Reviewerly/exHarmony huggingface dataset:
Advertiser-Assisted Behavioral Ad-Targeting via Denoised Distribution Induction

Nowadays, advertising (ad) deliveries are conducted in a targeted manner to improve their effectiveness and efficiency. However, human behavior data in ad-platforms such as Web browsing history is complex and contains a lot of “noise”. On the other hand, information in the advertiser's domain (e.g. e-commerce sites) seems to contain less noise (e.g. product browsing history) with respect to ad-targeting. We introduce a new denoising method for behavioral ad-targeting based on the idea of feature distribution alignment induced by the advertiser's domain. This denoised distribution induction can be achieved by employing domain adversarial training with stabilization techniques. We evaluate our model on real world data originating from an e-commerce site and an ad-platform. The results of an ablation study have demonstrated the advantage of utilizing an advertiser's domain for denoising human behavior data of an ad-platform domain.
Effectiveness Undersampling Method and Feature Reduction in Credit Card Fraud Detection

Credit card fraud is an issue that has affected Indonesia payment system over a decade.Sometimes, the result of the fraud used for terrorism and other crimes.Financial loss is not the only problem that is affected caused by credit card fraud but also Indonesia images in international trade, e-commerce, and the merchant.Currently, a trusted and secured banking payment system is crucial for both customers and banks.The problem from credit card fraud dataset is the data have many features and imbalanced class, this problem leads the paper to propose undersampling technique and feature reduction methods.In this paper we proposed two stage-feature reduction technique because a stage feature reduction could not find the optimal features.On the other hands, we are also applied Instance Hardness Threshold sampling and Random undersampling to deal with imbalance data.The two-stage feature reduction is chosen to eliminate the ineffective feature that cannot eliminate using only one feature reduction.The model from the implemented machine learning methods is evaluated using accuracy, specificity, recall, and Matthews Correlation Coefficient.We implemented our proposed approaches in the ULB credit card fraud detection dataset.According to the result, the undersampling gives a boost in performances which improve the recall and MCC score, the IHT undersampling provide goods results, and in some cases, the result can predict all the test set correctly.However, the two-stages feature reduction fails to improve the accuracy, precision, recall, and MCC score.In one case, the method reduced the accuracy score to 0.302.
Analysis of the Sports Teaching and Training System Based on the VR Technology

With the continuous development of the social science and technology, various scientific and technological products are pouring into our lives. From 2D and 3D technologies to the VR (virtual reality) technology, the application fields are extremely wide. This paper expounds the definition and research basis of VR, explores the application of the VR technology in the physical education and teaching, probes into the problems and achievements in the application of the VR technology, analyses the value and disadvantages of the virtual reality technology in the physical education and teaching, and finally puts forward its own assumptions and prospects for the application of the VR technology in the physical education and learning, aiming at providing ideas for expanding the new ways of the sports learning and increasing fun and effect of sports learning.
ANALISIS KONDISI AKTUAL PENCEMARAN MERKURI BERDASARKAN PETA KERENTANAN PENCEMARAN AIR PERMUKAAN PADA PENAMBANGAN EMAS RAKYAT DESA PANCURENDANG, KECAMATAN AJIBARANG, BANYUMAS

ABSTRACTThe mining and processing of people's gold produces impacts on the surrounding environment. Some residents immediately dumped the waste gold processing results into the environment. It is necessary to analyze the actual condition of mercury pollution based on a map of the level of pollution vulnerability to determine the size of the level of difficulty and the ease of polluted substances to affect surface water quality. The purpose of this study is to analyze the actual condition of the level of vulnerability of surface water pollution around the study area. The method used in this study is a survey method and field mapping, sampling methods: purposive sampling, laboratory analysis methods, mathematical methods, and descriptive evaluation methods. Calculation of the level of vulnerability of surface water is the PCSM (Point Count System Model) method with 3 parameters, namely land use, slope, and rainfall. Overlay is done between the level of pollution vulnerability map with the actual conditions of pollution in the field. The results showed the study area has a level of vulnerability to surface water pollution in the study area including the classification of quite vulnerable and very vulnerable. Based on the results of the study it can be concluded that the total score of 36-43 included in the vulnerability class is quite vulnerable. The total score of 43 - 50 is included in the very vulnerable vulnerability class. Actual mercury levels in the study area were known in a row AP1-AP6 samples were 0,00046 mg / L; &lt;0.00006 mg / L; &lt;0.00006 mg / L; &lt;0.00006 mg / L; 0,00039 mg / L and &lt;0,00006 mg / L. These results indicate that surface water in the study area has not been contaminated with mercury because its value is brought to all quality standards.Â Â
Virtual Reality System for Ship Handling Simulations: A Case Study on Nautical Personnel Performance, Observed Behaviour, Sense of Presence and Sickness

In this paper we introduce the virtual reality ship simulator we designed and the results of an experimental session, in which a manoeuvring task was proposed. In particular, we considered three conditions: (i) the kind of visualisation setup, i.e. a non-immersive system based on standard monitors and an immersive system using a head mounted display; (ii) the path users were instructed to follow, Elliptic or Eight shape path; (iii) the boat type, Slow and Fast. We analyzed three different aspects: performances, defined as the correctness of the followed path; cybersickness, assessed by the Simulator Sickness Questionnaire and physiological measurements (heart rate and skin conductance); sense of presence, determined through the Igroup Presence Questionnaire and the participants’ head rotation. In order to evaluate the proposed system from the point of view of experts, tests were conducted on 20 volunteer skilled users, specifically students of a naval academy. Results show that: (i) expert users are able to follow the predefined path in a quite accurate manner; (ii) both visualization systems do not introduce serious undesired effects or stress and the use of immersive virtual reality itself does not explain the increase of user malaise state; (iii) immersive virtual reality systems allow users to feel more involved and present in the simulation scenario; (iv) there are no appreciable differences with respect to the degree of knowledge of virtual reality systems, thus indicating that such simulator can be also used for training users without specific technological skills.
Unsupervised Adaptation of Acoustic Models for ASR Using Utterance-Level Embeddings from Squeeze and Excitation Networks

This paper proposes the adaptation of neural network-based acoustic models using a Squeeze-and-Excitation (SE) network for automatic speech recognition (ASR). In particular, this work explores to use the SE network to learn utterance-level embeddings. The acoustic modelling is performed using Light Gated Recurrent Units (LiGRU). The utterance embed-dings are learned from hidden unit activations jointly with LiGRU and used to scale respective activations of hidden layers in the LiGRU network. The advantage of such approach is that it does not require domain labels, such as speakers and noise to be known in order to perform the adaptation, thereby providing unsupervised adaptation. The global average and attentive pooling are applied on hidden units to extract utterance-level information that represents the speakers and acoustic conditions. ASR experiments were carried out on the TIMIT and Aurora 4 corpora. The proposed model achieves better performance on both the datasets compared to their respective baselines with relative improvements of 5.59% and 5.54% for TIMIT and Aurora 4 database, respectively. These experiments show the potential of using the conditioning information learned via utterance embeddings in the SE network to adapt acoustic models for speakers, noise, and other acoustic conditions.
Jordanian consumers satisfaction with electrical appliances after-sale services

The study aimed to identify the extent of Jordanian costumers' satisfaction with electrical appliances' after-sale services offered by both LG and Samsung companies.The researcher used the analytic descriptive approach for data collection from the primary resources through developing a questionnaire for the purposes of this study.The sample of the study included 400 clients for L.G and Samsung companies.The results of the study indicate a relationship between after-sale services offered by both companies and consumers' satisfaction.In addition, there was a relationship between quality guarantee certificate offered by LG and Samsung companies for electrical appliances and consumers' satisfaction.In light of the results, the study recommended with the necessity of the existence of a specialized team within the company to offer maintenance services, offering financial compensations in case there is a technical error with any product or substituting the product.
V-LSTM: An Efficient LSTM Accelerator Using Fixed Nonzero-Ratio Viterbi-Based Pruning

Long Short-Term Memory (LSTM) has been widely adopted in tasks with sequence data, such as speech recognition and language modeling. LSTM brought significant accuracy improvement by introducing additional parameters to Recurrent Neural Network (RNN). However, increasing number of parameters and computations also led to inefficiency in computing LSTM on edge devices with limited on-chip memory size and DRAM bandwidth. In order to reduce the latency and energy of LSTM computations, there has been a pressing need for model compression schemes and suitable hardware accelerators. In this paper, we first propose the Fixed Nonzero-ratio Viterbi-based Pruning, which can reduce the memory footprint of LSTM models by 96% with negligible accuracy loss. By applying additional constraints on the distribution of surviving weights in Viterbi-based Pruning, the proposed pruning scheme mitigates the load-imbalance problem and thereby increases the processing engine utilization rate. Then, we propose the V-LSTM, an efficient sparse LSTM accelerator based on the proposed pruning scheme. High compression ratio of the proposed pruning scheme allows the proposed accelerator to achieve 24.9% lower per-sample latency than that of state-of-the-art accelerators. The proposed accelerator is implemented on Xilinx VC-709 FPGA evaluation board running at 200MHz for evaluation.
Penerapan Cisco Packet Tracer Sebagai Media Pembelajaran Jaringan Untuk Meningkatkan Hasil Belajar Siswa Pada Kelas X TKJ di SMK 2 Ibrahimy Sukorejo

To improve the quality of learning can be done by applying strategies or methods of effective learning in the classroom, empower students and utilize existing resources in the school environment. Student activity in teaching and learning activities is marked by the participation of students in carrying out the task given by the teacher. Low student learning activities will have an impact on student’s non-completeness. One of the factors that influence the lesson that is applied by the teacher is less arousing student’s learning. The purpose of this research is to know the increase of activity and student learning outcomes trought application of Learning Media Using Cisco Packet Tracer Application To Improve Student Learning Outcomes of Class X In Basic Network Subject In SMK 2 Ibrahimy Sukorejo Lesson year 2018/2019. This research was conducted from28 April – 1 Mei 2018 with the object of research that is students class X TKJ as many 24 student. To obtain data in research using the method of observation, test and documentation. Data analysis in this research use qualitative descriptive analysis. The results obtained in the study showed improvement in student learning outcomes. In the pre-cycle of of classical completeness 54%, after the action of research daily repetition in cycle I obtained 66% classical completeness. This indicates an increase in student learning outcomes, althought the Cisco Packet Tracer application learning in cycle I has not yet achieved the caliber of learning. The Cisco Packet Tracer application is continued in cycle II. The result of daily test analysis on cycle II was 87% classical completeness, it shows that the result achievement of learning completeness in classical. The conclusion in this rearch that the learning model used by the teacher is very influence to the activity and result of student learning, it is proven that in applying of learning Cisco Packet Tracer application can improve result of activity and result of student learning of X TKJ class on basic network subjek to scores of 85% classically
LPAC: A Low-Precision Accelerator for CNN on FPGAs

Low bit quantization of neural network is required on edge devices to achieve lower power consumption and higher performance. 8bit or binary network either consumes a lot of resources or has accuracy degradation. Thus, a full-process hardware-friendly quantization solution of 4A4W (activations 4bit and weights 4bit) is proposed to achieve better accuracy/resource trade-off. It doesn't contain any additional floating operations and achieve accuracy comparable to full-precision. We also implement a low-precision accelerator for CNN (LPAC) on the Xilinx FPGA, which takes full advantage of its DSP by efficiently mapping convolutional computations. Through on-chip reassign management and resource-saving analysis, high performance can be achieved on small chips. Our 4A4W solution achieves 1.8x higher performance than 8A8W and 2.42x increase in power efficiency under the same resource. On ImageNet classification, the accuracy has a gap less than 1% to full-precision in Top-5. On the human pose estimation, we achieve 261 frames per second on ZU2EG, which is 1.78x speed up compared to 8A8W and the accuracy has only 1.62% gap to full-precision. This proves that our solution has better universality.
Improved ant colony optimization for quantum cost reduction

Heuristic algorithms play a significant role in synthesize and optimization of digital circuits based on reversible logic yet suffer with multiple disadvantages for multiqubit functions like scalability, run time and memory space. Synthesis of reversible logic circuit ends up with trade off between number of gates, quantum cost, ancillary inputs and garbage outputs. Research on optimization of quantum cost seems intractable. Therefore post synthesis optimization needs to be done for reduction of quantum cost. Many researchers have proposed exact synthesis approaches in reversible logic but focussed on reduction of number of gates yet quantum cost remains undefined. The main goal of this paper is to propose improved Ant Colony Optimization (ACO) algorithm for quantum cost reduction. The research efforts reported in this paper represent a significant contribution towards synthesis and optimization of high complexity reversible function via swarm intelligence based approach. The improved ACO algorithm provides low quantum cost based toffoli synthesis of reversible logic function without long computation overhead.
The Impact of Cyber Governance in Reducing the Risk of Cloud Accounting in Jordanian Commercial Banks - from the Perspective of Jordanian Auditing Firms

The objective of this study is identifying the impact of cyber governance on reducing the risk of cloud accounting in the Jordanian commercial banks. To achieve the objectives of this study, the descriptive and analytical approach was used; the study community is composed of external legal accountants who practices auditing in Jordan, (477) of them are practicing external auditing at the end of (2018) according to the statistics of the Jordanian association of certified public accountants (JACPA). Due to the difficulty and cost of the comprehensive survey, a simple random sample was taken. The sample included (213) auditors. The questionnaire was distributed to the sample of the study by the researchers personally and through e-mails, (182) questionnaires were recovered, after excluding (7) for the incompetence, of which (175) were valid for the statistical analysis. Thus, the percentage of retrieved and analyzed questionnaires was (82.2%), which is statistically acceptable. and In order to analyze the study data and test hypotheses, the statistical package for social sciences (SPSS) was used in the various statistical analyses, which are the descriptive statistics and coefficient of internal consistency (Cronbach&amp;#39;s alpha). also, The multiple correlation test was used, using the Pearson correlation coefficient, Multiple linear regression and stepwise regression analysis. The study came to find several results , the most important was The presence of a statistically significant impact of cyber security governance (cybersecurity security governance requirements, cybersecurity program, cyber security policy, cyber information management, evaluating and managing cyber risks) in reducing cloud accounting risks in Jordanian commercial banks, The most important recommendations are the need for Jordanian commercial banks to adopt the cyber governance as a basic reference to their banking policy to address the risks associated with the use of cloud accounting, As well as the need to establish a special department for human resources management within the bank which would have a pioneering intellectual orientation to cope with modern trends in cyber governance.
Persepsi Siswa Terhadap Penggunaan Alat Peraga dalam Pembelajaran Matematika

This article aims to analyze students' perceptions of the use of teaching aids in mathematics learning.This is icluding the cause that may appear from the use of teaching aids and solutions offered.The method used in this research was descriptive qualitative approach.The subjects in this study were 15 students majoring in pharmacy at SMK Adi Husada Malang.The instruments used in this study were questionnaires and interviews.The results of the study indicated that students' perceptions of the use of teaching aids in mathematics that were seen from four aspects 1) the use of teaching aids that used by teacher as learning media were very rare; 2) students' attitude towards the use of teaching aids was very good; 3) the frequency of using teaching aids was only three times, those were used in the material of building space, statistics and matrices, and 4) the use of teaching aids was very beneficial for students.As a result, this could be used as an alternative solution that teachers should always use variety of teaching aids in the learning process and intervene in school policies in order to improve the quality of teachers and mathematics learning.
Professional Honor in the Pedagogical Activity of the Future Teacher

The state of development of the problem of education of professional honor in the future teachers is revealed on the basis of generalization of philosophical, psychological and pedagogical literature.The essence and content of the key concepts of the research are defined as follows: "honor", "professional honor", "professional honor of the teacher".The professional honor of the teacher is defined as an integral personal-professional quality, which manifests itself in the process of fulfilling professional duties, defines a decent behavior in relation to subjects of pedagogical interaction and recognizes the child as the highest value, and also undergoes public assessment and recognition.It is proved that the process of educating the professional honor of the teacher will contribute to the creation of a "new model teacher", as well as raise the prestige, social status of this profession in our society.The substantive-component analysis of the investigated concept was carried out, which made it possible to identify and map out the leading functions of the teacher's professional honor.Among them: the contentforming, normative-regulatory, integrating and incentive functions.The structure of the teacher's professional honor defines the cognitive (knowledge and understanding of moral qualities of the individual), emotional-value (the ability to know and accept others, the ability to self-esteem) and activitybehavioral components (moral and ethical responsibility and responsible attitude to the performance of professional duties), which allow a deeper understanding of the content of the studied definition.
Scale factor calibration method for integrating-bucket sinusoidal phase shifting interferometry

In order to recover the phase map φ(x, y) from sinusoidally phase-shifted interferograms obtained with the integrating-bucket method, two images Ψs and Ψc should be calculated first which are proportional to sin(φ) and cos(φ) respectively, and one of them needs to be scaled by a scale factor before they are used to solve the phase with the arctangent function. Because parameters deciding the scale factor such as the sinusoidal phase modulation amplitude and initial phase are difficult to be set accurately, miscalibration of it can always happen and result in significant error in the phase demodulation. Therefore, in this paper we propose a simple scale factor calibration method which performs the calibration directly with the phase-shifted interferograms and avoids the need for accurate parameter setting. First, we introduce high-density fringes to the interferograms; then, we construct a new image variable Γ with the squares of Ψs and Ψc; last, we find the value of the scale factor by minimizing the second harmonic amplitude in Γ's spatial spectrum. Simulation results show that the proposed method is robust against noise and fringe distortion, and it was tested in a fiber-optic fringe projection profilometer.
Hacia un nuevo paradigma de aprendizaje de segundas lenguas móvil, abierto y social

This article explores the design and development of an innovative didactic paradigm for second 12languages at a distance, social and open (with a triple linguistic, methodological and technological 13dimension), whose core is defined by mobile technology and the Learning of Languages Assisted 14by Mobile or MALL, not only as an additional tool that can be used to perform certain tasks (in a 15more or less structured way), but asthe main way in which distance students of second languages 16can interact and carry out their learning in a way effective.