Reviewerly/exHarmony huggingface dataset:
Video Denoising for Scenes With Challenging Motion: A Comprehensive Analysis and a New Framework

Challenging motion, which tends to cause artifacts, is a key problem in the video denoising task. Recent video denoising methods have attempted to address this problem. However, they usually provide general performance evaluation on the overall dataset and cannot provide a comprehensive analysis for the influence of different motion levels. Thus, we questioned whether these methods can effectively deal with different scene motions. To this end, we synthesize a dataset containing videos with different motion levels and capture a new dataset that consists of videos involving large-scale motion. Then, we provide a comprehensive analysis on the elaborately collected datasets and find that, as the motion level increases, the performance of the denoising models based on implicit motion estimation (IME) declines sharply, while explicit motion estimation (EME) contributes to a more robust denoising quality. Therefore, in this work, we present an EME-embedded progressive denoising framework that fully considers the relationship between the noise removal and motion estimation. Specifically, we decouple video denoising into spatial denoising, EME-based frame reconstruction, and temporal refining processes. Spatial denoising improves the accuracy of EME process in the case of videos suffering from heavy noise, while the temporal refining process refines the denoised frame by utilizing temporal redundancy of the reconstructed motion-free frames. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art methods, especially for videos containing large-scale motion.
Breast cancer anomaly detection based on the possibility theory with a clustering paradigm

Breast cancer early diagnosis is a major concern for reducing deadly cases. Related breast tissue anomalies are masses and micro-calcifications, which characterization is essential to obtain a correct diagnosis. At this step, high false negative rates are observed, revealing a high ambiguity, which are crucial to handle with conventional approaches. Possibility theory offers a powerful paradigm enabling to handle a high uncertainty level. For enhancing the classification’s accuracy in case of scattered classes, we have redefined our classes based on clustering. Under this paradigm, we adopt in the clustering optimization a criteria based on the Dubois and Prade possibility transformation. In this research, we propose a new possibilistic modeling where a clustering based aggregation of features is applied followed by a fusion of the possibility distributions, for decision making. The above strategy has been applied on two case studies in breast cancer screening: mass and micro-calcification detection, where a set of challenging features are considered. Validation on the CBIS-DDSM public dataset has been undertaken. The proposed system, yielding a mass and micro-calcification detection accuracy respectively of 95.4% and 99.4% (at a specificity respectively of 96% and 100% and with an AUC respectively of 0.96 and 0.996), can be used to assist medical practitioners. • Development of general formalism for possibility transformation of medium level features and their fusion, using the asymmetric Dubois and Prade transformation. • Optimization of the clustering parameters (number of adequate clusters for each feature) based on a new related metric, which is the AUC taking as a confidence degree level the possibility consistency measure. • High rates of breast tissues anomaly detection accuracy: for the mass detection, an accuracy rate of 95.4% is obtained, while an accuracy rate of 99.4% is obtained for the micro-calcification detection.
Geolocated Data Generation and Protection Using Generative Adversarial Networks

Data mining techniques allow us to discover patterns in large datasets. Nonetheless, data may contain sensitive information. This is especially true when data is georeferenced. Thus, an adversary could learn about individual whereabouts, points of interest, political affiliation, and even sexual habits. At the same time, human mobility is a rich source of information to analyze traffic jams, health care accessibility, food desserts, and even pandemics dynamics. Therefore, to enhance privacy, we study the use of Deep Learning techniques such as Generative Adversarial Network (GAN) and GAN with Differential Privacy (DP-GAN) to generate synthetic data with formal privacy guarantees. Our experiments demonstrate that we can generate synthetic data to maintain individuals’ privacy and data quality depending on privacy parameters. Accordingly, based on the privacy settings, we generated data differing a few meters and a few kilometers from the original trajectories. After generating fine-grain mobility trajectories at the GPS level through an adversarial neural networks approach and using GAN to sanitize the original trajectories together with differential privacy, we analyze the privacy provided from the perspective of anonymization literature. We show that such $$\epsilon $$ -differentially private data may still have a risk of re-identification.
Re-calibrating Machine Learning Models Using Confidence Interval Bounds

In this article we propose a novel technique for the re-calibration of Machine Learning (ML) models. This technique is based on the computation of confidence intervals for the probability scores provided by any ML model. Compared to existing and commonly used calibration methods, the proposed approach has two important advantages: first, under weak assumptions it provides theoretical guarantees about calibration; second, this method does not require any further data other than the training set used for ML model development. We illustrate the effectiveness of the proposed approach on a benchmark dataset for COVID-19 diagnosis, by comparing the proposed method against commonly used re-calibration techniques.
Deep Attributed Graph Embeddings

Graph Representation Learning aims to learn a rich and low-dimensional node embedding while preserving the graph properties. In this paper, we propose a novel Deep Attributed Graph Embedding (DAGE) that learns node representations based on both the topological structure and node attributes. DAGE a is able to capture, in a linear time and with a limited number of trainable parameters, the highly non-linear properties of attributed graphs. The proposed approach outperforms the current state-of-the-art approaches on node classification and node clustering tasks at a lower computational costs.
Design of Distance Teaching System for Interior Design Course Based on Internet of Things and Education Platform

Distance education system plays an important role in promoting the integration of professional characteristics and application practice. In order to solve the problem of network throughput decline caused by communication channel data conflict, a distance teaching system of interior design course is designed based on Internet of Things and education platform. In the hardware part, the audio acquisition circuit and video acquisition circuit are designed to ensure the quality of signal transmission. In the software part, the communication architecture is set based on the Internet of Things technology, and a course data anticollision algorithm is proposed, which uses the spread spectrum code to separate the corresponding data to avoid data collision. Based on the education platform, the function structure of distance education system is designed to complete the course management and maintenance. The experimental results show that the maximum throughput of the distance education system designed in this paper is 3.54 MB/s, which is 0.86 MB/s and 0.97 MB/s more than the system based on big data technology and artificial intelligence, and it can effectively avoid the problem of data conflict.
Construction of Primary and Secondary School Teachers’ Competency Model Based on Improved Machine Learning Algorithm

In order to quantitatively evaluate the competence of primary and secondary school teachers, a competency model of primary and secondary school teachers based on an improved machine learning algorithm is proposed. The fitting parameter analysis model of primary and secondary school teachers’ competency is constructed, and the fitting benefit degree parameter of primary and secondary school teachers’ competency is extracted based on the analysis results of reliability index parameters. The improved machine learning algorithm is used to carry out quantitative analysis and characteristic element analysis in the process of primary and secondary school teachers’ competency evaluation and determine the competency elements of the model. According to the machine learning model, the competency elements are conceptualized and classified, and the theoretical parameter analysis model of online teaching competency of primary and secondary school teachers is constructed to realize the assessment and quantitative analysis of primary and secondary school teachers’ competency. Factor analysis and reliability tests were performed using the KMO test and Bartlett test. The empirical simulation analysis results show that the reliability and accuracy of the evaluation of primary and secondary school teachers’ competence by this method are good, and the level of credibility is high.
A recursive reinforced blockchain performance evaluation and improvement architecture: Maximising diversity to improve scalability

Summary Nowadays, Blockchain technology has received widespread attention because of its ability to effectively solve the trust problem in transactions. However, the throughput of crypto currency using Blockchain technology has never been comparable to that of centralized payment institutions. The fundamental reason is the architecture of the Blockchain itself, and the existing methods often focus on the part of the Blockchain but lack the overall control. In this paper, we design, a novel Recursively Reinforced Blockchain Architecture Search (BORAS) architecture, from the perspective of the overall structure of the Blockchain, which can search the various attributes of the various levels in a adaptive manner, aiming at selecting the best configuration in a dynamical environment. First, we introduce a recursive and scalable reinforcement learning framework, to find the optimal block size and block interval of the Blockchain, so as to improve the throughput of the Blockchain system. Second, we add the selection scheme of consensus algorithm to the dynamic space and also use the framework to select the optimal consensus mechanism to realize the optimal configuration of the Blockchain system in the dynamic environment. Third, we adopt a recursive method, which greatly reduces the time complexity required for the optimal configuration of the Blockchain system and improves the configuration efficiency. Comprehensive experiments on three representative Blockchain platforms demonstrate that BOBAS can consistently improve the throughput, while also reducing latency, with remarkable and efficient performances.
Journey to the center of the words: Word weighting scheme based on the geometry of word embeddings

A notable amount of work has been done to find sentence embeddings using compositional models in recent years. These works have shown that one of the simplest and most effective approaches to obtaining sentence embeddings is simple vector averaging of off-the-shelf word embeddings trained on large corpora. Recent literature introduced word weighting schemes based on the words frequency distribution into the simple averaging model. The frequency-based weighted averaging models augmented with the denoising steps are shown to outperform many complex deep learning models. However, these frequency-based weighting schemes derive the word weights solely based on their raw counts and ignore the diversity of contexts in which these words occur. This paper proposes an alternative weighting scheme that captures the contextual diversity in the word embedding space. The proposed weighting algorithm is simple, unsupervised, and non-parametric. Experimental results on semantic textual similarity tasks show that the proposed weighting method outperforms all the baseline models with significant margins and performs competitively to the current frequency-based state-of-the-art weighting approach. Furthermore, as the frequency distribution-based approaches and the proposed word embeddings geometry-based weighting approach capture two different properties of the words, we define hybrid weighting schemes to combine both the varieties. We also empirically demonstrate that the hybrid weighting methods perform consistently better than the corresponding individual weighting schemes.
Harmonizing Privacy Regarding Data Retention and Purging

Data privacy requirements are a complex and quickly evolving part of the data management domain. Especially in Healthcare (e.g., United States Health Insurance Portability and Accountability Act and Veterans Affairs requirements), there has been a strong emphasis on data privacy and protection. Data storage is governed by multiple sources of policy requirements, including internal policies and legal requirements imposed by external governing organizations. Within a database, a single value can be subject to multiple requirements on how long it must be preserved and when it must be irrecoverably destroyed. This often results in a complex set of overlapping and potentially conflicting policies. Existing storage systems are lacking sufficient support functionality for these critical and evolving rules, making compliance an underdeveloped aspect of data management. As a result, many organizations must implement manual ad-hoc solutions to ensure compliance. As long as organizations depend on manual approaches, there is an increased risk of non-compliance and threat to customer data privacy.
An algebraic attack to the Bluetooth stream cipher E0

In this paper we study the security of the Bluetooth stream cipher E0 from the viewpoint it is a “difference stream cipher”, that is, it is defined by a system of explicit difference equations over the finite field GF(2). This approach highlights some issues of the Bluetooth encryption such as the invertibility of its state transition map, a special set of 14 bits of its 132-bit state which when guessed implies linear equations among the other bits and finally a small number of spurious keys, with 83 guessed bits, which are compatible with a keystream of about 60 bits. Exploiting these issues, we implement an algebraic attack using Gröbner bases, SAT solvers and Binary Decision Diagrams. Testing activities suggest that the version based on Gröbner bases is the best one and it is able to attack E0 in about 279 seconds on an Intel i9 CPU. To the best of our knowledge, this work improves any previous attack based on a short keystream, hence fitting with Bluetooth specifications.
Reply to Zou et al.: Neoarchean eclogite-facies oceanic crust in the North China Craton

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...
ApaNet: adversarial perturbations alleviation network for face verification

Albeit Deep neural networks (DNNs) are widely used in computer vision, natural language processing and speech recognition, they have been discovered to be fragile to adversarial attacks. Specifically, in computer vision, an attacker can easily deceive DNNs by contaminating an input image with perturbations imperceptible to humans. As one of the important vision tasks, face verification is also subject to adversarial attack. Thus, in this paper, we focus on defending against the adversarial attack for face verification to mitigate the potential risk. We learn a network via an implementation of stacked residual blocks, namely adversarial perturbations alleviation network (ApaNet), to alleviate latent adversarial perturbations hidden in the input facial image. During the supervised learning of ApaNet, only the Labeled Faces in the Wild (LFW) is used as the training set, and the legitimate examples and corresponding adversarial examples produced by projected gradient descent algorithm compose supervision and inputs respectively. By leveraging the middle and high layer's activation of FaceNet, the discrepancy between an image output by ApaNet and the supervision is calculated as the loss function to optimize ApaNet. Empirical experiment results on the LFW, YouTube Faces DB and CASIA-FaceV5 confirm the effectiveness of the proposed defender against some representative white-box and black-box adversarial attacks. Also, experimental results show the superiority performance of the ApaNet as comparing with several currently available techniques.
More than words can say: a multimodal approach to understanding meaning and sentiment in social media

The authors develop a multimodal social listening analysis (MSLA) approach as a framework for managers to understand how meaning is constructed in social media posts using both text and other media. The research adds to AI and text analysis approaches by considering the whole meaning of a post rather than an analysis of subsets of information in text and other media. The use of MSLA is validated across the social media platforms of Facebook, Twitter and Instagram. The findings show that MSLA helps (i) reveal structures in what appear to be unstructured multimodal posts; (ii) identify all the sentiment items in a post; (iii) identify implicit meanings, such as irony, humour and sarcasm; and (iv) further identify emotions and judgements in multimodal communication. Importantly, this paper explains how decisions and opinions are made online and how marketing strategies can be tailored towards meanings derived from multimodal communication in social media.
On the Transformations of the Square of Opposition from the Point of View of Institution Model Theory

In recent decades, research in the square of opposition has been increased. New interpretations, extensions, and generalizations have been suggested, both Aristotelian and non-Aristotelian ones. The paper attempts at comparing different versions of the square of opposition. For this reason, we appeal to the wider categorical model-theoretic framework of the theory of institutions.
Turnstile Figures of Opposition

We present many figures of opposition (triangles and hexagons) for simple and double turnstiles. We start with one-sided turnstiles, corresponding to sets of tautologies, and then we go to double-sided turnstiles corresponding to consequence relations. In both cases, we consider proof-theoretic (with the simple turnstile) and model-theoretic (with the double turnstile) figures. By so doing, we discuss various central aspects of notations and conceptualizations of modern logic.
Investigation on Equity and Otherness in the Interaction Design Process: A Systematic Mapping

This article presents a systematic literature mapping (SLM) process to determine which interaction design practices promote otherness and equity in their executions. The main contribution of this study is the description of the interaction design of each study selected in the SLM. One of the main goals of interaction design is to produce a user-friendly interaction that reduces the negative aspects of the user experience. Essentially, it consists of developing interactive products that are easy, efficient, and pleasant to use from a user perspective. In designing computer systems, it is necessary to include a broad spectrum of user profiles and understand their wishes and needs. Therefore, the comprehensive access to all subjects in the design of a computer system is necessary. The interaction design process must be equity and changeable to ensure that users' particularities are included and most of them are represented in the system to be developed. The results highlight the importance of designers and performers in interaction design practices to determine if equity and otherness are included in the performance of interaction design practices. However, these specific terms are not generally utilized to highlight usage.
Complete ADS-B Security Solution

We propose a complete cryptographic solution for ADS-B security in this chapter, ensuring privacy and integrity without modifying existing ADS-B protocols or upgrading current ADS-B transponders, thereby being well-suited to large-scale and low-cost deployments. The main idea of our solution is to separate the aircraft identifier from its associated geographic locations to guarantee the privacy of general aviation, maximize the use of reserved fields in the ADS-B message format to protect the integrity of messages, and reduce package loss and disorder for increasingly congested 1090ES data links.
ADS-B Broadcast Authentication

Since the ADS-B communication protocol does not have an inherent message authentication mechanism, ADS-B messages are vulnerable to message injection and modification attacks, so the authenticity and integrity of ADS-B broadcast messages cannot be guaranteed. Some broadcast authentication schemes have been proposed to ensure such authenticity and integrity, but they are not suitable for ADS-B communication scenarios where ADS-B transponders are resource-limited and ADS-B data links are low bandwidth. Specifically, most of the existing broadcast authentication schemes are certificate-based, and the verification and transmission of the certificate chain require a lot of computing and communication resources. Therefore, non-certificate-based broadcast authentication schemes are essential to achieve the authenticity and integrity of ADS-B messages with less verification time and lower communication bandwidth.
Logical framework of information technology: Systematization of software development research

This article aims to present a comprehensive Logical Framework for Information Technology (IT) Research, specifically for developing customized IT applications or software. The methodology of writing this article uses a content analysis with the main source of literature review, Focus Discussion Group, and also based on the experience and knowledge of the authors as lecturers of Software Engineering and Software Project Management. This article shows that although current IT development approaches or methodologies (especially software development methodology) continue to develop, good IT design is carried out through six main stages, namely planning, analysis, design, construction, implementation, and maintenance. The success of IT implementation depends on the good process of all stages of IT design. The involvement of all actors/ stakeholders in IT design is essential to be accommodated at all stages of IT design. Quality also becomes the main goal and controls every process of IT development.