Reviewerly/exHarmony huggingface dataset:
Software technologies skills

Software design and development technologies evolve very fast and in unpredicted rates, posing many challenges for programmers who strive to use them properly and to be up-to-date, especially since software development demands teamwork and collaboration. As a result, Question and Answer (Q&A) sites, like Stack Overflow, have seen large growth. The questions are characterized by tags, which support developers to easily trace their topic of interest. Very often, these tags refer to technologies that are connected or serve the same purpose. This work is motivated by the fact that despite the volume of questions and technologies change over time, tags inter-connections carry insightful information since they can be utilized to monitor the technology trends and their dynamics given technologies fast simultaneous evolution over time. This work recognizes the value of such connections, to reveal associations of technologies and to support the scientific community, which can be highly inspired by this study, using advanced data analysis tools. Benefits of the present study include early notification of the labor market needs, competency discovery and directions for educational planning. In the present study, graph theory principles and tools were employed to profile chronologically the evolution and the associations of technologies related to tags, and the experimentation carried out has involved the entire set of Stack Overflow questions posted during a decade, between 2008 and 2018. Very interesting conclusions are summarized based on the tags related to Computer Science Technologies (Hard Skills) analysis, revealing non-evident software technologies skills, associations and dynamics.
Image Enhancement Techniques for Gastric Diseases Detection using Ultrasound Images

The abdominal, gallbladder, pancreatic and stomach diseases are the largest and usually occurring gastric diseases among various age groups. To test the stomach related disease in a wide range, ultrasound testing is frequently used. In an ultrasound testing, high-frequency sound waves are transmitted in body tissues, to transfer the image information to a computer to display the output. The ultrasound testing is an easy, comfortable and safe procedure with no side effects. In ultrasound imaging system, soft tissue structures, such as liver, pancreas, kidney gallbladder organs and body parts are diagnosed. However, the ultrasound images carry noise and got corrupted. This paper presents application of three filtering techniques namely middle, unsharp and wiener filtering for removal of unwanted noise from corrupted ultrasound images. The comparative analysis is carried out by measurement of entropy of final set of images. The Matlab results indicates the best entropy and hence visibility qualities are obtained with unsharp filtering.
CloudBC-A Secure Cloud Data acess Management system

Data security and Storage Management becomes the most promising task for any IT operation in the future. The business and service provider's mange to finds the technology like cloud computing for pooled computing resources. The enormous amounts of research works are initiated in parallel to protect data on the cloud environment. This leads to the strong evidence of unanswered questions related to the data security over the cloud data centres. The Cloud computing model used as a primary technology in many IT business operations across the millions of users. To ensure the data security and modification over the data stored on the cloud considered as a major issue among the community cloud users. The Proposed research work aims to protect the cloud data by implementing the new approach using blockchain technology. The Hybrid approach to combine the BlockChain(BC) Technology with cloud computing to ensure the data security among the community cloud users. is Hybrid approach will restrict the malicious data access and modification on the cloud servers. The secure hash blocks are generated and maintained by the cloud storage devices enhance the data security on the cloud. The block chains allow the user to trace back the malicious data access and modifications.
The future of academic publishing: Revolution or evolution revisited

This article presents an analysis of key trends affecting the academic publishing industry. It reviews funding and sales, scholarly collaboration networks, developments in OA, and monograph publishing. These key areas were identified in an earlier article that asked whether academic publishing was evolving or going through a revolution. The review of funding and sales presented here shows a mixed market with growth being challenged as library funds and purchase choices affect expenditure. Scholarly collaboration networks were previously identified as a major threat to traditional publishing, and a review of this area demonstrates that greater sharing of content is empowering librarians and academics to take a stronger stand in publisher negotiations. OA continues to be debated, and a review of responses to Plan S (the most coordinated policy to date) demonstrates conflicting views within academia, funders, and publishers. OA has yet to revolutionize the existing business models. Similarly, monograph publishing remains slow to evolve, although there are increasing initiatives to adopt more open, and online, models. The conclusion of this review is that libraries are likely to be the key promoters of change in the publishing environment and are more influential than other players in forcing evolution.
SAIS: Self-Adaptive Identification of Security Bug Reports

Among various bug reports (BRs), security bug reports (SBRs) are unique because they require immediate concealment and fixes. When SBRs are not identified in time, attackers can exploit the vulnerabilities. Prior work identifies SBRs via text mining, which requires a predefined keyword list and trains a classifier with known SBRs and non-security bug reports (NSBRs). The former approach is not reliable, because (1) as the contexts of security vulnerabilities and terminology of SBRs change over time, the predefined list will become out-dated; and (2) users may have insufficient SBRs for training. We introduce a semi-supervised learning-based approach, Sais, to adaptively and reliably identify SBRs. Given a project's BRs containing some labeled SBRs, many more NSBRs, and unlabeled BRs, Sais iteratively mines keywords, trains a classifier based on the keywords from the labeled data, classifies unlabeled BRs, and augments its training data with the newly labeled BRs. Our evaluation shows that Sais is useful for identifying SBRs.
Problems with Saliency Maps

Despite the popularity that saliency models have gained in the computer vision community, they are most often conceived, exploited and benchmarked without taking heed of a number of problems and subtle issues they bring about. When saliency maps are used as proxies for the likelihood of fixating a location in a viewed scene, one such issue is the temporal dimension of visual attention deployment. Through a simple simulation it is shown how neglecting this dimension leads to results that at best cast shadows on the predictive performance of a model and its assessment via benchmarking procedures.
Comparative Studies on Activity Recognition of Elderly People Living Alone

The social phenomenon of empty nesters is becoming more and more ubiquitous, and it is difficult to keep a watchful eye on their conditions. The advancement of computer technology and the spread of related applications have promoted the study of pervasive computing and smart cities. Ambient assisted living (AAL) systems have also been born in response to trends and demands. Regarding to medical surveillance, the AAL systems can care for the elderly people living alone full time, provide health advice, and initiate an early warning model when an emergency is detected. Main intention of this paper is to make comparative studies on activity recognition of elderly people living alone utilizing 6 classic classification algorithms, namely decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), naive Bayes (NB), linear discriminant analysis (LDA), and ensemble learning (EL). And we adopt these models to recognize 10 activities of daily living (ADLs), namely Meal_Preparation, Relax, Eating, Work, Sleeping, Wash_Dishes, Bed_to_Toilet, Enter_Home, Leave_Home and Housekeeping. In this work, we employ the Aruba annotated open dataset that obtained in a smart house where a voluntary single elderly woman has lived for 220 days. After structuring and cleaning data, we slice data according to entire activity and then extract features from these activity units. Results show that most classification algorithms perform well except for NB based on the activity features we extract. In the future, we can boost performance through improving algorithms, automatically extracting features, and changing the way of reproducing activity representation.
Infrared Image Segment and Fault Location for Power Equipment

Infrared imaging technology relies on the characteristics of non-contact, high fault diagnosis efficiency, intuitive and accurate data, night monitoring, high equipment sensitivity, strong environmental adaptability, etc., and has become a very important tool in the field of online real-time monitoring of power system. This paper introduces the application of image segmentation in power system fault detection using infrared image, divides the image into fault area and non-fault area, realizes the recognition and location of equipment fault area, and introduces several methods of mainstream image segmentation. In this paper, the application of infrared image segmentation in fault maintenance of power equipment is discussed in detail by taking the fault location of electrical insulator as an example, and the segmentation methods are compared.
Comparative Analysis of Cauchy Mutation and Gaussian Mutation in Crazy PSO

This paper provides an analysis of the use of Cauchy mutation and Gaussian mutation individually in a popular improved version of particle swarm optimization techniques for the improvement of convergence to final global solution. The particle swarm optimization is one of the famous techniques of swarm intelligence which has exhibited a standard performance in different real world as well as benchmark problems. But it has also some limitations, which forces the researchers to introduce some modifications for improvement of its convergence. In this paper, we propose two enhanced PSO alternate i.e. Cauchy mutated Crazy PSO and Gaussian mutated Crazy PSO, where the concept of Cauchy and Gaussian mutation are introduced respectively. Crazy PSO is one of the widely used modifications of in the earlier improved versions of PSO. Simulation results show that the Cauchy mutated crazy PSO has shown better results in comparison to the Gaussian mutated crazy PSO. The result also shown that both the modifications have exhibited better performance than the normal crazy PSO algorithm. The introduction of Cauchy mutation and Gaussian mutation helps in improvement of the performance of the crazy PSO algorithm which can be easily verified by the variation of dimensions along with population size.
Solar Irradiation Data Processing using estimator MatriceS (SIMS) validated for Portugal (southern Europe)

Accurate solar resource assessment is essential in all the different phases of solar energy systems design and implementation. On a local scale, the solar resource is best assessed from ground measurements and, if available, with the existence of complete time-series of hourly values for the long-term resource estimation. However, these usually suffer from the occurrence of data gaps that can be as large as several months for remote and/or less maintained stations. Such gaps hinder the correct assessment of solar availability and, for that matter, their filling is a crucial first step to perform an appropriate assessment. In this context, a method for Solar Irradiation Data Processing using estimator MatriceS (SIMS) has been developed and is presented here. The algorithm allows to determine long-term linear correlations between a network of spatially distributed stations and, with these, to identify possible outliers and to fill data gaps through the selection of the median value from the obtained estimations. The method is validated against global horizontal irradiation (GHI) data from a network that comprises 89 ground-measuring stations, being maintained by the Portuguese Meteorology Service (IPMA - Instituto Português do Mar e da Atmosfera), considering a period of 17 years (from 2001 to 2017). Two important assertions are made for coefficients between stations: (1) coefficients only decrease slightly with the distance between stations (with a median reduction of ∼0.0003 km−1), for the considered network; (2) asymptotic long-term coefficients are reached with only one year of data. Taking advantage of the SIMS method, GHI assessment is presented here in the form of availability maps over the Portuguese mainland, with the respective values being listed in a table for future reference. The present assessment confirms that Portugal is a suitable region for the implementation of solar energy systems, with GHI having availabilities up to 2028.4 kWh/m2/year ±3.4% in Sagres (southernmost part of Portugal).
Blockchain Based Aadhaar

Blockchain Based Aadhaar uses Blockchain platform for securing Aadhaar information. The system stores the Aadhaar data in a group of computers interconnected to each other. The Blockchain uses a hashing function to transform the data and produces a hash code. The hash code ensures the identity of user is kept secure. The data is stored in blocks in the nodes. The system also uses Ethereum Smart Contracts to ensure to only the authenticated data is accessed by the third parties. The Blockchain based Aadhaar uses distributed databases (decentralized databases) math and cryptography to record transactions, transactional data is secured and the data is stored in distributed small chunks and spreads across the entire network of computers, and so it is difficult to hack these systems than hacking the centralized or traditional servers.
Backpropagation With N-D Vector-Valued Neurons Using Arbitrary Bilinear Products

Vector-valued neural learning has emerged as a promising direction in deep learning recently. Traditionally, training data for neural networks (NNs) are formulated as a vector of scalars; however, its performance may not be optimal since associations among adjacent scalars are not modeled. In this article, we propose a new vector neural architecture called the Arbitrary BIlinear Product NN (ABIPNN), which processes information as vectors in each neuron, and the feedforward projections are defined using arbitrary bilinear products. Such bilinear products can include circular convolution, 7-D vector product, skew circular convolution, reversed-time circular convolution, or other new products that are not seen in the previous work. As a proof-of-concept, we apply our proposed network to multispectral image denoising and singing voice separation. Experimental results show that ABIPNN obtains substantial improvements when compared to conventional NNs, suggesting that associations are learned during training.
Evaluation Of Distance Measures In Content Based Image Retrieval

Content-Based Image retrieval (CBIR) is an application of computer vision which tries to cope up with the hindrance of image retrieval i.e. searching of an image from the large repositories. In CBIR, images are denoted as features in the dataset. The two key parameters involved in retrieving the actual content includes the extracting of image features and features matching. Once the image features are extracted, the homogeneous attributes are measured with the help of similarity metrics. Similarity matrix is one of the key issues in CBIR. Their exits number of similarity matrix. Most popularly used similarity metrics are described and evaluated in this paper. They are evaluated on standard Wang Image dataset which is considered as a benchmark database for CBIR. Result shows that Chebychev Metrics gives the highest average accuracy of 92.56% whereas in respect of response time, Euclidean Metrics gives the best time performance with average elapsed time of 1.275573 seconds.
Combining Clock and Voltage Noise Countermeasures Against Power Side-Channel Analysis

The power side-channel continues to be a major vulnerability in many critical systems. Numerous countermeasures have been proposed since its discovery as a serious vulnerability, including both hardware and software implementations. Each countermeasure has its own drawback, with some of the more popular countermeasures incurring 3-4x overhead in area and power. While this is acceptable for some devices, other lightweight devices can not tolerate this amount of overhead. In addition, most countermeasures are quite invasive to the design process, requiring modification to the design and additional validation. This work explores two relatively non-invasive countermeasures, both the detailed implementations and the interactions: 1) clock randomization, and 2) supply voltage noise, by real collecting power traces from a state-of-the-art FPGA board and using the Correlation Power Analysis (CPA) attack. Counter-attacks and their impact on the countermeasure efficacy are also explored. The key result is that the combined effects of the two countermeasures is greater than the impact of either countermeasure when used independently.
An Advanced Pyramid Network Technology for Optical Character Recognition

Abstract Optical Character Recognition (OCR) technology can quickly convert text and digital information in pictures into text information, which has been widely used in actual scenes. However, in some complex scenes, such as different light, angle or occlusion, the existing OCR systems still cannot reach the required accuracy. This paper proposes an advanced pyramid network structure, which uses multiple different scales and parallel pyramid network structures to deal with the problem of different character scales and misalignment. Each pyramid network structure has at least four convolutional layers. At the same time, in each pyramid network connection, the proportionally discarding parameters is introduced to increase the calculation speed further. The advantage of this network structure is very robust to more differently sized characters and increases the number of valid parameters obtained through training. Experiments on open source data sets show that the method has better recognition accuracy and speed.
Understanding user needs in videogame moment retrieval

Videogames are a rich domain for scholarship, and their dynamic content makes them a new and unique challenge for information retrieval (IR). Recent work has made it possible to cite specific moments in a videogame (like pages in a book), but currently finding those moments to cite is laborious because there are no videogame moment search engines. We conducted an in-depth interview study with ten users across a variety of user profiles: developers, educators, speedrunners, scholars, and streamers. From these interviews, we identify the unique needs of each user profile for retrieving moments from videogames. We outline implications for future research in retrieval and design implications for new tools focused on interactive media.
Effectiveness of Oral Health Education Program using Home-using Portable Device for Children

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Exploring Emojis for Emotion Recognition in Portuguese Text

New forms of communication, like emojis, are frequent today in social media. Having in mind their strong connection with expressed emotions, we exploit emojis towards the creation of a model for emotion recognition in Portuguese. We gather short texts from Twitter and follow a traditional text classification task, where emojis are used as labels. After the process of feature engineering, two types of Naive Bayes and SVM classifiers are trained: one for classifying emotion, based on related emojis; another for predicting emojis. Interesting but debatable results were obtained on the former task, while the latter revealed to be more challenging, mainly due to emoji similarity. Yet, this also suggests that we can rely on them as an alternative to manually labelling emotions.
FPGA Implementation of Present Algorithm with Improved Security

In recent times, the need of information security has grown widely and especially the study of lightweight symmetric ciphers among all the cryptographic algorithms has gained huge interest. The reason for this enlarging demand for cryptography is due to the ever-increasing insecurities particularly in the resource limited devices like the IoT (Internet of Things). The prime focus of this paper is to consider the variety of hardware architectures of a lightweight cipher PRESENT, which acquired its fame as a resource conservator as well as a tough cipher. This lightweight cipher can be used in Home automation and IoT related devices. In this paper, individual focus is laid on area minimization and security improvement. The 4-bit iterative architecture implemented in this paper saves area which makes it suitable for resource constraint devices like RFID Tags. Also, a modified key-dependent architecture is implemented with high potential to resist security threats which can be used in IoT setups where data suffers severe breaches.
Monte-Carlo Search for Prize-Collecting Robot Motion Planning with Time Windows, Capacities, Pickups, and Deliveries

Logistics operations often require a robot to pickup and deliver objects from multiple locations within certain time frames. This is a challenging task-and-motion planning problem as it intertwines logical and temporal constraints about the operations with geometric and differential constraints related to obstacle avoidance and robot dynamics. To address these challenges, this paper couples vehicle routing over a discrete abstraction with sampling-based motion planning. On the one hand, vehicle routing provides plans to effectively guide sampling-based motion planning as it explores the vast space of feasible motions. On the other hand, motion planning provides feasibility estimates which vehicle routing uses to refine its plans. This coupling makes it possible to extend the state-of-the-art in multi-goal motion planning by also incorporating capacities, pickups, and deliveries in addition to time windows. When not all pickups and deliveries can be completed in time, the approach seeks to minimize the violations and maximize the profit.