Reviewerly/exHarmony huggingface dataset:
A Survey of Automatic Generation of Code Comments

Code comments are a valuable form of documentation attached to code that is the most intuitive and efficient way for programmers to understand software code. Good code comments can help programmers quickly understand the role of source code and facilitate understanding of programs and software maintenance tasks. However, in practice, most programmers only pay attention to the code and ignore the comments and documents, which makes the program's readability and maintainability greatly reduced. Based on the meaning of code comments, this paper discusses the current progress in the field of code comments research, adopts the comparative analysis method, focuses on the classification research of the methods and tools for automatic generation of code comments, expounds its advantages and disadvantages, and reveals the issues that need further study.
A multi-cloud world requires a multi-cloud security approach

More than a third of global organisations store data in shared or public cloud environments. And around the same proportion store data in a private cloud. As organisations move to multi-cloud architectures, malicious actors are looking to capitalise on any new attack vectors that this opens up, such as data and application sprawl and possible misconfiguration of cloud infrastructure. A holistic cyber security strategy will help to mitigate the risks inherent in a multi-cloud environment, while enabling the organisation to reap the business and technology rewards associated with it, explains Rory Duncan of NTT UK. We are increasingly living in a multi-cloud world. According to the ‘2019 Risk:Value Report’, over a third of respondents in global organisations say their company currently stores its data in a shared or public cloud environment, such as Amazon Web Services (AWS), Microsoft Azure or Google. Around the same proportion stores data in a private cloud. These numbers are set to go up as more and more businesses transition their processes and applications to the cloud.
Learning Quality Improved Word Embedding with Assessment of Hyperparameters

Deep learning practices have a large impact on many areas. Big data and key hardware developments in GPU and TPU are the main reasons behind deep learning success. The recent progress in the text analysis and classification using deep learning has been significant as well. The quality of word representation that has become much better by using methods such as Word2Vec, FastText and Glove has been important in this improvement. In this study, we aimed to improve Word2Vec word representation, which is also called embedding, by tuning its hyperparameters. The minimum word count, vector size, window size, and the number of iterations were used to improve word embeddings. We introduced two approaches, which are faster than grid search and random search, to set the hyperparameters. The word embeddings were created using documents with approximately 300 million words. A deep learning classification model that uses documents consisting of 10 different classes was applied to evaluate the quality of word embeddings. A 9% increase in classification success was achieved only by improving hyperparameters.
What Design Methods do DataVis Practitioners Know and Use?

Data visualization as a profession has been growing rapidly in recent years. Although some initiatives are in place to increase engagement between the academic and practitioner communities, we currently do not have a good understanding of how practitioners do their design work, including what methods, approaches, and principles they know and use in their everyday practice. We present a subset of results of a survey in which 87 DataVis practitioners identified their familiarity with popular design methods and the frequency with which they use them in their own work. We also discuss follow-up work to develop a deeper understanding of practitioners' perspectives on design methods.
Self-adaptive learning for hybrid genetic algorithms

Abstract Local search can be introduced into genetic algorithms to create a hybrid, but any improvement in performance is dependent on the learning mechanism. In the Lamarckian model, a candidate solution is replaced by a fitter neighbour if one is found by local search. In the Baldwinian model, the original solution is retained but with an upgraded fitness if a fitter solution is found in the local search space. The effectiveness of using either model or a variable proportion of the two within a hybrid genetic algorithm is affected by the topology of the fitness function and the details of the hybrid algorithm. This paper investigates an intelligent adaptive approach to decide on the learning mechanism to be used by an individual over the course of the search. Evolution is used to self-adapt both the frequency of a steepest-descent local search and the relative proportions of Lamarckian and Baldwinian inheritance. Experiments have shown that this form of adaptive learning can improve the ability to find high-quality solutions and can accelerate the hybrid search without the need to find optimal control parameters for the learning process.
Goal driven network pruning for object recognition

Pruning studies up to date focused on uncovering a smaller network by removing redundant units, and fine-tuning to compensate accuracy drop as a result. In this study, unlike the others, we propose an approach to uncover a smaller network that is competent only in a specific task, similar to top-down attention mechanism in human visual system. This approach doesn’t require fine-tuning and is proposed as a fast and effective alternative of training from scratch when the network focuses on a specific task in the dataset. Pruning starts from the output and proceeds towards the input by computing neuron importance scores in each layer and propagating them to the preceding layer. In the meantime, neurons determined as worthless are pruned. We applied our approach on three benchmark datasets: MNIST, CIFAR-10 and ImageNet. The results demonstrate that the proposed pruning method typically reduces computational units and storage without harming accuracy significantly.
Development of the formal model of the optical recognition mechanism for tabular documents

Abstract The article describes the tasks of the optical recognition system for tabular documents and the development of a formal model of the optical recognition mechanism for tabular documents. The aim of the study is to use the developed system of automatic analysis of platform-type documentation as expert and training to support decision making, semantic analysis or audit of any content, including in other industries based on the principle of automatic filling of ontologies. The proposed system will allow to overcome this barrier in a cardinal way – by automatically creating a database. In this case, the user’s role will be reduced to the choice of sources for filling ontologies (the choice of thesauruses, corpus of texts in machine-readable or natural language, etc.) and the configuration of inference rules, i.e., essentially, to the configuration operations.
Texture Feature Extraction From Free-Viewing Scan Paths Using Gabor Filters With Downsampling

Texture-based features computed on eye movement scan paths have recently been proposed for eye movement biometric applications. Feature vectors were extracted within this prior work by computing the mean and standard deviation of the resulting images obtained through application of a Gabor filter bank. This paper describes preliminary work exploring an alternative technique for extracting features from Gabor filtered scan path images. Namely, features vectors are obtained by downsampling the filtered images, thereby retaining structured spatial information within the feature vector. The proposed technique is validated at various downsampling scales for data collected from 94 subjects during free-viewing of a fantasy movie trailer. The approach is demonstrated to reduce EER versus the previously proposed statistical summary technique by 11.7% for the best evaluated downsampling parameter.
Solar Atlas of New Zealand from satellite imagery

Alan C. Brent* , James (Jim) Hinkley , Daniel Burmester & Ramesh Rayudu Sustainable Energy Systems, School of Engineering and Computer Science, Victoria University of Wellington, New Zealand
Implementation of AES cryptography and twofish hybrid algorithms for cloud

Abstract Data exchange is an inevitable issue in this digital age. Especially data in a cloud system. The data must be secured so as not to be abused by people who are not entitled to. Algorithms to secure data is already widely known. It would be more secure if data security combines at least two algorithms for Cryptography. The goal is to strengthen the results of the data encryption in the case will be hacked, unauthorized people. This research will incorporate two Cryptographic Algorithms, namely AES (Advanced Encryption Standard) and Twofish with 256 bits key generated by the HASH function SHA 256. This new algorithm will give more security to the data uploaded or downloaded in the Cloud system.
A Gaze-Based Web Browser with Multiple Methods for Link Selection

This paper presents a gaze-based web browser that allows hands-free navigation through five different link selection methods (namely, Menu, Discrete Cursor, Progressive Zoom, Quick Zoom, and Free Pointing) and two page scrolling techniques. For link selection, the purpose of this multi-approach solution is two-fold. On the one hand, we want users to be able to choose either their preferred methods or those that, in each specific case, are the most suitable (e.g., depending on the kind of link to activate). On the other hand, we wanted to assess the performance and appreciation level of the different approaches through formal tests, to identify their strengths and weaknesses. The browser, which is conceived as an assistive technology tool, also includes a built-in on-screen keyboard and the possibility to save and retrieve bookmarks.
A New Targeted Password Guessing Model

TarGuess-I is a leading targeted password guessing model using users’ personally identifiable information (PII) proposed at ACM CCS 2016 by Wang et al. Owing to its superior guessing performance, TarGuess-I has attracted widespread attention in password security. Yet, TarGuess-I fails to capture popular passwords and special strings in passwords correctly. Thus we propose TarGuess-I $$ ^+ $$ : an improved password guessing model, which is capable of identifying popular passwords by generating top-300 most popular passwords from similar websites and grasping special strings by extracting continuous characters from user-generated PII. We conduct a series of experiments on 6 real-world leaked datasets and the results show that our improved model outperforms TarGuess-I by 9.07% on average with 1000 guesses, which proves the effectiveness of our improvements.
A Hierarchical Trust Management Architecture Based on Blockchain for Crossover Service

As an emerging business model of the modern service industry (MSI), crossover service integrates services from enterprises and organizations in different fields. By combining computer and communication technologies, crossover service can improve the efficiency and quality of services. However, due to the complexity and heterogeneity among different industries, there still remain huge challenges regarding the service convergence process. One of the most important issues is the trust management in crossover services. The establishment of trust is the basic requirement of service convergence. In this paper, we first present the overview of crossover service and the issue of trust management by analyzing the specific application case of crossover service. Besides, based on blockchain technology, we will propose a decentralized trust management architecture to address the trust management issue in crossover service. The introduction of blockchain will significantly enhance the security of the crossover service. Moreover, a complete trust delivery relationship among different objects will be presented for the proposed trust management architecture. Finally, the feasibility of our proposed trust management architecture and the future research challenges will be illustrated in detail.
Perceiving heavily occluded human poses by assigning unbiased score

The problem of human pose estimation has been largely solved by the prevailing Deep Convolutional Neural Networks (DCNNs). However, heavily occluded human poses still represent great challenges. In this paper, we propose a new scoring method to perceive the detection of heavily occluded poses unbiasedly. The typical way of assigning scores to detected poses is to use the mean confidence of each joint. This makes poses with occlusion suppressed durng evaluation since invisible joints may have a much lower confidence than visible joints. We address this by identifying the visibility of each joint, an occlusion aware network is designed to predict both heatmaps and visible values of joints simultaneously. Thus, the degree of occlusion of a pose can be grasped, and a much fairer score is able to be set. Furthermore, a KS-net is proposed to predict the KS (Keypoint Similarity) between each estimated joint location and its matched ground-truth. The predicted KS calibrates localization accuracy better than the maximum heat value in heatmap. Pose score is calculated using the predicted visibility and KS value of each joint. The efficacy of our method is demonstrated on the most widely used MS-COCO pose dataset. Extensive experiments show that using our scoring approach can significantly improve the average precision of heavily occluded poses for the provided detections.
A Method for Optimizing Opaque Filter Queries

An important class of database queries in machine learning and data science workloads is the opaque filter query: a query with a selection predicate that is implemented with a UDF, with semantics that are unknown to the query optimizer. Some typical examples would include a CNN-style trained image classifier, or a textual sentiment classifier. Because the optimizer does not know the predicate's semantics, it cannot employ standard optimizations, yielding long query times. We propose voodoo indexing, a two-phase method for optimizing opaque filter queries. Before any query arrives, the method builds a hierarchical "query-independent" index of the database contents, which groups together similar objects. At query-time, the method builds a map of how much each group satisfies the predicate, while also exploiting the map to accelerate execution. Unlike past methods, voodoo indexing does not require insight into predicate semantics, works on any data type, and does not require in-query model training. We describe both standalone and SparkSQL-specific implementations, plus experiments on both image and text data, on more than 100 distinct opaque predicates. We show voodoo indexing can yield up to an 88% improvement over standard scan behavior, and a 79% improvement over the previous best method adapted from research literature.
3D‐quantum interferometer using silicon microring‐embedded gold grating circuit

Abstract A 3D (three‐dimensional) quantum interferometer consisting of a silicon microring circuit proposed. The interferometer based on the electron spin cloud projections generated by microring‐embedded gold grating. The electron cloud oscillations result from the excitation of the gold grating at the center of the silicon microring by the dark soliton pulse of 1.50 μm center wavelength. The electron cloud spin‐down, spin‐up automatically formed in the two axes ( x , y , respectively) and propagated along the z ‐axis. In this proposal, the sensing mechanism of the circuit is manipulated by varying the reflector gold lengths of the sensing arm. The electron cloud spin coupled and changed by changing the gold lengths. The sensitivity measurement of the 3D quantum interferometer for three gold layer lengths of 100 nm, 500 nm, and 1,000 nm is (47.62 nm fs −1 , ±0.4762 fs −1 , ±0.01 nm −1 ), (238.10 nm fs −1 , ±0.4762 fs −1 , ±0.002 nm −1 ), (476.20 nm fs −1 , ±0.4762 fs −1 , ±0.001 nm −1 ), respectively. The used circuit parameters are the real ones that can be fabricated by the currently available technology. Moreover, the silicon micro ring circuit acts as a plasmonic antenna, which can apply for wireless quantum communication. The electron cloud spin projection space–time control can apply for quantum cellular automata.
Visualization Systems for Linked Datasets

The wide adoption of the RDF data model, as well as the Linked Open Data initiative, have made available large linked datasets that have the potential to offer invaluable knowledge. Accessing, evaluating and understanding these datasets as published, though, requires extensive training and experience in the field of the Semantic Web, making these valuable sources of information inaccessible to a wider audience. In the recent years, there have been many efforts to create systems that allow the visualization and exploration of this information. Some of there systems rely on techniques that allow them to limit the volume of the displayed information, by providing aggregated, filtered or summarized access to the datasets while others initialize the exploration of the dataset based on actions performed by the users, such as keyword searches and queries. The underlying technique is key for the sustainability of the system, the definition of the requirements that the input must comply with, the datasets that can be visualized as well as the visualization types provided. We present here a survey on these techniques, their strengths and weaknesses as well as the datasets that they can support. The survey will provide the reader with a deep understanding of the challenges regarding the visualization of large linked datasets, a categorization of the developed techniques to resolve them as well as an overview of the available systems and their functionalities.
An improved modeling method for profile-based personalized search

A Personalized search system aims to provide tailor-made results for each user's query according to his preferences. Building such a system depends mainly on creating profiles that represent real user interests. In this paper, we present a method for modeling users and creating accurate profiles by implicitly tracking and collecting user-browsing data. Furthermore, we examine techniques to enhance profile accuracy through combining multiple browsing data sources, distinguishing important concepts from irrelevant ones in a user profile, and the concept levels required from a reference ontology to describe user's interests.
Multi-Channel Feature Pyramid Networks for Prostate Segmentation, Based on Transrectal Ultrasound Imaging

Accurate segmentation for transrectal ultrasound imaging (TRUS) is often a challenging medical image processing task. The problem of weak boundary between adjacent prostate tissue and non-prostate tissue, and high similarity between artifact area and prostate area has always been the difficulty of TRUS image segmentation. In this paper, we construct a multi-channel feature pyramid network (MFPN) based on deep convolutional neural network-based prostate segmentation method to process multi-scale feature maps. Each level enhances the edge characteristics of the prostate by controlling the scale of the channel. The optimized regression mechanism of the target area was used to accurately locate the prostate. Experimental results showed that the proposed method achieved the key indicator Dice similarity coefficient and average absolute distance of 0.9651 mm and 0.504 mm, which outperformed state-of-the-art approaches.
SparkFuzz

With more than 1200 contributors, Apache Spark is one of the most actively developed open source projects. At this scale and pace of development, mistakes are bound to happen. In this paper we present SparkFuzz, a toolkit we developed at Databricks for uncovering correctness errors in the Spark SQL engine. To guard the system against correctness errors, SparkFuzz takes a fuzzing approach to testing by generating random data and queries. Spark-Fuzz executes the generated queries on a reference database system such as PostgreSQL which is then used as a test oracle to verify the results returned by Spark SQL. We explain the approach we take to data and query generation and we analyze the coverage of SparkFuzz. We show that SparkFuzz achieves its current maximum coverage relatively fast by generating a small number of queries.
Age-Related Differences in Word Recognition Task according to the Interference Types: Evidence from Eye-Tracking

Objectives: There is considerable controversy with respect to language processing in the elderly.The purpose of the study was to investigate age-related differences in word recognition tasks according to interference types with Eye-tracker.Methods: A total of 46 participants (24 young and 22 elderly adults) participated in the study.A word recognition task and an online eye-tracking analysis were used.The stimuli consisted of 80 Korean nouns with four types: target, phonologically related objects to the target, semantically related objects to the target, and unrelated objects.Each critical trial display included four types.Participants were asked to select one of the pictures after the target was auditorily presented.Results: The elderly group had lower accuracy and slower reaction time than the young group.There were significant interactions between the group and time-window.The young group fixated the target longer than the elderly group in the time window of 1,000-1,800 ms.According to the proportion data of the interference type, there were significant interactions between types and time-window.Both groups fixated the phonologically related picture longer than semantically related picture or unrelated picture in 1,000-1,400 ms time-window.Conclusion: Elderly adults demonstrated delayed processing when compared with younger adults, whereas age-group differences did not emerge as a function of the interference types.There was aging-related decline in online processing of the word recognition abilities, but differential effects by the interference types did not efficiently discriminate the groups, indicating that their online processing abilities to inhibit phonologically and semantically related foils may be intact, although elderly adults suffered from slowed processing.
A fuzzy model to enhance user profiles in microblogging sites using deep relations

Social Networking Sites (SNS) have entailed a revolution for society. They have given a say to everyone regardless of their status and this has been translated into loads of data. The task of profiling users constitutes a way to learn from this data in order to show users only the content that is relevant to them. Several recommendation system techniques have been used to address this problem, being mainly based on what the user explicitly says about themselves, on what the user publishes in the SNS and on the similarities between users. However, in social media context, it is also possible to use relations between users. Considering basic relations like follower or followee to extract information from them may result in noise, since they do not imply that users share interest or even ideas. In this work, we present a fuzzy framework to enrich user profiles with complex properties in order to have an even better representation of them. We use basic relations defined by SNSs to complete the information available in user profiles with topics of interest and ideas towards them and to define deep relations that will enable new ways of analysis. We use these deep relations to create clusters of similar users that, ultimately, will allow the expansion of properties from known users to the rest of the cluster. We tested our proposal with a dataset of Tweets in Spanish related to a political event. Our experiments prove the potential that this approach has for a lot of applications in microblogging context.