Reviewerly/exHarmony huggingface dataset:
Ontology based Semantic Query Expansion for Searching Queries in Programming Domain

Information on the web is growing rapidly; learning programming languages has become one of the most widely searched topics.Programmers and people of several backgrounds are now using Web search engines to acquire programming information, including information about a specific language or topic.Nonetheless, due to a lack of programming knowledge, many laypeople have difficulties in forming appropriate queries to articulate their inquiries, so they used to write short queries, which leads to vocabulary problems and contextless query.Besides, semantics is almost neglected in traditional search query since it is just keyword-based searches, which deem their search to be imprecise with irrelevant results, due to the use of unclear keywords.A Semantic query expansion method is proposed for disambiguating queries in computer programming domain using ontology.The integration of Cosine similarity method into the proposed model improved the expanded query, and accordingly, the searching results.The proposed system has been tested with several ambiguous and misspelled queries and the generated extensions proved to retrieve more relevant results, when applied to a search engine.The quality of the retrieved results for the expanded queries, are much higher than that for the crude queries.The proposed technique was implemented, and then tested with external independent testers.They confirmed the mechanical test results and displayed its improvement with an average of precision @10 82.2% and 91.1% respectively.The results were promising and therefore open further research directions.
ZKSENSE: A Friction-less Privacy-Preserving Human Attestation Mechanism for Mobile Devices

Abstract Recent studies show that 20.4% of the internet traffic originates from automated agents. To identify and block such ill-intentioned traffic, mechanisms that verify the humanness of the user are widely deployed, with CAPTCHAs being the most popular. Traditional CAPTCHAs require extra user effort (e.g., solving mathematical puzzles), which can severely downgrade the end-user’s experience, especially on mobile, and provide sporadic humanness verification of questionable accuracy. More recent solutions like Google’s reCAPTCHA v3, leverage user data, thus raising significant privacy concerns. To address these issues, we present zkSENSE: the first zero-knowledge proof-based humanness attestation system for mobile devices. zkSENSE moves the human attestation to the edge: onto the user’s very own device, where humanness of the user is assessed in a privacy-preserving and seamless manner. zkSENSE achieves this by classifying motion sensor outputs of the mobile device, based on a model trained by using both publicly available sensor data and data collected from a small group of volunteers. To ensure the integrity of the process, the classification result is enclosed in a zero-knowledge proof of humanness that can be safely shared with a remote server. We implement zkSENSE as an Android service to demonstrate its effectiveness and practicality. In our evaluation, we show that zkSENSE successfully verifies the humanness of a user across a variety of attacking scenarios and demonstrate 92% accuracy. On a two years old Samsung S9, zkSENSE’s attestation takes around 3 seconds (when visual CAPTCHAs need 9.8 seconds) and consumes a negligible amount of battery.
Efficient Criteria of Quantumness for a Large System of Qubits

In order to model and evaluate large-scale quantum systems, e.g., quantum computer and quantum annealer, it is necessary to quantify the “quantumness” of such systems. In this paper, we discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems, which could be used to characterize their degree of quantumness. Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution, i.e., the accessibility index . Applying it to the case of D-Wave One superconducting quantum annealing device, we find that its operation as described falls well within the quantum domain.
Object-Action Association Extraction from Knowledge Graphs

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is of utmost importance and a long-lasting goal in Artificial Intelligence (AI) research. Training systems with relevant data is a common approach; yet, it is not always feasible to find the data needed, especially since a big portion of this knowledge is commonsense. In this paper, we propose a novel method for extracting and evaluating relations between objects and actions from knowledge graphs, such as ConceptNet and WordNet. We present a complete methodology of locating, enriching, evaluating, cleaning and exposing knowledge from such resources, taking into consideration semantic similarity methods. One important aspect of our method is the flexibility in deciding how to deal with the noise that exists in the data. We compare our method with typical approaches found in the relevant literature, such as methods that exploit the topology or the semantic information in a knowledge graph, and embeddings. We test the performance of these methods on the Something-Something Dataset.
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept

With the advent of direct models in automatic speech recognition (ASR), the formerly prevalent frame-wise acoustic modeling based on hidden Markov models (HMM) diversified into a number of modeling architectures like encoder-decoder attention models, transducer models and segmental models (direct HMM).While transducer models stay with a frame-level model definition, segmental models are defined on the level of label segments directly.While (soft-)attention-based models avoid explicit alignment, transducer and segmental approach internally do model alignment, either by segment hypotheses or, more implicitly, by emitting so-called blank symbols.In this work, we prove that the widely used class of RNN-Transducer models and segmental models (direct HMM) are equivalent and therefore show equal modeling power.It is shown that blank probabilities translate into segment length probabilities and vice versa.In addition, we provide initial experiments investigating decoding and beam-pruning, comparing time-synchronous and label-/segment-synchronous search strategies and their properties using the same underlying model.
Large scale data analysis using MLlib

Recent advancements in the internet, social media, and internet of things (IoT) devices have significantly increased the amount of data generated in a variety of formats. The data must be converted into formats that is easily handled by the data analysis techniques. It is mathematically and physically expensive to apply machine learning algorithms to big and complicated data sets. It is a resource-intensive process that necessitates a huge amount of logical and physical resources. Machine learning is a sophisticated data analytics technology that has gained in importance as a result of the massive amount of data generated daily that needs to be examined. Apache Spark machine learning library (MLlib) is one of the big data analysis platforms that provides a variety of outstanding functions for various machine learning tasks, spanning from classification to regression and dimension reduction. From a computational standpoint, this research investigated Apache Spark MLlib 2.0 as an open source, autonomous, scalable, and distributed learning library. Several real-world machine learning experiments are carried out in order to evaluate the properties of the platform on a qualitative and quantitative level. Some of the fundamental concepts and approaches for developing a scalable data model in a distributed environment are also discussed.
A study of barriers and benefits of ICT adoption among SMEs

Purpose: The objective of the study is to explore the factors preventing adoption of ICT in SMEs and discuss the benefits due to ICT adoption by SMEs. Research Methodology: The intensive literature is reviewed to know the factors preventing ICT and benefits due to adoption of ICT in SMEs. The secondary data is collected from sources like research journal, books, thesis, reports, working paper etc. Finding of the Study: The undertaken study found that awareness of ICT, Cost of the ICT and ICT infrastructures are preventing factors of ICT adoption in SMEs & benefits due to ICT adoption are increase in productivity, enhancement in buyer and seller relationship and improvement among internal as well as external stake holders. Practical Implication: This study may help SMEs to understand the importance of ICT adoption and to know factors which prevent them from use of ICT. SMEs may formulate strategy to minimize preventing factors of ICT and maximize the benefits from ICT adoption.
Optimized Polynomial Classiﬁer for Classiﬁcation of M-PSK Signals

Automatic modulation classiﬁcation (AMC) is the emerging research area for military and civil applications. In this paper, M-PSK signals are classiﬁed using the optimized polynomial classiﬁer. The distinct features i.e., higher order cumulants (HOC’s) are extracted from the noisy received signal and the dataset is generated with different number of samples, various SNR’s and on several fading channels. The proposed classiﬁer structure classiﬁes the overall modulation classiﬁcation problem into binary sub-classiﬁcations. In each sub-classiﬁcation, the extracted features are expanded using polynomial expansion into higher dimension space. In higher dimension space numerous non-linearly separable classes becomes linearly separable. The performance of the proposed classiﬁer is evaluated on Rayleigh and Rician fading channels in the presence of additive white gaussian noise (AWGN). The polynomial classiﬁer performance is optimized using one of the famous heuristic computational techniques i.e., Genetic Algorithm (GA). The extensive simulations have been carried with and without optimization, which shows relatively better percentage classiﬁcation accuracy (PCA) as compared with the state of art existing techniques.
Egyptian Shabtis Identification by Means of Deep Neural Networks and Semantic Integration with Europeana

Ancient Egyptians had a complex religion, which was active for longer than the time that has passed since Cleopatra until our days. One amazing belief was to be buried with funerary statuettes to help the deceased carry out his/her tasks in the underworld. These funerary statuettes, mainly known as shabtis, were produced in different materials and were usually inscribed in hieroglyphs with formulas including the name of the deceased. Shabtis are important archaeological objects which can help to identify the owners, their jobs, ranks or their families. They are also used for tomb dating because, depending on different elements: color, formula, tools, wig, hand positions, etc., it is possible to associate them to a concrete type or period of time. Shabtis are spread all over the world, in excavations, museums or private collections, and many of them have not been studied and identified because this process requires a deep study and reading of the hieroglyphs. Our system is able to solve this problem using two different YOLO v3 networks for detecting the figure itself and the hieroglyphic names, which provide identification and cataloguing. Until now, there has been no other work on the detection and identification of shabtis. In addition, a semantic approach has been followed, creating an ontology to connect our system with the semantic metadata aggregator, Europeana, linking our results with known shabtis in different museums. A complete dataset has been created, a comparison with previous technologies for similar problems has been provided, such as SIFT in the ancient coin classification, and the results of identification and cataloguing are shown. These results are over similar problems and have led us to create a web application that shows our system and is available on line.
Towards Boosting the Accuracy of Non-latin Scene Text Recognition

Scene-text recognition is remarkably better in Latin languages than the non-Latin languages due to several factors like multiple fonts, simplistic vocabulary statistics, updated data generation tools, and writing systems. This paper examines the possible reasons for low accuracy by comparing English datasets with non-Latin languages. We compare various features like the size (width and height) of the word images and word length statistics. Over the last decade, generating synthetic datasets with powerful deep learning techniques has tremendously improved scene-text recognition. Several controlled experiments are performed on English, by varying the number of (i) fonts to create the synthetic data and (ii) created word images. We discover that these factors are critical for the scene-text recognition systems. The English synthetic datasets utilize over 1400 fonts while Arabic and other non-Latin datasets utilize less than 100 fonts for data generation. Since some of these languages are a part of different regions, we garner additional fonts through a region-based search to improve the scene-text recognition models in Arabic and Devanagari. We improve the Word Recognition Rates (WRRs) on Arabic MLT-17 and MLT-19 datasets by 24.54% and 2.32% compared to previous works or baselines. We achieve WRR gains of 7.88% and 3.72% for IIIT-ILST and MLT-19 Devanagari datasets.
Prediction System for the Spread of Corona Virus in Central Java with K-Nearest Neighbor (KNN) Method

Abstract—Corona Virus is currently spreading very rapidly in many parts of Indonesia, including Central Java Province. According to the current data of corona database in Central Java, today on 17th of August 2021, the number of confirmed cases is; Confirmed in Treatment (Active Cases): 16.344, Confirmed Recovered: 408.697, and Confirmed Dead: 29.148. Therefore, the total number of cases is 454.189, obtained from the sum of the number of being treated, recovered, and dead. Corona Virus is a collection of viruses that can infect the respiratory system, generally mild, such as common cold, although, some forms of diseases like; SARS, MERS, and COVID-19 are more deadly. In anticipating this case, the government has created some policies which include; limiting activities outside the house, having school activities done from home, working from home, and even having religious activities done from home too. The purpose of this study was to predict the possible rate of new cases in one of Central Java areas with confirmed cases of corona virus. Thus, it can be used as information material for the public to anticipate early. The research method applied in this research is problem analysis and literature study, data collection and implementation. The application of the K-Nearest Neighbor (KNN) method is expected to be able to predict the level of spread of COVID-19 in Central Java. The results of the research on testing the prediction system for the new cases level were tested in the Sragen area. Testing is carried out by taking samples for new cases, namely Kudu Regency/City, Confirmed: 17,599, Treated: 89, Recovered: 18,303, Died: 1,721, Suspected: 87 and Discarded Suspected: 1,711. After doing the prediction with K-NN algorithm, it showed the Condition: High.
C2 Piecewise Cubic Bezier Curve Based Smoothing Path for Mobile Robot

Good path planning result of mobile robot can not only make better navigation system, but also optimize the travelling time of robot.Most path planning method just can extract the waypoints or line segments for guiding robot to desired target.By implementing of smoothing path, robot can easily archive target position without constantly stopping and turning many times.In this works, we construct continuous C 2 path by using Piecewise Cubic Bezier Curve that respect to initial pose and target pose of robot.The results in experiment shown the acceptable performance of our robot in the presence of proposed smoothing path algorithm.
Confidence interval for the parameter of the zero-truncated Poisson distribution

This paper introduces a confidence interval for the parameter in a zero-truncated Poisson distribution.We adjust the profile likelihood method to construct this confidence interval by using a function of parameter as a nuisance.The performance of the proposed estimator is investigated through simulations, and compared with the conventional Wald confidence interval.From the results, the proposed estimator provides a good performance in terms of coverage probability in all cases in the study.It also has the short interval length.The practicality of our approach is confirmed by application to two real datasets, on a choleraepidemic and on mortality rates of infants on an estate.
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding

Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages.Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models.In this paper we focus on mitigating noise in augmented data.We develop a denoising training approach.Multiple models are trained with data produced by various augmented methods.Those models provide supervision signals to each other.The experimental results show that our method outperforms the existing state of the art by 3.05 and 4.24 percentage points on two benchmark datasets, respectively.The code will be made open sourced on github.
SapiAgent: A Bot Based on Deep Learning to Generate Human-Like Mouse Trajectories

The growing interest in bot detection can be attributed to the fact that fraudulent actions performed by bots cause surprisingly high economical damage.State-of-the-art bots aim at mimicking as many as possible aspects of human behavior, ranging from response times and typing dynamics to humanlike phrasing and mouse trajectories.In order to support research on bot detection, in this paper, we propose an approach to generate human-like mouse trajectories, called SapiAgent.To implement SapiAgent, we employ deep autoencoders and a novel training algorithm.We performed experiments on our publicly available SapiMouse dataset which contains human mouse trajectories collected from 120 subjects.The results show that SapiAgent is able to generate more realistic mouse trajectories compared with Bézier curves and conventional autoencoders.
Token-Level Multilingual Epidemic Dataset for Event Extraction

In this paper, we present a dataset and a baseline evaluation for multilingual epidemic event extraction. We experiment with a multilingual news dataset which we annotate at the token level, a common tagging scheme utilized in event extraction systems. We approach the task of extracting epidemic events by first detecting the relevant documents from a large collection of news reports. Then, event extraction (disease names and locations) is performed on the detected relevant documents. Preliminary experiments with the entire dataset and with ground-truth relevant documents showed promising results, while also establishing a stronger baseline for epidemiological event extraction.
Introduction of Indonesian Poem (Pantun) as a Creative Effort of Elementary School Students in Improving Language Skills in the Covid-19 Pandemic Era

Learning activities are very impactful in the Covid-19 Pandemic Era so students are directed to study at home. Assistance from educators is very important to develop good literacy. With the Thematic Community Service Program, the purpose it is hoped that there will be an increase in student interest in learning and make it easier for educators and parents to carry out online learning activities. Including the introduction of Pantun in improving Language Skills in the Ciparay Hilir Region which is carried out online. The method in this study uses quantitative research. The results of this study are still many students who do not know the type of poem (known as pantun). Students' understanding of Pantun material, Types, and Language Skills by delivering material via PowerPoint or YouTube which is done online, and using the google meet application with educators. The results of the research show that the average post-test score of students is 70% greater than the average pre-test score of 37%, so the difference is 43%. Therefore, during the Covid-19 pandemic, there were still many efforts to increase literacy.
On the Benefits of Randomly Adjusting Anytime Weighted A*

Anytime Weighted A*---an anytime heuristic search algorithm that uses a weight to scale the heuristic value of each node in the open list---has proven to be an effective way to manage the trade-off between solution quality and computation time in heuristic search. Finding the best weight, however, is challenging because it depends on not only the characteristics of the domain and the details of the instance at hand, but also the available computation time. We propose a randomized version of this algorithm, called Randomized Weighted A*, that randomly adjusts its weight at runtime and show a counterintuitive phenomenon: RWA* generally performs as well or better than AWA* with the best static weight on a range of benchmark problems. The result is a simple algorithm that is easy to implement and performs consistently well without any offline experimentation or parameter tuning.
Utilization of Internet of Things on Food Supply Chains in Food Industry

This study aims to analyze the use of the Internet of Things (IoT) in supporting the management of food supply chains (FSCs) in the food industry. This research used qualitative research methods. The results obtained from this study are increasing the effectiveness and efficiency of the existing food supply chain in the food industry by applying the IoT concept to food supply chain management. These results can be obtained because the IoT concept is supported by various systems and technologies that can be implemented and developed so that IoT can help identify and deal with existing problems more quickly while being able to assist in the decision-making process with information obtained through IoT technology so that it will support development food supply chain management in the food industry. This study was conducted to see how much influence the internet of things (IoT) has on food supply chain management in the food industry.
Performance Analysis of CloudLinux-based Web Server at the Embassy of the Kingdom of Morocco in Jakarta

The rapid development of Information Technology (IT) has made a high availability web server a necessity. CloudLinux is a CentOS-based operating system specifically for the needs of servers with good high availability. This study will implement and analyze a CloudLinux-based web server for the Embassy of the Kingdom of Morocco in Jakarta to replace the current CentOS-based web server that does not meet high availability standards. The research methodology used in this study is the PPDIOO Life-cycle from Cisco. Designing a network according to the customer's needs requires identifying several elements in it, including the goals and constraints faced by the organization. Cisco creates a network life-cycle that can help these problems into six phases: Prepare, Plan, Design, Implement, Operate, and Optimize (PPDIOO). This study aims to determine how well the uptime, response time, and load impact are on the CloudLinux-based web server. The uptime test results on the CloudLinux-based web server have a value of an average of 99.971%. Testing the response time on the CloudLinux-based web server has an average value of 684.75ms (milliseconds). The results of the load impact test on the CloudLinux-based web server using ten virtual users (10VU) has a total value of 7595 requests, load impact using fifteen virtual users (15VU) has a total value of 11315 requests, and finally, load impact using twenty-five virtual users (25VU) has a total value of 18631 requests.