Reviewerly/exHarmony huggingface dataset:
Pengujian Black Box pada Aplikasi Penjualan Berbasis Web Menggunakan Metode Equivalents Partitions (Studi Kasus: PT Arap Store)

In a software test, validation is a process of checking whether the software meets the specifications and intended purposes. Data validation process that has not been maximized can result in data that is stored in the database is not appropriate, including the login process that becomes an error. This can be detrimental to the owner and user in using this application. Therefore, it is necessary to improve the quality of validation more accurately, so that it can help the owner in the accuracy of the data.Applications that are designed in such a way must go through a testing phase to ensure the quality of the software itself. A good test case is if the test has the possibility of finding an error that is not revealed. Among the many ways of testing Black Box, in this study the Equivalence Partition testing method was chosen. This method tests solving or dividing the input domain from the program into data classes so that a test case can be obtained, which is considered quite relevant to test sales applications at PT Arap Store. Stages of research carried out begins by determining the functionality to be tested, designing test scenarios, determining the data to be tested, determining input can be numeric values, range values, related value collections or Boolean conditions in accordance with the database structure that has been created, conducting experiments testing, documenting research results, and drawing conclusions.Based on the results of tests that have been carried out, it gives a conclusion that this software can be used properly, after a repair has been made on the errors found. However, this test can be said to be imperfect, because it is only done by using several sample forms. From the results of this test it is expected that the application can be used according to user requirements.
Achieving Differential Privacy in Smart Home Scenarios

With the growth of the Internet of Things (IoT) and Smart Homes, there is an ever-growing amount of data coming from within people’s houses. These data are intrinsically private and should be treated carefully, despite their high value for analysis. In this work, we propose a differentially private strategy to estimate frequencies of values in the context of Smart Home data.
Investigating Profiled Side-Channel Attacks Against the DES Key Schedule

Recent publications describe profiled single trace side-channel attacks (SCAs) against the DES key-schedule of a “commercially available security controller”. They report a significant reduction of the average remaining entropy of cryptographic keys after the attack, with surprisingly large, key-dependent variations of attack results, and individual cases with remaining key entropies as low as a few bits. Unfortunately, they leave important questions unanswered: Are the reported wide distributions of results plausible - can this be explained? Are the results device-specific or more generally applicable to other devices? What is the actual impact on the security of 3-key triple DES? We systematically answer those and several other questions by analyzing two commercial security controllers and a general purpose microcontroller. We observe a significant overall reduction and, importantly, also observe a large key-dependent variation in single DES key security levels, i.e. 49.4 bit mean and 0.9 % of keys &lt; 40 bit (first investigated security controller; other results similar). We also observe a small fraction of keys with exceptionally low security levels that can be called weak keys. It is unclear, whether a device’s side-channel security should be assessed based on such rare weak key outliers. We generalize results to other leakage models by attacking the hardware DES accelerator of a general purpose microcontroller exhibiting a different leakage model. A highly simplified leakage simulation also confirms the wide distribution and shows that security levels are predictable to some extent. Through extensive investigations we find that the actual weakness of keys mainly stems from the specific switching noise they cause. Based on our investigations we expect that widely distributed results and weak outliers should be expected for all profiled attacks against (insufficiently protected) key-schedules, regardless of the algorithm and specific implementation. Finally, we describe a sound approach to estimate actual 3-key triple-DES security levels from empirical single DES results and find that the impact on the security of 3-key triple-DES is limited, i.e. 96.1 bit mean and 0.24 % of key-triples &lt; 80 bit for the same security controller.
Total Physical Response (TPR) Method in Improving English Vocabulary Acquisition of 5-6 Years Old Children

Nowadays, English has become one of the subjects that must be mastered. Thus, English learning should be done from an early age. This research aimed to describe how Total Physical Response (TPR) method can improve English vocabulary achievement in 5-6 years old children. TPR is the language learning approach which stimulates the children in acquiring their mother language which implemented in teaching foreign languages. The samples of this research were 5-6 years students of TK (Kindergarten) An-Nisa, Rokan Hilir, Riau Province, Indonesia with the total samples 15 students. This research used qualitative research. The data was analyzed by using descriptive statistics, analyzing the scores of English vocabularies. The results showed that noun categories had the highest percentage in vocabulary acquisition (97.78 %), then followed by verbs (86.67 %) and adjectives (62.22 %). Meanwhile, the percentages of vocabularies that used by children were verbs (24.44 %), nouns (11.11 %), while the children were still not capable in using adjectives (0 %). The results of this research is expected to contribute to the development of English learning for young learners.
UNISEL Bot: Designing Simple Chatbot System for University FAQs

The UNISEL Bot system was developed for helping marketing department in order to help on giving information in interactive mode for marketing purpose. The current problem is the information are not served in interactive ways, manually serving information using portal and paper are complicated and there is no real-time customer support to help on question and answer. In the era of technology, the information should be served in an interactive platform. The interactive information tends to gather more user attention. Therefore, this project aims to develop marketing assistant Chatbot system for a private academic institution which known as UNISEL Bot. The Chatbot system development is expected to assist the marketing department to use smarter marketing and interactive ways, for instance; to receive FAQs from student and provide real-time feedback whilst encourage people to engage with latest technology. Agile methodology was used in the development of this Chatbot system. Qualitative data gathering using interview method with students and University staffs was implemented. Multiple diagram is presented in this paper to describe the process flow of UNISEL Bot system. UNISEL Bot system was made up of seven main modules including the Ask Question, Feedback, Registration, Event, Appointment, Survey and Map. In future, this Chatbot system can work effectively to replace the traditional method of manual customer service and can also helping people in capturing user data for building analytic data.
Stereo calibration of binocular ultra-wide angle long-wave infrared camera based on an equivalent small field of view camera

In this paper, a stereo calibration method for binocular ultra-wide angle long-wave infrared camera is proposed on the basis of an equivalent small field of view camera. Extrinsic parameters are calibrated through the corrected images from the left and right cameras. They can be viewed as images taken by a small field of view camera. The calibration procedure consists of three steps: monocular calibration, distortion correction and extrinsic parameters calibration. In order to evaluate the accuracy of the method, stereo vision of the camera is modelled and a 3D reconstruction approach is presented. A series of experiments, including intrinsic parameters, extrinsic parameters and 3D reconstruction, are conducted to validate the proposed method. The results show that the baseline length error decreases to 0.67%, and the relative error for the 3D reconstruction of corners is smaller than 8.11%. In contrast to the common stereo calibration method, it improves calibration accuracy.
U-Net Training with Instance-Layer Normalization

Normalization layers are essential in a Deep Convolutional Neural Network (DCNN). Various normalization methods have been proposed. The statistics used to normalize the feature maps can be computed at batch, channel, or instance level. However, in most of existing methods, the normalization for each layer is fixed. Batch-Instance Normalization (BIN) is one of the first proposed methods that combines two different normalization methods and achieve diverse normalization for different layers. However, two potential issues exist in BIN: first, the Clip function is not differentiable at input values of 0 and 1; second, the combined feature map is not with a normalized distribution which is harmful for signal propagation in DCNN. In this paper, an Instance-Layer Normalization (ILN) layer is proposed by using the Sigmoid function for the feature map combination, and cascading group normalization. The performance of ILN is validated on image segmentation of the Right Ventricle (RV) and Left Ventricle (LV) using U-Net as the network architecture. The results show that the proposed ILN outperforms previous traditional and popular normalization methods with noticeable accuracy improvements for most validations, supporting the effectiveness of the proposed ILN.
CREATIVE COMPETENCE AS A COMPONENT OF THE PROFESSIONAL COMPETENCE OF A FUTURE ENGINEER

The article considers the structure of creative competence in the context of the formation of the creative potential of a future engineer. The analysis of the phenomenon provided an opportunity to propose a structure of creative competence for students of a technical university, which includes motivational-axiological, cognitive, activity and reflective components, and determine the content of its components. The result of a scientific search was to clarify the essence of the concept of creative competence of the future activity of a technical university student as a complex personality education, covering the sphere of intelligence, emotions, moral values and acting on a fundamentally new, integrative level of transferring acquired competencies from one industry to another in order to either achieve a fundamentally new result of activity, or the implementation of activities at a fundamentally new qualitative level.
A Null-Space-Based Verification Scheme for Coded Edge Computing against Pollution Attacks

Edge computing is attracting more and more attention in recent years to fulfill the requirements of latency-critical and computation-intensive applications. By using the coding redundancy, coded edge computing has emerged to optimize the total computation latency. Compared with the servers in cloud computing, edge devices located at the edge of network may not be reliable and trustworthy. In coded edge computing, even one incorrect intermediate result will lead to the incorrect final result. Therefore, considering the low computation capabilities of edge devices and low latency requirements of user, we study the result verification problem for coded edge computing. Specifically, we propose an efficient Orthogonal Mark (OM) verification scheme by the properties of linear space. We also conduct solid theoretical analysis to show the successful verification probabilities under two kinds of attack models, respectively. Finally, we conduct extensive simulations to show the effectiveness of the proposed OM verification scheme when comparing with basic coded edge computing scheme and Decoding Comparison (DC) scheme.
Viewport Prediction for Panoramic Video with Multi-CNN

Real-time interaction has become increasingly important. At the same time, panoramic video has gradually become popular. In this paper, the problem we study is predicting the Field-of-View(FoV) at the future moment when people are enjoying a dynamic panoramic immersive video. Existing methods either estimate the future viewing area based on the previous trajectory, or predict the FoV based on salient region in video frames. Here, we design a new model to predict the viewing points in future moments. Firstly, we predict a point from the viewer's previous viewing trajectory using LSTM(Long Short-Term Memory) network. At the mean time, panoramic video frames are mapped to 6 patches by cube map in advance. The modified VGG-16 network is used for each patch image to perform saliency detection. Then, these 6 salient maps are combined to a single salient map as output. A 3layer convolutional neural network to refined the salient map is utilized. Finally, the salient map of corresponding moment is combined with the predicted point by LSTM input to a two-layer fully connected network to produce the final predicted point. The experiment results show that our model's prediction accuracy is higher than the traditional prediction algorithm and has better performance than the model without using the second CNN network.
Efficient Denoising Framework for Mammogram Images with a New Impulse Detector and Non-Local Means

The survival rates of breast cancer are increasing as screening and diagnosis improve. The removal of noise is revealed to be a significant step for automatic - computer aided detection (CAD) of microcalcification in digital mammography.In this paper, a combined approach for eradicating impulse noise from digital mammograms is proposed. The process is achieved in two stages, detection of noise followed by filtering of noise. The detection of noise is carried out by using Modified Robust Outlyingness Ratio (mROR) trailed by an extended NL (Non-Local)-means filter for filtering mechanism.According to the value of mROR, all pixels in mammogram images are divided into four distinct groups. In each cluster, many decision rules are then applied for detecting the impulse noise. Filtering is done with NL-means filter by providing a reference mammogram image.The comparative analysis and evaluated results are compared with some existing filters which indicate that the proposed structure outperforms the analysed result of others.
Aftershock Detection with Multi-scale Description Based Neural Network

Aftershocks refer to the smaller earthquakes that occur following large earthquakes, in the same area of the main shock. The task of aftershocks detection, as a crucial and challenging issue in disaster monitoring, has attracted wide research attention in relevant fields. Compared with the traditional detection methods like STA/LTA algorithms or heuristic matching, neural network techniques are regarded as an advanced choice with better pattern recognition ability. However, current neural network-based solutions mainly formulate the seismic wave as ordinary time series, where existing techniques are directly deployed without adaption, and thus fail to obtain competitive performance on the intensive and highly-noise waveforms of aftershocks. To that end, in this paper, we propose a novel framework named Multi-Scale Description based Neural Network (MSDNN) for enhancing aftershock detection. Specifically, MSDNN contains a delicately-designed network structure for capturing both short-term scale and long-term scale seismic features. Therefore, the unique characteristics of seismic waveforms can be fully-exploited for aftershock detection. Furthermore, a multi-task learning strategy is introduced to model the seismic waveforms of multiple monitoring stations simultaneously, which can not only refine the detection performance but also provide additionally quantitative clues for discovering homologous earthquakes. Finally, comprehensive experiments on the data set from aftershocks of the Wenchuan M8.0 Earthquake have clearly validated the effectiveness of our framework compared with several state-of-the-art baselines.
Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset

Weather forecast uncertainty is a key element for energy market volatility. By intelligently considering uncertainties on the schedule development, renewable energy systems with storage could improve dispatching accuracy, and therefore, effectively participate in electricity wholesale markets. Deterministic forecasts have been traditionally used to support dispatch planning, representing reduced or no uncertainty information about the future weather. Aiming at better representing the uncertainties involved, probabilistic forecasts have been developed to increase forecasting accuracy. For the dispatch planning, this can highly influence the development of a more precise schedule. This work extends a dispatch planning method to the use of probabilistic weather forecasts. The underlying method used a schedule optimizer coupled to a post-processing machine learning algorithm. This machine learning algorithm was adapted to include probabilistic forecasts, considering their additional information on uncertainties. This post-processing applied a calibration of the planned schedule considering the knowledge about uncertainties obtained from similar past situations. Simulations performed with a concentrated solar power plant model following the proposed strategy demonstrated promising financial improvement and relevant potential in dealing with uncertainties. Results especially show that information included in probabilistic forecasts can increase financial revenues up to 15% (in comparison to a persistence solar driven approach) if processed in a suitable way.
Self-focus Deep Embedding Model for Coarse-Grained Zero-Shot Classification

Zero-shot learning (ZSL), i.e. classifying patterns where there is a lack of labeled training data, is a challenging yet important research topic. One of the most common ideas for ZSL is to map the data (e.g., images) and semantic attributes to the same embedding space. However, for coarse-grained classification tasks, the samples of each class tend to be unevenly distributed. This leads to the possibility of learned embedding function mapping the attributes to an inappropriate location, and hence limiting the classification performance. In this paper, we propose a novel regularized deep embedding model for ZSL in which a self-focus mechanism, is constructed to constrain the learning of the embedding function. During the training process, the distances of different dimensions in the embedding space will be focused conditioned on the class. Thereby, locations of the prototype mapped from the attributes can be adjusted according to the distribution of the samples for each class. Moreover, over-fitting of the embedding function to known classes will also be mitigated. A series of experiments on four commonly used zero-shot databases show that our proposed method can attain significant improvement in coarse-grained data sets.
Impact of Knowledge Management (KM) on Human Resource Management (HRM) Performance Based on the ACHIEVE Model in Selected Banks of Amman – Jordan

The purpose of this study is to explore the influence of knowledge management on human resource management performance based on the ACHIEVE model in selected banks of Amman. The population consisted of 43 employees in 4 Jordanian banks. Data was gathered by a questionnaire according to performance factors of ACHIEVE model. To identify and get rid of irrelevant variables, reliability analysis was conducted. The Kolmogorov-Smirnov normality test was used to examine if variables are normally distributed. As the dependent variable being measured is ordinal, the Friedman test was performed. Furthermore, one-sample t-tests were conducted to investigate the influence of KM on HRM performance. In light of results, KM is significantly impact HRM performance. According to ACHIEVE model, Out of 5 factors, 4 of them were affected by KM with varying degrees. The incentive was the highest factor impact of KM, while an environment is the lowest. The research presents managerial guidance for decision making to develop HRM performance factors, in addition to many theoretical and practical implications. In the end, directions for further research are also included.
Using Augmented Reality for Masonry and Concrete Embed Coordination

Missing or misaligned embeds, sleeves and penetrations for concrete and masonry construction have historically caused disruptions and errors in commercial construction resulting in delays, rework and cost overruns.In order to locate embeds, sleeves and penetrations through the traditional process requires a special set of coordination drawings to be produced called "lift drawings."The "lift drawings" are essentially elevation views of critical wall and foundations elements which show the exact location and alignment for the embeds or penetrations.Producing these "lift drawings" is a time consuming and tedious process that is typically undertaken by the construction management professionals in order to assure that these elements are located properly within the building.In the age of BIM this process of producing "lift drawings" for embeds, sleeves and penetrations should be coordinated and streamlined using BIM and augmented reality to reduce the work effort and provide greater efficiencies.This paper seeks to discover ways in which BIM combined with augmented reality and traditional surveying hardware can improve the process of embed, sleeve and penetration coordination.
Opinion Mining and Product Review Summarization in E-Commerce

With the immense increase of the number of users of the internet and simultaneously the massive expansion of the e-commerce platform, millions of products are sold online. To improve user experience and satisfaction, online shopping platform enables every user to give their reviews for each and every product that they buy online. Reviews are long and contain only a few sentences which are related to a particular feature of that product. It becomes very difficult for the user to understand other customer views about different features of the product. So, we need accurate opinion-based review summarization which will help both customers and product manufacture to understand and focus on a particular aspect of the product. In this chapter, the authors discuss the abstractive document summarization method to summarize e-commerce product reviews. This chapter has an in-depth explanation about different types of document summarization and how that can be applied to e-commerce product reviews.
Artificial intelligence, algorithms and antitrust

The field of artificial intelligence or ‘AI’ has been reshaping virtually every industry built on the idea that machines could be used to simulate human intelligence through so-called ‘machine learning’. Antitrust interest in this topic has been generated among regulators, policy-makers, academics and business in the EU and internationally. This article explores the extent to which AI may raise competition or other concerns for consumer welfare and whether existing legal and policy instruments are appropriate to deal with the emerging opportunities and challenges.