Reviewerly/exHarmony huggingface dataset:
Nonlocal Adaptive Biharmonic Regularizer for Image Restoration

In this paper, we propose a nonlocal adaptive biharmonic regularization term for image denoising and restoration, combining the advantages of fourth order models (without the staircase effect while preserving slopes) and nonlocal methods (preserving texture). For its numerical solution, we employ the $$L^2$$ gradient descent and finite difference methods to design explicit, semi-implicit, and implicit schemes. Numerical results for denoising and restoration are shown on synthetic images, real images, and texture images. Comparisons with local fourth order regularizer and the nonlocal total variation are made, which help illustrate the advantages of the proposed model.
A jackknife entropy-based clustering algorithm for probability density functions

This paper proposes a new unsupervised learning algorithm called jackknife entropy-based clustering algorithm for grouping families of probability density functions (pdfs). The fitness function is used to choose the best threshold values of similarity in the proposed algorithm. We demonstrate the correctness and robustness of the proposed algorithm on a synthetic data set. Finally, we apply the algorithm to texture clustering.
Author Identification with Chicken Swarm Optimization Algorithm and Adaboost Approaches

Author identification takes an important place in Natural Language Processing (NLP). Each written document carries the trail of its author. In this study, we aim to realize the author identification via the traces belonging to author be retrieved from the text. A raw dataset was created with 25 columnists and randomly selected 2024 texts from different newspapers in the Turkish language. A dataset with character and lexical features with natural language processing methods were prepared over the raw dataset. The feature selection process was realized with the combination of the Chicken swarm optimization and the ensemble learning algorithms on the prepared dataset. The results were evaluated before and after the feature selection method was applied. The highest success rate with 93.99% was achieved when Adaboost with J48 algorithm was applied after the feature selection process carried out.
A Study on Web Based Online Examination System

Online examination system is a web-based examination system where examinations are given online. Either through the internet or intranet using computer system. The main goal of this online examination system is to effectively evaluate the student thoroughly through a totally automated system that not only reduce the required time but also obtain fast and accurate results. The main objective of our software is to efficiently evaluate the candidate thoroughly through a fully automated system there is no need of paper and pen. The user can write exam without going to the exam centre. Also the website will provide a good.
Secure and Fast Implementation of ARX-Based Block Ciphers Using ASIMD Instructions in ARMv8 Platforms

In Internet of Things services, various types of embedded devices are employed. Among them, ARM-based devices have been widely used as clients. Since these devices communicate with each other in wirelessly, transmitted data needs to be protected with secure block ciphers. Recently, several Add-Rotate-XOR (ARX)-based block ciphers, such as HIGHT and revised CHAM, have been developed for efficient encryption on embedded devices. In this paper, we present secure and fast implementations of ARX-based block ciphers HIGHT and revised CHAM in ARMv8 platforms. For performance efficiency, we basically apply task and data parallel processing mechanism by fully utilizing NEON architecture embedded in ARMv8 platforms. Typically, it is required to duplicate round key in NEON register to utilize the NEON architecture to process multiple data blocks simultaneously. In our implementations, we propose an optimal approach minimizing the cost of round key duplication and efficient key scheduling for task parallelism. For secure implementation, we develop efficient software countermeasures against realistic fault attack models. Thus, we present efficient software countermeasure based on intra-instruction redundancy. Especially, we propose enhanced random shuffling method which is the core operation for the proposed countermeasure. With the proposed random shuffling method, we can significantly reduce the overhead for preventing fault attacks. We present two versions of the software: a version providing highly fast (HF) performance without fault attack countermeasures and a version providing highly secure (HS) against fault attacks. Compared with referenced software, HF with HIGHT, revised CHAM-64/128, CHAM-128/128, and CHAM-128/256 provides about 8 times, 38 times, 13 times and 13 times of enhanced performance, respectively. Compared with previous best results having fault attack countermeasure, HS with HIGHT, revised CHAM-64/128, CHAM-128/128, and CHAM-128/256 provides about 50%, 30%, 80%, and 70% of enhanced performance, respectively. Both our HS and HF achieve better performance and higher security compared with related works.
Exploiting dynamic changes from latent features to improve recommendation using temporal matrix factorization

Recommending sustainable products to the target users in a timely manner is the key drive for consumer purchases in online stores and served as the most effective means of user engagement in online services. In recent times, recommender systems are incorporated with different mechanisms, such as sliding windows or fading factors to make them adaptive to dynamic change of user preferences. Those techniques have been investigated and proved to increase recommendation accuracy despite the very volatile nature of users’ behaviors they deal with. However, the previous approaches only considered the dynamics of user preferences but ignored the dynamic change of item properties. In this paper, we present a novel Temporal Matrix Factorization method that can capture not only the common users’ behaviours and important item properties but also the change of users’ interests and the change of item properties that occur over time. Experimental results on a various real-world datasets show that our model significantly outperforms all the baseline methods.
VPNs as boundary objects of the internet: (mis)trust in the translation(s)

How do we come to trust, use and govern virtual private networks (VPNs)?How do these objects of the internet tack back and forth between metaphor and technical processes as they garner usership and critique?This paper aims to answer these questions by considering VPNs as boundary objects.We follow Susan Leigh Star's (2010) call to further explore the 'tacking' back and forth of boundary objects as both symbolic and technical objects.This is applied within internetspace and governance-space through empirical methods that walkthrough a typical user experience for acquiring VPN services, while also offering a systemic account of the discourse that such a user would experience in coming to understand VPNs and their function.
A Study on Agile Transformation in the New Digital Age

In the face of recent digital and digital transformation, companies and industries are trying to be agile to adapt and respond to change. Agile paradigm is spreading beyond the boundaries of existing applications such as IT-related projects and software development. In this regard, this study, we analyzed the diffusion of agile paradigm by text mining abstracts of research papers from 2001 to 2019. In addition, we discussed agile transformation in the Fourth Industrial Revolution. Through this study, we confirmed that we are studying agile transformation in various fields such as business environment, corporate organizational culture, manufacturing industry, and supply chain. The results of this study will contribute to understanding the meaning and role of agile as a basic paradigm for digital transformation in the Fourth Industrial Revolution.
Intra-frame Adaptive Transform Size for Video Steganography in H.265/HEVC Bitstreams

A video steganography method based on H.265/HEVC (High Efficiency Video Coding) video adaptive transform block size is proposed in this paper. With the adoption of new quadtree splitting structure in H.265/HEVC video compression standard, secret information could be embedded into carrier bitstreams by adaptive modifying the transform unit (TU) size during the procedures of searching best coding syntax elements set. Different from traditional video steganography methods, the proposed method could achieve at most modifying the transform unit splitting once while multiple secret information is embedded, which can achieve a high visual quality for digital carrier video. Moreover, the proposed method is only manipulated on the splitting decision process of TUs. Large volume quantity of TUs in compressed carrier video could guarantee a high embedding capacity. The experimental results have been proven the superiority of efficiency and performance about the proposed method. Moreover, the proposed method can be better applied to high-definition (HD) or higher video applications, and provides a practical tool for the protection of privacy data about digital video, as well as the contents of digital video itself.
Path Planning of Multiple Mobile Robots in a Dynamic 3D Environment

In this paper, we present a theoretical exposition into the application of an artificial potential field method, that is, the Lyapunov-based control scheme. A motion planner of mobile robots navigating in a dynamic environment is proposed. The dynamic environment includes multiple mobile robots, fixed spherical and cylindrical-shaped obstacles. The motion planner exploits the minimum distance technique for the avoidance of the cylindrical obstacles. The mobile robots navigate in a bounded environment, avoiding all the obstacles and each other whilst enroute to its target. The effectiveness of the suggested nonlinear velocity governing laws is verified by a computer simulation which proves the efficiency and robustness of the control technique.
Convolutional Neural Networks for Traffic Signs Recognition

The application fields of traffic signs recognition are multiple, including autonomous vehicles, self-driving cars, Advanced Driver Assistance Systems (ADAS), etc. The ultimate goal of these systems is to save lives on roads by reducing the number of accidents. Specifically, the incidents caused by drivers’ distraction and inattention. Efficient traffic signs recognition systems represent then one of the major components in making cars safer, not only for drivers, but for pedestrians as well. In this domain of research, Deep Learning approaches have gained more popularity due to their appealing performances. However, these approaches still face many difficulties, especially, those related to adverse conditions and real time response. Furthermore, these methods are very demanding in terms of hardware requirements, computational load and training data. From this perspective, our work aims to create an efficient Deep Learning model, for the classification of traffic signs, under adverse and challenging conditions. The objective of the research is to reach a high recognition accuracy, via a faster learning process, while using limited data and hardware resources. The obtained results show that CNNs could reach an accuracy of more than 99.6% for signs with no challenges. However, their accuracy decreases when dealing with more challenging traffic signs. The results shows also that lighter architectures are efficient in terms of generalization, accuracy and speed.
The geometry of integration in text classification RNNs

Despite the widespread application of recurrent neural networks (RNNs) across a variety of tasks, a unified understanding of how RNNs solve these tasks remains elusive. In particular, it is unclear what dynamical patterns arise in trained RNNs, and how those patterns depend on the training dataset or task. This work addresses these questions in the context of a specific natural language processing task: text classification. Using tools from dynamical systems analysis, we study recurrent networks trained on a battery of both natural and synthetic text classification tasks. We find the dynamics of these trained RNNs to be both interpretable and low-dimensional. Specifically, across architectures and datasets, RNNs accumulate evidence for each class as they process the text, using a low-dimensional attractor manifold as the underlying mechanism. Moreover, the dimensionality and geometry of the attractor manifold are determined by the structure of the training dataset; in particular, we describe how simple word-count statistics computed on the training dataset can be used to predict these properties. Our observations span multiple architectures and datasets, reflecting a common mechanism RNNs employ to perform text classification. To the degree that integration of evidence towards a decision is a common computational primitive, this work lays the foundation for using dynamical systems techniques to study the inner workings of RNNs.
DNN Placement and Inference in Edge Computing

The deployment of deep neural network (DNN) models in software applications is increasing rapidly with the exponential growth of artificial intelligence. Currently, such models are deployed manually by developers in the cloud considering several user requirements, while the decision of model selection and user assignment is difficult to take. With the rise of edge computing paradigm, companies tend to deploy applications as close as possible to the user. Considering this system, the problem of DNN model selection and the inference serving becomes harder due to the introduction of communication latency between nodes. We present an automatic method for DNN placement and inference in edge computing; a mathematical formulation to the DNN Model Variant Selection and Placement (MVSP) problem is presented, it considers the inference latency of different model-variants, communication latency between nodes, and utilization cost of edge computing nodes. Furthermore, we propose a general heuristic algorithm to solve the MVSP problem. We provide an analysis of the effects of hardware sharing on inference latency, on an example of GPU edge computing nodes shared between different DNN model-variants. We evaluate our model numerically, and show the potentials of GPU sharing, with decreased average latency by 33% of millisecond-scale per request for low load, and by 21% for high load. We study the tradeoff between latency and cost and show the pareto optimal curves. Finally, we compare the optimal solution with the proposed heuristic and showed that the average latency per request increased by more than 60%. This can be improved using more efficient placement algorithms.
The Hybrid GMDH-Neo-fuzzy Neural Network in Forecasting Problems in Financial Sphere

The hybrid evolving GMDH-neo-fuzzy system was suggested and investigated. The application of GMDH based on self-organization principle enables to build optimal structure of neo-fuzzy system and train weights of neural network in one procedure. The suggested approach allows to prevent the drawbacks of deep learning such as vanishing or exploding of gradient. As a node of neo-fuzzy system neo-fuzzy neuron with small number of tunable parameters is suggested. This enables to cut training time and accelerate convergence of training. The experimental studies of hybrid neo-fuzzy network were carried out in the task of forecasting of industrial output index, share prices and NASDAQ index. The forecasting efficiency of the suggested hybrid neo-fuzzy system in macro-economy and financial sphere was estimated and its sensitivity to variation of tuning parameters was investigated.
Using Cyclic Noise as the Source Signal for Neural Source-Filter-Based Speech Waveform Model

Neural source-filter (NSF) waveform models generate speech waveforms by morphing sine-based source signals through dilated convolution in the time domain. Although the sine-based source signals help the NSF models to produce voiced sounds with specified pitch, the sine shape may constrain the generated waveform when the target voiced sounds are less periodic. In this paper, we propose a more flexible source signal called cyclic noise, a quasi-periodic noise sequence given by the convolution of a pulse train and a static random noise with a trainable decaying rate that controls the signal shape. We further propose a masked spectral loss to guide the NSF models to produce periodic voiced sounds from the cyclic noise-based source signal. Results from a large-scale listening test demonstrated the effectiveness of the cyclic noise and the masked spectral loss on speaker-independent NSF models in copy-synthesis experiments on the CMU ARCTIC database.
Towards Provably Correct Probabilistic Flight Systems

Safety envelopes are meant to determine under which conditions and state space regions a probabilistic property of a data-driven system can be asserted with high confidence. Dynamic data-driven applications systems (DDDAS) can make use of safety envelopes to be cognizant of the formal warranties derived from their models and assumptions. An example of safety envelopes is presented as the intersection of two simpler concepts: $$z$$ -predictability and $$\tau $$ -confidence; which correspond to state estimation and classification, respectively. To illustrate safety envelopes, stall detection from signal energy is shown with data gathered by piezo-electric sensors in a composite wing inside a wind tunnel under varying angles of attack and airspeed configuration. A formalization of these safety envelopes is presented in the Agda proof assistant, from which formally proven sentinel code can be generated.
Building a Guideline to Reinforce Agile Software Development with the Basic Profile of ISO/IEC 29110 in Very Small Entities

The importance of Very Small Entities (VSEs) in the development chain of the software industry, highlights the needs to provide support for them to develop quality software products within budget and schedule. However, most VSEs do not have experience in the implementation of software engineering standards. Additionally, a large percentage of them are using agile methods in an effort to produce software that meets the time requested by the market. This paper presents a proposal of an international guide, it will provide a guide for VSEs that want to reinforce their agile environment to develop software using an agile approach based on Scrum and XP with practices of the ISO/IEC 29110. The agile guide will also provide a guide that facilitates the implementation of an agile approach for VSEs that are already using the software Basic profile of the ISO/IEC 29110 series. This paper presents the problems that VSEs using an agile method have, the process followed to develop the agile guide, and a description of the agile guide.
SACA Net: Cybersickness Assessment of Individual Viewers for VR Content via Graph-Based Symptom Relation Embedding

Recently, cybersickness assessment for VR content is required to deal with viewing safety issues. Assessing physical symptoms of individual viewers is challenging but important to provide detailed and personalized guides for viewing safety. In this paper, we propose a novel symptom-aware cybersickness assessment network (SACA Net) that quantifies physical symptom levels for assessing cybersickness of individual viewers. The SACA Net is designed to utilize the relational characteristics of symptoms for complementary effects among relevant symptoms. The proposed network consists of three main parts: a stimulus symptom context guider, a physiological symptom guider, and a symptom relation embedder. The stimulus symptom context guider and the physiological symptom guider extract symptom features from VR content and human physiology, respectively. The symptom relation embedder refines the stimulus-response symptom features to effectively predict cybersickness by embedding relational characteristics with graph formulation. For validation, we utilize two public 360-degree video datasets that contain cybersickness scores and physiological signals. Experimental results show that the proposed method is effective in predicting human cybersickness with physical symptoms. Further, latent relations among symptoms are interpretable by analyzing relational weights in the proposed network.
Research on Unstructured Data Processing Technology in Executing Audit Based on Big Data Budget

Abstract The wave of big data has promoted the transformation of auditing technology, which has brought about tremendous changes to auditing models and auditing methods. The traditional audit data analysis methods cannot analyze semi-structured and unstructured data, nor can they meet the requirements of the development of audit informatization in the context of big data. New ideas and methods for audit data analysis are urgently needed. In this context, the article proposes an audit data analysis framework based on text mining, and describes the detailed audit data analysis process of collecting and storing, mining and analysing, summarizing and publishing. By using text mining technology to mine the unstructured original audit data collected, different text mining models are established according to the clear audit requirements, the audit data is analysed, and then audit doubts are found, and ultimately formable audit evidence and audit trail. The construction of the framework aims to provide new ideas for big data audits to reduce the risk of big data audits and improve audit quality.
On enumeration of irreducible polynomials and related objects over a finite field with respect to their trace and norm

In this paper we enumerate irreducible polynomials of degree m and related objects over a finite field F q with respect to their trace and norm. Our approach makes use of both generating functions and exponential sums, and we improve all previously known results in the literature when m > q .
A Survey on Cloud Workflow Collaborative Adaptive Scheduling

Objectively speaking, cloud workflow requires task assignment and virtual resource provisioning to work together in a collaborative manner for adaptive scheduling, thus to balance the interests of both supply and demand in the cloud service under the service level agreements. In this study, we present a survey on the current cloud workflow collaborative adaptive scheduling from the perspectives of resource provisioning and job scheduling, together with the existing cloud computing research, and look into the key problems to be solved and the future research.
The Use of Artificial Intelligence in the Future of Competition Law Enforcement

In a future in which the evidential underpinnings for initiating competition law investigations could begin to shift towards a reliance upon the use of artificial intelligence, this paper outlines some of the key opportunities and risks for businesses and regulators. In October 2017, the European Commission (the Commission) published a consultancy tender seeking advice on how artificial intelligence (AI) could improve its processes of evidence management, legal drafting and—as is the focus of this paper—its market intelligence gathering.1 More recently, in its AI White Paper of February 2020, the Commission again expressed an intention to understand how AI can equip ‘law enforcement authorities with appropriate tools’.2 The utility of AI in the processing of large amounts of data and pattern recognition—particularly against the backdrop of a rapidly digitalising European economy—offers considerable opportunities for the future of European competition law enforcement. One only needs to look at the duration of, and scale of evidence gathered in, the Commission’s Google investigations to understand the potentially significant benefits to the Commission of future proofing its enforcement powers with AI-powered analytical tools.3