Reviewerly/exHarmony huggingface dataset:
A robust and distributed architecture for 5G-enabled networks in the smart blockchain era

Internet-of-Things (IoT) integrates ubiquitous computing to extend the Internet connectivity to the various application environments such as e-Health, smart cities, cyber–physical systems, etc. It connects the real-time objects to experience high-speed data transfer in 5G network deployment. It may periodically collect and process the data to control the access mechanism that utilizes a dedicated network protocol to fetches the information from different network domains. However, most of the access mechanisms cannot be applicable to the industrial application as it is based on a centralized architecture. This architecture has a complexity of computation overhead to deteriorate the performance of device-to-device (D2D) communication. In the industrial sectors, security and privacy preservation are majorly concerned to perform expensive operations and to prevent malicious cloud servers. Moreover, the real-time objects should be coordinated to optimize communication efficiencies. Thus, this paper presents a robust blockchain-based lightweight distributed architecture (RB-LDA) for 5G-enabled networks. The proposed RB-LDA authenticates the device access that advertises genuine data possession to restrict the key exposure. Moreover, the qualitative analysis proves the identity management continuously monitors the activities of IoT devices to perform a better auditing process. Above all, the experimental analysis shows that the proposed RB-LDA can achieve better data privacy to preserve sensitive information against the trusted third parties.
Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection

Breast cancer has become the second leading cause of death among women worldwide. In India, a woman is diagnosed with breast cancer every four minutes. There has been no known basis behind it, and detection is extremely challenging among medical scientists and researchers due to unknown reasons. In India, the ratio of women being identified with breast cancer in urban areas is 22:1. Symptoms for this disease are micro calcification, lumps, and masses in mammogram images. These sources are mostly used for early detection. Digital mammography is used for breast cancer detection. In this study, we introduce a new hybrid wavelet filter for accurate image enhancement. The main objective of enhancement is to produce quality images for detecting cancer sections in images. Image enhancement is the main step where the quality of the input image is improved to detect cancer masses. In this study, we use a combination of two filters, namely, Gabor and Legendre. The edges are detected using the Canny detector to smoothen the images. High-quality enhanced image is obtained through the Gabor–Legendre filter (GLFIL) process. Further image is used by classification algorithm. Animal migration optimization with neural network is implemented for classifying the image. The output is compared to existing filter techniques. Ultimately, the accuracy achieved by the proposed technique is 98%, which is higher than existing algorithms.
Factors Influencing Pre-service Teachers' Intention to Use Digital Learning Materials: A Study Conducted During the COVID-19 Pandemic in Germany

The COVID-19 pandemic necessitated an abrupt change in university teacher education, in that most face-to-face courses were replaced by online education, which had a profound impact on students. Pandemic distance learning required students to possess a high degree of self-regulation concerning their learning environment and to find new ways of communicating with their peers and instructors. At the same time, the novel situation offered opportunities to experience new educational applications. To learn more about the possible benefits of distance learning, this study examines how the first online semester during the pandemic contributed to pre-service teachers' intentions to use digital learning materials in the future. Pre-service teachers enrolled in a German university (n = 348) answered an online questionnaire at the end of the summer term of 2020. Findings from structural equation modeling showed that the perceived quality of teacher training during the online semester and self-reported improvements in digital skills predicted significantly students' intentions to use digital learning materials for future teaching. Moreover, results revealed that attentional regulation predicted perceived quality of teacher training and self-reported improvements in digital skills during distance learning. Thus, attentional regulation had a significant indirect effect on pre-service teachers' behavioral intentions. The indirect effects of other resource management strategies (effort and time management) and intrinsic motivation were not significant. Our results show that the quality of online instruction was an important factor in student teachers' learning processes during the pandemic. Based on our results, we discuss implications for the promotion of pre-service teachers' intentions to use digital learning materials for teaching in schools.
CoPE

Human interactions with items are being constantly logged, which enables advanced representation learning and facilitates various tasks. Instead of generating static embeddings at the end of training, several temporal embedding methods were recently proposed to learn user and item embeddings as functions of time, where each entity has a trajectory of embedding vectors aiming to encode the full dynamics. However, these methods may not be optimal to encode the dynamical behaviors on the interaction graphs in that they can not generate "fully''-temporal embeddings and do not consider information propagation. In this paper, we tackle the issues and propose CoPE (Co ntinuous P ropagation and E volution). We use an ordinary differential equation based graph neural network to model information propagation and more sophisticated evolution patterns. We train CoPE on sequences of interactions with the help of meta-learning to ensure fast adaptation to the most recent interactions. We evaluate CoPE on three tasks and prove its effectiveness.
Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model

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Fact-based visual question answering via dual-process system

Fact-based visual question answering (FVQA) requires the model to answer questions based on the observed images and external knowledge. The key is to enable the agent to understand questions and images and then reason on the knowledge base to find the correct answer. Founded on the dual-process theory in cognitive science, an effective framework for the FVQA is proposed in this study by coordinating a perception module (System 1) and an explicit reasoning module (System 2). When a question and an image are given, System 1 first learns the joint representation of them, and then System 2 predicts the answer via reasoning on a fact graph and a semantic graph. Specifically, System 1 is implemented by a two-parallel BERT-style model, while System 2 by a graph neural network (GNN) with a dual-level attention mechanism. Experiments on two public datasets, i.e., FVQA and OK-VQA datasets, show that our model outperforms other baselines. Moreover, the proposed model also provides the interpretation of the reasoning process in addition to a correct answer to the question.
Automated Extraction and Time-Cost Prediction of Contractual Reporting Requirements in Construction Using Natural Language Processing and Simulation

Due to a lack of suitable methods, extraction of reporting requirements from lengthy construction contracts is often completed manually. Because of this, the time and costs associated with completing reporting requirements are often informally approximated, resulting in underestimations. Without a clear understanding of requirements, contractors are prevented from implementing improvements to reporting workflows prior to project execution. This study developed an automated reporting requirement identification and time–cost prediction framework to overcome this challenge. Reporting requirements are extracted using Natural Language Processing (NLP) and Machine Learning (ML), and stochastic simulations are used to predict overhead costs and durations associated with report preparation. Functionality and validity of the framework were demonstrated using real contracts, and an accuracy of over 95% was observed. This framework provides a tool to rapidly and efficiently retrieve requirements and quantify the time and costs associated with reporting, in turn providing necessary insights to streamline reporting workflows.
3D Object Detection and Tracking Methods using Deep Learning for Computer Vision Applications

3D multi-object detection and tracking is an essential constituent for many applications in today's world. Object detection is a technology related to computer vision and image processing that allows us to detect instances of certain classes. There are numerous applications like robotics, autonomous driving and augmented reality. A bounding box often defines the region of interest and then is used to classify into respective categories. Due to identical appearance and shape of various objects and the interference of lighting and shielding, object detection has always been a challenging problem in computer vision. Conventional 2D object detection yields four degrees of freedom axis-aligned bounding boxes with centre (x, y) and 2D size (w, h), the 3D bounding boxes generally have 6 Degrees of freedom: 3D physical size (w, h, l), 3D centre location (x, y, z). 2D object detection and tracking methods do not provide depth information to perform essential tasks in various computer vision applications. One among them is the Autonomous driving. 3D object detection includes depth information that provides more information on the structure of detected object. More information is required to make decisions accurately in different fields where 3D object detection and tracking can be applied. In this paper various 3D object detection and tracking methods are elaborated for various computer vision applications, this includes various fields such as robotics, driving, space field and also in the military.
Mu

Serverless computing platforms simplify development, deployment, and automated management of modular software functions. However, existing serverless platforms typically assume an over-provisioned cloud, making them a poor fit for Edge Computing environments where resources are scarce. In this paper we propose a redesigned serverless platform that comprehensively tackles the key challenges for serverless functions in a resource constrained Edge Cloud.
Explainable Artificial Intelligence for Data Science on Customer Churn

Machine learning, as a tool, has become critical for decision-making mechanisms in the modern world. It has applications in a wide range of areas, including finance, healthcare, justice, and transportation. Unfortunately, machine learning is often considered as a “black box”. As such, recommendations made by machine learning techniques, as well as the reasoning behind those recommendations, are not easily understood by humans. In this paper, we present an explainable artificial intelligence (XAI) solution that integrates and enhances state-of-the-art techniques to produce understandable and practical explanations to end-users. To evaluate the effectiveness of our XAI solution for data science, we conduct a case study on applying our solution to explaining a random forest-based predictive model on customer churn. Results show the practicality and usefulness of our XAI solution in practical applications such as data science on customer churn.
Chatbot design approaches for fashion E-commerce: an interdisciplinary review

Chatbots can bring innovation in online assistance and communication with customers. Due to the growth of e-commerce, fashion brands have been adopting chatbots to provide personalised consumer experiences. Research in the area of chatbots for fashion e-commerce has addressed technological advancements and consumer behaviour, but little has been done on analysing chatbot features through a holistic point of view. The aim of this paper is to offer an interdisciplinary review through a comprehensive categorisation of recent studies on the theme and inform future research in the area. To achieve that, a theme-based literature review was carried out through the analysis of specialised research. The collected work was categorised addressing both computational and non-computational perspectives. The findings show that Deep Learning, recommendation systems, audio recognition and integration of chatbots with other fashion applications are a few design opportunities to be applied in both research and practice.
The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy

Privacy-preserving data analysis is a rising challenge in contemporary statistics, as the privacy guarantees of statistical methods are often achieved at the expense of accuracy. In this paper, we investigate the tradeoff between statistical accuracy and privacy in mean estimation and linear regression, under both the classical low-dimensional and modern high-dimensional settings. A primary focus is to establish minimax optimality for statistical estimation with the (ε,δ)-differential privacy constraint. By refining the “tracing adversary” technique for lower bounds in the theoretical computer science literature, we improve existing minimax lower bound for low-dimensional mean estimation and establish new lower bounds for high-dimensional mean estimation and linear regression problems. We also design differentially private algorithms that attain the minimax lower bounds up to logarithmic factors. In particular, for high-dimensional linear regression, a novel private iterative hard thresholding algorithm is proposed. The numerical performance of differentially private algorithms is demonstrated by simulation studies and applications to real data sets.
Quantifying and Mitigating Privacy Risks of Contrastive Learning

Data is the key factor to drive the development of machine learning (ML) during the past decade. However, high-quality data, in particular labeled data, is often hard and expensive to collect. To leverage large-scale unlabeled data, self-supervised learning, represented by contrastive learning, is introduced. The objective of contrastive learning is to map different views derived from a training sample (e.g., through data augmentation) closer in their representation space, while different views derived from different samples more distant. In this way, a contrastive model learns to generate informative representations for data samples, which are then used to perform downstream ML tasks. Recent research has shown that machine learning models are vulnerable to various privacy attacks. However, most of the current efforts concentrate on models trained with supervised learning. Meanwhile, data samples' informative representations learned with contrastive learning may cause severe privacy risks as well. In this paper, we perform the first privacy analysis of contrastive learning through the lens of membership inference and attribute inference. Our experimental results show that contrastive models trained on image datasets are less vulnerable to membership inference attacks but more vulnerable to attribute inference attacks compared to supervised models. The former is due to the fact that contrastive models are less prone to overfitting, while the latter is caused by contrastive models' capability of representing data samples expressively. To remedy this situation, we propose the first privacy-preserving contrastive learning mechanism, Talos, relying on adversarial training. Empirical results show that Talos can successfully mitigate attribute inference risks for contrastive models while maintaining their membership privacy and model utility.
Multiple-view flexible semi-supervised classification through consistent graph construction and label propagation

Graph construction plays an essential role in graph-based label propagation since graphs give some information on the structure of the data manifold. While most graph construction methods rely on predefined distance calculation, recent algorithms merge the task of label propagation and graph construction in a single process. Moreover, the use of several descriptors is proved to outperform a single descriptor in representing the relation between the nodes. In this article, we propose a Multiple-View Consistent Graph construction and Label propagation algorithm (MVCGL) that simultaneously constructs a consistent graph based on several descriptors and performs label propagation over unlabeled samples. Furthermore, it provides a mapping function from the feature space to the label space with which we estimate the label of unseen samples via a linear projection. The constructed graph does not rely on a predefined similarity function and exploits data and label smoothness. Experiments conducted on three face and one handwritten digit databases show that the proposed method can gain better performance compared to other graph construction and label propagation methods.
VistrongerDet: Stronger Visual Information for Object Detection in VisDrone Images

Existing methods are especially difficult to detect objects accurately in videos and images captured by UAV. In the work, we carefully analyze the characteristics of VisDrone DET 2021 dataset, and the main reasons for the low detection performance are tiny objects, wide scale span, long-tail distribution, confusion of similar classes. To mitigate the adverse influences caused thereby, we propose a novel detector named VistrongerDet, which possesses Stronger Visual Information. Our framework integrates the novel components of FPN level, ROI level and head level enhancements. Benefitted from the overall enhancements, VistrongerDet significantly improves the detection performance. Without bells and whistles, VistrongerDet is plug-gable which can be used in any FPN-based two-stage detectors. It achieves 1.23 points and 1.15 points higher Average Precision (AP) than Faster R-CNN and Cascade R-CNN on VisDrone-DET test-dev set.
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks

State-of-the-art object detectors are vulnerable to localized patch hiding attacks, where an adversary introduces a small adversarial patch to make detectors miss the detection of salient objects. The patch attacker can carry out a physical-world attack by printing and attaching an adversarial patch to the victim object; thus, it imposes a challenge for the safe deployment of object detectors. In this paper, we propose DetectorGuard as the first general framework for building provably robust object detectors against localized patch hiding attacks. DetectorGuard is inspired by recent advancements in robust image classification research; we ask: can we adapt robust image classifiers for robust object detection? Unfortunately, due to their task difference, an object detector naively adapted from a robust image classifier 1) may not necessarily be robust in the adversarial setting or 2) even maintain decent performance in the clean setting. To address these two issues and build a high-performance robust object detector, we propose an objectness explaining strategy: we adapt a robust image classifier to predict objectness (i.e., the probability of an object being present) for every image location and then explain each objectness using the bounding boxes predicted by a conventional object detector. If all objectness is well explained, we output the predictions made by the conventional object detector; otherwise, we issue an attack alert. Notably, our objectness explaining strategy enables provable robustness for "free": 1) in the adversarial setting, we formally prove the end-to-end robustness of DetectorGuard on certified objects, i.e., it either detects the object or triggers an alert, against any patch hiding attacker within our threat model; 2) in the clean setting, we have almost the same performance as state-of-the-art object detectors. Our evaluation on the PASCAL VOC, MS COCO, and KITTI datasets further demonstrates that DetectorGuard achieves the first provable robustness against localized patch hiding attacks at a negligible cost (< 1%) of clean performance.
High-Speed Hardware Architectures and FPGA Benchmarking of CRYSTALS-Kyber, NTRU, and Saber

Post-Quantum Cryptography (PQC) has emerged as a response of the cryptographic community to the danger of attacks performed using quantum computers. All PQC schemes can be implemented in software and hardware using conventional (non-quantum) computing systems. PQC is the biggest revolution in cryptography since the invention of public-key schemes in the mid-1970 s. Lattice-based key exchange schemes have emerged as leading candidates in the NIST PQC standardization process due to their relatively short public keys and ciphertexts. This paper presents novel high-speed hardware architectures for four lattice-based Key Encapsulation Mechanisms (KEMs) representing three NIST PQC finalists: NTRU (with two distinct variants, NTRU-HPS and NTRU-HRSS), CRYSTALS-Kyber, and Saber. We benchmark these candidates in terms of their performance and resource utilization in today's FPGAs. Our best architectures outperform the best designs from other groups reported to date in terms of the area-time product by factors ranging from 1.01 to 2.88, depending on the algorithm and security level. Additionally, our study demonstrates that CRYSTALS-Kyber and Saber have very similar hardware performance. Both outperform NTRU in terms of execution time by a factor 36-62 for key generation and 3-7 for decapsulation, assuming the same security level.
Succinct LWE Sampling, Random Polynomials, and Obfuscation

We present a construction of indistinguishability obfuscation (iO) that relies on the learning with errors (LWE) assumption together with a new notion of succinctly sampling pseudorandom LWE samples. We then present a candidate LWE sampler whose security is related to the hardness of solving systems of polynomial equations. Our construction improves on the recent iO candidate of Wee and Wichs (Eurocrypt 2021) in two ways: first, we show that a much weaker and simpler notion of LWE sampling suffices for iO; and secondly, our candidate LWE sampler is secure based on a compactly specified and falsifiable assumption about random polynomials, with a simple error distribution that facilitates cryptanalysis.
MirChecker: Detecting Bugs in Rust Programs via Static Analysis

Safe system programming is often a crucial requirement due to its critical role in system software engineering. Conventional low-level programming languages such as C and assembly are efficient, but their inherent unsafe nature makes it undesirable for security-critical scenarios. Recently, Rust has become a promising alternative for safe system-level programming. While giving programmers fine-grained hardware control, its strong type system enforces many security properties including memory safety. However, Rust's security guarantee is not a silver bullet. Runtime crashes and memory-safety errors still harass Rust developers, causing damaging exploitable vulnerabilities, as reported by numerous studies.