Reviewerly/exHarmony huggingface dataset:
Enhanced Evolutionary Generative Adversarial Networks

In the existing GANs, the realness of an input sample is estimated by the discriminator using a single scalar. Such a concise measurement may convey insufficient information to guide the generator, potentially leading to mode collapse and gradient vanishing. Unlike existing GANs, we represent the concept of realness as a realness distribution rather than a single scalar. So that the corresponding discriminator estimates realness of an input sample from multiple angles, providing more informative guidance to generator. In particular, we introduce the realness measure objectives as mutation operations which generate multifarious individuals to adapt the min-max game. Then, we utilize a new least-squares fitness function to measure the performance of generated individuals. The measurement results provide an important reference that we only preserve the well-performing generators and remove others. In the next step, we use well-performing generated individuals as parents for the next training. In this way, our GAN has the ability of effectively and steadily capturing the underlying data distribution and always preserves the best offspring to fool the discriminator. Numerous experiments are conducted on the publicly available datasets demonstrate that our method can reduce the gradient vanishing and suppress the mode collapse in standard GANs.
Representation, justification, and explanation in a value-driven agent: an argumentation-based approach

Ethical and explainable artificial intelligence is an interdisciplinary research area involving computer science, philosophy, logic, and social sciences, etc. For an ethical autonomous system, the ability to justify and explain its decision-making is a crucial aspect of transparency and trustworthiness. This paper takes a Value-Driven Agent (VDA) as an example, explicitly representing implicit knowledge of a machine learning-based autonomous agent and using this formalism to justify and explain the decisions of the agent. For this purpose, we introduce a novel formalism to describe the intrinsic knowledge and solutions of a VDA in each situation. Based on this formalism, we formulate an approach to justify and explain the decision-making process of a VDA, in terms of a typical argumentation formalism, Assumption-based Argumentation (ABA). As a result, a VDA in a given situation is mapped onto an argumentation framework in which arguments are defined by the notion of deduction. Justified actions with respect to semantics from argumentation correspond to solutions of the VDA. The acceptance (rejection) of arguments and their premises in the framework provides an explanation for why an action was selected (or not). Furthermore, we go beyond the existing version of VDA, considering not only practical reasoning, but also epistemic reasoning, such that the inconsistency of knowledge of the VDA can be identified, handled, and explained.
Recognition of English information and semantic features based on SVM and machine learning

With the deepening of people’s research on event anaphora, a large number of methods will be used in the identification and resolution of event anaphora. Although there has been some progress in the resolution of the current event, the difficult problems have not yet been completely resolved. This study analyzes the English information anaphora resolution based on SVM and machine learning algorithms and uses the CNN three-layer network as the basis to model the structure. Moreover, this study improves the semantic features by adding semantic roles and analyzes and compares the performance of the improved semantic features with those before the improvement. In addition, this study combines semantic features to compare and analyze each feature combination and uses a dual candidate model to improve the system. Finally, this study analyzes the experimental results. The results show that the performance of the system using the dual candidate model is better than that of the single candidate model system.
Enhanced Spatial and Extended Temporal Graph Convolutional Network for Skeleton-Based Action Recognition

In the skeleton-based human action recognition domain, the spatial-temporal graph convolution networks (ST-GCNs) have made great progress recently. However, they use only one fixed temporal convolution kernel, which is not enough to extract the temporal cues comprehensively. Moreover, simply connecting the spatial graph convolution layer (GCL) and the temporal GCL in series is not the optimal solution. To this end, we propose a novel enhanced spatial and extended temporal graph convolutional network (EE-GCN) in this paper. Three convolution kernels with different sizes are chosen to extract the discriminative temporal features from shorter to longer terms. The corresponding GCLs are then concatenated by a powerful yet efficient one-shot aggregation (OSA) + effective squeeze-excitation (eSE) structure. The OSA module aggregates the features from each layer once to the output, and the eSE module explores the interdependency between the channels of the output. Besides, we propose a new connection paradigm to enhance the spatial features, which expand the serial connection to a combination of serial and parallel connections by adding a spatial GCL in parallel with the temporal GCLs. The proposed method is evaluated on three large scale datasets, and the experimental results show that the performance of our method exceeds previous state-of-the-art methods.
The Role of Governance Attributes in Corporate Social Responsibility (CSR) Practices Evidence From Jordan

The importance of information disclosure is increasing for stakeholders, mainly the non-financial disclosure, and the primary objective of the current study is to investigate the impact of a set of governance attributes on the level of corporate social responsibility disclosure in the Jordanian context. The study sample consisted of 51 industrial companies listed during 2012 to 2017; a set of statistical analyzes were used, such as descriptive statistics and multiple regression. Empirical evidence shows that the board size and audit committee play a crucial role in the social responsibility disclosure, while other factors (board activity, board compensation, non-executive directors, and audit company type) have no effect on disclosure. The findings are expected to have potential effects on the capital market in Jordan in terms of focusing on the strengths that support the social responsibility disclosure and the development of guidelines that contribute to promoting a disclosure culture between the listed companies, which support government plans in achieving sustainability.
CANet: Concatenated Attention Neural Network for Image Restoration

In this article, we present a general framework for low-level vision tasks including image compression artifacts reduction and image denoising. Under this framework, a novel concatenated attention neural network (CANet) is specifically designed for image restoration. The main contributions of this article are as follows: First, we establish a concise network with a recursive topology by abandoning most complex artificially designed topology of the network. Second, we did not use the down-sampling mechanism in our network and a lightweight attention mechanism is adopted for this. Third, we rethink the feature fusion mechanism in the field of image restoration and improve this by applying concise but effective concatenation and feature selection mechanism which promotes further extraction of more cleaner features in images. Lastly, we demonstrate that CANet achieves better results than previous state-of-the-art approaches with sufficient experiments in compression artifacts removing and image denoising.
Examination and Comparison of TOSCA Orchestration Tools

The use of orchestration and automation has been growing in recent years. This can be especially evident in cloud infrastructures where OASIS TOSCA orchestration standard can be used to provide independence and prevent vendor lock-in. In this paper we examine different TOSCA compliant orchestration tools, test them with TOSCA templates and present a comparison between these tools. This comparison should be used to decide which tool is easier to use for both the companies and the developers according to their requirements.
Hybrid Model-Based Emotion Contextual Recognition for Cognitive Assistance Services

Endowing ubiquitous robots with cognitive capabilities for recognizing emotions, sentiments, affects, and moods of humans in their context is an important challenge, which requires sophisticated and novel approaches of emotion recognition. Most studies explore data-driven pattern recognition techniques that are generally highly dependent on learning data and insufficiently effective for emotion contextual recognition. In this article, a hybrid model-based emotion contextual recognition approach for cognitive assistance services in ubiquitous environments is proposed. This model is based on: 1) a hybrid-level fusion exploiting a multilayer perceptron (MLP) neural-network model and the possibilistic logic and 2) an expressive emotional knowledge representation and reasoning model to recognize nondirectly observable emotions; this model exploits jointly the emotion upper ontology (EmUO) and the n-ary ontology of events HTemp supported by the NKRL language. For validation purposes of the proposed approach, experiments were carried out using a YouTube dataset, and in a real-world scenario dedicated to the cognitive assistance of visitors in a smart devices showroom. Results demonstrated that the proposed multimodal emotion recognition model outperforms all baseline models. The real-world scenario corroborates the effectiveness of the proposed approach in terms of emotion contextual recognition and management and in the creation of emotion-based assistance services.
Vaas

We demonstrate Vaas, a video analytics system for large-scale datasets. Vaas provides an interactive interface to rapidly develop and experiment with different workflows for solving a video analytics task. Users express these workflows as Vaas queries, which specify data flow graphs where nodes may be implemented by machine learning models, custom code, or basic built-in operations (e.g., cropping, selecting detections of by class, filtering tracks by bounding boxes). For example, the problem of detecting lane change events in dashboard camera video could be solved directly as an activity recognition task, by training a model to classify whether a segment of video contains a lane change, or decomposed into a set of simpler tasks, such as detecting lane markers and then identifying shifts in the detected lanes. Our system interface incorporates a query composition tool, where users can rapidly compose operations to implement a workflow, and an exploration tool, where users can experiment with a query by applying it over samples from the dataset to fix bugs and tune parameters. Vaas incorporates recent work in approximate video query processing to support the fast, interactive execution of queries, and accelerates the annotation process of hand-labeling examples to train models by allowing users to annotate over the outputs of previously expressed queries rather than the entire video dataset.
Divide and capture: An improved cryptanalysis of the encryption standard algorithm RSA

RSA is a well known standard algorithm used by modern computers to encrypt and decrypt messages. In some applications, to save the decryption time, it is desirable to have a short secret key d compared to the modulus N. The first significant attack that breaks RSA with short secret key given by Wiener in 1990 is based on the continued fraction technique and it works with d<1184N.25. A decade later, in 2000, Boneh and Durfee presented an improved attack based on lattice technique which works with d < N.292. Until this day, Boneh–Durfee attack remain as the best attack on RSA with short secret key. In this paper, we revisit the continued fraction technique and propose a new attack on RSA. Our main result shows that when d<t(22+8/3)N.75/e, where e is the public exponent and t is a chosen parameter, our attack can break the RSA with the running time of O(tlog (N)). Our attack is especially well suited for the case where e is much smaller than N. When e ≈ N, the Boneh–Durfee attack outperforms ours. As a result, we could simultaneously run both attacks, our new attack and the classical Boneh–Durfee attack as a backup.
Deep Learning Algorithms in Medical Image Processing for Cancer Diagnosis: Overview, Challenges and Future

Health care sector is entirely different from other industrial sector owing to the value of human life and people gives the highest priority. Medical image processing is a research domain where advance computer-aided algorithms are used for disease prognosis and treatment planning. Machine learning comprises of neural networks and fuzzy logic algorithms that have immense applications in the automation of a process. The deep learning algorithm is a machine learning technique that does not relies on feature extraction unlike classical neural network algorithms. Computer-aided automatic processing is in high demand in the medical field due to the improved accuracy and precision. The coupling of machine learning algorithms with high-performance computing gives promising results in medical image analysis like fusion, segmentation, registration and classification. This chapter proposes the applications of deep learning algorithms in cancer diagnosis specifically in the CT/MR brain and abdomen images, mammogram images, histopathological images and also in the detection of diabetic retinopathy. The overview of deep learning algorithms in cancer diagnosis, challenges and future scope is also highlighted in this work. The pros and cons of various types of deep learning neural network architectures are also stated in this work. The intelligent machines in future will be using the deep learning algorithms for the disease diagnosis, treatment planning and surgery.
Data in the time of COVID-19: a general methodology to select and secure a NoSQL DBMS for medical data

As the COVID-19 crisis endures and the virus continues to spread globally, the need for collecting epidemiological data and patient information also grows exponentially. The race against the clock to find a cure and a vaccine to the disease means researchers require storage of increasingly large and diverse types of information; for doctors following patients, recording symptoms and reactions to treatments, the need for storage flexibility is only surpassed by the necessity of storage security. The volume, variety, and variability of COVID-19 patient data requires storage in NoSQL database management systems (DBMSs). But with a multitude of existing NoSQL DBMSs, there is no straightforward way for institutions to select the most appropriate. And more importantly, they suffer from security flaws that would render them inappropriate for the storage of confidential patient data.This paper develops an innovative solution to remedy the aforementioned shortcomings. COVID-19 patients, as well as medical professionals, could be subjected to privacy-related risks, from abuse of their data to community bullying regarding their medical condition. Thus, in addition to being appropriately stored and analyzed, their data must imperatively be highly protected against misuse.This paper begins by explaining the five most popular categories of NoSQL databases. It also introduces the most popular NoSQL DBMS types related to each one of them. Moreover, this paper presents a comparative study of the different types of NoSQL DBMS, according to their strengths and weaknesses. This paper then introduces an algorithm that would assist hospitals, and medical and scientific authorities to choose the most appropriate type for storing patients' information. This paper subsequently presents a set of functions, based on web services, offering a set of endpoints that include authentication, authorization, auditing, and encryption of information. These functions are powerful and effective, making them appropriate to store all the sensitive data related to patients.This paper presents an algorithm to select the most convenient NoSQL DBMS for COVID-19 patients, medical staff, and organizations data. In addition, the paper proposes innovative security solutions that eliminate the barriers to utilizing NoSQL DBMSs to store patients' data. The proposed solutions resolve several security problems including authentication, authorization, auditing, and encryption. After implementing these security solutions, the use of NoSQL DBMSs will become a much more appropriate, safer, and affordable solution to storing and analyzing patients' data, which would contribute greatly to the medical and research effort against COVID-19. This solution can be implemented for all types of NoSQL DBMSs; implementing it would result in highly securing patients' data, and protecting them from any downsides related to data leakage.
A combination of RANSAC and DBSCAN methods for solving the multiple geometrical object detection problem

In this paper we consider the multiple geometrical object detection problem. On the basis of the set $$\mathcal {A}$$ containing data points coming from and scattered among a number of geometrical objects not known in advance, we should reconstruct or detect those geometrical objects. A new efficient method for solving this problem based on the popular RANSAC method using parameters from the DBSCAN method is proposed. Thereby, instead of using classical indexes for recognizing the most appropriate partition, we use parameters from the DBSCAN method which define the necessary conditions proven to be far more efficient. Especially, the method is applied to solving multiple circle detection problem. In this case, we give both the conditions for the existence of the best circle as a representative of the data set and the explicit formulas for the parameters of the best circle. In the illustrative example, we consider the multiple circle detection problem for the data point set $$\mathcal {A}$$ coming from 5 intersected circles not known in advance. The method is tested on numerous artificial data sets and it has shown high efficiency. The comparison of the proposed method with other well-known methods of circle detection in real-world images also indicates a significant advantage of our method.
Detecting Code Comment Inconsistency using Siamese Recurrent Network

Comments are the internal documentation of corresponding code blocks, which are essential to understand and maintain a software. In large scale software development, developers need to analyze existing codes, where comments assist better readability. In practice, developers commonly ignore comments' updating with respect to changing codes, which leads the code comment inconsistency. Traditionally researchers detect these inconsistencies based on code-comment tokens. However, sequence ordering in codecomments is ignored in existing solution, as a result inconsistencies for invalid sequences of codes and comments are neglected. This paper solves these inconsistencies using siamese recurrent network which uses word tokens in codes and comments as well as their sequences in corresponding codes or comments. Proposed approach has been evaluated with a benchmark dataset, along with the ability of detecting invalid code comment sequence is examined.
Db2 event store

The requirements of Internet of Things (IoT) workloads are unique in the database space. While significant effort has been spent over the last decade rearchitecting OLTP and Analytics workloads for the public cloud, little has been done to rearchitect IoT workloads for the cloud. In this paper we present IBM Db2 Event Store ™ , a cloud-native database system designed specifically for IoT workloads, which require extremely high-speed ingest, efficient and open data storage, and near real-time analytics. Additionally, by leveraging the Db2 SQL compiler, optimizer and runtime, developed and refined over the last 30 years, we demonstrate that rearchitecting for the public cloud doesn't require rewriting all components. Reusing components that have been built out and optimized for decades dramatically reduced the development effort and immediately provided rich SQL support and excellent run-time query performance.
Alibaba hologres

In existing big data stacks, the processes of analytical processing and knowledge serving are usually separated in different systems. In Alibaba, we observed a new trend where these two processes are fused: knowledge serving incurs generation of new data, and these data are fed into the process of analytical processing which further fine tunes the knowledge base used in the serving process. Splitting this fused processing paradigm into separate systems incurs overhead such as extra data duplication, discrepant application development and expensive system maintenance. In this work, we propose Hologres, which is a cloud native service for hybrid serving and analytical processing (HSAP). Hologres decouples the computation and storage layers, allowing flexible scaling in each layer. Tables are partitioned into self-managed shards. Each shard processes its read and write requests concurrently independent of each other. Hologres leverages hybrid row/column storage to optimize operations such as point lookup, column scan and data ingestion used in HSAP. We propose Execution Context as a resource abstraction between system threads and user tasks. Execution contexts can be cooperatively scheduled with little context switching overhead. Queries are parallelized and mapped to execution contexts for concurrent execution. The scheduling framework enforces resource isolation among different queries and supports customizable schedule policy. We conducted experiments comparing Hologres with existing systems specifically designed for analytical processing and serving workloads. The results show that Hologres consistently outperforms other systems in both system throughput and end-to-end query latency.
Tuning the Turkish Text Classification Process Using Supervised Machine Learning-based Algorithms

Text classification is the process of determining categories or tags of a document depending on its content. Although it is a well-known process, it has many steps that require tuning to have better mathematical models. In this context, as an agglutinative language, especially the Turkish text classification process requires some extra tuning and preprocessing steps. This paper proposes a methodology and expresses key-points for tuning the Turkish text classification process using supervised machine learning algorithms. For this purpose, we perform intensive experiments on an open Turkish news dataset. Our study shows that our methodology improves categorization results based on F1-score.