Reviewerly/exHarmony huggingface dataset:
Website Quality Analysis of Evaluation Development Monitoring System Using The Webqual 4.0 Method

In order to improve the implementation of good governance more empowered, managed to, clean and responsible, as well as in the framework of the realization of good Governance, the Government has issued various guidelines and rules that became the basis for the local government, either the province, district or city, to carry out monitoring and evaluation of development in the regions.
A Tractable, Expressive, and Eventually Complete First-Order Logic of Limited Belief

In knowledge representation, obtaining a notion of belief which is tractable, expressive, and eventually complete has been a somewhat elusive goal. Expressivity here means that an agent should be able to hold arbitrary beliefs in a very expressive language like that of first-order logic, but without being required to perform full logical reasoning on those beliefs. Eventual completeness means that any logical consequence of what is believed will eventually come to be believed, given enough reasoning effort. Tractability in a first-order setting has been a research topic for many years, but in most cases limitations were needed on the form of what was believed, and eventual completeness was so far restricted to the propositional case. In this paper, we propose a novel logic of limited belief, which has all three desired properties.
Sistem Pendukung Keputusan Penentuan Calon Penerima Beasiswa Prestasi di Universitas Amikom Yogyakarta

Amikom Yogyakarta University provides various types of scholarships to students, one of which is an achievement scholarship. the selection process requires precision and also fast, so that the results obtained are in accordance with the provisions, if the selection process is done manually then it will take a long time and easy errors occur. Decision support systems using the Tsukamoto fuzzy method can help select scholarship recipients by performing mathematical calculations by the system and produce weighting criteria for each student as a reference in decision makers. Based on the results of the research of each criteria weight, namely GPA, achievement and assessment of parents obtained test results with an accuracy of 80%.
Predicting the Argumenthood of English Prepositional Phrases

Distinguishing between arguments and adjuncts of a verb is a longstanding, nontrivial problem. In natural language processing, argumenthood information is important in tasks such as semantic role labeling (SRL) and prepositional phrase (PP) attachment disambiguation. In theoretical linguistics, many diagnostic tests for argumenthood exist but they often yield conflicting and potentially gradient results. This is especially the case for syntactically oblique items such as PPs. We propose two PP argumenthood prediction tasks branching from these two motivations: (1) binary argumentadjunct classification of PPs in VerbNet, and (2) gradient argumenthood prediction using human judgments as gold standard, and report results from prediction models that use pretrained word embeddings and other linguistically informed features. Our best results on each task are (1) acc. = 0.955, F1 = 0.954 (ELMo+BiLSTM) and (2) Pearson’s r = 0.624 (word2vec+MLP). Furthermore, we demonstrate the utility of argumenthood prediction in improving sentence representations via performance gains on SRL when a sentence encoder is pretrained with our tasks.
Extract and Merge: Merging extracted humans from different images utilizing Mask R-CNN

Selecting human objects out of the various type of objects in images and merging them with other scenes is manual and day-to-day work for photo editors. Although recently Adobe photoshop released "select subject" tool which automatically selects the foreground object in an image, but still requires fine manual tweaking separately. In this work, we proposed an application utilizing Mask R-CNN (for object detection and mask segmentation) that can extract human instances from multiple images and merge them with a new background. This application does not add any overhead to Mask R-CNN, running at 5 frames per second. It can extract human instances from any number of images or videos from merging them together. We also structured the code to accept videos of different lengths as input and length of the output-video will be equal to the longest input-video. We wanted to create a simple yet effective application that can serve as a base for photo editing and do most time-consuming work automatically, so, editors can focus more on the design part. Other application could be to group people together in a single picture with a new background from different images which could not be physically together. We are showing single-person and multi-person extraction and placement in two different backgrounds. Also, we are showing a video example with single-person extraction.
ATTENet: Detecting and Explaining Suspicious Tax Evasion Groups

In this demonstration, we present ATTENet, a novel visual analytic system for detecting and explaining suspicious affiliated-transaction-based tax evasion (ATTE) groups. First, the system constructs a taxpayer interest interacted network, which contains economic behaviors and social relationships between taxpayers. Then, the system combines basic features and structure features of each group in the network with network embedding method structure2Vec, and then detects suspicious ATTE groups with random forest algorithm. Last, to explore and explain the detection results, the system provides an ATTENet visualization with three coordinated views and interactive tools. We demonstrate ATTENet on a non-confidential dataset which contains two years of real tax data obtained by our cooperative tax authorities to verify the usefulness of our system.
RoLZ - The Reduced Offset LZ Data Compression Algorithm

The paper unveils an exotic data compression algorithm, called Reduced Offset Lempel Ziv (RoLZ). Unlike classical Lempel Ziv implementations, RoLZ uses a ‘reduced’ subset from which a possible ‘match’ set is chosen and also it minimizes the information needed to describe this match-length set. The big advantage of such approach is higher compression ratio, at some decompression speed expense. Our three algorithm embodiments for LZSS, LZP and RoLZ are tested against five types of data, and in all our tests, the compression ratio of RoLZ is far superior to LZSS’ or LZP's.
Polarity Classification Tool for Sentiment Analysis in Malay Language

&lt;p&gt;The popularity of the social media channels has increased the interest among researchers in the sentiment analysis(SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool(MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data.&lt;/p&gt;
Decision Support System for Restaurant Selection in Badung Regency with Geography Visualization

Decision Support System (DSS) is a system that can assist a person in making accurate decisions and on target. Many problems can be solved by using the DSS, which one is the selection of restaurants in Badung Region. There are several methods that can be used in building a DSS include Weighted Product (WP). WP is a method that the most widely used in solving problems with multiple criteria, such as DSS for selection of restaurants in Badung Region.&#x0D; This research uses the WP method for selection of restaurants in Badung Region. In determining the best choice of restaurants, there are several criterias that will use on taken the decisions, there are the quality of food, price, service, atmosphere, and distance. The value of each of these criteria derived from the reviews or ratings of other users, so these values are dynamic. User can freely set the value of the weight of each criterion to be compared in order to obtain the best results.&#x0D; The end result of this research is built a system that can help users to compare several restaurants that the results will be sorted from the best based on the results of the comparison weight that has been entere.
MakeSense: An IoT Testbed for Social Research of Indoor Activities

There has been increasing interest in deploying IoT devices to study human behaviour in locations such as homes and offices. Such devices can be deployed in a laboratory or `in the wild' in natural environments. The latter allows one to collect behavioural data that is not contaminated by the artificiality of a laboratory experiment. Using IoT devices in ordinary environments also brings the benefits of reduced cost, as compared with lab experiments, and less disturbance to the participants' daily routines which in turn helps with recruiting them into the research. However, in this case, it is essential to have an IoT infrastructure that can be easily and swiftly installed and from which real-time data can be securely and straightforwardly collected. In this paper, we present MakeSense, an IoT testbed that enables real-world experimentation for large scale social research on indoor activities through real-time monitoring and/or situation-aware applications. The testbed features quick setup, flexibility in deployment, the integration of a range of IoT devices, resilience, and scalability. We also present two case studies to demonstrate the use of the testbed, one in homes and one in offices.
SoS Paradigm Benefits SaaS Integration: Novel Approach and First Results

To fulfill its complex business demands, an organization may require the integration of many SaaS applications. A main challenge in this regard is to select the suitable SaaS offerings that satisfy all the business requirements. Due to the diversity of QoS needs and the large number of SaaS providers, selecting the suitable set of SaaS applications to be integrated may result in QoS degradation and higher costs. In this paper, we propose to use a System-of-Systems (SoS)-based approach for the selection and integration of SaaS. We believe that the use of SoS and its dynamic properties could improve the service selection at SaaS level. To do so, we propose three Mixed Integer Programming models that optimize the global SaaS behavior by minimizing the overall delivery costs, and maximizing the global functional and QoS utilities. The implementation of the approach and the experimental results highlight the efficiency of our proposed solution.
Convex hull algorithms based on some variational models

Seeking the convex hull of an object is a very fundamental problem arising from various tasks. In this work, we propose two variational convex hull models using level set representation for 2-dimensional data. The first one is an exact model, which can get the convex hull of one or multiple objects. In this model, the convex hull is characterized by the zero sublevel-set of a convex level set function, which is non-positive at every given point. By minimizing the area of the zero sublevel-set, we can find the desired convex hull. The second one is intended to get convex hull of objects with outliers. Instead of requiring all the given points are included, this model penalizes the distance from each given point to the zero sublevel-set. Literature methods are not able to handle outliers. For the solution of these models, we develop efficient numerical schemes using alternating direction method of multipliers. Numerical examples are given to demonstrate the advantages of the proposed methods.
Designing Reading Materials For ESP Students Vocational High School Of Medical Science

The study attempted to design reading materials for Medical Science students at one of Vocational High Schools in Serang. To produce satisfactory teaching materials, the researchers conducted the following steps: doing needs analysis, reviewing the principles of materials design and reading in a foreign language, designing course framework, syllabus, the reading materials, and implementing the sample lessons. Researchers employed qualitative methods in gathering the data. The instruments used were questionnaire and interview. The questionnaire was addressed to students, alumni, teachers and institution. The interview was collected from the users. The needs analysis was carried out by distributing questionnaires. The result of needs analysis, became the basis for the researchers to design course framework. The course framework was then developed into a syllabus. Finally the syllabus became the basis for designing reading materials. Reading materials for Vocational High School applied a topical syllabus. Each lesson or unit discusses different topics, greeting and introduction. Introduction about nurse, health, dialogue between nurse and patient, eat healthy food, profile a nurse, tools, disease, nutrition, the healthy diet pyramid. Each lesson also adopted various reading skills or strategies. They were skimming, scanning, and guessing meaning from context. When the materials had been designed, the researchers implemented six out of ten lessons of the coursebook. The results were satisfactory. Most students mentioned that the coursebook was related to their field of study. The majority of the students said that the materials were understandable and interesting. They also said that they were satisfied with the reading materials
Cross-Router Covert Channels

Many organizations protect secure networked devices from non-secure networked devices by assigning each class of devices to a different logical network. These two logical networks, commonly called the host network and the guest network, use the same router hardware, which is designed to isolate the two networks in software. In this work we show that logical network isolation based on host and guest networks can be overcome by the use of cross-router covert channels. Using specially-crafted network traffic, these channels make it possible to leak data between the host network and the guest network, and vice versa, through the use of the router as a shared medium. We performed a survey of routers representing multiple vendors and price points, and discovered that all of the routers we surveyed are vulnerable to at least one class of covert channel. Our attack can succeed even if the attacker has very limited permissions on the infected device, and even an iframe hosting malicious JavaScript code can be used for this purpose. We provide several metrics for the effectiveness of such channels, based on their pervasiveness, rate and covertness, and discuss possible ways of identifying and preventing these leakages.
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch Noise

Batch Normalization (BN)(Ioffe and Szegedy 2015) normalizes the features of an input image via statistics of a batch of images and hence BN will bring the noise to the gradient of the training loss. Previous works indicate that the noise is important for the optimization and generalization of deep neural networks, but too much noise will harm the performance of networks. In our paper, we offer a new point of view that self-attention mechanism can help to regulate the noise by enhancing instance-specific information to obtain a better regularization effect. Therefore, we propose an attention-based BN called Instance Enhancement Batch Normalization (IEBN) that recalibrates the information of each channel by a simple linear transformation. IEBN has a good capacity of regulating noise and stabilizing network training to improve generalization even in the presence of two kinds of noise attacks during training. Finally, IEBN outperforms BN with only a light parameter increment in image classification tasks for different network structures and benchmark datasets.
Digital video watermarking tools: an overview

Piracy and copyright infringement is a serious concern with internet connectivity becoming a necessity rather than luxury. Due to this, there is a constant need to come up with new copyright protection algorithms and also new watermarking tools to suit user's needs. This paper provides a survey on various video watermarking tools available in the market. This paper summarises the basic concepts in video watermarking, new attacks and latest applications of video watermarking which are evolving. Each tool has been described highlighting its pros and cons for its applicability. The challenges involved in watermarking of video content have also been detailed.
Improving Fluency Skill in English Speaking Through Role Playing Learning Strategy at English Study Program, Administration Business Department, State Polytechnic of Madiun

In the preliminary study, the researchers found that the average value of fluency in English speaking is 2.25. This is because of the teacher always uses monotonous, less enjoyable learning strategy and the teacher only focuses on completing the material so that the learning process can only transfer knowledge so that the level of mastery of the material is low which ultimately decreases student achievement. To overcome this problem, researchers propose a strategy in teaching speaking skill, namely role-playing learning strategy. This study is designed to improve fluency in English speaking by using role playing learning strategy. This study aims to explain how role-playing learning strategy can improve fluency in English speaking in the 4th semester, English Study Program, Business Administration Department, State Polytechnic of Madiun. This study uses a Classroom Action Research (CAR) design which is collaborative in nature where researchers and teacher collaborate in carrying out this research. Researcher act as observer while English teacher become collaborator of researcher in implementing role playing learning strategy. This research was carried out in a cycle that refers to procedures for action research, namely planning, implementing, observing, and reflecting. Each cycle in this study consisted of two meetings. The research data was collected through several instruments, namely evaluation of English speaking, questionnaires, and field notes. The subjects of this study were 4th semester of D3 Program students, English Study Program, Business Administration Department, State Polytechnic of Madiun. The results of the overall average score of fluency in English speaking in the first cycle of 2.44, in the second cycle of 2.89, and in the third cycle of 3.19. Classroom action research can be stopped in cycle III because it is in accordance with the average criteria of success is 3.01. The interesting of students towards the implementation of role-playing learning strategy shows that students are happy and interested in implementing role playing learning strategy.
Learning Conceptual-Contextual Embeddings for Medical Text

External knowledge is often useful for natural language understanding tasks. We introduce a contextual text representation model called Conceptual-Contextual (CC) embeddings, which incorporates structured knowledge into text representations. Unlike entity embedding methods, our approach encodes a knowledge graph into a context model. CC embeddings can be easily reused for a wide range of tasks just like pre-trained language models. Our model effectively encodes the huge UMLS database by leveraging semantic generalizability. Experiments on electronic health records (EHRs) and medical text processing benchmarks showed our model gives a major boost to the performance of supervised medical NLP tasks.
A Generalized Network for MRI Intensity Normalization

Image normalization, the correction for intra-volume inhomogeneities in magnetic resonance imaging (MRI) data has little significance for visual diagnosis, but is a crucial step before automated radiotherapy solutions. There are several well-established normalization methods, however they are usually time expensive and difficult to tune for a specific dataset. In this study, we show how an artificial neural network (ANN) can be trained on non-medical images --- making the model general --- for intensity normalization on medical MRI images. Compared to one of the most well-known correction methods, N4ITK, the trained network achieves a higher accuracy with a speedup-factor of almost 70.
Assessment of Enjoyment and Intensity of Physical Activity in Immersive Virtual Reality on the Omni-Directional Omni Treadmill and Icaros Flight Simulator in the Context of Recommendations for Health

The aim of the study is to assess enjoyment and intensity of physical exercise while practicing physical activity (PA) in immersive virtual reality (IVR) using innovative training devices (omni-directional Omni treadmill and Icaros Pro flight simulator). The study also contains the results of subjective research on the usefulness of such a form of PA in the opinion of users. In total, 61 adults (10 women and 50 men) took part in the study. To assess the enjoyment level (EL) Interest/Enjoyment subscale of Intrinsic Motivation Inventory (IMI) was used. Exercise intensity was assessed during 10-minute sessions of active video games (AVGs) in IVR based on heart rate (HR). The average enjoyment level during physical exercise in IVR on the tested training devices was relatively high (Omni 5.74 points, Icaros 5.60 points). In the opinion of the majority of participants, AVGs on IVR training devices constitute a sufficiently useful form of movement to meet the needs of PA practiced in free time, and they can even replace some of the classic forms of movement. Intensity of PA during games on training devices was at the level recommended for health benefits for 92% (Omni) and 84% (Icaros Pro) of its duration. Based on the conducted research, it can be assumed that AVGs in IVR using a multi-directional treadmill and a flight simulator can be an effective tool for increasing participation in health-oriented PA.
Real-Time Elderly Healthcare Monitoring Expert System Using Wireless Sensor Network.

Elderly chronic diseases are the main cause of death in the world, accounting 60% of all death. Because elderly with chronic diseases at the early stages has no observed symptoms, and then symptoms starts to appear, it is critical to observe the symptoms as early as possible to avoid any complication. This paper presents an expert system for an Elderly Health Care (EHC) at elderly home tailored for the specific needs of Elderly. The proposed EHC aims to develop an integrated and multidisciplinary method to employ communication technologies and information for covering real health needs of elderly people, mainly of people at high risk due to social and geographic isolation in addition to specific chronic diseases. The proposed EHC provides personalized intervention plans covering chronic diseases such as (body temperature (BT), blood pressure (BP), and Heart beat rate (HR)). The processes and architecture of the proposed EHC are based on the server side and three main clients, one for the elderly and another two for the nurse and the physicians whom take care of them. The proposed EHC model is discussed for proving the usefulness and effectiveness of the expert system.