Reviewerly/exHarmony huggingface dataset:
Assessing feature selection method performance with class imbalance data

Identifying the most informative features is a crucial step in feature selection. This paper focuses primarily on wrapper feature selection methods designed to detect important features with F1-score as the target metric. As an initial step, most wrapper methods order features according to importance. However, in most cases, the importance is defined according to the classification method used and varies with the characteristics of the data set. Using synthetically simulated data, we examine four existing feature ordering techniques to find the most desirable and the most effective ordering mechanism to identify informative features. Using the results, an improved method is suggested to extract the most informative feature subset from the data set. The method uses the sum of absolute values of the first k principal component loadings to order the features where k is a user-defined application-specific value. It also applies a sequential feature selection method to extract the best subset of features. We further compare the performance of the proposed feature selection method with results from the existing Recursive Feature Elimination (RFE) by simulating data for several practical scenarios with a different number of informative features and different imbalance rates. We also validate the method using a real-world application on several classification methods. The results based on the accuracy measures indicate that the proposed approach performs better than the existing feature selection methods.
EM-X-DL: Efficient Cross-device Deep Learning Side-channel Attack With Noisy EM Signatures

This work presents a Cross-device Deep-Learning based Electromagnetic (EM-X-DL) side-channel analysis (SCA) on AES-128, in the presence of a significantly lower signal-to-noise ratio (SNR) compared to previous works. Using a novel algorithm to intelligently select multiple training devices and proper choice of hyperparameters, the proposed 256-class deep neural network (DNN) can be trained efficiently utilizing pre-processing techniques like PCA, LDA, and FFT on measurements from the target encryption engine running on an 8-bit Atmel microcontroller. In this way, EM-X-DL achieves &gt;90% single-trace attack accuracy. Finally, an efficient end-to-end SCA leakage detection and attack framework using EM-X-DL demonstrates high confidence of an attacker with &lt;20 averaged EM traces.
Identifying vulgarity in Bengali social media textual content

The presence of abusive and vulgar language in social media has become an issue of increasing concern in recent years. However, research pertaining to the prevalence and identification of vulgar language has remained largely unexplored in low-resource languages such as Bengali. In this paper, we provide the first comprehensive analysis on the presence of vulgarity in Bengali social media content. We develop two benchmark corpora consisting of 7,245 reviews collected from YouTube and manually annotate them into vulgar and non-vulgar categories. The manual annotation reveals the ubiquity of vulgar and swear words in Bengali social media content (i.e., in two corpora), ranging from 20% to 34%. To automatically identify vulgarity, we employ various approaches, such as classical machine learning (CML) classifiers, Stochastic Gradient Descent (SGD) optimizer, a deep learning (DL) based architecture, and lexicon-based methods. Although small in size, we find that the swear/vulgar lexicon is effective at identifying the vulgar language due to the high presence of some swear terms in Bengali social media. We observe that the performances of machine leanings (ML) classifiers are affected by the class distribution of the dataset. The DL-based BiLSTM (Bidirectional Long Short Term Memory) model yields the highest recall scores for identifying vulgarity in both datasets (i.e., in both original and class-balanced settings). Besides, the analysis reveals that vulgarity is highly correlated with negative sentiment in social media comments.
VidVRD 2021

ACM Multimedia 2021 Video Relation Understanding Challenge is the third grand challenge which aims at exploring the relationship of subjects and objects appearing in videos for fine-grained and high-level video understanding. Given a video, the video relation detection model should output a serious of relation triplet subject, predicate, object and the corresponding trajectories of subject and object. The goal of this task is to promote research on developing video semantic understanding model, so as to perform complex inferences and mining of visual knowledge in videos. In this paper, we make a comprehensive and detailed introduction of this task, conclude the proposed algorithms in the last few years, and propose future direction for research in this task.
Performance Analysis of Otsu-Based Thresholding Algorithms: A Comparative Study

Image thresholding is a widely used technology for a lot of computer vision applications, and among various global thresholding algorithms, Otsu-based approaches are very popular due to their simplicity and effectiveness. While the usage of Otsu-based thresholding methods is well discussed, the performance analyses of these methods are rather limited. In this paper, we first review nine Otsu-based approaches and categorize them based on their objective functions, preprocessing, and postprocessing strategies. Second, we conduct several experiments to analyze the model characteristics using different scene parameters both on synthetic images and real-world cell images. We put more attention to examine the variance of foreground object and the effect of the distance between mean values of foreground and background. Third, we explore the robustness of algorithms by introducing two typical kinds of noises under different intensities and compare the running time of each method. Experimental results show that NVE, WOV, and Xing’s methods are more robust to the distance of mean values of foreground and background. The large foreground variance will cause a larger threshold value. Experiments on cell images show that foreground miss detection becomes serious when the intensities of foreground pixels change drastically. We conclude that almost all algorithms are significantly affected by Salt&amp;Pepper and Gaussian noises. Interestingly, we find that ME increases almost linearly with the intensity of Salt&amp;Pepper noise. In terms of algorithms’ time cost, methods with no preprocessing and postprocessing steps have more advantages. All these findings can serve as a guideline for image thresholding when using Otsu-based thresholding approaches.
Intel’s Hyperscale-Ready Infrastructure Processing Unit (IPU)

Major Advantages of IPUs Separation of Infrastructure & Tenant Guest can fully control the CPU with their SW, while CSP maintains control of the infrastructure and Root of Trust Infrastructure Offload Accelerators help process these task efficiently. Minimize latency and jitter and maximize revenue from CPU Diskless Server Architecture Simplifies data center architecture while adding flexibility for the CSP
One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search

Convolutional neural networks (CNNs) are used in numerous real-world applications such as vision-based autonomous driving and video content analysis. To run CNN inference on various target devices, hardware-aware neural architecture search (NAS) is crucial. A key requirement of efficient hardware-aware NAS is the fast evaluation of inference latencies in order to rank different architectures. While building a latency predictor for each target device has been commonly used in state of the art, this is a very time-consuming process, lacking scalability in the presence of extremely diverse devices. In this work, we address the scalability challenge by exploiting latency monotonicity --- the architecture latency rankings on different devices are often correlated. When strong latency monotonicity exists, we can re-use architectures searched for one proxy device on new target devices, without losing optimality. In the absence of strong latency monotonicity, we propose an efficient proxy adaptation technique to significantly boost the latency monotonicity. Finally, we validate our approach and conduct experiments with devices of different platforms on multiple mainstream search spaces, including MobileNet-V2, MobileNet-V3, NAS-Bench-201, ProxylessNAS and FBNet. Our results highlight that, by using just one proxy device, we can find almost the same Pareto-optimal architectures as the existing per-device NAS, while avoiding the prohibitive cost of building a latency predictor for each device.
Homotopy Theory in Digital Topology

Digital topology is part of the ongoing endeavor to understand and analyze digitized images. With a view to supporting this endeavor, many notions from algebraic topology have been introduced into the setting of digital topology. But some of the most basic notions from homotopy theory remain largely absent from the digital topology literature. We embark on a development of homotopy theory in digital topology, and define such fundamental notions as function spaces, path spaces, and cofibrations in this setting. We establish digital analogues of basic homotopy-theoretic properties such as the homotopy extension property for cofibrations, and the homotopy lifting property for certain evaluation maps that correspond to path fibrations in the topological setting. We indicate that some depth may be achieved by using these homotopy-theoretic notions to give a preliminary treatment of Lusternik–Schnirelmann category in the digital topology setting. This topic provides a connection between digital topology and critical points of functions on manifolds, as well as other topics from topological dynamics.
Multi-label classification of research articles using Word2Vec and identification of similarity threshold

Abstract Every year, around 28,100 journals publish 2.5 million research publications. Search engines, digital libraries, and citation indexes are used extensively to search these publications. When a user submits a query, it generates a large number of documents among which just a few are relevant. Due to inadequate indexing, the resultant documents are largely unstructured. Publicly known systems mostly index the research papers using keywords rather than using subject hierarchy. Numerous methods reported for performing single-label classification (SLC) or multi-label classification (MLC) are based on content and metadata features. Content-based techniques offer higher outcomes due to the extreme richness of features. But the drawback of content-based techniques is the unavailability of full text in most cases. The use of metadata-based parameters, such as title, keywords, and general terms, acts as an alternative to content. However, existing metadata-based techniques indicate low accuracy due to the use of traditional statistical measures to express textual properties in quantitative form, such as BOW, TF, and TFIDF. These measures may not establish the semantic context of the words. The existing MLC techniques require a specified threshold value to map articles into predetermined categories for which domain knowledge is necessary. The objective of this paper is to get over the limitations of SLC and MLC techniques. To capture the semantic and contextual information of words, the suggested approach leverages the Word2Vec paradigm for textual representation. The suggested model determines threshold values using rigorous data analysis, obviating the necessity for domain expertise. Experimentation is carried out on two datasets from the field of computer science (JUCS and ACM). In comparison to current state-of-the-art methodologies, the proposed model performed well. Experiments yielded average accuracy of 0.86 and 0.84 for JUCS and ACM for SLC, and 0.81 and 0.80 for JUCS and ACM for MLC. On both datasets, the proposed SLC model improved the accuracy up to 4%, while the proposed MLC model increased the accuracy up to 3%.
A kNN approach based on ICP metrics for 3D scans matching: an application to the sawing process

The Canadian wood industry use sawing simulators to digitally break a log into a basket of lumbers. However, those simulators tend to be computationally intensive. In some cases, this renders them impractical as decision support tools. Such a use case is the problem of dispatching large volume of wood to several sawmills in order to maximise total yield in dollars. Fast machine learning metamodels were recently proposed to address this issue. However, the approach needs a feature extraction step which could result in a loss of information. Conversely, it was proposed to directly make use of the raw information, available in the 3D scans of the logs typically used by a recent sawmill simulator, in order to retain that information. Here, we improve upon that method by reducing the computational cost incidental with the processing of those raw scans.
Accurate pancreas segmentation using multi-level pyramidal pooling residual U-Net with adversarial mechanism

A novel multi-level pyramidal pooling residual U-Net with adversarial mechanism was proposed for organ segmentation from medical imaging, and was conducted on the challenging NIH Pancreas-CT dataset.The 82 pancreatic contrast-enhanced abdominal CT volumes were split via four-fold cross validation to test the model performance. In order to achieve accurate segmentation, we firstly involved residual learning into an adversarial U-Net to achieve a better gradient information flow for improving segmentation performance. Then, we introduced a multi-level pyramidal pooling module (MLPP), where a novel pyramidal pooling was involved to gather contextual information for segmentation, then four groups of structures consisted of a different number of pyramidal pooling blocks were proposed to search for the structure with the optimal performance, and two types of pooling blocks were applied in the experimental section to further assess the robustness of MLPP for pancreas segmentation. For evaluation, Dice similarity coefficient (DSC) and recall were used as the metrics in this work.The proposed method preceded the baseline network 5.30% and 6.16% on metrics DSC and recall, and achieved competitive results compared with the-state-of-art methods.Our algorithm showed great segmentation performance even on the particularly challenging pancreas dataset, this indicates that the proposed model is a satisfactory and promising segmentor.
Reversible Data Hiding Based on Multiple Adaptive Two-Dimensional Prediction-Error Histograms Modification

Pairwise prediction-error expansion (pairwise PEE) is an efficient reversible data hiding technique based on two-dimensional (2D) histogram modification. However, without considering the image content, its embedding manner lacks adaptivity. In this paper, we propose the adaptive modification for multiple prediction-error histograms (PEHs). An iterative self-learning optimization algorithm is devised to adaptively generate the 2D mapping, based on the PEH and payload. A loss function is employed and the optimization can be solved linearly to reduce the time cost. To determine the appropriate expansion bins, we generate multiple 2D PEHs and establish multiple candidate mappings. The optimal combination of the adaptive 2D mappings is determined by using exhaustive searching. Our method is flexible as the 2D mapping generation has a broad dynamic range. The experimental results show that the proposed method can outperform the pairwise PEE and also provide a competitive performance compared with the other state-of-the-art 2D RDH methods.
A Hybrid Approach of Opinion Mining and Comparative Linguistic Analysis of Restaurant Reviews

The existing research on sentiment analysis mainly utilized data curated in limited geographical regions and demography (e.g., USA, UK, China) due to commercial interest and availability of review data.Since the user's attitudes and preferences can be affected by numerous sociocultural factors and demographic characteristics, it is necessary to have annotated review datasets belong to various demography.In this work, we first construct a review dataset BanglaRestaurant that contains over 2300 customer reviews towards a number of Bangladeshi restaurants.Then, we present a hybrid methodology that yields improvement over the best performing lexicon-based and machine learning (ML) based classifier without using any labeled data.Finally, we investigate how the demography (i.e., geography and nativeness in English) of users affect the linguistic characteristics of the reviews by contrasting two datasets, BanglaRestaurant and Yelp.The comparative results demonstrate the efficacy of the proposed hybrid approach.The data analysis reveals that demography plays an influential role in the linguistic aspects of reviews.
Analysis of Image Processing Using Morphological Erosion and Dilation

Abstract Digital image processing is important for image information extraction. One of the image processing methods is morphological image processing. This technique uses erosion and dilation operations to enhance and improve the image quality by shrinking and enlarging the image foreground. However, morphological image processing performance depends on the characteristics of structuring elements and their foreground image that need to be extracted. This paper studies how the structuring elements affect the performance of morphological erosion and dilation on binary images. The experimental result shows that choosing the right structuring element for morphological erosion and dilation can significantly influence the foreground and background structure of the output image.
Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation

Predictive health monitoring systems help to detect human health threats in the early stage. Evolving deep learning techniques in medical image analysis results in efficient feedback in quick time. Fibrous dysplasia (FD) is a genetic disorder, triggered by the mutation in Guanine Nucleotide binding protein with alpha stimulatory activities in the human bone genesis. It slowly occupies the bone marrow and converts the bone cell into fibrous tissues. It weakens the bone structure and leads to permanent disability. This paper proposes the study of FD bone image analyzing techniques with deep networks. Also, the linear regression model is annotated for predicting the bone abnormality levels with observed coefficients. Modern image processing begins with various image filters. It describes the edges, shades, texture values of the receptive field. Different types of segmentation and edge detection mechanisms are applied to locate the tumor, lesion, and fibrous tissues in the bone image. Extract the fibrous region in the bone image using the region-based convolutional neural network algorithm. The segmented results are compared with their accuracy metrics. The segmentation loss is reduced by each iteration. The overall loss is 0.24% and the accuracy is 99%, segmenting the masked region produces 98% of accuracy, and building the bounding boxes is 99% of accuracy.
Teleportation of the entangled state of two superconducting qubits

Abstract An efficient scheme is proposed to teleport an entangled state of two superconducting (SC) qubits from Alice's to Bob's lab. This type of two-level systems has recently attracted a lot of attention due to the possible tunability of the coupling strength of the qubits with each other. To achieve the purpose, we first generate the GHZ state as the necessary teleportation channel. Then, appropriate interactions are performed in two processes between two of the five qubits, each with a certain frequency modulative external magnetic field which is applied on specific one of the qubits. Next, via applying proper gates and measurements in each lab, we observe that the teleportation can be successfully performed with maximum possible values of fidelity and success probability. At last, to make the protocol close to reality, decay rates of SC qubits are also taken into account, showing that our protocol still works well, satisfactorily.
A Systematic Review of Density Grid-Based Clustering for Data Streams

Various applications, such as electronic business, satellite remote sensing, intrusion discovery, and network traffic monitoring, generate large unbounded data stream sequences at a rapid pace. The clustering of data streams has attracted considerable interest due to the increasing usage of evolving data streams. In particular, evolving data streams affect clustering because they introduce numerous challenges, such as time and memory limits and one-pass clustering. Furthermore, researchers need to be able to determine arbitrarily shaped clusters present in evolving data streams from applications. Due to these characteristics, conventional density grid-based clustering techniques cannot be used. Moreover, the existing density grid-based clustering algorithms have low cluster quality for clustering evolving data streams. This study conducted a systematic literature review (SLR) and noted numerous research-related issues encountered in solving the aforementioned problems. We summarized numerous grid-based clustering algorithms that have been used and determined their distinctive and limited features. We also observed how these algorithms address the challenges affecting the clustering of evolving data streams and studied their advantages and disadvantages. SLR was based on 104 articles published between 2010 and 2021. Numerous challenges remain for grid-based clustering algorithms, particularly in terms of time-limited and high-dimensional data handling. Last, our findings indicated a variety of active studies on density grid-based clustering.
New concepts of media science in the sociocultural system of the information civilization

The object of analysis in this article is the integration process of a number of humanities studying the media sphere of the information age. A new synthetic science of the globalized world is formed through integration – mediology, which studies different directions of human spiritual life in the digital revolution. Since the study is interdisciplinary in nature, the methodological basis for the analysis is a conceptual-systemic approach, which makes it possible to use both general scientific and cultural-historical, social-analytical, and contextual-competence research methods. The study is based on the analysis of different approaches of foreign and Russian scientists (philosophers, cultural experts, sociologists, teachers) to the problems of media science in its historical context and modern status. The main research result is the proof that global transformations of the era in the humanities and, accordingly, in the education system have led to new research objects: the theory and history of media culture, media philosophy and media policy, media pedagogy, and media management. The author comes to the conclusion that over the past quarter-century, a new complex media science has developed in the humanitarian sphere, the theory of which is significantly ahead of educational practice, which still has many problem areas in media pedagogy as a factor of forming a citizen of the globalized world.