Reviewerly/exHarmony huggingface dataset:
PENILIKAN KESALAHAN BERBAHASA INDONESIA YANG BAIK DAN BENAR DALAM KONTEKS SOSIAL-MASYARAKAT DI RUANG PUBLIK

This research is a qualitative descriptive study. Data and data sources used in this study are in the form of pictures or photographs in the field. One characteristic of qualitative research is the data collected in the form of words, images, and not numbers. The researcher also uses field notes in the form of observation notes and other sources. The data needed to answer the problems that want to be solved are (1) data on the types of errors in Indonesian that are good and correct in the public space, (2) data regarding social-community responses to errors in Indonesian that are good and correct in public space. Data collection techniques used in this study are observation and documentation. The results of the research obtained are (1) errors in Indonesian that are good and correct in the public space are divided into three parts, namely spelling mistakes, diction, and structure, (2) the most dominant use of Indonesian language is diction errors or word choices, ( 3) errors in public spaces contained in posters or nameplate / institutions / stores, contained in newspapers or mass media, and contained in social media, (3) responses in the social-community environment are indifferent and do not know that there are errors in Indonesian that are good and correct in the public space.
Application of mobile frequency-resonance methods of satellite images and photo images processing for water accumulations searching

Summary The results of additional experimental studies are presented with the aim of studying the possibility of using methods of frequency resonance processing and interpretation of satellite and photo images for the aquifers searching. Investigations of a reconnaissance character were carried out on a mineral water deposit, the territory of a summer cottage, the offshore of the Atlantic coast of the USA, in the areas of lakes and the Aral Sea, as well as in certain regions of longevity. Additional materials have been obtained that testify to the synthesis of water at a depth of 69 km in volcanic complexes of a certain type. A survey of longevity zones in various regions showed that all of them are located within volcanic structures filled with basalts. In these volcanoes, water is synthesized at a depth of 69 km and hydrogen migrates. Hydrogen-enriched water promotes longevity. The results of the developed direct-prospecting methods testing in various regions of the world testify to their efficiency, effectiveness and practical applicability in the search and exploration of aquifers, as well as ore and combustible minerals.
VisImages: A Large-scale, High-quality Image Corpus in Visualization Publications.

Images in visualization publications contain rich information, such as novel visual designs, model details, and experiment results. Constructing such an image corpus can contribute to the community in many aspects, including literature analysis from the perspective of visual representations, empirical studies on visual memorability, and machine learning research for chart detection. This study presents VisImages, a high-quality and large-scale image corpus collected from visualization publications. VisImages contain fruitful and diverse annotations for each image, including captions, types of visual representations, and bounding boxes. First, we algorithmically extract the images associated with captions and manually correct the errors. Second, to categorize visualizations in publications, we extend and iteratively refine the existing taxonomy through a multi-round pilot study. Third, guided by this taxonomy, we invite senior visualization practitioners to annotate visual representations that appear in each image. In this process, we borrow techniques such as gold standards and majority voting for quality control. Finally, we recruit the crowd to draw bounding boxes for visual representations in the images. The resulting corpus contains 35,096 annotated visualizations from 12,267 images with 12,057 captions in 1397 papers from VAST and InfoVis. We demonstrate the usefulness of VisImages through the following four use cases: 1) analysis of color usage in VAST and InfoVis papers across years, 2) discussion of the researcher preference on visualization types, 3) spatial distribution analysis of visualizations in visual analytic systems, and 4) training visualization detection models.
Analyses and Encryption Implementation of a New Chaotic System Based on Semitensor Product

Semitensor product theory can deal with matrices multiplication with different numbers of columns and rows. Therefore, a new chaotic system for different high dimensions can be created by employing a semitensor product of chaotic systems with different dimensions, so that more channels can be selected for encryption. This paper proposes a new chaotic system generated by semitensor product applied on Qi and Lorenz systems. The corresponding dynamic characteristics of the new system are discussed in this paper to verify the existences of different attractors. The detailed algorithms are illustrated in this paper. The FPGA hardware encryption implementations are also elaborated and conducted. Correspondingly, the randomness tests are realized as well, and compared to that of the individual Qi system and Lorenz system, the proposed system in this paper owns the better randomness characteristic. The statistical analyses and differential and correlation analyses are also discussed.
Research on Forensics of Social Network Relationship Based on Big Data

Abstract With the development of technologies such as big data, cloud computing, and mobile Internet, many cybercrimes have changed from the previous crime mode of single-person and single-machine to the crime mode of multi-person and multi-machine. Due to the popularity of mobile phones and various mobile terminal applications, many criminals often communicate with other members and commit crimes by mobile phones. Therefore, it is very important for the forensics of the electronic data in the instant communication tools. Much valuable information such as personal common contacts, chat content can be found in the mobile phone, which can help the investigators take the evidence of social network relationship and find many key clues. This paper expounds the forensics mode of social network relationship and the forensics process of mobile phones, and puts forward the forensics method of social network relationship based on Wechat platform, analyses the instance data set, obtains the social network diagram, and intuitively and clearly shows the relationship and intimacy between multiple members. The forensics of Social network relationship can help the investigation organizations narrow the scope of investigation and improve the efficiency of handling cases.
Dynamic fringe phase estimation in controllable phase-shifting interferometry by adaptive Wiener filtering

This paper presents the results of using adaptive Wiener filtering to dynamically estimate the parameters of interferometric signals. It is shown that the interference-fringe parameters, including the phase of the fringes, can be directly computed from the coefficients obtained by an adaptive Wiener filter and the criterion of the minimum error variance. The method considered here introduces no limitations on the number and size of the given phase shifts and makes it possible to determine the actual phase shift. Estimates are given of the errors of the method when observation noise and random deviations of the phase-shift step are present.
New Trends of Deep Learning in Clinical Cardiology

Deep Learning (DL) is a novel type of Machine Learning (ML) model. It is showing an increasing promise in medicine, study and treatment of diseases and injuries, to assist in data classification, novel disease symptoms and complicated decision making. Deep learning is one of form of machine learning typically implemented via multi-level neural networks. This work discusses the pros and cons of using DL in clinical cardiology that is also applied in medicine in general while proposing certain directions as more viable for clinical use. DL models called Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) have been applied to arrhythmias, electrocardiogram, ultrasonic analysis, genomes and endomyocardial biopsy. Convincingly, the results of the trained model are satisfactory, demonstrating the power of more expressive deep learning algorithms for clinical predictive modeling. In the future, more novel deep learning methods are expected to make a difference in the field of clinical medicines.
Improvement of One-Dimensional Fisherface Algorithm to extract the Features (Case study: Face Recognition)

Abstract Recently, computer vision research results have supported many sectors to assist and solve problems. One of branch of the computer vision fields is biometric system. Many modalities have been implemented to depict the human characteristics. Face is one of the modalities that has been employed to recognize the human. A crucial problem of the face recognition is high dimensionality. The problem would impact on the computational performance, and even it could cause the process failure. Feature extraction is the solution to reduce the dimensionality. However, many cases have shown that feature extraction could fail as singularity problem. In this research, we proposed the improvement of the fisherface algorithm to solve the singularity problem. We have modified the singularity covariance matrix so that the matrix can be further handled and processed. The purpose of the paper is to improve the performance of the fisherface algorithm. We have verified our proposed algorithm by using the Olivetty Research Laboratory face image. We applied 7-cross validations to evaluate our proposed algorithm, the evaluation results achieved more than 92% accuracy.
Dishcover Indonesia: Android Cooking Game Design, Mechanics and Development

Video games is a growing medium that has been around for decades, and the mobile device market for video games has started exponentially evolving from recent years. Indonesia's game development community is still budding and has more room to expand in continuing years. With Indonesia's rich culture it's a wonder how there are not many popular games that revolve around it, especially one that includes its unique cuisine. Subsequently, cooking games too have slowed down within the market where current games are not able to satisfy consumers therefore none are playing games from the genre. The authors decided to develop a game that utilizes both the cooking game genre and Indonesia's cuisine to appeal to the Android market. This is done through a survey for market research and player testing to accomplish a game worthy of the expectations of the current market.
Two-Way Recommendation System for Intelligent Employment of College Students Based on Data Mining

Based on big data and the new era, the information employment service technology revolution in China’s smart universities brought about by high-tech applications such as mobile internet, internet of things and cloud computing has put forward new technological transformation points and demands for the construction of the ideology of information service for smart employment and the mode of employment, work and life of Chinese college students. In order to make the employment of college students and the recruitment of enterprises more efficient, we have designed a “two-way recommendation system for intelligent employment of college students” by means of data mining and other means, so as to achieve the effect of intelligent integration, reorganization and intelligent sharing of high-quality relevant employment resources and improve the efficiency of employment services for college students [1].
Hybrid Load-Balanced Scheduling in Scalable Cloud Environment

Cloud computing is a high computational distributed environment with high reliability and quality of service. It is playing an important role in the next generation of computing with pay per use model and high elasticity. With increased requirement for cloud resources, load over the cloud servers has increased, which makes cloud use a more efficient algorithm to maintain its performance and quality of service to users. The performance metrics that define the performance of task scheduling include execution time, finish time, scheduling time, task completion cost, and load balancing on each computing resources. So, to overcome existing solutions and provide better QoS performance, a neural-network-based GA-ANN scheduling algorithm is proposed in this paper, which outperforms the existing solutions. To simulate the proposed GA-ANN model, cloudsim3.0 toolkit is used, and the performance is evaluated by comparing simulation time, average start time, average finish time, execution time, and utilization percentage of computing resources (VMs).
Spatio‐Temporal Generative Adversarial Networks

We designed a spatiotemporal generative adversarial network which given some initial data and random noise, generates a consecutive sequence of spatiotemporal samples that have a logical relationship. We build spatial discriminators and temporal discriminators to distinguish whether the samples generated by the generator meet the requirements for time and space coherence. The model is trained on the skeletal dataset and the Caltrans Performance Measurement System District 7 dataset. In contrast to traditional Generative adversarial networks (GANs), the proposed spatiotemporal GAN can generate logically coherent samples with the corresponding spatial and temporal features while avoiding mode collapse. In addition, we show that our model can generate different styles of spatiotemporal samples given different random noise inputs. This model will extend the potential range of applications of GANs to areas such as traffic information simulations and multiagent adversarial simulations.
Recommendation System for E-Commerce by Memory Based and Model Based Collaborative Filtering

Usage of internet is growing rapidly and became more and more important in every aspect of life. Everyone is addicted to use the internet and enjoy its advantages. One of the key advantages with the internet is E-commerce. E-commerce being an online market facilitates the users to a greater extent. In the past, people used to buy the goods by going to the shops and markets but now everyone is using E-commerce to buy the goods. In past if people want to search for a product, they could directly ask the shop owner and he would provide it, if he had it. But in the E-commerce it is headache of the customer to search for the product as it is vast. To avoid this, recommendation systems are used. These recommendation systems recommend products for the users and help the users to take correct decision and also help for the growth of E-commerce. There are different types of recommendation systems such as content based, collaborative, hybrid etc. Variety of algorithms are been used by various researchers based on the application area and the requirements of the end user. In this paper, we propose a collaborative filtering recommendation system.
DPARM: Differentially Private Association Rules Mining

Association analysis is critical in data analysis performed to find all co-occurrence relationships ($i.e$ ., frequent itemsets or confident association rules) from the transactional dataset. An association rule can improve the ability of users to discover patterns and develop corresponding strategies. The data analysis process can be summarized as a set of queries, where each query is a real-valued function of the dataset. However, unless restrictions and protections are implemented, accessing the dataset to answer the queries may lead to the disclosure of the private information of individuals. In this paper, we propose an original differentially private association rules mining (DPARM) algorithm, which uses multiple support thresholds to reduce the number of candidate itemsets while reflecting the real nature of the items and uses random truncation and uniform partition to reduce the dimensionality of the dataset. Both of these elaborated approaches can aid in reducing the sensitivity of the queries, and this dramatically reduces the scale of the required noise and improves the utility of the mining results. We significantly stabilize the noise scale by adaptively allocating the privacy levels and bound the overall privacy loss. Through a series of experiments, we prove that our DPARM algorithm outperforms the literature in the accuracy of data mining while satisfying differential privacy. To the best of our knowledge, our work is the first DPARM algorithm to adopt multiple support thresholds while using a set of elaborated approaches to bound the overall privacy loss of the mining process.
Enabling XCSF to cope with dynamic environments via an adaptive error threshold

The learning classifier system XCSF is a variant of XCS employed for function approximation. Although XCSF is a promising candidate for deployment in autonomous systems, its parameter dependability imposes a significant hurdle, as a-priori parameter optimization is not feasible for complex and changing environmental conditions. One of the most important parameters is the error threshold, which can be interpreted as a target bound on the approximation error and has to be set according to the approximated function. To enable XCSF to reliably approximate functions that change during runtime, we propose the use of an error threshold, which is adapted at run-time based on the currently achieved approximation error. We show that XCSF with an adaptive error threshold achieves superior results over static thresholds in dynamic scenarios, where in general there exists no one-fits-all static threshold.
Do High-Performance Work Practices Influence Organizational Innovation? Mediating Role of Human Capital

Due to globalization and competitive business environment, organizations need to update their information to remain innovative and competitive. Moreover, high-performance work practices (HPWPs) play a vital role that determines organizational success. The current study is performed to scrutinize the effect of high-performance work practices on the organizational innovation with the mediating effect of human capital. Survey questionnaire method was adopted, and data were collected from 202 employees by applying simple random sampling technique. Scales validation has been checked by confirmatory factor analysis (CFA) by using Smart PLS. Moreover, the structure equation modeling technique (SEM) has also been used to test the hypotheses. Findings revealed that there is a positive and significant relationship between high-performance work practices (participation, empowerment, work-life balance, recruitment &amp; selection) and organizational innovation. Additionally, results also purported that human capital significantly mediates the relationship b/w high-performance work practices (participation, empowerment, work-life balance, recruitment &amp; selection) and organizational innovation.
Continuous tracking of moving objects in long-distance imaging through a turbulent medium using a 3D point cloud analysis

Preserving object detections and maintaining continuous, uninterrupted tracking routes of moving objects is a difficult challenge in long-distance imaging through a turbulent medium that extremely degrades the video. We introduce a new method for the continuous tracking of moving objects in such conditions. First, a number of steps are applied, including global video stabilization, reduction of local oscillations, and finding spatio-temporal “suspicious sections” in the video that may contain moving objects. This phase produces a 3D point cloud that represents suspicious moving objects. Subsequently, the possible routes in the cloud are analyzed, producing the final routes. A comparison to the current standard shows an improvement in the object tracking stability.
Use of 3D Animation Software in Visualizing Architectural Works

Abstract The architect is needed something to explain their product result from each stage of the design process. The tool is something that is used by the architect in explaining their product result with a 3D presentation. The purpose of this study is to describe how influencing and important of 3D Presentation (such as the animation) with the architectural works for architects. The software that was used for the visualization is the lumion, which is often used by professional architects. The method of this research used the interview and questionnaires with some architects. Based on the result, the animation can make easier for the architect to explain and elaborate their concepts to the others as well as their Client. The lumion software is the easier one to apply for the architect to present their design.
Statistical Analysis of the Frequency of Damage Accumulation in the Structure of Epoxy Composites Under Tensile Loads

Statistical analysis of the frequency of damage accumulation in the structure of epoxy polymer samples subjected to tensile loads was performed. Samples of polymers produced on the basis of epoxy modified binder Etal-247 cured with hardener Etal-45M, which is a mixture of aromatic and aliphatic di- or polyamines, were chosen as the object of research. The main criterion for determining the coordinates of “critical” points was the results obtained using the author’s method for estimating changes in the fractality index based on the use of the least coverage method. The values of tensile stresses and relative elongations registered with high reading frequency (0.01 s) were used as input data. The levels of “critical” states corresponded to the values of fractality indices less than 0.5. The levels of accumulated damage were revealed with the excess leading to sample destruction. It has been established that in order to obtain the most objective results in the study of the damage accumulation kinetics, it is advisable to analyze not a single “most representative” sample, but a number of samples of the studied structure.
BesFS: A {POSIX} Filesystem for Enclaves with a Mechanized Safety Proof

New trusted computing primitives such as Intel SGX have shown the feasibility of running user-level applications in enclaves on a commodity trusted processor without trusting a large OS. However, the OS can still compromise the integrity of an enclave by tampering with the system call return values. In fact, it has been shown that a subclass of these attacks, called Iago attacks, enables arbitrary logic execution in enclave programs. Existing enclave systems have very large TCB and they implement ad-hoc checks at the system call interface which are hard to verify for completeness. To this end, we present BesFS--the first filesystem interface which provably protects the enclave integrity against a completely malicious OS. We prove 167 lemmas and 2 key theorems in 4625 lines of Coq proof scripts, which directly proves the safety properties of the BesFS specification. BesFS comprises of 15 APIs with compositional safety and is expressive enough to support 31 real applications we test. BesFS integrates into existing SGX-enabled applications with minimal impact to TCB. BesFS can serve as a reference implementation for hand-coded API checks.