Reviewerly/exHarmony huggingface dataset:
Machine Learning Approach for Bird Detection

The Artificial Intelligence has been observing a memorial heightening in bridging the gap between the competence of human being and device. The progress in Computer Vision with Deep Learning has been established and is becoming better with time, essentially, over one special algorithm - a Convolutional Neural Network. Here, we use CNN algorithm to recognize the image. Convolutional Neural Network is a Deep Learning algorithm which can take image as an input, allocate priority in different object to Figure & capable of differentiating one from the other. Our research contains 95.52% validation accuracy. This approach obtained from the experimentation will help to identify a bird from any picture.
Orbuculum - Predicting When Users Intend to Leave Large Public Displays

We present a system, predicting the point in time when users of a public display are about to leave. The ability to react to users' intention to leave is valuable for researchers and practitioners alike: users can be presented additional content with the goal to maximize interaction times; they can be offered a discount coupon for redemption in a nearby store hence enabling new business models; or feedback can be collected from users right after they have finished interaction without interrupting their task. Our research consists of multiple steps: (1) We identified features that hint at users' intention to leave from observations and video logs. (2) We implemented a system capable of detecting such features from Microsoft Kinect's skeleton data and subsequently make a prediction. (3) We trained and deployed a prediction system with a Quiz game which reacts when users are about to leave (N=249), achieving an accuracy of 78%. The majority of users indeed reacted to the presented intervention.
Are Similar Bugs Fixed with Similar Change Operations? An Empirical Study

Fine-grained change operations can help software developers fix software bugs more accurately and efficiently. However, the current fine-grained change operations are only used in specific fixing process, such as fixing of If statement. In this paper, we conducted an empirical study to explore the fine-grained change operations for bug fixing. Based on the Mozilla bug data, we examined whether similar bugs are fixed with similar change operations. The results show that: First, for bug reports with similar descriptions or bug-fix commits with similar descriptions, their corresponding fine-grained change operations are not related; Second, in the case where the descriptions of both bug reports and bug-fix commits are similar, the fine-grained change operations in patch code are not related; Third, by classifying bug reports, we find that the change operations in the same bug report category are similar; Finally, by analyzing the fine-grained change operations for each bug, we present some combined patterns that are often used together.
Design and Implementation of a Social Distancing and Contact Tracing Wearable

The outbreak of the COVID-19 pandemic early 2020 has presented humanity with the unprecedented challenge to tackle a global health emergency that spreads through close social contact on an overpopulated planet. After easing nationwide lockdown measures, and with socio-economic activities starting to normalise, there is an increased awareness for the importance of social distancing to prevent further spreading of the virus. The implementation of a contact tracing system to identify infected individuals and isolate them from society through rapid quarantining requires technological assistance on a scale that does not yet exist. In an effort to solve the problem, a novel design of a social distancing and contact tracing wearable is presented. The wearable uses RF communication both to exchange information with other devices, and to estimate proximity to each other. The demonstrated prototype shows the successful implementation of a highly autonomous, user friendly, robust, and technically capable wearable that can be deployed in critical environments where social distancing is difficult or impossible to enforce.
Stochastic gradient descent‐based support vector machines training optimization on Big Data and HPC frameworks

Summary Support vector machines (SVM) is a widely used machine learning algorithm. With the increasing amount of research data nowadays, understanding how to do efficient training is more important than ever. This article discusses the performance optimizations and benchmarks related to providing high‐performance support for SVM training. In this research, we have focused on a highly scalable gradient descent‐based approach to implementing the core SVM algorithm. In providing a scalable solution, we have designed optimized high‐performance computing and dataflow‐oriented SVM implementations. A high‐performance computing approach means the algorithm is implemented with the bulk synchronous parallel (BSP) model. In addition, we analyzed the language level optimizations and math kernel optimizations on a prominent HPC modeling programming language (C++) and dataflow modeling programming language (Java). In the experiments, we compared the performance of classic HPC models, classic dataflow models, and hybrid models designed on classic HPC and dataflow programming models. Our research illustrates a scientific approach in designing the SVM algorithm at scale in classic HPC, dataflow, and hybrid systems.
Empirical Investigation on the Effects of Culture on Knowledge Sharing and Organization Citizenship Behaviour: Study from UAE

Cross-cultural research studies with regard to organisation citizenship behaviour (OCB) focus primarily on testing various dimensions in different cultures. We have proposed in our study that culture is positively associated with knowledge sharing (KS) and OCB in the context of UAE and have further hypothesised this association is mediated by responsible leadership (RL). The data has been collected from employees and their respective supervisors of 46 mid-sized organisations from all the Emirates of UAE. The data regarding knowledge sharing and culture has been collected directly from 437 employees belonging to diverse industry sectors and cultures. The data regarding employees’ OCB has been collected from 117 supervisors of those employees. The results depict that culture has positive relationship with KS and OCB. RL is found to partially mediate the relationship between culture and KS and culture and OCB. Further, the findings, implications on managerial practices, and future research directions are discussed.
Comparison of Nazief-Adriani and Paice-Husk algorithm for Indonesian text stemming process

Abstract Stemming is a process contained in the pre-processing stage that recognizes basic words (stem word) by combining or solving each of the variants of a word. Every language is unique, the most popular stemming algorithm for Indonesian text is Nazief-Adriani algorithm. Therefore, this study aims to compare Nazief-Adriani algorithm with another stemming algorithm for Indonesian text, that is Paice-Husk stemming algorithm which is commonly used for English. Beside, Nazief-Adriani and Paice-Husk algorithm for stemming process, this study use McCabe Cyclometic Complexity Metrix to evaluate the complexity of algorithm. Based on the experiment result with 20 sentences as data with a thousand words, the accuracy of the Nazief-Adriani algorithm is better than the Paice-Husk algorithm, which is 91.87% compared to 64.43%. Likewise, in terms of complexity, the algorithm is still more complex Paice-Husk than Nazief-Adriani. However, in terms of processing time, the Paice-Husk algorithm is slightly faster than the Nazief-Adriani algorithm. These results indicate that the Paice-Husk algorithm requires a more complete implementation of Indonesian morphological and grammatical rules to produce the better Indonesian stem words.
Fully Dynamic Multi-Key FHE Without Gaussian Noise

Fully dynamic multi-key fully homomorphic encryption (FHE) that allows an unlimited number of homomorphic operations for unconstrained parties.That is to say, it supports performing as many computational procedures on inputs (which are encrypted by an unrestricted number of parties) as needed.The existed fully dynamic multi-key FHE scheme is based on the learning with errors (LWE) problem.However, the LWE problem suffers from the intricate and laborious Gaussian sampling which extremely weakens the schemes' efficiencies.What's more, it has been lately revealed that Gaussian sampling creates lots of possible side-channel vulnerabilities that result in the disclosure of secret keys.The paper proposed a fully dynamic multi-key FHE that based on the learning with rounding (LWR) problem which leaves out the time-consuming Gaussian sampling required in the LWE problem while sustaining almost the same security level.
Layered Framework for Mobile Forensics Analysis

Mobile device seems to be smaller as compared to laptop and personal computers but performs almost every task that is performed on computer, such as internet connectivity, transferring files, multimedia file sharing, online shopping and transactions, booking of hotel/flight tickets, trading in stock exchange, email services etc. Organizations have an ever increasing amount of information/ data that is shared via various sources available in the market. The analysis of mobile phone is challenging at technical, legal and evidential level. Lack of technical knowledge of handling tools may lead to damage of crucial evidence while lack of standard legislation imposes legal challenges. Extracting and preserving evidence present on mobile device in proper manner is also important. Noting down each and every step/ activity performed while carrying forensic process is essential, therefore documentation is necessary. Forensic tools play crucial role in the process as they are used in almost every phase of the process, so the examiner must have complete knowledge of the tool and their working.
Measuring software quality with usability, efficiency, and portability characteristics

Abstract The paper aims to measure the quality of the “Application of software cost estimation (AoSCE)” produced by the author in the previous study. The quality characteristics measured include usability, efficiency, and portability using the ISO 9126-1 standard. The measurement results indicate that the three characteristics are good, but the usability characteristics still need improvement. The interesting of this paper is that testing procedures and worksheets can be applied to measure the quality of other software by adjusting the conditions of the test object.
Efficient Three-Dimensional Video Cybersecurity Framework Based on Double Random Phase Encoding

With the rapidly increasing rate of using online services and social media websites, cybercriminals have caused a great deterioration in the network security with enormous undesired consequences. Encryption techniques may be ... | Find, read and cite all the research you need on Tech Science Press
Simplifying IoT data stream enrichment and analytics in the edge

Edge devices are usually limited in resources. They often send data to the cloud, where techniques such as filtering, aggregation, classification, pattern detection, and prediction are performed. This process results in critical issues such as data loss, high response time, and overhead. On the other hand, processing data in the edge is not a simple task due to devices' heterogeneity, resource limitations, a variety of programming languages and standards. In this context, this work proposes STEAM, a framework for developing data stream processing applications in the edge targeting hardware-limited devices. As the main contribution, STEAM enables the development of applications for different platforms, with standardized functions and class structures that use consolidated IoT data formats and communication protocols. Moreover, the experiments revealed the viability of stream processing in the edge resulting in the reduction of response time without compromising the quality of results.
A Query Conundrum: The Mental Challenges of Using a Cognitive Assistant

Cognitive assistants use vocal interfaces and artificial intelligence to assist humans with complex tasks. While much research has focused on the application of these devices, a few studies have addressed how these devices affect the way humans work. To fill this gap, this research studied the effects of a cognitive assistant on mental workload, frustration, and effort. Participants worked with a Wizard-of-Oz style assistant and completed the Wisconsin Card-Sorting Task and engaged in a peripheral-detection task in a two-sample study that compared participants (n = 21) who worked with the assistant to those who did not. Follow-up interviews were also completed. Results suggest that onboarding techniques, such as tutorials, are important for developing analogical trust before regular use. Additionally, results suggest that keeping the mental model of the CA clear, simple, and intuitive is important to reduce the mental effort that is required to account for the CA and interactions with it while working. Cognitive assistants offer a broad range of advantages but also have distinct challenges for users: primarily the lack of physical affordances that can be linked to functionality.
Movie Recommendation using YouTube Movie Trailer Data as the Side Information

The user feedback data such as likes, dislikes, comments on movie trailers posted on YouTube can be a useful information source for movie recommender systems. In this paper, we study the effect of adding the feedback data on trailers as a type of the side information to the movie rating data. We propose a recommendation framework that can integrate the trailer and rating data adopting different integration strategies: integrating all the trailer data as movie features, using sentiment scores derived from the trailer comments as a rating matrix to integrate with the movie rating matrix and treating others as the movie features, or only integrating the sentiment score based rating matrix with the movie rating matrix. Our experiment shows that if we include the movie trailer data, recommendation accuracy is improved. We also find that the most accurate result is achieved if all the trailer feedback data is integrated as movie features. To design our system, we use both Matrix Factorization (MF) and Deep Neural Network (DNN) Models. We find that the DNN model performs better than the MF model.
Reasoning over temporal knowledge graph with temporal consistency constraints

Knowledge graph reasoning or completion aims at inferring missing facts by reasoning about the information already present in the knowledge graph. In this work, we explore the problem of temporal knowledge graph reasoning that performs inference on the graph over time. Most existing reasoning models ignore the time information when learning entities and relations representations. For example, the fact (Scarlett Johansson, spouse Of, Ryan Reynolds) was true only during 2008 - 2011. To facilitate temporal reasoning, we present TA-TransRILP, which involves temporal information by utilizing RNNs and takes advantage of Integer Linear Programming. Specifically, we utilize a character-level long short-term memory network to encode relations with sequences of temporal tokens, and combine it with common reasoning model. To achieve more accurate reasoning, we further deploy temporal consistency constraints to basic model, which can help in assessing the validity of a fact better. We conduct entity prediction and relation prediction on YAGO11k and Wikidata12k datasets. Experimental results demonstrate that TA-TransRILP can make more accurate predictions by taking time information and temporal consistency constraints into account, and outperforms existing methods with a significant improvement about 6-8% on Hits@10.
Swag: A Wrapper Method for Sparse Learning

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Deep Learning Based Methods for Processing Data in Telemarketing-Success Prediction

In recent years, the importance of data has been increasing day by day. This has led companies to choose and use them actively, especially for reaching valuable information. Thanks to the interpretation of data, companies can save both time, labor, and costs for these operations in many application areas such as finance, security, e-commerce, data mining, etc. One critical area focuses on the use of finance, in which if the companies properly interpret and use this data, they can directly achieve more successful results in terms of their offering to customers with more accurate campaigns. In this paper, some deep learning methods (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple Recurrent Neural Network (SimpleRNN)) are used to predict the possibility of subscribing to deposit after the customer is called within the scope of the bank telemarketing campaign. Implemented models are tested with the used dataset and experimental results were compared and interpreted. To improve the obtained accuracy level different approaches are applied to the dataset. Because of the unbalanced structure of the used dataset, SMOTE approach was used to reach more accurate results. After the dataset is processed to be a balanced form, some deep learning methods are applied to it. Obtained results had compared with other proposals. Experimental results showed that the proposed algorithms gave a very acceptable prediction, and it is expected to be used in the finance sector.
Quantitative-integration-based TSK fuzzy classification through improving the consistency of multi-hierarchical structure

In traditional hierarchical fuzzy classifiers, there exist some main problems. These issues include the output of the previous training layer influencing with the input of the next layers, the inconsistency between the intra-layer and inter-layer, and the improvement of consequent parameters. Based on the above troubles, a novel quantitative-integration-based hierarchical Takagi–Sugeno–Kang (TSK) is proposed to handle the challenges in existing hierarchical fuzzy classifiers. As a novel hierarchical structure, the proposed classifier is built in a stacked manner. Each base building unit consists of an optimized zero-order TSK fuzzy classifier. For good interpretability of each base building unit, the antecedent parameters are solved by random selections of input features, random combinations of fuzzy rules, divisions of fuzzy partitions and generations of cluster centers. In addition, an improved method based on classical ridge regression is proposed to solve the problem of the consistency between intra-layer and inter-layer. In order to enhance the classification performance of the fuzzy classifier, the input of each layer is optimized to effectively resolve the problem that the output of the previous layer affects the input of the next base building unit. Moreover, a method that randomly selects a part of the sample from the original training set is adopted to open the manifold structure of input set. Furthermore, the experimental results indicate that this method can indeed make the QI-TSK-FC fuzzy classifier obtain good or comparative classification performance. To improve the generalization ability of the fuzzy classifier, a method that quantitatively solves the integrated output is considered so that the QI-TSK-FC model can quickly obtain the satisfactory optimal solution. Finally, the classification performance and interpretability of the proposed fuzzy classifier are verified through the MIT-BIH sleep datasets.
Lossless Discrete Atomic Compression of Full Color Digital Images

In this paper, we consider discrete atomic compression (DAC) of full color digital images. DAC is a lossy compression algorithm. We show that lossless compression can be provided without significant changes of this algorithm. The main idea is to add compressed information about differences between the source image and the reconstructed one to the corresponding DAC-file. These data in combination with “lossy” part of DAC-file provide reconstruction of the image without any distortions. Also, we investigate efficiency of such an approach.
Implementation of the Gauss-Circle Map for encrypting and embedding simultaneously on digital image and digital text

Abstract This paper discusses implementation of Gauss-Circle Map (GCM) in cryptography and steganography process simultaneously. Cryptography is used for securing data confidentiality, while steganography is used to protect the existence of data. The objects that used in this thesis are digital text and digital images. This research was conducted by designing algorithms for encryption and embedding simultaneously, as well as extraction and decryption simultaneously then implement it with python programming. Results obtained from the observation shows that GCM had randomness level 100% using NIST test with chosen parameter x 0 (1) = x 0 (2) = 0, α (1) = α (2) = 9, β (1) = β (2) = 0.481, K (1) = K (2) = 1000000, and Ω (1) = Ω (2) = 0.5. Algorithm that have been designed have varying degrees of sensitivity according to different parameters, and high key spaces that reaches 2.6244 × 10 1269 . Encrypted image is uniformly distributed since it passes goodness of fit test. Correlation coefficient values of the stego image are at interval [0.89,1] and very close to correlation coefficient values of the cover image. However, Peak Signal to Noise Ratio (PSNR) of the stego image did not meet standard (above 40 dB). Here, the extracted-decrypted stego image have perfect similarity with the original image.
Sentiment analysis and topic modeling of BPJS Kesehatan based on twitter crawling data using Indonesian Sentiment Lexicon and Latent Dirichlet Allocation algorithm

Abstract In today’s era, company performance is influenced by quick and easy responses to interacting with users. Twitter is one of the social media which is believed that public opinion on Twitter can influence the government or companies to make policies. Public criticism on Twitter is more quickly responded than people who contacting customer service directly, this is because the companies or government do not want their image to be bad due to delays in responding to public complaints on Twitter. Studies related to opinion writing on social media can use the method of topic modeling and sentiment analysis in order to get what topics are currently being discussed and also the value of their sentiments. Modeling of the topic was carried out using Latent Dirichlet Allocation and sentiment analysis using the Indonesian Sentiment Lexicon. A case study of public opinion on BPJS Kesehatan using Twitter data for 3 months from February to April 2020, obtained 5 main topics with the BPJS Kesehatan’s New Contribution Rate as a trending topic with a sentiment value of 61.7% positive and 38.3% negative.
Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions

While object detection modules are essential functionalities for any autonomous vehicle, the performance of such modules that are implemented using deep neural networks can be, in many cases, unreliable. In this paper, we develop abstraction-based monitoring as a logical framework for filtering potentially erroneous detection results. Concretely, we consider two types of abstraction, namely data-label abstraction and post-algorithm abstraction. Operated on the training dataset, the construction of data-label abstraction iterates each input, aggregates region-wise information over its associated labels, and stores the vector under a finite history length. Post-algorithm abstraction builds an abstract transformer for the tracking algorithm. Elements being associated together by the abstract transformer can be checked against consistency over their original values. We have implemented the overall framework to a research prototype and validated it using publicly available object detection datasets.