Reviewerly/exHarmony huggingface dataset:
Feature models to boost the vulnerability management process

Vulnerability management is a critical and very challenging process that allows organisations to design a procedure to identify potential vulnerabilities, assess the level of risk, and define remediation mechanisms to address threats. Thus, the large number of configuration options in systems makes it extremely difficult to identify which configurations are affected by vulnerabilities and even assess how systems may be affected. There are several repositories to store information on systems, software vulnerabilities, and exploits. However, they are largely scattered, offer different formats and information, and their use has limitations, complicating vulnerability management automation. For this reason, we introduce a discussion concerning modelling in vulnerability management and the proposal of feature models as a means to collect the variability of software and system configurations to facilitate the vulnerability management process. This paper presents AMADEUS-Exploit, a feature model-based solution that provides query and reasoning mechanisms that make it easier for vulnerability management experts. The power of AMADEUS-Exploit is shown and evaluated in three different ways: first, the solution is compared with other vulnerability management tools; second, the solution is faced with another in a complex scenario with 4,000 vulnerabilities and 700 exploits; and finally, our solution was used in a real project demonstrating the usability of reasoning operations to determine potential vulnerabilities.
Universal Beamforming: A Deep RFML Approach

We introduce, design, and evaluate a set of universal receiver beamforming techniques. Our approach and system DEFORM, a Deep Learning (DL)-based RX beamforming achieves significant gain for multi-antenna RF receivers while being agnostic to the transmitted signal features (e.g., modulation or bandwidth). It is well known that combining coherent RF signals from multiple antennas results in a beamforming gain proportional to the number of receiving elements. However in practice, this approach heavily relies on explicit channel estimation techniques, which are link specific and require significant communication overhead to be transmitted to the receiver. DEFORM addresses this challenge by leveraging Convolutional Neural Network to estimate the channel characteristics in particular the relative phase to antenna elements. It is specifically designed to address the unique features of wireless signals complex samples, such as the ambiguous 2π phase discontinuity and the high sensitivity of the link Bit Error Rate. The channel prediction is subsequently used in the Maximum Ratio Combining algorithm to achieve an optimal combination of the received signals. While being trained on a fixed, basic RF settings, we show that DEFORM's DL model is universal, achieving up to 3 dB of SNR gain for a two-antenna receiver in extensive evaluation demonstrating various settings of modulations and bandwidths.
Face Mask Detection Based Entry Control Using XAI and IoT

Today it has become mandatory for all the citizens to wear a face mask to protect them from COVID-19. Also taking two doses of vaccine is a must to visiting public places and currently, the only method to verify whether a person is fully vaccinated is by showing a vaccine certificate. The proposed application is helpful for elderly people who find it difficult to use smart phones. The shop owners, offices, banks, or any public place can check for restrictions of entry if anyone is not wearing a mask. As a result, no need for any guard to keep an eye on people. Machine learning techniques with Explainable AI (XAI) can solve these problems easily and results are made understandable to end-users because of the explaining ability and interpretability of neural network models. The system performs well for prediction and gives more accurate and trustworthy predictions. Hence XAI is more reliable in healthcare systems. The proposed system is implemented completely on Raspberry Pi allowing a complete embedded application. The application is developed using Python and HTML. PyCharm/Visual Studio Code with the help of an open-source library is used for training, defining, etc. Machine learning models used for the system are Tensorflow.js, Keras, OpenCV, etc. The whole application can run on a microcontroller such as Raspberry Pi, which allows one to simply plug and play the system at any time.
Fuzzy Spatiotemporal Data Modeling and Operations in RDF

With the emergence of a large number of fuzzy spatiotemporal data on the Web, how to represent and operate fuzzy spatiotemporal data has become an important research issue. Meanwhile, the Resource Description Framework (RDF) is a standard data and knowledge description language of the Semantic Web and has been applied in many application areas, such as geographic information systems and meteorological systems. In this paper, a model for representing fuzzy spatiotemporal data is proposed and a set of algebraic operations for the model are investigated. First, a representation method of fuzzy spatiotemporal RDF data and a fuzzy spatiotemporal RDF graph model are proposed. In addition, a formal fuzzy spatiotemporal RDF algebra is proposed and a set of algebraic operations for manipulating fuzzy spatiotemporal RDF data are developed. The algebraic operations include: set operation, selection operation, projection operation, join operation, and construction operation. Finally, the existing SPARQL query language is extended and an example that shows how to apply the proposed algebraic operations to capture the queries expressed by the extended SPARQL query language is given.
Attention-calibration based double-branch cross-domain person re-identification

Existing cross-domain pedestrian re-identification methods tend to utilize domain adaptation or domain generalization strategies to eliminate the differences between domains, but these methods fail to fully characterize the characteristics of unknown domain samples, resulting in poor recognition performance in unknown domain. By learning specific features and invariant features of different domains, this paper proposes a new attention-calibration double-branch cross-domain pedestrian re-identification network (ACDBNet). Firstly, the attention calibration module is utilized to extract the channel information and spatial information of an image, and locate the distinguishable local features; then, the global branch and local branch are introduced to fully learn the domain specific feature and domain invariant feature respectively, and the features in these two branches are linearly weighted fused. Experimental results on a large number of public datasets demonstrate that the proposed method has excellent performance in various environments.
SCF: Structured collaborative filtering with heterogeneous implicit feedback

Recommendation systems aim to analyze users’ historical behaviors to recommend items that suit their preferences. In the real world, users’ feedback is usually heterogeneous, such as purchases and examinations, and there are two fundamental challenges, namely the heterogeneity of users’ implicit feedback and the uncertainty of users’ preferences. Previous works have attempted to exploit heterogeneous implicit feedback, but most of them are based on traditional preference assumptions and may not fully learn users’ preferences. To overcome this limitation, we propose a novel solution, i.e., structured collaborative filtering (SCF), which can effectively utilize the complementarity of heterogeneous feedback. Specifically, we address the heterogeneity challenge by designing two types of structured information, i.e., a set of examined items and a group of neighboring users, which integrate examinations into the task of learning users’ purchase preferences. Furthermore, we reduce the uncertainty of users’ preferences by leveraging two preference assumptions defined on randomly constructed item-sets or user-groups. On this basis, we derive four algorithms, including SCF(i,i), SCF(u,i), SCF(i,u), and SCF(u,u), which take advantage of pointwise preference learning to accurately model the latent representations of users and items. Extensive experimentation on three real-world datasets demonstrates that our SCF outperforms the state-of-the-art methods.
Deep metric learning via group channel-wise ensemble

Deep metric learning aims at learning the distance metric for data samples by deep neural networks. Essentially, it derives an embedding space where the mappings of semantically related samples are much closer than those of irrelevant ones. When learning a single metric using all samples, it fails to well encode the sample similarity in several aspects, such as color, shape, and appearance. To handle this issue, some works simply divide the linear layer into multiple sub-layers, and the data subsets for learning multiple metrics are randomly generated at sample level. This usually makes the samples in the same class appear in different subspaces. Therefore, we propose a Group Channel-wise Ensemble (GCE) method that devises a group channel-wise convolution block in convolution networks for learning multiple distance metrics. Its core idea is that the channels of convolution layer encode different similarity metrics in groups. Specifically, the embedding space is partitioned into several subspaces, where one base learner captures one distance metric. Moreover, the class-proxy based meta-class update strategy is adopted for generating data subsets. This strategy learns discriminative distance metrics by optimizing the triplet loss on training samples. Empirical studies on four benchmarks demonstrate that GCE achieves promising performances in image retrieval and clustering tasks.
An enhanced adaptive Kalman filtering for linear systems with inaccurate noise statistics

Abstract This paper investigates the simultaneous state and noise covariance estimation for linear systems with inaccurate noise statistics. An enhanced adaptive Kalman filtering (EAKF) based on dynamic recursive nominal covariance estimation (DNRCE) and modified variational Bayesian (VB) inference is presented. The EAKF realizes the concurrently estimation of state and noise covariance matrices by introducing a nominal parameter in the traditional recursive covariance estimation and designing a new adaptive forgotten factor for the dynamic model of the estimated information propagation. The simulation of a target tracking example shows that, compared with the existing filters, the proposed filter has good adaptive performance for inaccurate and time‐varying noise covariance matrices.
Lightweight Mutual Authentication for Healthcare IoT

“Smart medical” applications refer to the fusion of technology and medicine that connects all linked sensor equipment with the patients, including those that measure physiological signals, such as blood pressure, pulse, and ECG. In addition, these physiological signal data are highly private and should be safely protected. It takes much longer to complete authentication processes in the traditional way, either based on public key infrastructure or attribute-based encryption, which is a burden for IoT devices. Hence, on the basis of attribute-based encryption, we propose lightweight authentication to shorten the time spent on authentication. Moreover, we use the patients’ data and timestamps as seeds to generate random numbers for authentication. The experiments show that the lightweight authentication using Xeon E3-1230 computer is about 4.45 times faster than complete authentication and 5.8 times faster than complete authentication when using Raspberry Pi. Our proposal significantly improves the disadvantages of IoT devices that lack computing power.
Type and class vectors and matrices in ℤ<sub><i>n</i></sub>. Application to ℤ<sub>6</sub>, ℤ<sub>7</sub>, and ℤ<sub>12</sub>

In post-tonal theory, set classes are normally elements of Z12 and are characterized by their interval-class vector. Those being non-inversionally-symmetrical can be split into two set types related by inversion, which can be characterized by their trichord-type vector. In this paper, I consider the general case of set classes and types in Zn and their m-class and m-type vectors, m ranging from 0 to n, which are properly grouped into matrices. As well, three relevant cases are considered: Z6 (hexachords), Z7 (heptatonic scales), and Z12 (chromatic scale), where all those type and class matrices are computed and provided in supplementary files; and, in the first two cases, also in the form of tables. This completes the corresponding information given in previous publications on this subject and can directly be used by researchers and composers. Moreover, two computer programs, written in MATLAB, are provided for obtaining the above-mentioned and other related matrices in the general case of Zn. Additionally, several theorems on type and class matrices are provided, including a complete version of the hexachord theorem. These theorems allow us to obtain the type and class matrices by different procedures, thus providing a broader perspective and better understanding of the theory.
Comparison of Different Modeling Techniques for Flemish Twitter Sentiment Analysis

Microblogging websites such as Twitter have caused sentiment analysis research to increase in popularity over the last several decades. However, most studies focus on the English language, which leaves other languages underrepresented. Therefore, in this paper, we compare several modeling techniques for sentiment analysis using a new dataset containing Flemish tweets. The key contribution of our paper lies in its innovative experimental design: we compared different preprocessing techniques and vector representations to find the best-performing combination for a Flemish dataset. We compared models belonging to four different categories: lexicon-based methods, traditional machine-learning models, neural networks, and attention-based models. We found that more preprocessing leads to better results, but the best-performing vector representation approach depends on the model applied. Moreover, an immense gap was observed between the performances of the lexicon-based approaches and those of the other models. The traditional machine learning approaches and the neural networks produced similar results, but the attention-based model was the best-performing technique. Nevertheless, a tradeoff should be made between computational expenses and performance gains.
The characteristics of facial emotions expressed in Memojis

Memoji represents humans in online communications, and their emotional conveyance is attracting much attention. Despite this, there is a lack of studies exploring the emotional conveyance level of Memoji using various methods. In this study, we examined the emotional quality of Memojis and compared them with photography using the 5-Likert questionnaire and an AI- driven method. An online survey was conducted to collect user assessments of emotional expressions in Memojis. Twenty-eight photographs from the Japanese and Caucasian Facial Expressions of Emotion (JACFEE) database depicting seven emotions were employed. Memojis of these photographs were made with an iPhone using iOS 15.2. The emotional content of individual Memojis was evaluated and compared with those of the photographs. We confirmed each emotion with an AI-driven prediction and analyzed it with human assessment. In general, Memojis were less efficient in communicating emotions than photography. Happiness and sadness showed robust results, while fear and contempt were relatively inaccurately perceived. When partic-ipants were less confident with a Memoji's facial expression, they tended to prefer photography even more. The tendency was coherent between human judgments and AI-supported predictions. We also discuss the limitations and challenges of the proper use of Memojis for improved figure-based and non-verbal communication.
Off-road Quad-Bike Detection Using CNN Models

Off-road vehicles are rapidly being employed for transportation, military activities, and sports racing. However, in monitoring and maintaining the race’s safety and reliability, quad-bike detection receives less attention than on-road vehicle recognition utilizing DL approaches. In this paper, we used transfer-learning approaches on pre-trained models of cutting-edge architectures, notably Yolov4, Yolov4-tiny, and Yolov5s, to detect quad-bikes from images and videos. A quad-bike dataset acquired from YouTube (https://youtu.be/ZyE3t3lG-vU. Accessed on April 10, 2022) was used to train and assess these designs. In this paper, we show that the Yolov4-tiny architecture outperforms the Yolov4, and Yolov5s in terms of mAP@50 and computing time per image.
Deep Unfolded Projected Alternating Minimization Algorithm for Blind Image Super-resolution

Blind super-resolution (BSR) has a wide range of applications in fruitful fields, such as pattern recognition, image processing, and signal processing. This problem focuses on recovering the original high-resolution (HR) details and blur kernel from a low-resolution (LR) blurry image. In these years, learning-based BSRs have evolved to become increasingly powerful through more extensive labelled data, better-designed end-to-end model and stronger training algorithm. However, the classic model-based methods typically provide good interpretability for applying prior information and generalizability of dealing with variational factors (blur kernels) with respect to their explicit alternative optimization framework, while the learning-based methods generally highly rely on the training data and are typically perform less effective on the unseen kernels. In this paper, a learning-aided model-based algorithm is proposed to solve the aforementioned issues by unfolding a well-designed model-based BSR approach, the projected alternating minimization (PAM) algorithm, that establishes an explicit alternative optimization iterations with data-driven network-based update module. As a result, the proposed unfolding PAM (UPAM) inherits the good interpretability and generalizability of model-based methods towards solving arbitrary blur kernels, while retaining the high performance of learning-based methods. Extensive experiments verify that the proposed UPAM outperforms both of the model-based and learning-based BSR methods, and gains significantly better performance when dealing with the blur kernels.
Transparent Registration-Based Encryption through Blockchain

Garg et al. (TCC 2018) defined the notion of registration-based encryption (RBE) where the private key generator (PKG) is decoupled from key management and replaced by a key curator (KC). KC does not possess any cryptographic secrets and only plays the role of aggregating the public keys of all the registered users and updating the public parameters whenever a new user joins the system, which solves the key escrow issue. Notwithstanding, RBE still places a significant amount of trust in KC, whose actions are not accountable, e.g., it could secretly register multiple keys for already registered users. In this article, we propose a blockchain-based RBE framework, which provides total transparency and decentralization of KC by leveraging smart contracts. Our framework transfers the right of key management from KC to individual participants and keeps publicly upgradable parameters on-chain. We provide a basic construction that calculates the public parameter on-chain and an extended construction with better efficiency, which merely calculates the roots of trees on-chain. Our basic version is theoretically feasible, while the extended version is practically feasible. In particular, the enhanced scheme reduces computing complexity to a constant level. Our prototype implementation and evaluation results demonstrate that our extended construction is satisfactorily efficient.
An Enhanced Exploration of Sentimental Analysis in Health Care

The medical dataset replicates the patient's crucial information, such as important details regarding the patient's health. It includes disease diagnoses, interventions, and descriptions of the examined results. Also, detecting the mindset of an acute disease-affected patient is a primary challenging task. Though sentiment analysis plays a role in seeing their perspective, the significant broad medical application does not yet meet the analysis of patient mindset. So here we identified major shortcoming exists while studies the diversified disease-affected people mindset. Hence, we introduce a practical framework to analyse patients' perspectives using a socio-medical dataset that contains various reviews and feedback of critical diseases-affected people—initially, we applied a pre-processing technique, including Lowercase Conversion, removing special characters, removing stop words, Number to word conversion, Stemming, and lemmatization over dataset. Next, N-gram tokenization methodology is used to extract the valuable features followed by assigning polarity score to each sentiment we extract and calculate the overall polarity of the context. Finally, a probabilistic LDA model was employed to combine the review. Furthermore, various machine learning classifiers are explored to evaluate the performance of the proposed framework.
On Generalization Bounds for Deep Networks Based on Loss Surface Implicit Regularization

The classical statistical learning theory implies that fitting too many parameters leads to overfitting and poor performance. That modern deep neural networks generalize well despite a large number of parameters contradicts this finding and constitutes a major unsolved problem towards explaining the success of deep learning. While previous work focuses on the implicit regularization induced by stochastic gradient descent (SGD), we study here how the local geometry of the energy landscape around local minima affects the statistical properties of SGD with Gaussian gradient noise. We argue that under reasonable assumptions, the local geometry forces SGD to stay close to a low dimensional subspace and that this induces another form of implicit regularization and results in tighter bounds on the generalization error for deep neural networks. To derive generalization error bounds for neural networks, we first introduce a notion of stagnation sets around the local minima and impose a local essential convexity property of the population risk. Under these conditions, lower bounds for SGD to remain in these stagnation sets are derived. If stagnation occurs, we derive a bound on the generalization error of deep neural networks involving the spectral norms of the weight matrices but not the number of network parameters. Technically, our proofs are based on controlling the change of parameter values in the SGD iterates and local uniform convergence of the empirical loss functions based on the entropy of suitable neighborhoods around local minima.
Supervised learning of sheared distributions using linearized optimal transport

Abstract In this paper we study supervised learning tasks on the space of probability measures. We approach this problem by embedding the space of probability measures into $$L^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> spaces using the optimal transport framework. In the embedding spaces, regular machine learning techniques are used to achieve linear separability. This idea has proved successful in applications and when the classes to be separated are generated by shifts and scalings of a fixed measure. This paper extends the class of elementary transformations suitable for the framework to families of shearings, describing conditions under which two classes of sheared distributions can be linearly separated. We furthermore give necessary bounds on the transformations to achieve a pre-specified separation level, and show how multiple embeddings can be used to allow for larger families of transformations. We demonstrate our results on image classification tasks.