Reviewerly/exHarmony huggingface dataset:
An Image Forensic Technique Based on SIFT Descriptors and FLANN Based Matching

Doctored images are prevalent everywhere since the easy availability of photo-editing tools. The research in image forensics focuses mainly on developing techniques that can help discriminate between doctored and legitimate content in an image. There are various kinds of forgeries possible in an image. Here, we present a robust algorithm for copy-move forgery detection(CMFD). We exploit the simple linear iterative clustering (SLIC) algorithm to divide the source image into nonoverlapping, irregular-sized blocks and then use Scale Invariant Feature Transform (SIFT) to determine the feature keypoints with their descriptors. After that, keypoints between blocks are matched using Fast Library for Approximate Nearest Neighbors (FLANN). Forged regions are chalked out accurately employing some morphological operations and analysis using correlation coefficient. To prove the effectiveness of the proposed algorithm, we have tested it on four standard datasets and found out the proposed scheme is performing satisfactorily well. It is helpful after scaling, rotation, and JPEG compression operations too.
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy

Pseudo-labeling (PL), a semi-supervised learning (SSL) method where a seed model performs self-training using pseudo-labels generated from untranscribed speech, has been shown to enhance the performance of end-to-end automatic speech recognition (ASR). Our prior work proposed momentum pseudo-labeling (MPL), which performs PL-based SSL via an interaction between online and offline models, inspired by the mean teacher framework. MPL achieves remarkable results on various semi-supervised settings, showing robustness to variations in the amount of data and domain mismatch severity. However, there is further room for improving the seed model used to initialize the MPL training, as it is in general critical for a PL-based method to start training from high-quality pseudo-labels. To this end, we propose to enhance MPL by (1) introducing the Conformer architecture to boost the overall recognition accuracy and (2) exploiting iterative pseudo-labeling with a language model to improve the seed model before applying MPL. The experimental results demonstrate that the proposed approaches effectively improve MPL performance, outperforming other PL-based methods. We also present in-depth investigations to make our improvements effective, e.g., with regard to batch normalization typically used in Conformer and LM quality.
On the Existence of Pairs of Primitive and Normal Elements Over Finite Fields

Let $$\mathbb {F}_{q^n}$$ be a finite field with $$q^n$$ elements, and let $$m_1$$ and $$m_2$$ be positive integers. Given polynomials $$f_1(x), f_2(x) \in \mathbb {F}_{q^n}[x]$$ with $$\deg (f_i(x)) \le m_i$$ , for $$i = 1, 2$$ , and such that the rational function $$f_1(x)/f_2(x)$$ satisfies certain conditions which we define, we present a sufficient condition for the existence of a primitive element $$\alpha \in \mathbb {F}_{q^n}$$ , normal over $$\mathbb {F}_q$$ , such that $$f_1(\alpha )/f_2(\alpha )$$ is also primitive.
Application of XR-Based Virtuality-Reality Coexisting Course

Significant advances in new emerging technologies such as the 5th generation mobile networks (5G), Expand the reality (XR), and Artificial Intelligence (AI) enable extensive three-dimensional (3D) experience and interaction. The vivid 3D virtual dynamic displays and immersive experiences will become new normal in near future. The XR-based virtuality-reality co-existing classroom goes beyond the limitations of Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Such technology also enables integration of the digital and physical worlds and further creates a smart classroom featuring co-existed virtuality and reality. In this paper, we show an application of the XR enabling human-environment interaction. Through the theory explanation, practice platform and combination of virtual and reality, using XR we construct a new type of class. We develop the teaching methods using digital enabling, building information model library, and offline XR immersive teaching, and design a mixed teaching method which is virtual and real, and is available in online and offline formats. In our developed system the working scene as it is seen in practice is shown to the students. We consider the steel structure construction technology course in civil engineering and architecture as an example, to help the learners develop their theoretical understanding and practical skills. We also discuss the application and development of the class based on virtual and reality. This work shows the feasibility and significance of learning methods and the reconstruction and transformation of learning spaces empowered by new technologies in the digital era.
A lower bound on the number of inequivalent APN functions

In this paper, we establish a lower bound on the total number of inequivalent APN functions on the finite field with 22m elements, where m is even. We obtain this result by proving that the APN functions introduced by Pott and the second author [22], which depend on three parameters k, s and α, are pairwise inequivalent for distinct choices of the parameters k and s. Moreover, we determine the automorphism group of these APN functions.
Data services in ocean science with a focus on the biology

Biological ocean science has a long history; it goes back millennia, whereas the related data services have emerged in the recent digital era of the past decades. To understand where we come from—and why data services are so important—we will start by taking you back to the rise in the study of marine biology—marine biodiversity—and its key players, before immersing ourselves in the data life cycle, past and present joint global initiatives, and systems that allow(ed) scientists to more easily access biological data, online services through some simple keyboard strokes, and the many challenges we still encounter on a daily basis when dealing with these types of data.
Preliminary Study on Motion Planning with Obstacle Avoidance for Hard Constrained Redundant Robotic Manipulators

The motion planning for robotic manipulators is one of the most important and challenging problems of the robot control. The primary task of a robotic manipulator is to move its end-effector from an initial position and orientation to a predefined goal position and orientation. This research considers motion planning with obstacle avoidance for planar redundant robots with constrained joint space. It analyses their kinematics and defines the different types of inverse kinematics solutions. On the basis of those types of solutions in the robot workspace are defined zones corresponding to each solution type. The research proposes a numerical approach for finding the specific points at which the robot can change its solution type. Those points are of interest when the robot has to execute a motion which passes through different zones of the workspace which are not intersecting. In such cases the robot is required to pass through some of those points in order to be able to change the solution type and to execute the desired motion. The approach applicability is validated through conducted experiments.
Robustness Estimation and Optimisation for Semantic Web Service Composition With Stochastic Service Failures

Service-oriented architecture (SOA) is a widely adopted software engineering paradigm that encourages modular and reusable applications. One popular application of SOA is web service composition, which aims to loosely couple web services to accommodate complex goals not achievable through any individual web service. Many approaches have been proposed to construct composite services with optimized Quality of Service (QoS), assuming that QoS of web services never changes. However, the constructed composite services may not perform well and may not be executable later due to its component services' failure. Therefore, it is important to build composite services that are robust to stochastic service failures. Two challenges of building robust composite services are to efficiently generate service composition with near-optimal quality in a large search space of available services and to accurately measure the robustness of composite services considering all possible failure scenarios. This article proposes a novel two-stage GA-based approach to robust web service composition with an adaptive evolutionary control and an efficient robustness measurement. This approach can generate robust composite service at the design phase, which can cope with stochastic service failures and maintain high quality at the time of execution. We have conducted experiments with benchmark datasets to evaluate the performance of our proposed approach. Our experiments show that our method can produce highly robust composite services, achieving outstanding performance consistently in the event of stochastic service failures, on service repositories with varying sizes.
Introducing innovative educational technologies into the undergraduate curricula to promote individual work of students: Case of Turkmenistan

As computer technology and network systems continue to spread out all over the world, educational sectors are also being continuously updated and course curricula are being revised in accordance with the solutions based on Information and Communication Technology (ICT). On the other hand, developing undergraduate curricula aiming at individual work of students, using innovative educational technologies, is a challenging task. Moreover, because gaining individual learning and computer skills is the key element of quality education, it is highly important to work out concrete directions and methodologies which can be overcome by introducing ICT-based innovative educational technologies into educational curriculum. Therefore, innovative educational methodologies and ICT-based solutions are continuously being implemented in the educational system of Turkmenistan. In this sense, the paper presents the analysis, strategies or methods of benefiting from innovative educational technologies to develop individual-work-based undergraduate curricula of higher educational institutions of Turkmenistan, taking into account the national conceptions, execution plans, and guidelines, work packages of an international project, SDG targets and other related charters.
A Method of English Test Knowledge Graph Construction

English is one of the key subjects of basic education in many countries; more and more students tend to learn English online.This paper takes middle school English texts as the research object and proposes a method of English test knowledge graph construction.Through acquiring the data, preprocessing the corpus, and designing feature vectors, this paper realizes to extract the English knowledge points from the tests based on SVM model and construct an English test knowledge graph.It is important to the standardization, automation and systematization of online education learning.
Beyond “Alt-Text”

Data visualization is a reliable tool for professional communication practitioners for synthesizing and presenting data to a variety of audiences. However, data visualizations have a range of accessibility concerns including: visual acuity, color/contrast difficulties, color blindness and size/scale issues. Alt-text is not enough to make these visuals accessible and therefore more advanced web coding techniques, such as the Scalable Vector Graphic (SVG) format should be used to create data visualizations for the web. The use of SVG allows for greater coded semantic and contextual information to be added to data visualizations resulting in graphics that can be better interacted with by users with a variety of accessibility software.
Knowledge Enhanced Fact Checking and Verification

As the Internet and social media offer increasing opportunities for organizations and individuals to publicize online contents, it has become essential to develop effective means to identify misinformation like fake news. Recently, fact checking systems have been regarded as a promising tool to automatically deal with large amounts of information. How to effectively take advantage of existing unstructured document knowledge bases and structured knowledge graphs to build robust fact checking systems, however, remains to be a challenge. In this paper, we propose a knowledge enhanced fact checking system, which leverages the Wikidata5M knowledge graph and Wikipedia documents to incorporate external knowledge into the claim to be checked for more robust and accurate fact checking. First, we devise a contextualized knowledge graph selection method to identify the most relevant sub-graph with the checked claim from the large knowledge graph. We then construct a novel claim-evidence-knowledge graph and use a graph attention network to integrate natural language evidence with structured knowledge triplets by allowing them to propagate information among each other. By integrating the claim, retrieved evidence and selected knowledge triplets in a unified claim-evidence-knowledge graph, our method improves the label accuracy of predicted claims by more than 4% on the FEVER dataset over state-of-the-art fact checking models.
Effect of the Opponent’s Appearance on Interpersonal Cognition that Affects User-to-User Relationship in Virtual Whole-Body Interaction

People infer the internal characteristics (attitude, intent, thoughts, ability, relationship, etc.) of others (interpersonal cognition, IC) from the impressions they form from the personality or attributes of those others (impression formation). Studies premised on interpersonal communication in a seated condition have confirmed that, regardless of whether the communication is in the real world or in a media environment, the appearance of the other person affects IC and the outcome of the communication. People also develop relationships based on impressions or images of the other party. The psychological relationship manifests in physical relationships, that is, the relative positions of the body or the movement. In this study, we evaluate the effects of the appearance of the opponent’s avatar on the players’ IC in whole-body interaction taking place in a virtual reality (VR) space. Moreover, we examine the feasibility of constructing a method of changing the players’ relationship in interpersonal interactions that accompany the control and interference of the entire body, “whole-body interaction,” by manipulating their appearances. In this study, we selected the party game Twister as a case model of whole-body interaction and developed a system that allows users to play Twister in VR space. Using this system environment, we conducted an experiment to evaluate the players’ IC based on the gender and realism of the opponent’s avatar. The results showed that differences in the appearance of the opponent’s avatar affected the IC of male players. We also indicated that changes in IC observed in the experiment can affect the players’ relationship, thus identifying issues that must be resolved in order to realize the method.
Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks

[Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases from requirements, automatically extracting such causal statements are a basic necessity. [Problem:] We lack an approach that is able to extract causal relations from natural language requirements in fine-grained form. Specifically, existing approaches do not consider the combinatorics between causes and effects. They also do not allow to split causes and effects into more granular text fragments (e.g., variable and condition), making the extracted relations unsuitable for automatic test case derivation. [Objective & Contributions:] We address this research gap and make the following contributions: First, we present the Causality Treebank, which is the first corpus of fully labeled binary parse trees representing the composition of 1,571 causal requirements. Second, we propose a fine-grained causality extractor based on Recursive Neural Tensor Networks. Our approach is capable of recovering the composition of causal statements written in natural language and achieves a F1 score of 74% in the evaluation on the Causality Treebank. Third, we disclose our open data sets as well as our code to foster the discourse on the automatic extraction of causality in the RE community.
New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data

In this paper, we developed a new robust part-based model for facial landmark localization and detection via affine transformation. In contrast to the existing works, the new algorithm incorporates affine transformations with the robust regression to tackle the potential effects of outliers and heavy sparse noises, occlusions and illuminations. As such, the distorted or misaligned objects can be rectified by affine transformations and the patterns of occlusions and outliers can be explicitly separated from the true underlying objects in big data. Moreover, the search of the optimal parameters and affine transformations is cast as a constrained optimization programming. To mitigate the computations, a new set of equations is derived to update the parameters involved and the affine transformations iteratively in a round-robin manner. Our way to update the parameters compared to the state of the art of the works is relatively better, as we employ a fast alternating direction method for multiplier (ADMM) algorithm that solves the parameters separately. Simulations show that the proposed method outperforms the state-of-the-art works on facial landmark localization and detection on the COFW, HELEN, and LFPW datasets.
$$\mathrm 3D^2Unet$$: 3D Deformable Unet for Low-Light Video Enhancement

Video recording suffers from noise, artifacts, low illumination, and weak contrast under low-light conditions. With such difficulties, it is challenging to recover a high-quality video from the corresponding low-light one. Previous works have proven that convolutional neural networks perform well on low-light image tasks, and these methods are further extended to the video processing field. However, existing video recovery methods fail to fully exploit the long-range spatial and temporal dependency simultaneously. In this paper, we propose a 3D deformable network based on Unet-like architecture (\(\mathrm 3D^2Unet\)) for low-light video enhancement, which recovers RGB formatted videos from RAW sensor data. Specifically, we adopt a spatial temporal adaptive block with 3D deformable convolutions to better adapt the varying features of videos along spatio-temporal dimensions. In addition, a global residual projection is employed to further boost learning efficiency. Experimental results demonstrate that our method outperforms state-of-the-art low-light video enhancement works.
How are test smells treated in the wild? A tale of two empirical studies

Developing test code may be a time­-consuming process that requires much effort and cost, especially when done manually. In addition, during this process, developers and testers are likely to adopt bad design choices, which may lead to introducing the so­-called test smells in the test code. As the test code with test smells size increases, these tests might become more complex, and as a consequence, much more challenging to understand and evolve them correctly. Therefore, test smells may harm the test code quality and maintenance and break the whole software testing activities. In this context, this study aims to understand whether software testing practitioners unintentionally insert test smells when they implement test code. We first carried out an expert survey to analyze the usage frequency of a set of test smells and then interviews to reach a deeper understanding of how practitioners deal with test smells. Sixty professionals participated in the survey, and fifty professionals participated in the interviews. The yielded results indicate that experienced professionals introduce test smells during their daily programming tasks, even when using their companies’ standardized practices. Additionally, tools support test development and quality improvement, but most interviewees are not aware of test smells’ concepts.
Detecting Bots to Distinguish Hate Speech on Social Media

Social Media Platforms were developed with the intention to bring people together and share their experiences. A social bot is typically engineered to pass as a human, often with the intent to manipulate online discussion. On the other hand they can be designed for doing malicious activities, such as spreading vast misinformation, fake ratings and review or show themselves as fraudulent. Due to rising use of social media activity in users, it has been the need of the hour to take down the bots that manipulate the users on the platform in the wrong direction. For this research work, Twitter tweets were mined by using Tweepy and used for analysis. These raw tweets were preprocessed to find the bots that spread hate speech on the platform through sentimental analysis of data.
Evaluation of Local Thresholding Algorithms for Segmentation of White Matter Hyperintensities in Magnetic Resonance Images of the Brain

White matter hyperintensities are distinguished in magnetic resonance images as areas of abnormal signal intensity. In clinical research, determining the region and position of these hyperintensities in brain MRIs is critical; it is believed this will find applications in clinical practice and will support the diagnosis, prognosis, and therapy monitoring of neurodegenerative diseases. The properties of hyperintensities vary greatly, thus segmenting them is a challenging task. A substantial amount of time and effort has gone into developing satisfactory automatic segmentation systems. In this work, a wide range of local thresholding algorithms has been evaluated for the segmentation of white matter hyperintensities. Nine local thresholding approaches implemented in ImageJ software are considered: Bernsen, Contrast, Mean, Median, MidGrey, Niblack, Otsu, Phansalkar, Sauvola. Additionally, the use of other local algorithms (Local Normalization and Statistical Dominance Algorithm) with global thresholding was evaluated. The segmentation accuracy results for all algorithms, and the parameter spaces of the best algorithms are presented.
Blockchain Technology Enables Healthcare Data Management and Accessibility

Blockchain can be described as an immutable ledger, logging data entries in a decentralized manner. It has been argued that this emerging technology disrupts a wide variety of data-driven domains, including the health domain. In terms of data integrity, immutability, audit, data provenance, flexible access, confidence, privacy, and security, today's healthcare data management systems face critical challenges. A significant proportion of current healthcare networks leveraged for data management are often clustered, raising possible risks of single point collapse in the event of natural disasters. Blockchain is a decentralized, evolving, and revolutionary platform that has the ability to radically revolutionize, reshape and change the way of data accessibility in the healthcare sector. This chapter explores how the use of blockchain for data processing systems and accessibility in healthcare will stimulate advances and deliver substantial changes. This chapter presents a case study to show the practicality of blockchain technology for various healthcare applications.