Reviewerly/exHarmony huggingface dataset:
Mental Workload Impact of a Visual Language on Understanding SQL Queries

The Structured Query Language (SQL) is an essential topic in introductory database courses. Despite its declarative syntax, the specification of SQL queries can be confusing. Visual Query Languages (VQLs) are an alternative to reduce this complexity. However, these VQLs do not cover many complex constructions. To overcome this limitation, we present the Diagrammatic SQL (DSQL). Since increased Mental Workload (MW) during task performance may increase fatigue and facilitate errors. Our objective is to investigate the MW impact of DSQL on understand SQL queries. In an experiment, we compare the accuracy, the time, and the ease to understand complex queries using both languages. The results indicate that has no significant difference.
TEKNIK PENYEMBUNYIAN PESAN TEKS TERENKRIPSI ALGORITMA MERKLE-HELLMAN KNAPSACK MENGGUNAKAN METODE PIXEL VALUE DIFFERENCING KE DALAM CITRA DIGITAL

Optimization of data security techniques is very necessary so that the data or information that is secured is really safe from attack. Data that has been encrypted based on cryptographic techniques very quickly raises suspicion that the data is confidential or important. Therefore, this technique is better combined with steganography techniques. Utilization of steganography techniques can minimize the attacker's suspicion of data that is secured, because by using steganography data techniques can be hidden on certain objects. This study discusses how to encode a text based on the Merkle-Hellman Knapsack algorithm and the resulting password is hidden in a grayscale digital image as a hiding object based on the pixel value differencing algorithm. This is done to minimize suspicion and make it difficult for attackers to find out confidential or important data.Keywords: cryptography,steganography, merkle-hellmankanpsack, PVD, image
Mouse, touch, or fich

Finger touch and mouse-based interaction are today's predominant modalities to interact with screen-based user interfaces. Related work suggests that new techniques interweaving pre-touch sensing and touch are useful future alternatives. In this paper, we introduce Fich, a novel pre-touch technique that augments conventional touch interfaces with tooltips and further "fingerover" effects, opening up the space in front of the screen for user interaction. To study Fich in-depth, we developed a Fich-enabled weather application and compared user experience and interface discovery ("serendipity") of Fich against the traditional input modalities Mouse and finger Touch in a user study with 42 subjects. We report on the results, implying Fich's user experience to be rated significantly higher in terms of hedonic quality and significantly lower in terms of pragmatic quality, as compared to traditional input modalities.
Research on the dam foundation pit hydrogeological problems in Dadu river deep overburden layer area

Abstract Western Sichuan is located in the two and three steps of transition zone of our country on the terrain, in the area of Dadu River valley was “V” type, the valley is widely distributed deep overburden layer. The existence of deep overburden layer not only greatly increases the difficulty of hydropower development in the southwest region, but also gives birth to a series of new scientific problems. This paper analyze the basic feature and formation of deep overburden layer in the Dadu River, and then discuss the may encounter hydrogeological problems with the pit water gushing, piping and flow sand.
Particle Swarm Algorithm for the Optimization of Modular Neural Networks in Pattern Recognition

In this paper a Particle Swarm Algorithm (PSO) is applied for the optimization of modular neural networks. This method is used for optimizing modular neural network in medical image recognition (Echocardiograms). As echocardiograms, these images help to diagnose heart diseases by the specialists and so can reduce the number of deaths by disease. Simulation results show that the scaled conjugate gradient (trainscg method) offers much higher performance with an average 81.4% recognition rate and with the gradient descent with adaptive learning (traingda method) an average of 76.3% recognition rate.
Researching digital communication practices among German-speaking expats in China: Study design

The present paper develops the study design of a linguistic analysis of digital communication practices among German-speaking expats in China using WeChat.Within digital communication in China, the messenger application WeChat is the most widely used means of communication, boasting roughly one billion users.There has not been relevant linguistic research on communication making use of this application to date.We especially lack studies addressing the communication practices with WeChat by native speakers of German, who form a relatively large group of expatriates in China (20,000+ Germans alone), mostly working in the automobile industry.After a short introduction (1.) which addresses the relevant research question to be tackled, the previous research on WeChat is summarized (2.).The attention WeChat has received in the media of the German language area becomes visible, but we can also see that there is no systematic scholarly discourse focusing on the application just yet.This is followed by a section on the communicative features and functionality of WeChat (3.): here it is pointed out that WeChat has multiple functions which go significantly further than messaging applications such as WhatsApp provide their users.These features comprise e.g.online payment, GPS tracking and navigation, money transfers, and monetary gifts.Furthermore, WeChat combines features of WhatsApp, Facebook, and Twitter.A thorough description of these features provides the basis for laying out the study design of the online survey (4.).The online survey comprises more than twenty questions on the use of WeChat with special regard to the expatriate setting in China.The paper ends with a section on the expected results (5.).
Automatic Correction of Free Format MCQ Tests

Although the technology for the automatic grading of multiple-choice exams exists, it is neither efficient nor as automatic as it claims to be. All proposed methods have a predefined answer sheet format that looks like a crosswords table or a chessboard. Because of this format, all questions must have the same number of choices. Such an answer sheet is not clear, and candidates taking the exam can and will accidentally mark the wrong cell in the table. Most of them assume that there is only one possible answer for every question. This article proposes an algorithm that does not require any special format, works with all scanning resolutions and is actually fast.
Peningkatan Keterampilan Membaca Pemahaman Tingkat Terampil Melalui Metode SQ3R Pada Mahasiswa Semester VII STBA JIA

Dokkai or reading understanding in Japanese is one of the lessons that combines several skills such as kanji, sentence pattern, translation, and culture. From all those several skills dokkai is a lesson that need accuracy. The obstacles in learning dokkai as the error understanding and error translation is a problem that must be overcome. This research is using the method of SQ3R, that are survey, question, read, recite, and review. These stages can describes reading or discourse into several part that can be easily understood. This research is the action research that using two cycle, each cycle consists of 2 meetings. The object of the research is the students of the 7thsemester that took Jyoukyuu Dokkai as the lesson. The result of the research from the action that using this method is the skills of the students in understanding discourse become increase, finish is study and the activities become better than before. The student has the new method in understanding new reading and discourse.&#x0D;
PERBAIKAN KUALITAS CITRA MAPS MENGGUNAKAN METODE CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION (CLAHE)

Map images that are on Google Maps can also be screenshooted, when taking image maps / maps the image quality is not optimal, this is because the ability of the computer used to take the image does not support high graphics. This map image can be used as material for the purposes of work that uses images such as graphic design, namely printing.The ability of computers that do not support large graphics processing in getting a good image is not a major problem in image improvement. Because the image that is not maximized, its color elements can be manipulated with digital image processing techniques, one of the methods for manipulating elements of image colors is the CLAHE method. The CLAHE method is applied and calculates the pixel values that exist in each RGB layer. In the field of graphic design images that have less intensity can be improved by increasing contrast. Image contrast enhancement can be done by the CLAHE method.Keywords: Maps Imagery, Contrast, CLAHE
Advances in Usability of Formal Methods for Code Verification with Frama-C

Industrial usage of code analysis tools based on semantic analysis, such as the Frama-C platform, poses several challenges, from the setup of analyses to the exploitation of their results.  In this paper, we discuss two of these challenges.  First, such analyses require detailed information about the code structure and the build process, which are often not documented, being part of the implicit build chain used by the developers.  Unlike heuristics-based tools, which can deal with incomplete information, semantics-based tools require stubs or specifications for external library functions, compiler builtins, non-standard extensions, etc.  Setting up a new analysis has a high cost, which precludes industrial users from trying such tools, since the return on investment is not clear in advance: the analysis may reveal itself of little use w.r.t. the invested time.  Improving the usability of this first step is essential for the widespread adoption of formal methods in software development.  A second aspect that is essential for successful analyses is understanding the data and navigating it.  Visualizing data and rendering it in an interactive manner allows users to considerably speed up the process of refining the analysis results.  We present some approaches to both of these issues, derived from experience with code bases given by industrial partners.
SITM: See-In-The-Middle Side-Channel Assisted Middle Round Differential Cryptanalysis on SPN Block Ciphers.

Side-channel analysis constitutes a powerful attack vector against cryptographic implementations. Techniques such as power and electromagnetic side-channel analysis have been extensively studied to provide an efficient way to recover the secret key used in cryptographic algorithms. To protect against such attacks, countermeasure designers have developed protection methods, such as masking and hiding, to make the attacks harder. However, due to significant overheads, these protections are sometimes deployed only at the beginning and the end of encryption, which are the main targets for side-channel attacks.In this paper, we present a methodology for side-channel assisted differential cryptanalysis attack to target middle rounds of block cipher implementations. Such method presents a powerful attack vector against designs that normally only protect the beginning and end rounds of ciphers. We generalize the attack to SPN based ciphers and calculate the effort the attacker needs to recover the secret key. We provide experimental results on 8-bit and 32-bit microcontrollers. We provide case studies on state-of-the-art symmetric block ciphers, such as AES, SKINNY, and PRESENT. Furthermore, we show how to attack shuffling-protected implementations.
CRYPTOCURRENCY: ACQUISITION AND USAGE

The article deals with the investigation of acquisition cryptocurrency in time retrospective and aspects of its usage taking point to the change of the value and number of transactions by the most popular cryptocurrency Bitcoin, its usage, comparing with the most popular payment platforms and resources consumed per transaction. The methods of analysis of scientific resources and statistical data, systematisation and critical evaluation were used during the research process. Evaluation of the acquisition and usage of cryptocurrency, according to analysed aspects reveals various sides of the processes.
Machine Learning-enhanced Realistic Framework for Real-time Seismic Monitoring - The Winning Solution of the 2017 International Aftershock Detection Contest.

Identifying the arrival times of seismic P-phases plays a significant role inreal-time seismic monitoring, which provides critical guidance for emergencyresponse activities. While considerable research has been conducted on thistopic, efficiently capturing the arrival times of seismic P-phases hiddenwithin intensively distributed and noisy seismic waves, such as those generatedby the aftershocks of destructive earthquakes, remains a real challenge sincemost common existing methods in seismology rely on laborious expertsupervision. To this end, in this paper, we present a machine learning-enhancedframework based on ensemble learning strategy, EL-Picker, for the automaticidentification of seismic P-phase arrivals on continuous and massive waveforms.More specifically, EL-Picker consists of three modules, namely, Trigger,Classifier, and Refiner, and an ensemble learning strategy is exploited tointegrate several machine learning classifiers. An evaluation of theaftershocks following the MS 8.0 Wenchuan earthquake demonstrates thatEL-Picker can not only achieve the best identification performance but alsoidentify 120% more seismic P-phase arrivals as complementary data. Meanwhile,experimental results also reveal both the applicability of different machinelearning models for waveforms collected from different seismic stations and theregularities of seismic P-phase arrivals that might be neglected during manualinspection. These findings clearly validate the effectiveness, efficiency,flexibility and stability of EL-Picker. Note that this paper is the Englishversion of our work published in Science China Information Science(http://engine.scichina.com/doi/10.1360/SSI-2020-0214).
Coalgebraic Geometric Logic

Using the theory of coalgebra, we introduce a uniform framework for adding modalities to the language of propositional geometric logic. Models for this logic are based on coalgebras for an endofunctor T on some full subcategory of the category Top of topological spaces and continuous functions. We compare the notions of modal equivalence, behavioural equivalence and bisimulation on the resulting class of models, and we provide a final object for the corresponding category. Furthermore, we specify a method of lifting an endofunctor on Set, accompanied by a collection of predicate liftings, to an endofunctor on the category of topological spaces.
Pengembangan Multimedia Interaktif Aksara Jawa untuk Sekolah Dasar

&lt;p class="Abstrak"&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; The purpose of the research and development that is producing a eligible interactive multimedia Javanese alphabet products with valid criteria, practical, interesting, and effective. This research and development using model Lee &amp;amp; Owens. They use some following steps; (1) analysis assessment, (2) designs, (3) development, (4) implementation, and (5) evaluation. The results showed that interactive multimedia were very valid with an average percentage of 86,93%, very practical with an average percentage of 89,83%, very interesting with an average of 88,97%. Interactive multimedia is also very effective, as evidenced by the results of the posttest for 91.66% of students who have received complete marks.&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt; Tujuan penelitian dan pengembangan ini yaitu untuk menghasilkan multimedia interaktif aksara Jawa yang layak dengan memenuhi kriteria valid, praktis, menarik, dan efektif. Penelitian dan pengembangan ini menggunakan model dari Lee &amp;amp; Owens. Tahapan pengembangan yaitu (1) analisis kebutuhan dan analisis awal-akhir, (2) desain, (3) pengembangan, (4) implementasi, dan (5) eveluasi. Hasil penelitian didapatkan bahwa multimedia interaktif sangat valid dengan rata-rata persentase 86,93%, sangat praktis dengan rata-rata presentase 89,83%, sangat menarik dengan rata-rata persentase 88,97%. Multimedia interaktif ini juga sangat efektif dibuktikan dengan hasil &lt;em&gt;posttest &lt;/em&gt;sebesar 91,66% siswa telah mendapatkan nilai tuntas.
Leveraging Uncertainty in Adversarial Learning to Improve Deep Learning Based Segmentation

This paper proposes a new framework that combines Bayesian SegNet with adversarial learning to obtain high-quality segmented objects of interest. The proposed architecture takes in the form of two discriminator networks that are trained separately. The first network discriminates between segmentation maps coming either from the SegNet or the ground truth. The second network discriminates between the model uncertainty obtained from SegNet and an ideal solution that does not include uncertainty. The process is very similar to the fusion of sensor information for better decision making. Uncertainty is considered as a measure of mistakes. Hence, learning from it will help improve the performance of neural networks. Our results show that we obtain higher accuracies compared to Bayesian SegNet. Training is performed on a small-sized dataset called CamVid and a large-sized dataset Sun RGB-D. The paper shows that dealing with uncertainties is beneficial for decision making in neural networks, especially in applications with highly uncertain environments. Examples include self-driving cars and medical imaging in cancer treatment.
A hybrid encryption method handling big data vulnerabilities

As big data hits the maximum number of companies in all domains, secured data transfer can be done by cryptography. With increasing threats to big data, the security must focus on to avoid the attackers from the formation of any pattern to gain access to the information. Big data deals with the linguistic data which consists of low secured data and high secured data as well. Hence, the system should focus on providing multi-fold security and should avoid high-security common to all data categories. This paper presents a hybrid model for big data that ensures data confidentiality, data integrity, access control and sequential freshness by combining three symmetric key algorithms AES, DES and blowfish for the encryption and decryption process in any desired order. Based on the level of security the combination of the algorithms can vary. This method of encryption and decryption process ensures safe data transformation between source and destination.
Bigger versus Similar: Selecting a Background Corpus for First Story Detection Based on Distributional Similarity

The current state of the art for First Story Detection (FSD) are nearest neighbourbased models with traditional term vector representations; however, one challenge faced by FSD models is that the document representation is usually defined by the vocabulary and term frequency from a background corpus.Consequently, the ideal background corpus should arguably be both large-scale to ensure adequate term coverage, and similar to the target domain in terms of the language distribution.However, given these two factors cannot always be mutually satisfied, in this paper we examine whether the distributional similarity of common terms is more important than the scale of common terms for FSD.As a basis for our analysis we propose a set of metrics to quantitatively measure the scale of common terms and the distributional similarity between corpora.Using these metrics we rank different background corpora relative to a target corpus.We also apply models based on different background corpora to the FSD task.Our results show that term distributional similarity is more predictive of good FSD performance than the scale of common terms; and, thus we demonstrate that a smaller recent domain-related corpus will be more suitable than a very largescale general corpus for FSD.
PENERAPAN METODE BACKWARD CHAINING PADA SISTEM PAKAR UNTUK MENDETEKSI PENYAKIT TANAMAN JAGUNG

C orn plant disease is one of the obstacles to success in cultivating corn in Ngkari-ngkari Village. Farmers do not yet fully know how to deal with the symptoms of corn plant disease. This study aims to create an expert system application to detect maize plant diseases, to determine the type of disease and the solution used is the backward chaining method approach starting from the Goal then looking for facts that match the premise data. This research resulted in an application that can help users to detect symptoms of corn plant diseases and provide prevention or control solutions if there are corn farmers that are detected by disease symptoms. Keyword: Backward Chaining, Expert system, Corn plant
On social engineering

Social engineering is a term that emerged from the hacker and security communities for the social process of compromising information security. This process focuses on the manipulation, deception, or influence of the people involved in information security as a means to gain access to otherwise secure information or computer systems. This chapter briefly traces the history of social engineering, common tactics and tools used by social engineers, and research on psychological and other individual traits exploited in social engineering. Future research trajectories will also be discussed.
KLASIFIKASI AYAT AL-QURAN TERJEMAHAN BAHASA INGGRIS MENGGUNAKAN K-NEAREST NEIGHBOR (KNN) DAN INFORMATION GAIN

Al-Quran is a holy book that contains instructions and instructions for the life of Muslims. In the Al-Quran there are interpretations quoted from the previous verse and have an implied meaning, so to be able to obtain these verses textually and contextually it is necessary to classify the interpretation of the Al-Quran to facilitate Muslims in finding topics in theAl-Quran. In this study, it is proposed to classify the topic of Al-Quran verses in English translation which consists of three classifications, namely commands, prohibitions and others. In this research the system design is done by collecting datasets, preprocessing to get clean data, selecting features using gain information, classifying using the K-Nearest Neighbor (KNN) method, and testing the system. The results of the tests conducted resulted in a value 64,10% for accuracy, 63% for precision, and 62.68% for recall using the value of k = 17 and the dataset containing data testing and data training of 1:9, respectively.Keywords: classification, topics of Al-Quran, K-Nearest Neighbor, Information gain.
Modified Early Warning Score (MEWS) as Predictor of Deterioration Risk on Patient with Stroke in Emergency Unit in Malang

Stroke is a acute nerve function disorder due to disruption of blood circulation in the brain that occurs suddenly or quickly causes symptoms and signs that correspond to the affected focal area. Stroke is one of the diseases that cause death, disability, reduce a person's productivity, and increase the economic burden. This is related to the deterioration condition of the patient while in the emergency unit.