Reviewerly/exHarmony huggingface dataset:
Would Your Clothes Look Good on Me? Towards Transferring Clothing Styles with Adaptive Instance Normalization

Several applications of deep learning, such as image classification and retrieval, recommendation systems, and especially image synthesis, are of great interest to the fashion industry. Recently, image generation of clothes gained lot of popularity as it is a very challenging task that is far from being solved. Additionally, it would open lots of possibilities for designers and stylists enhancing their creativity. For this reason, in this paper we propose to tackle the problem of style transfer between two different people wearing different clothes. We draw inspiration from the recent StarGANv2 architecture that reached impressive results in transferring a target domain to a source image and we adapted it to work with fashion images and to transfer clothes styles. In more detail, we modified the architecture to work without the need of a clear separation between multiple domains, added a perceptual loss between the target and the source clothes, and edited the style encoder to better represent the style information of target clothes. We performed both qualitative and quantitative experiments with the recent DeepFashion2 dataset and proved the efficacy and novelty of our method.
ShuffleBlock: Shuffle to Regularize Convolutional Neural Networks

Deep neural networks have enormous representational power which has lead them to overfit on most datasets. Thus, regularizing them is important in order to reduce overfitting and to enhance their generalization capability. This paper studies the operation of channel patch shuffle as a regularization technique in deep convolutional networks. We propose a novel regularization technique called ShuffieBlock where we show that randomly shuffling small patches or blocks between channels significantly improves their performance. The patches to be shuffled are picked from the same spatial locations in the feature maps such that a patch, when transferred from one channel to another, acts as a structured noise for the later channel. The ShuffieBlock module is easy to implement and improves the performance of several baseline networks for the task of image classification on CIFAR and ImageNet datasets.
Arabic Documents Clustering using Bond Energy Algorithm and Genetic Algorithm

Text clustering is a popular data mining process used in data indexing and information retrieval. However, Existing clustering techniques suffer from several shortcomings such as sensitivity to the initial value, slow convergence, etc... In this paper, the Bond Energy Algorithm (BEA) has been used for clustering Arabic documents. The bond energy algorithm has been modified with the help of a Genetic algorithm to enhance the accuracy of the results. The efficiency of modified BEA has been compared to BEA baseline and K-means clustering in terms of precision, recall, and F-Score. The results show that Modified BEA can be used for clustering Arabic Documents, and the efficiency of the algorithm can be enhanced in the future to give a better result.
Transformer-Based Deep Learning for Sarcasm Detection with Imbalanced Dataset: Resampling Techniques with Downsampling and Augmentation

Sarcasm is a speech in which the speaker says something positive and means something negative to insult others. Sarcasm detection is a sentiment analysis problem in natural language processing (NLP) with no well-defined borders. Extracting the meaning of sarcastic content can impact the analysis of people’s tendencies in a particular situation. Due to the massive availability of online text data, storage resources, and the evolution of computing powers, deep learning techniques can be deployed in sarcasm detection. Thus, in this paper, different deep learning techniques and mechanisms are used to classify a given text as sarcastic or non-sarcastic in an imbalanced dataset. The transfer learning practice is adopted in this work by training pretrained transformer-based models, the BERT and the RoBERTa. In addition, three RNN-based models are used: the LSTM, BiLSTM, and BiGRU. The models are trained on a dataset from the SemEval2022 competition. The dataset is imbalanced and in the English language. The dataset is processed in two ways: the downsampling and the augmentation to produce a balanced dataset. The models are trained on the two resultant datasets and the original one. The results of the conducted training show that the ensemble of the transformer-based models trained on the original imbalanced dataset outperforms the baseline model in terms of F1-Score with a value of 0.43.
Application of near infrared spectroscopy in sub-surface monitoring of petroleum contaminants in laboratory-prepared soils

This study was conducted to determine the suitability of near-infrared (NIR) spectroscopy for subsurface monitoring of crude oil contaminants in different soil types under different moisture conditions. Calibration tests, carried out in both wet and dry soils, with crude oil contents 5–30% indicate an inverse relationship between spectra and oil content with R2 values ≥0.98 for dry soil mixtures. A derived index termed “Oil Index” was generated by calculating a spectral ratio between the main oil peak in the NIR region and a spectrally inactive part of the spectrum within the visible region. It was used in a series of experiments designed to test whether NIR probes could yield useful information about the movement of oil through different soils. Synthetic crude oil was dropped into a series of soil samples of different particle size distribution and moisture content. Analyses of the 3D distribution of values of the Oil Index demonstrate that it is possible to estimate and map synthetic crude oil concentration in the subsurface of the soil samples. Results showed that while the Oil Index provided a reasonable estimation for oil concentration in dry samples, this was not the case for wet samples, although the Index was useful in understanding the pattern of movement of oil contaminant in wet soils. The work indicates that this technique may enhance field investigation of oil contamination, providing an accurate in-field technique.
Distributed and Scalable Healthcare Data Storage Using Blockchain and KNN Classification

Medical data from patients is saved electronically in the healthcare industry. The paper suggests storing medical data on a distributed on-chain utilizing InterPlanetary File Storage (IPFS) and blockchain. The use of InterPlanetary File Storage can help an organization manage the storage space efficiently. The proposed method gives details about the intersection of artificial intelligence and blockchain. This proposed approach eliminates the requirement for a trusted centralized authority, intermediates, and transaction records, resulting in increased efficiency and safety while retaining high integrity, scalability, reliability, and security. It also emphasizes smart contracts to manage and control all interactions and transactions between all healthcare participants. Using artificial intelligence, a revolutionary new system can be built in which a huge amount of medical data can be processed efficiently and can be used to predict diseases. The paper utilizes the assistance of binary classification method by K-nearest neighboring algorithm for machine learning to foresee the patient's heart health. Using InterPlanetary File Storage (IPFS) improves the storage system framework by making data distributed and, subsequently, more reliable, scalable, and accessible to the clients.
How Do Pre-/In-Service Mathematics Teachers Reason for or against the Use of Digital Technology in Teaching?

The role and the availability of digital technology in society is growing, which is why educators need to increasingly more often decide which types of digital technology to integrate into their teaching and when to integrate them. Thus, those decision-making skills need to be developed and measured especially for pre-service mathematics teachers. Therefore, we conducted an explorative interview study to understand the portfolio of argumentation on whether to use digital technology in different teaching phases and what criteria are used when making those decisions. Our results are based on ten interviews with pre- and in-service mathematics teachers in Germany. The analysis shows that (1) different levels of argumentation can be distinguished and (2) there are indications that teachers need to be aware of digital technology when deciding whether or not to use digital technology in a teaching setting. In addition, (3) besides the teaching-phase perspective and the learner-perspective, we expanded current research by formalizing the educator-perspective in a list of decision criteria. The compiled list of decision criteria was theoretically validated through the literature. In combination with the applied teaching phase framework and taxonomy of digital technology the list could aid in the development of the decision-making skills and potentially could result in a more reflective use of digital technology by pre-service and in-service teachers.
Detecting COVID-19 Implication on Education and Economic in Arab World Using Sentiment Analysis Techniques of Twitter Data

Coronavirus (COVID-19) is a contagious and dangerous infection that initially surfaced in Wuhan, China, in December of 2019 and has infected millions around the world. The rapid transmission of this disease leads to all governments’ taking a lockdown decision. This decision has a negative effect on the two main sectors of any country: education and the economy. This study explores public opinion about the impact of COVID-19 on these two sectors by collecting Arabic tweets from Twitter from the MENA region. Then, we applied NLP and sentiment analysis process to the dataset for each sector after applying the pre-processing steps. We built state-of-the-art machine learning models to use them in the prediction process that are Random Forest (RF), Logistic Regression (LR), Na¨ıve Bayes (NB), and Voting classifier (VC). The results showed that the NB and VC gave the best performance results in two datasets. The results showed that these models produced outcomes that were useful to many decision-makers in the government in taking important and suitable decisions related to daily life in the Arab World.
Fast algorithms for incremental and decremental semi-supervised discriminant analysis

Incremental and decremental problems are challenging tasks in semi-supervised learning. The incremental semi-supervised discriminant analysis (ISSDA) method proposed by Dhamecha et al. is an efficient method for incremental semi-supervised learning. However, one deficiency of the ISSDA method is that the total scatter matrix remains unchanged during incremental learning, which is impractical in practice. On the other hand, there may be a series of incorrectly artificial labeling in the public data set, and it is interesting to consider the decremental problem in semi-supervised learning. To the best of our knowledge, however, there are few decremental algorithms for semi-supervised discriminant analysis. The contributions of this work are as follows. First, a new incremental semi-supervised discriminant analysis method is proposed, in which we consider updating the total scatter matrix and the between-class scatter matrix simultaneously when new samples are added. Second, we show how to solve the large eigenproblem of the updated total scatter matrix efficiently. Third, we propose two decremental algorithms for semi-supervised discriminant analysis. Numerical experiments demonstrate the superiority of the proposed algorithms over many state-of-the-art algorithms for semi-supervised discriminant analysis.
Digital Footprints and Data-Security Risks for Political Scientists

ABSTRACT As greater shares of research and data are digitized, political scientists are increasingly confronted with questions pertaining to data security. Yet, data-management plans rarely evaluate the risks pertaining to a researcher’s data across all project phases. This article highlights distinct risks related to key phases of the research process that deserve more attention by scholars. We emphasize risks during a project’s inception and pre-data-collection phases, as well as risks associated with data publication and its afterlife. We discuss how shifts in political context and (re)newed politicization of topics can present new security risks for both the researcher and the researched communities long after a project has been completed. We provide a framework for recognizing and mitigating data risks, thereby contributing to the growing interest in data-security best practices.
Sentiment Analysis and Vector Embedding: A Comparative Study

Automatic intent/sentiment classification can be done with various machine learning approaches as well as methods. But the success of these techniques majorly depends on the representation of words or documents in vector space. That can be easily consumed by machine toward learning the hidden pattern of text/corpus. To achieve this, various methods have been proposed, and many are commercially accepted as well. In deep learning architecture for intent/sentiment analysis, the vector embedding plays a crucial role. It represents feature extraction. In this paper, various methods for vector embedding are discussed along with their comparison.
Design of an Integrated Cryptographic SoC Architecture for Resource-Constrained Devices

One of the active research areas in recent years that has seen researchers from numerous related fields converging and sharing ideas and developing feasible solutions is the area of hardware security. The hardware security discipline deals with the protection from vulnerabilities by way of physical devices such as hardware firewalls or hardware security modules rather than installed software programs. These hardware security modules use physical security measures, logical security controls, and strong encryption to protect sensitive data that is in transit, in use, or stored from unauthorized interferences. Without mechanisms to circumvent the ever-evolving attacking strategies on hardware devices and the data that they process or store, billions of dollars will always be lost to attackers who ply their trade by targeting such vulnerable devices. This paper, therefore, proposes an integrated cryptographic SoC architecture solution to this menace. The proposed architecture provides security by way of key exchange, management, and encryption. The proposed architecture is based on a True Random Number generator core that generates secret keys that are used in Elliptic Curve Diffie-Hellman Key Exchange to perform elliptic curve scalar multiplication to obtain public and shared keys after the exchange of the public keys. The proposed architecture further relies on a Key Derivation Function based on the CubeHash algorithm to obtain Derived Keys that provide the needed security using the ChaCha20_Poly1305 Authenticated Encryption with Associated (AEAD) Data Core. The proposed Integrated SoC architecture is interconnected by AMBA AHB-APB on-chip bus and the system is scheduled and controlled using the PicoRV32 opensource RISC-V processor. The proposed architecture is tested and verified on the Virtex-4 FPGA board using a custom-designed GUI desktop application.
Image Steganography Technique based on Singular Value Decomposition and Discrete Wavelet Transform

Steganography is a technique of hiding information in digital media. In recent years plenty of work has been done in this domain, and the work can be compared on various parameters such as high robustness and large capacity to achieve a goal. This paper proposed the method of steganography in digital media using Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT). The DWT is a frequency-domain technique comprising DWT which comparatively offers better robustness and high PSNR value of stego image over other techniques. The proposed method works well for information hiding against AWGN (additive white Gaussian noise) attack and fulfills the objective to achieve high robustness and high PSNR.
Adaptive feedback connection with a single‐level feature for object detection

From the perspective of detector optimisation, detecting objects using only a one-level feature cannot provide good performance for a wide range of scales. Various complex feature pyramidal structures address this problem using the divide-and-conquer strategy and multi-scale feature fusion. However, this requires adding too many additional convolutional layers and fusion operations. To address the issue, a simple detection part is proposed, which includes three components, namely a one-level feature map for detection, the encoder structure with feedback connection, and a decoupled head. The redesigned encoder and decoupled head can successfully address the performance decline caused by the one-level feature-based detection. Moreover, the proposed method can accelerate the convergence of the detector and achieve a faster inference time. Based on the optimised detection part, an adaptive feedback connection with a single-level feature (AFS) is proposed for object detection. The experiments conducted on the MS COCO 2017 benchmark show that the proposed method can achieve comparable results with its multi-scale pyramid counterpart, You Only Look Once v4 (YOLOv4). In addition, AFS can help the YOLOv4 achieve 44.9 mAP at 27 frame per second and converging 82 epochs earlier under the image size of 608×608, which represents a 42.1% improvements in the convergence speed.
A Characterization Study of Merge Conflicts in Java Projects

In collaborative software development, programmers create software branches to add features and fix bugs tentatively, and then merge branches to integrate edits. When edits from different branches textually overlap (i.e., textual conflicts ) or lead to compilation and runtime errors (i.e., build and test conflicts ), it is challenging for developers to remove such conflicts. Prior work proposed tools to detect and solve conflicts. They investigate how conflicts relate to code smells and the software development process. However, many questions are still not fully investigated, such as what types of conflicts exist in real-world applications and how developers or tools handle them. For this article, we used automated textual merge, compilation, and testing to reveal three types of conflicts in 208 open-source repositories: textual conflicts, build conflicts (i.e., conflicts causing build errors), and test conflicts (i.e., conflicts triggering test failures). We manually inspected 538 conflicts and their resolutions to characterize merge conflicts from different angles. Our analysis revealed three interesting phenomena. First, higher-order conflicts (i.e., build and test conflicts) are harder to detect and resolve, while existing tools mainly focus on textual conflicts. Second, developers manually resolved most higher-order conflicts by applying similar edits to multiple program locations; their conflict resolutions share common editing patterns implying great opportunities for future tool design. Third, developers resolved 64% of true textual conflicts by keeping complete edits from either a left or right branch. Unlike prior studies, our research for the first time thoroughly characterizes three types of conflicts, with a special focus on higher-order conflicts and limitations of existing tool design. Our work will shed light on future research of software merge.
An Intelligent Breast Ultrasound System for Diagnosis and 3D Visualization

Background: Ultrasonography is the main examination method for breast diseases. Ultrasound imaging is currently relied upon by doctors to form statements of characteristics and locations of lesions, which severely limits the completeness and effectiveness of ultrasound image information. Moreover, analyzing ultrasonography requires experienced ultrasound doctors, which are not common in hospitals. Thus, this work proposes a 3D-based breast ultrasound system, which can automatically diagnose ultrasound images of the breasts and generate a representative 3D breast lesion model through typical ultrasonography. Methods: In this system, we use a weighted ensemble method to combine three different neural networks and explore different combinations of the neural networks. On this basis, a breast locator was designed to measure and transform the spatial position of lesions. The breast ultrasound software generates a 3D visualization report through the selection and geometric transformation of the nodular model. Results: The ensemble neural network improved in all metrics compared with the classical neural network (DenseNet, AlexNet, GoogLeNet, etc.). It proved that the ensemble neural network proposed in this work can be used for intelligent diagnosis of breast ultrasound images. For 3D visualization, magnetic resonance imaging (MRI) scans were performed to achieve their 3D reconstructions. By comparing two types of visualized results (MRI and our 3D model), we determined that models generated by the 3D-based breast ultrasound system have similar nodule characteristics and spatial relationships with the MRI. Conclusions: In summary, this system implements automatic diagnosis of ultrasound images and presents lesions through 3D models, which can obtain complete and accurate ultrasound image information. Thus, it has clinical potential.
FPGA Implementation of High-Performance s-box Model and Bit-level Masking for AES Cryptosystem

The inadequacies inherent in the existing cryptosystem have driven the development of exploit the benefits of cipher key characteristics and associated key generation tasks in cryptosystems for high-performance security systems. In this paper, cipher key-related issues that exists in conventional symmetric AES crypto system is considered as predominant issues and also discussed other problems such as lack of throughput rate, reliability and unified key management problems are considered and solved using appropriate hierarchical transformation measures. The inner stage pipelining is introduced over composite field based s-box transformation models to reduce the path delay. In addition to that, this work also includes some bit level masking technique for AES. The improved diffusion and confusion metrics of bit masking transformation model mitigates key management related issues. An extensive analysis of data rate proved the performance metrics of proposed AES model. And finally, FPGA implementation is carried out to validate the performance metrics in real time.
Colloids and Nanoparticles: Implications for Hydrothermal Precious Metal Ore Formation

Abstract Research on nanoscale processes is expanding in many scientific and technical disciplines, and economic geology is no exception. The idea that colloidal gold particles (or nanoparticles) may have played a significant role in ore formation was initially based on textural evidence from high-grade epithermal veins. More recently, the discovery of gold nanoparticles in some present-day geothermal systems and several orogenic gold deposits suggests that their formation could be a common process in gold-supersaturated hydrothermal solutions. Rare laboratory studies indicate that gold nanoparticles typically aggregate to form dendritic clusters. Over time, these dendrites perhaps can evolve to form coarser-grained crystals as subsequent annealing and recrystallization occurs. Due to the ephemeral nature and later recrystallization of dendrites, evidence of their former existence is commonly obscured. However, newer nanoscale imaging technologies have resulted in an increased recognition of their presence in hydrothermal gold ores, and thus their role in ore-forming processes merits further research. In particular, does their nucleation and deposition lead to forming higher-grade ores?