Reviewerly/exHarmony huggingface dataset:
Distinguishing Similar Design Pattern Instances through Temporal Behavior Analysis

Design patterns (DPs) encapsulate valuable design knowledge of object-oriented systems. Detecting DP instances helps to reveal the underlying rationale, thus facilitates the maintenance of legacy code. Resulting from the internal similarity of DPs, implementation variants, and missing roles, approaches based on static analysis are unable to well identify structurally similar instances. Existing approaches further employ dynamic techniques to test the runtime behaviors of candidate instances. Automatically verifying the runtime behaviors of DP instances is a challenging task in multiple aspects. This paper presents an approach to improve the verification process of existing approaches. To exercise the runtime behaviors of DP instances in cases that test cases of legacy systems are often unavailable, we propose a markup language, TSML (Test Script Markup Language), to direct the generation of test cases by putting a DP instance into use. The execution of test cases is monitored based on a trace method that enables us to specify runtime events of interest using regular expressions. To characterize runtime behaviors, we introduce a modeling and specification method employing Allen's interval-based temporal relations, which supports variant behaviors in a flexible way without hard-coded algorithms. A prototype tool has been implemented and evaluated on six open source systems to verify 466 instances reported by five existing approaches with respect to five DPs. The results show that the dynamic analysis increases the F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score by 53.6% in distinguishing similar DP instances.
Confidence-Based Voting for the Design of Interpretable Ensembles with Fuzzy Systems

In this study, a new voting procedure for combining the fuzzy logic based classifiers and other classifiers called confidence-based voting is proposed. This method combines two classifiers, namely the fuzzy classification system, and for the cases when the fuzzy system returns high confidence levels, i.e., the returned membership value is large, the fuzzy system is used to perform classification, otherwise, the second classifier is applied. As a result, most of the sample is classified by the explainable and interpretable fuzzy system, and the second, more accurate, but less interpretable classifier is applied only for the most difficult cases. To show the efficiency of the proposed approach, a set of experiments is performed on test datasets, as well as two problems of estimating the person’s emotional state with the data collected by non-contact vital sensors, which use the Doppler effect. To validate the accuracies of the proposed approach, the statistical tests were used for comparison. The obtained results demonstrate the efficiency of the proposed technique, as it allows for both improving the classification accuracy and explaining the decision making process.
Constraining deep representations with a noise module for fair classification

The recent surge in interest for Deep Learning (motivated by its exceptional performances on longstanding problems) made Neural Networks a very appealing tool for many actors in our society. One issue in this shift of interest is that Neural Networks are very opaque objects and it is often hard to make sense of their predictions.
Text Mining Strategy of Power Customer Service Work Order Based on Natural Language Processing Technology

In order to improve the level of power customer service, we need to start with natural language processing technology, and conduct in-depth text mining of power customer complaint work order. Combined with the format and characteristics of power customer service work order, this paper proposed a text mining strategy of power customer service work order based on natural language processing technology which includes the following processes: work order data cleaning, text segmentation, information characterization, model training and model evaluation is proposed. The TF-IDF algorithm and the model optimization method based on accuracy calculation are applied in this strategy, which greatly improves the level of power customer service.
Comparison of Pornographic Image Classification based on Texture, Color, and Shape Features

Content filtering application on the internet is very important to protect children from various negative content such as pornography and violence. It is necessary to find a detection tool for pornographic images with high accuracy and fairly low computational time. The classification algorithm is responsible for distinguishing pornographic images and non-pornographic images. This research aims to find the optimum classifier among available algorithms. To achieve these objectives, we compare the existing classification algorithm namely: K Nearest Neighbors (KNN), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Decision Tree (DT), Random Forest (RF), Multi Layers Perceptron (MLP), and Support Vector Machine (SVM). Classification algorithm needs features as the input. Therefore we need a feature extraction algorithm to do so. In this study, we evaluate some features which are Haralick texture (texture features), Color Histogram (color features), and Hu Moment (shape features). The test results show that the most suitable method for classification of pornographic images is the Random Forest (RF) with an accuracy of 91.04% and the computational time per image is 0.80 ms.
An Automated End-to-End Side Channel Analysis Based on Probabilistic Model

In this paper, we propose a new automated way to find out the secret exponent from a single power trace. We segment the power trace into subsignals that are directly related to recovery of the secret exponent. The proposed approach does not need the reference window to slide, templates nor correlation coefficients compared to previous manners. Our method detects change points in the power trace to explore the locations of the operations and is robust to unexpected noise addition. We first model the change point detection problem to catch the subsignals irrelevant to the secret and solve this problem with Markov Chain Monte Carlo (MCMC) which gives a global optimal solution. After separating the relevant and irrelevant parts in signal, we extract features from the segments and group segments into clusters to find the key exponent. Using single power trace indicates the weakest power level of attacker where there is a very slight chance of acquiring as many power traces as needed for breaking the key. We empirically show the improvement in accuracy even with presence of high level of noise.
The Use of Apriori Algorithm in the Formation of Association Rule at Lotteria Cibubur

Lotteria as one of the franchises that produce sales data every day, has not been able to maximize the utilization of that data. The sale data storage is still not optimal. By utilizing sales transaction data that have been stored in the database, the management can find out the menus purchased simultaneously, using the association rule. Namely, data mining techniques to find the association rules of a combination of items. The process of searching for associations uses the help of apriori algorithms to produce patterns of the combination of items and rules as important knowledge and information from sales transaction data. By using the minimum support parameters, the minimum and the month period of the sales transaction to find the association rules, the data mining application generates association rules between items in April 2019, where consumers who buy hot / ice coffee will then buy float together with support of 16% and 100% confidence. Knowing which menu products or items are the most sold, thus lotteria Cibubur can develop a sales strategy to sell other types of menu products by examining the advantages of the most sold menu with other menus and can increase the stock of menu ingredients.
Expressing disambiguation filters as combinators

Contrarily to most conventional programming languages where certain symbols are used so as to create non-ambiguous grammars, most recent programming languages allow ambiguity. These ambiguities are solved using disambiguation rules, which dictate how the software that parses these languages should behave when faced with ambiguities. Such rules are highly efficient but come with some limitations - they cannot be further modified, their behaviour is hidden, and changing them implies re-building a parser.
Developing a Mobile Application for Safety Engineers for Hazard Analysis

Hazards can be defined as any source of potential damage/harm and they have a high probability of happening in our daily life and workplaces. Hazards are the main reason behind occupational accidents and occupational health and safety problems. Hazard analysis techniques are methods that reduce or monitor hazards in our daily life and workplaces. In general, workplace environments have different types of hazard such as ergonomic, physical, biological, chemical, and others. Hazard analysis requires a process of observation, data collection and writing a report to identify and analyze hazards. The occupational safety and health related hazards can be reduced through using hazard analysis techniques. Since hazard analysis requires statistical data collection and writing report while safety engineers is observing the hazards, a tool such as a mobile application will reduce the time and the paper work by converting safety engineers' observation to a report immediately. Therefore, it is important to develop mobile application that will help safety engineers to assess hazard analysis in a quick and systematic way. In this paper, steps of developing a mobile application for hazard analysis for safety engineers are explained. The process of defining features, implementing features into the mobile application, and evaluation process of the mobile application are discussed in detail.
g-Inspector: Recurrent Attention Model on Graph

Graph classification problem is becoming one of research hotspots in the realm of graph mining, which has been widely used in cheminformatics, bioinformatics and social network analytics. Existing approaches, such as graph kernel methods and graph Convolutional Neural Network, are facing the challenges of non-interpretability and high dimensionality. To address the problems, we propose a novel recurrent attention model, called g-Inspector, which applies the attention mechanism to investigate the significance of each region to make the results interpretable. It also takes a shift operation to guide the inspector agent to discover the next relevant region, so that the model sequentially loads small regions instead of the entire large graph, to solve the high dimensionality problem. The experiments conducted on standard graph datasets show the effectiveness of our g-Inspector in graph classification problems.
Research on Rural Community Construction with Modern Rammed Earth Materials

Abstract The soil exists in the vast countryside. Rammed earth materials are an important part of the growth process of rural dwellings. With the emergence of the homogenization of architectural style and the lack of cultural atmosphere of rural characteristics in the construction of beautiful countryside, the development and application of modern rammed earth materials will help the future construction of rural communities. Taking rammed earth dwelling houses in Sichuan as the research object, this paper constructs the livability evaluation system of rammed earth community, analyses the evaluation of residents’ satisfaction, and thus obtains the optimization strategy of modern rammed earth dwelling houses. From the theoretical perspective of "Ternary mutualism", the relationship between modern rammed earth materials and rural communities is mutually beneficial, that is, modern rammed earth materials are the natural foundation, charm display window and practice platform of rural communities, and rural communities are the embodiment of the value of modern rammed earth materials, protection continuation and multiple innovation. Based on this, the construction path of modern rammed earth materials in rural communities is proposed, in order to provide reference for the construction of rural communities using modern rammed earth materials. The research results show that modern rammed earth material, as a unique construction material, is conducive to the construction of rural communities in harmony with the natural ecological environment of the local landscape, and plays a role in the protection and inheritance of local culture.
The Search and Improvement of DES Algorithm for Data Transmission Security in SCADA

With the application of SCADA (Supervisory Control and Data Acquisition) is becoming more and more extensive in the national critical infrastructure. At the same time, compared with conventional control system, the data communication of SCADA system becomes more diverse and complex, and the communication system is more likely to be attacked, so improving the level of information security of SCADA system is very important and urgent. However, the wide application of DES has a short key in the world, which cannot meet the demand of the current network security. In this paper, ADES (Advanced Data Encryption Standard) algorithm is proposed and implemented in the C++ language, which is based on the deep research of data encryption methods and DES algorithm particularly. The method extends the length of DES from 64 bits to 128 bits. The function of ADES algorithm is closed to SM4 by experimental result, so the ADES can well meet the requirement of data transmission security.
College Students Learning Behavior Analysis Based on SVM and Fisher-Score Feature Selection

With the development of modern educational methods, new modes and platforms for college students’ learning have appeared. Through the scientific statistics and analysis of students’ learning behaviors, we can find the regular pattern contained in these data, discover students’ interest goals and predict learning effects, and provide students with targeted and personalized learning guidance. At present, the common analysis methods are mainly K-means clustering method. Considering the support vector machine has higher classification accuracy, this paper proposes a support vector machine analysis method based on Fisher-Score feature selection for students learning behavior analysis. Firstly, through the Fisher-Score feature selection, the key features in the learning behavior are selected, and the honor features unrelated to the learning effect are removed, and then the data analysis is performed by the SVM classifier. The verification results show that our method has better accuracy.
Research on SSD base network

Abstract In this paper, eight neural networks are selected instead of VGG16 in the SSD model to construct the corresponding SSD model and were tested on ImageNet and VOC datasets. The accuracy, predict time and model size of the eight neural networks in object classification are analyzed. The mAP, FPS and model size of the eight neural networks as the SSD base network in the object detection are analyzed. The result can provide reference for object detection tasks under specific requirements.
Towards the Development of a Novel Service Cost Modeling: An ISP Perspective

Accurate costing provides insightful information to any Internet Service Provider (ISP) for better network planning, profits, and decision making. Developing accurate cost models for communication network services has always been a challenge for ISPs / cloud operators due to the complex nature of today's advanced shared cloud and network infrastructure. Currently, developing and maintaining such cost models require significant effort and time by the network planners in an ISP. The proposed novel methodology reduces the cycle time for the development of the cost model significantly, which leads to the ISP's operational cost savings. We have considered an Internet Protocol Virtual Private Network (IPVPN) service for the study. IPVPN creates a logical virtual network by providing an encrypted and dedicated data connection. We conducted simulations to validate the accuracy and effectiveness of the proposed model. We have compared our proposed method with the existing approach and compute the performance improvement cost gap for different network sizes. To prove the operational savings, we evaluated a quantitative example concerning both methods investigated. We also experimented with a machine learning technique for the study, which provided complementary results.
Decipherment of the Written and Printed Matter on the Charred Document using UV and IR Under Different Wavelengths

Disputed documents in forensic investigations are the documents whose authenticity is suspicious or uncertain. Guilty people, most of the time, try to burn the essential documents to hide their criminal activities and wrong intentions. This is usually achieved by exposing the document to fire with or without the use of an accelerant like kerosene or petrol. These burnt documents maybe later on linked to a substantial act of forgery, fraud, ransoms or any other serious offense. Contemporary work has been attempted to study experimentally, certain critical documents such official stamps for the effect of burning on these documents and further how these charred documents can be subjected to the decipherment of any important matter. For this purpose, some official stamps with specific pre-written/pre-printed matter were considered in the study. The sample documents were first analyzed for their various characteristics and security features and then after charring. The charred were then appropriately gathered and were then analyzed under equipment projectina inspec-8 by utilizing diverse light sources (non-destructive sources) for example, UV, IR, and Retro-reflective light viewed under different wavelengths. Outcomes of this work have demonstrated that with the careful examination of the charred documents using appropriate nondestructive and specific light techniques like UV/IR and retro light, the charred matter can be retrieved up to the extent that can be used further criminological investigations. The writings were comparatively more visible under different wavelengths of lights. The results were recorded through proper photographic techniques.
Bitcoin Data Analytics: Exploring Research Avenues and Implementing a Hadoop-Based Analytics Framework

Bitcoin is the most successful cryptocurrency since its inception in 2009 [30]. There are 18.1 million BTCs in circulation as of December 2019, which roughly translates to 149 Billion USD [12]. With Bitcoin’s substantial market capitalization and unique features like pseudo-anonymity and immutability, it draws much attention from the researchers across the world. Despite this enormous spotlight towards Bitcoin, it remains under-researched because of the large size of the Bitcoin Data, (Roughly 250 GB) and the inability to process this data in small time. To explore avenues for further research, this article presents a survey of the recent advancements done regarding the big data analytics of the Bitcoin Cryptocurrency. Furthermore, we propose an analysis framework based on the Apache Hadoop ecosystem.
Short Semantic Patterns: A Linguistic Pattern Mining Approach for Content Analysis Applied to Hate Speech

Microblog posts such as tweets frequently contain users’ opinions and thoughts about events, products, people, institutions, etc. However, the usage of social media to prop-agate hate speech is not an uncommon occurrence. Analyzing hateful speech in social media is essential for understanding, fighting and discouraging such actions. We believe that by extracting fragments of text that are semantically similar it is possible to depict recurrent linguistic patterns in certain kinds of discourse. Therefore, we aim to use these patterns to encapsulate frequent statements textually expressed in microblog posts. In this paper, we propose to exploit such linguistic patterns in the context of hate speech. Through a technique that we call SSP (Short Semantic Pattern) mining, we are able to extract sequences of words that share a similar meaning in their word embedding representation. By analyzing the extracted patterns, we reveal some kinds of discourses that are replayed across a dataset, such as racist and sexist statements. Afterwards, we experiment using SSP as features to build classifiers that detect if a tweet contains hate speech (binary classification) and to distinguish between sexist, racist and clean tweets (ternary classification). The SSP instances encountered in tweets containing sexism have shown that a large number of sexist tweets began with the introduction ‘I’m not sexist but’ and ‘Call me sexist but’. Meanwhile, SSP instances found in tweets reproducing racism revealed a prominence of contents against the Islamic religion, associated entities and organizations.
EVALUASI PENERAPAN SIM-RS MENGGUNAKAN COBIT 5 PADA RSUD LAWANG

Information technology (IT) has become a crucial part for companies or institutions with enterprise scale. The problem in this research is the service of patients in RSUD Lawang has not been entirely done using the SIM-RS application directly. This study focuses on the implementation of the SIM-RS in Lawang District Hospital in providing health services to the community. This research standard uses COBIT 5 standard with APO 07 domain which discusses the preparation of HR in Lawang District Hospital, BAI 07 discusses readiness in obtaining new information systems (SIM-RS), and DSS 01 discusses procedural development and treatment of all forms operations, infrastructure, and facilities related to the information system used. The conclusions of this study are: (1) The level reached by the APO 07 domain is at level 1. This is due to the application of the SIM-RS in the Lawang District Hospital, which has a base practice but in the design of activities that have not been adequately monitored and evaluated and the work products that have not been applied, controlled, and maintained as appropriate. (2) The level achieved by the BAI 07 domain is at level 2. This is due to the application of the SIM-RS in Lawang District Hospital, which has a planned base of practice, is well monitored and evaluated. Its implementation is following agency needs. But related to the implementation process is still not following established standards. (3) The level achieved by the DSS 01 domain is at level 1. This is due to the application of the SIM-RS in Lawang District Hospital having a base practice. Still, in the design of activities, it has not been adequately monitored and evaluated, and the work products they have not been implemented, controlled, and maintained as should be.
Symbols and Functions in Human Machine Interface: Are Google Icons a Possible Solution for Intercultural Usability?

We explored the possibility of taking icons from Android Smartphones, for the use in graphical user interfaces (GUI) in bakery sheeter. This idea was generated from the fact that Android Smartphones are sold worldwide and that the icons used in the menu should have a degree of popularity that most people could deal with. With this research question in mind we created a mixed method design for the study (qualitative and quantitative data). Results show that some icons were able to represent basic navigation function in industrial machines.