation. Our algorithm will also memorize all the optimal points found during the day for used as much as references and to add it’s as the elements that constitute these rules taking into account also the energy consumption of the system. Our system is developed from such a fate to be reliable, fair and tough.
Meta-Learning Applied to the Selection of the Classification Methods in Industrial Images

In industrial production, control of quality of product and analysis of state of the staff in charge are important factors, nevertheless, the collection and analysis of data imply large amounts of time and may involve a risk to health of the staff in case of quality control, to deal with these tasks, image capturing and classification tools have been used, however, there is a challenge to identify the most appropriate classification method when taking into account the type of image being studied, the challenge is greater when it is necessary for a system to process different products with different classification objectives.This paper presents a methodology based on Meta-Learning and CNN for the identification of the appropriate methods of classification of industrial images.As an object of study images of hot-rolled steel strip, shear pad of wagon train, welds x-rays, aluminum wheel x-rays and human faces were used, obtaining 96% accuracy, 99.7% AUC and 96.5% F-measure.
Study of Information Retrieval and Machine Learning-Based Software Bug Localization Models

Software bug localization (SBL) is a process of finding out the location of bug that causes the failure of some functionality in the application. There are many different methods of performing SBL like analysing of execution traces, information retrieval and manual debugging. Information retrieval (IR) based models works as same as simple search query model in which bug report is taken as query. In this paper, we perform an empirical study for verifying the effectiveness of VSM. Based on TFIDF modelling, the results are experimented on four datasets and evaluated with TOPK, MAP and MRR metrics. In addition to this, review of existing machine learning and deep learning-based SBL models are also presented because of their effective power and improved results in localization accuracy.
Construction of the Contemporary Chinese Common Verbs’ Semantic Framework Dictionary

Semantic lexicon and semantic framework are the primary support of natural language processing tasks such as information extraction, sentiment analysis, and machine translation. Therefore, it is essential to construct the contemporary Chinese common verbs’ semantic framework dictionary that covers rich semantic knowledge. Based on an analysis of current research results, this paper defines the lexical framework of common Chinese verbs. According to the predicate thematic roles, the semantic framework is divided into the basic semantic framework and extended semantic framework. Frameworks which are automatically extracted, taking semantics as the processing unit, and summarized based on large-scale lexical and thematic roles labeling corpus. The complete and simplified versions of the verb framework is constructed with the help of manual proofreading. The final verb framework contains a detailed description and corresponding example sentences of 2,782 common verbs with 4,516 meanings.
Scaling loss: updating gradient of loss for accurate object detection

L<sub>1</sub> loss function and Intersection over Union (IoU) are commonly used in object detection. However, minimizing the loss function through training process does not necessarily amount to maximizing IoUs. L<sub>1</sub> loss simply assigns equal weights to difference of the width, height, and center point between a prediction box and a ground truth box but pays less attention to the contribution of each shape property. Observing this, we propose scaling loss which can be easily embedded in convolutional neural networks for mitigating the gap between IoU and loss function. The key insight is to add in the loss function the adaptive weights for width, height, and center point that encode the shape properties of the bounding box. The contribution of each shape property will be adaptively adjusted according to the difference between a prediction box and a ground truth box, i.e. increasing the weight assigned to the bad-regressed shape property. By this means, the scaling loss is able to obtain more accurate prediction box. The proposed scaling loss was embedded in Faster R-CNN and SSD, and was validated on PASCAL VOC 2007. Experimental results verify that the proposed scaling loss can improve the detection accuracy over the smooth L<sub>1</sub> loss and Softer-NMS.
Controle de Sistemas Variantes no Tempo: uma abordagem alternativa à técnica de controle via transformação Lyapunov-Floquet

It is known in the literature that the Lyapunov-Floquet transformation control technique can be applied in time-varying systems.However, the asymptotic stability of the system is not mathematically guaranteed.In this work the robust control theory is approached aiming its application in time-varying systems.This method is based on obtaining the representation of the system as a convex combination of the maximum and minimum of the variant terms of the matrices that determine the system.As will be seen, the robust control technique allows to obtain sufficient stability condition for linear time-varying systems, via LMIs.Finally, applications of this technique are presented in two systems, one linear and the other nonlinear.
Multi-class Image Segmentation Using Theory of Weak String Energy and Fuzzy Set

Segmentation of a multi-class image is a major challenging work in image processing. The challenge arises as the uncertainties occur in the segmentation process. Here we present a novel method based on the concept of weak string energy to manage the uncertainties in the segmentation process. The concept of the weak string is utilized to find the location of the boundaries accurately among the segments. The segments of an image are generated based on the energy function in the fuzzy set domain in the proposed method. The accurate segments are generated when the function attains its minimum value. The segments are generated from an image without any prior knowledge about the total count of segments. The performance of the method is verified experimentally using different datasets and it is found to be quite satisfactory compared to the state-of-the-art methods.
E-Voting System Based on Multiple Ballot Casting

The use of electronic voting is gradually replacing the traditional one. However, the issue of creating an honest and reliable electronic voting system is still open. Creating a reliable e-voting system that meets all security requirements is a difficult task. The paper presents an electronic voting system based on the principle of blind intermediaries using multiple ballots. The basic requirements for systems are described: eligibility, fairness, individual verifiability, universal verifiability, vote-privacy, receipt-freeness, coercion-resistance, vulnerable software/hardware resistance. Blind intermediaries allow you to exclude the user’s vote from communicating with any authentication data. The principle of multiple ballot casting allows verification of the correctness of the ballot without the use of evidence with zero disclosure, as well as ensures compliance with other requirements for electronic voting systems. The system architecture is described, and the components involved in the voting process are described. The voting process is described, consisting of several stages: preparation, voting, counting of results. Cryptographic protocols are used, which are used for data transfer between system components. The compliance with the security requirements of the system is justified.
Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components

Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, they may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we investigate how to use the conformal prediction framework for assurance monitoring of CPS with machine learning components. In order to handle high-dimensional inputs in real-time, we compute nonconformity scores using embedding representations of the learned models. By leveraging conformal prediction, the approach provides well-calibrated confidence and can allow monitoring that ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. Empirical evaluation results using the German Traffic Sign Recognition Benchmark and a robot navigation dataset demonstrate that the error rates are well-calibrated while the number of alarms is small. The method is computationally efficient, and therefore, the approach is promising for assurance monitoring of CPS.
Scheduling Virtual Data Along with Data Servers: Case Study

It has been observed that cloud computing environments sometimes may provide significant benefits, including reconfiguring virtualized resources on demand, which may be very much beneficial toward deploying cloud services. Earlier, particularly in traditional data centers, usually, applications may be tied to specific physical servers to deal with the upper-bound assigned tasks. In that case, the data centers may be expensive to maintain low resource utilization associated with virtual technology. Of course, the cloud data centers are more flexible and secure while providing better support for on-demand allocation as well. It may improve server utilization and signifies appropriate virtualization technology. As the cost of current data centers may be mostly driven by their energy consumption, sometimes challenges may have to be faced regarding the cost of energy per each virtual machine while being associated with heterogeneous environment. Practically, while designing the private cloud, major challenges associated with cloud computing environment may be faced. As in this consideration, each virtual machine may be mapped toward the physical host in accordance with the available resource on the host machine, accordingly, quantifying the performance of scheduling, and allocating cloud infrastructure may be extremely challenging. In this paper, focused is on virtualized data and evaluation mechanisms associated with data servers as well as data centers.
The Manual Memex

Here we introduce a new column that explores IEEE Computer Magazine’s contribution to the computing literature and how it is laying the foundation for the future of computer science and engineering.