Reviewerly/exHarmony huggingface dataset:
CodeCV: Mining Expertise of GitHub Users from Coding Activities

The number of software projects developed collaboratively on social coding platforms is steadily increasing. One of the motivations for developers to participate in open-source software development is to make their development activities easier accessible to potential employers, e.g., in the form of a resume for their interests and skills. However, manual review of source code activities is time-consuming and requires detailed knowledge of the technologies used. Existing approaches are limited to a small subset of actual source code activity and metadata and do not provide explanations for their results. In this work, we present CodeCV, an approach to analyzing the commit activities of a GitHub user concerning the use of programming languages, software libraries, and higher-level concepts, e.g., Machine Learning or Cryptocurrency. Skills in using software libraries and programming languages are analyzed based on syntactic structures in the source code. Based on Labeled Latent Dirichlet Allocation, an automatically generated corpus of GitHub projects is used to learn the concept-specific vocabulary in identifier names and comments. This enables the capture of expertise on abstract concepts from a user's commit history. CodeCV further explains the results through links to the relevant commits in an interactive web dashboard. We tested our system on selected GitHub users who mainly contribute to popular projects to demonstrate that our approach is able to capture developers' expertise effectively.
IKAR: An Interdisciplinary Knowledge-Based Automatic Retrieval Method from Chinese Electronic Medical Record

To date, information retrieval methods in the medical field have mainly focused on English medical reports, but little work has studied Chinese electronic medical reports, especially in the field of obstetrics and gynecology. In this paper, a dataset of 180,000 complete Chinese ultrasound reports in obstetrics and gynecology was established and made publicly available. Based on the ultrasound reports in the dataset, a new information retrieval method (IKAR) is proposed to extract key information from the ultrasound reports and automatically generate the corresponding ultrasound diagnostic results. The model can both extract what is already in the report and analyze what is not in the report by inference. After applying the IKAR method to the dataset, it is proved that the method could achieve 89.38% accuracy, 91.09% recall, and 90.23% F-score. Moreover, the method achieves an F-score of over 90% on 50% of the 10 components of the report. This study provides a quality dataset for the field of electronic medical records and offers a reference for information retrieval methods in the field of obstetrics and gynecology or in other fields.
3D-MedTranCSGAN: 3D Medical Image Transformation using CSGAN

Computer vision techniques are a rapidly growing area of transforming medical images for various specific medical applications. In an end-to-end application, this paper proposes a 3D Medical Image Transformation Using a CSGAN model named a 3D-MedTranCSGAN. The 3D-MedTranCSGAN model is an integration of non-adversarial loss components and the Cyclic Synthesized Generative Adversarial Networks. The proposed model utilizes PatchGAN's discriminator network, to penalize the difference between the synthesized image and the original image. The model also computes the non-adversary loss functions such as content, perception, and style transfer losses. 3DCascadeNet is a new generator architecture introduced in the paper, which is used to enhance the perceptiveness of the transformed medical image by encoding-decoding pairs. We use the 3D-MedTranCSGAN model to do various tasks without modifying specific applications: PET to CT image transformation; reconstruction of CT to PET; modification of movement artefacts in MR images; and removing noise in PET images. We found that 3D-MedTranCSGAN outperformed other transformation methods in our experiments. For the first task, the proposed model yields SSIM is 0.914, PSNR is 26.12, MSE is 255.5, VIF is 0.4862, UQI is 0.9067 and LPIPs is 0.2284. For the second task, the model yields 0.9197, 25.7, 257.56, 0.4962, 0.9027, 0.2262. For the third task, the model yields 0.8862, 24.94, 0.4071, 0.6410, 0.2196. For the final task, the model yields 0.9521, 33.67, 33.57, 0.6091, 0.9255, 0.0244. Based on the result analysis, the proposed model outperforms the other techniques.
Path Planning of Unmanned Aerial Vehicle in Complex Environments Based on State-Detection Twin Delayed Deep Deterministic Policy Gradient

This paper investigates the path planning problem of an unmanned aerial vehicle (UAV) for completing a raid mission through ultra-low altitude flight in complex environments. The UAV needs to avoid radar detection areas, low-altitude static obstacles, and low-altitude dynamic obstacles during the flight process. Due to the uncertainty of low-altitude dynamic obstacle movement, this can slow down the convergence of existing algorithm models and also reduce the mission success rate of UAVs. In order to solve this problem, this paper designs a state detection method to encode the environmental state of the UAV’s direction of travel and compress the environmental state space. In considering the continuity of the state space and action space, the SD-TD3 algorithm is proposed in combination with the double-delayed deep deterministic policy gradient algorithm (TD3), which can accelerate the training convergence speed and improve the obstacle avoidance capability of the algorithm model. Further, to address the sparse reward problem of traditional reinforcement learning, a heuristic dynamic reward function is designed to give real-time rewards and guide the UAV to complete the task. The simulation results show that the training results of the SD-TD3 algorithm converge faster than the TD3 algorithm, and the actual results of the converged model are better.
Random feature selection using random subspace logistic regression

Feature selection becomes a prominent method in the big data era. The logistic regression model is a wrapper method that provides better classification or prediction accuracy but it is computationally expensive. In this study, we propose the random subspace logistic regression where features are randomly selected through bootstrap cycles. The random subspace regression method is applied to both standard and lasso logistic regression models. Using the simulated and empirical data, our proposed random subspace logistic regression shows favorable results and can be a promising alternative for flat feature selection.
Deep learning-based data privacy protection in software-defined industrial networking

The industrial Internet connects equipment to the network and utilizes the data generated to assist businesses. Industrial big data is the result of data accumulation; thus, the industrial Internet has to adopt new technologies—namely, software-defined industrial networks (SDIN) —to keep up with these developments. This study suggests a deep differential privacy data protection algorithm based on SDIN. The deep learning model is analyzed and integrated with differential privacy to provide the process framework for the deep differential privacy data protection algorithm. The equivalent model of the generative adversarial network is used to allow the attacker to obtain the optimal fake samples. The balance between dataset availability and privacy protection is achieved by implementing parameter tuning on the deep differential privacy model. The experimental results show that the proposed algorithm has strong industrial data privacy protection and high data availability and can effectively guarantee the privacy security of industrial data.
Digital marketing solutions based on consumer data and homomorphic encryption

Abstract In recent years, homomorphic encryption has attracted much research attention in the international cryptography community and is applied in various industries to enhance data confidentiality. With the advent of the era of big data, more and more data are available through online, using users another how to propose privacy protection models applicable to consumer data application scenarios under the premise of meeting data privacy, security and regulatory requirements, so that the models can formulate accurate marketing strategies more scientifically based on a large amount of consumer data, is the key to the current competition. In this process, more and more people are concerned about privacy confidentiality and security, and homomorphic encryption is of great significance for data processing security.
Compilation of Entangling Gates for High-Dimensional Quantum Systems

Most quantum computing architectures to date natively support multi-valued logic, albeit being typically operated in a binary fashion. Multi-valued, or qudit, quantum processors have access to much richer forms of quantum entanglement, which promise to significantly boost the performance and usefulness of quantum devices. However, much of the theory as well as corresponding design methods required for exploiting such hardware remain insufficient and generalizations from qubits are not straightforward. A particular challenge is the compilation of quantum circuits into sets of native qudit gates supported by state-of-the-art quantum hardware. In this work, we address this challenge by introducing a complete workflow for compiling any two-qudit unitary into an arbitrary native gate set. Case studies demonstrate the feasibility of both, the proposed approach as well as the corresponding implementation (which is freely available at https://github.com/cda-tum/qudit-entanglement-compilation).
Multivariate time-series anomaly detection via temporal convolutional and graph attention networks

Multivariate time series anomaly detection has been investigated extensively in recent years. Capturing long-term time series information is one of the challenges in this field. We propose a novel multivariate time series anomaly detection framework MTAD-TCGA comprising several modules that efficiently and accurately capture dependencies in long-term multivariate time series. The proposed model contains a temporal convolutional module and uses two parallel graph attention layers to learn the complex dependencies of time series in both the temporal and feature dimensions. A Gated Recurrent Unit layer, based on an improved attention mechanism, and an auto-regressive model is used for prediction, and the prediction model and reconstruction model are jointly optimized. Finally, the threshold is selected by extreme value theory, and then anomalies are identified. The experimental results on three public datasets show our framework is superior to other state-of-the-art models, achieving F1 scores uniformly at levels above 0.9, verifying the effectiveness and feasibility of the MTAD-TCGA method.
Email Security: Concept, Formulation, and Applications

Electronic mails are a popular communication tool in the digital era. However, communications across the untrusted Internet pose major security threats to e-mail communications. This work aimed to examine the cryptographic security concepts behind the e-PGP and S/MIME email protocols, present the mathematical concepts, and highlight the applications for improving e-mail security. The work presents a review of the security implications of PGP and S/MIME in protecting the relevant principles of email security, such as confidentiality, integrity, availability, authenticity, and non-repudiation.
Certain Investigation of Attacks in the Field of Internet of Things and Blockchain Technology

The Internet of Things (IoT) and Blockchain Technology are more trending which are gaining popularity all over the world in recent times. IoT is a collection of several things that consists of sensors, actuators, and many other devices to perform identification and sensing by aggregation of data, interacting with other devices, and advanced processes in a real-time environment. However, at the same time, Blockchain comprises small blocks where each block can have an identity and hash value. Due to the huge sector of applications that can be achieved with IoT devices, threats and huge attacks can occur and make it an insecure state. Therefore, security and privacy have become an integral part of the internet of things. To the best of our knowledge, extensive studies are made on the security threats and different types of attacks in every layer of the Internet of Things. Moreover, the article focuses on the integration of blockchain technology with the Internet of Things (IoT) and the classification of Blockchain IoT (BIoT) attacks. The threat levels and the prevention techniques of attacks in IoT and Blockchain are incorporated respectively.
Deep Learning-based Ship Detection on FPGAs

Detecting ships from remote sensing imagery is a crucial application for maritime surveillance. Object detection algorithms based on Deep Neural Networks (DNNs), such as YOLO, have enabled sophisticated accuracy for ship detection tasks. However, the deployment of DNN models on embedded accelerators raises several issues, including performance degradation, latency delays, and energy consumption due to the computational complexity and model size of these models. Thus, given the limitations of computational resources in embedded devices, the question of how to determine the balanced tradeoff between performance, inference latency, and energy efficiency has been a concern. In this study, we demonstrate the feasibility of the Versal ACAP FPGA (VCK190) for YOLO-based ship detection compared to Jetson TX2 GPU. Our results show that despite providing similar performance, the FPGA board can predict a YOLO model in less time, roughly half the time it takes the GPU to implement the same model in addition to the superior power efficiency.
Grid-Based Decimation for Wavelet Transforms With Stably Invertible Implementation

The constant center frequency to bandwidth ratio (Q-factor) of wavelet transforms provides a very natural representation for audio data. However, invertible wavelet transforms have either required non-uniform decimation -- leading to irregular data structures that are cumbersome to work with -- or require excessively high oversampling with unacceptable computational overhead. Here, we present a novel decimation strategy for wavelet transforms that leads to stable representations with oversampling rates close to one and uniform decimation. Specifically, we show that finite implementations of the resulting representation are energy-preserving in the sense of frame theory. The obtained wavelet coefficients can be stored in a timefrequency matrix with a natural interpretation of columns as time frames and rows as frequency channels. This matrix structure immediately grants access to a large number of algorithms that are successfully used in time-frequency audio processing, but could not previously be used jointly with wavelet transforms. We demonstrate the application of our method in processing based on nonnegative matrix factorization, in onset detection, and in phaseless reconstruction.
Deep Learning Algorithms for Brain Imaging

Advances in artificial intelligence (AI) and machine learning have led to successful applications of automated medical image analysis. Deep learning algorithms, a subset of machine learning, are particularly suited to learn complex patterns in data to make automated predictions for new images.1 Several studies have demonstrated that deep learning algorithms applied to medical images may have broad future clinical applications.2,3 If successfully embedded within future healthcare systems, AI may reduce the costs and time associated with reading and interpreting brain scans. However, using these tools in clinical practice has been elusive for various reasons.4,5
Face Recognition System Using Feature Extraction Method of 2-D Gabor Wavelet Filter Bank and Distance-Based Similarity Measures

Face recognition is a computer vision task which has a capability to recognize human according to their corresponding face features. Face recognition application in computer system have been developed in recent year including surveillance, human-computer interaction, and multimedia application. Some challenges will possibly be undergone in face recognition research mostly lie on deformation of face condition such as pose, expression, illumination, and accessories use that lead to a noticeable impact on recognition rate. In this research, two-dimensional Gabor wavelet filter bank was implemented to extract face image. Gabor filter has characteristic similar to wavelet that is localized in both time and frequency domain able to extract local feature on face image. Convolution process produces a face representation feature called Gaborface in a terms of magnitude information obtained from sinusoidal complex function. Features are formed as a column matrix concatenation which is called feature vector. At the end of process, one distance-based similarity measure, i.e., Euclidean Distance was implemented to perform a test measurement on data features vector with database features. Testing has been done by giving variation in some aspects, i.e., number of training data, face image deformation such pose and expression, and the variant of filter with variation in scale and orientation. The best system, based on this research, is achieved by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5 \times 8$</tex> filter bank with accuracy up to 98,67%. The proposed method also shows a very well performance in recognizing the expression changes, but it cannot accommodate properly in pose changes.
DySR: A Dynamic Graph Neural Network Based Service Bundle Recommendation Model for Mashup Creation

An increasing number and diversity of services are available, which results in significant challenges to effectively reuse service during mashup creation. Many works have modeled the mashup creation problem as a service recommendation task and have achieved remarkable results. However, the performance of these methods can be further improved. The main problems affecting these methods include the constraints among recommended services, the evolution of services, and the semantic gap existing in services and mashups. In this article, we model the mashup creation problem as a service bundle recommendation task that is formally defined to address the constraints among recommended services. And then, a dynamic graph neural network based model called DySR is proposed to tackle the evolution of service and the semantic gap between services and mashups. In order to quantitatively measure how significant the semantic gap between mashups and services is, a measurement method of a semantic gap is given. With it, experiments show that to what extent DySR can reduce the semantic gap in the context of mashup creation. In addition, new evaluation metrics are introduced to overcome the preference for popular services in traditional service recommendations. Extensive experiments conducted on a real-world dataset from ProgrammableWeb, and the experiment results show that DySR outperforms existing state-of-the-art methods.
Data-driven Cross-lingual Syntax: An Agreement Study with Massively Multilingual Models

Abstract Massively multilingual models such as mBERT and XLM-R are increasingly valued in Natural Language Processing research and applications, due to their ability to tackle the uneven distribution of resources available for different languages. The models’ ability to process multiple languages relying on a shared set of parameters raises the question of whether the grammatical knowledge they extracted during pre-training can be considered as a data-driven cross-lingual grammar. The present work studies the inner workings of mBERT and XLM-R in order to test the cross-lingual consistency of the individual neural units that respond to a precise syntactic phenomenon, that is, number agreement, in five languages (English, German, French, Hebrew, Russian). We found that there is a significant overlap in the latent dimensions that encode agreement across the languages we considered. This overlap is larger (a) for long- vis-à-vis short-distance agreement and (b) when considering XLM-R as compared to mBERT, and peaks in the intermediate layers of the network. We further show that a small set of syntax-sensitive neurons can capture agreement violations across languages; however, their contribution is not decisive in agreement processing.
Avatarian embodiment in Indigenous Futurisms 4D: the intersemiosis of intercultural encounters

ABSTRACTABSTRACTDrawing on VR affordances and its relevant discourse of embodiment, immersion and engagement, Indigenous Futurisms (IF) challenges mainstream stereotypes of indigenous cultures. This article explores how the VR movie Crow: The Legend mediates an intercultural experience of IF. The framework proposed for understanding the virtually-mediated IF intercultural experience is grounded in the integration of SF-MDA and postphenomenology. The suggested theoretical integration interprets the VR embodied affordances of navigation, body movement, and hand gestures. The dialogue established in Crow between the interactants and VR technology unravels the cultural particularities of futurisms' notions of 'non-linear temporality,' 'selfdetermination,' 'self-discovery,' 'selflessness' and 'diversity'.يسعى المفهوم المستقبلي للشعوب الأصلية IF إلى تغيير الصور النمطية السائدة باستخدام إمكانات الواقع الافتراضي وخطابه الذي يتسم بالتجسيد والانغماس والمشاركة. تعني هذه المقالة بتحليل كيف يعرض فيلم VR Crow: The Legend تجربة فريدة للقاء الثقافات .وفي هذا الشأن يرتكز الإطار التحليلي على الدمج بين SF-MDA وpostphenomenology .ويعمل هذا التكامل النظري المقترح على تفسير إمكانات الواقع الافتراضي المتجسدة في التنقل الإفتراضي وحركة الجسم وإيماءات اليد، كما انه يكشف عن الحوار الذي يحثه الفيلم بين المتفاعلين وتكنولوجيا الواقع الافتراضي وينتج عنه ترسيخ الخصائص الثقافية لمفاهيم المستقبل عن "الزمنية غير الخطية" و "تقرير المصير" و "اكتشاف الذات " و "نكران الذات " و "التنوع ".KEYWORDS: Indigenous Futurisms VRSF-MDApostphenomenologyavatarian embodimentintercultural encounter Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsRania Magdi FawzyRania Magdi Fawzy is an Associate Professor of Applied Linguistics. She is an editor for Discourse Context & Media, Elsevier. She has published a number of papers in international journals affiliated to reputable publishers such as Discourse, Context & Media, Discourse & communication, Visual Communication, Space & Culture and Social Semiotics.Reham Farouk El ShazlyReham El Shazly is an Associate Professor of Applied Linguistics at CLC at Arab Academy for Science, Technology & Maritime Transport, Cairo. Her research focuses on multimodality, CDA, cognitive linguistics, and foreign language acquisition. Her most recent publications appeared in Language and Intercultural Communication, Semiotica, and Expert Systems.