Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Overload and Theft Detection for Power Distribution System

Energy theft is a major threat that affects economy of our country and there necessitates to fulfil the energy demand as per the requirement of power consumption. GSM based energy meter is used to avoid the user to pay for excess power theft bill irrespective of the electricity usage. Practice of prepaid recharge system is an inexpensive way of giving power supply, reducing the problems of billing. Electricity theft detection solves the problem of human inspection as the system requires no manpower. Illegal electricity usages will be reduced. Internet of things and GSM are used for reporting of energy overload and power theft to the supplier. The overload and theft detection techniques that have been written in the paper are reviewed. Keywords— Overload, energy theft, prepaid recharge system, GSM, internet of things, microcontroller.
DETECTION AND PREVENTION OF BOTNET AND SYBIL ATTACK IN SOCIAL MEDIA

: A botmaster is a person who operates the command and control of botnets for remote process execution. The botnets are typically installed on compromised machines via various forms of remote code installation. Botmasters have started to invade SOCIAL MEDIA (facebook, twitter, youtube ) by spamming, stealing private data through more flexible C&C channels. Since relentless spammers exploit the established trust relationships between account owners and their friends to efficiently spread malicious spam, timely detection of compromised accounts are quite challenging now-a-days. Here we propose a set of social behavioral features that can effectively characterize the user’s social activities on facebook. The validation of the efficacy of these behavioral features by collecting and analyzing real user click streams in a facebook website to identify the compromised accounts is done . It evaluates the capability of the social behavioral profiles in distinguishing different facebook users and the proposed system shows that social behavioral profiles can accurately differ from individual facebook users and detect compromised accounts
Transfer Learning-Based Cough Representations for Automatic Detection of COVID-19

In the last months, there has been an increasing interest in developing reliable, cost-effective, immediate and easy to use machine learning based tools that can help health care operators, institutions, companies, etc. to optimize their screening campaigns. In this line, several initiatives emerged aimed at the automatic detection of COVID-19 from speech, breathing and coughs, with inconclusive preliminary results. The ComParE 2021 COVID-19 Cough Sub-challenge provides researchers from all over the world a suitable test-bed for the evaluation and comparison of their work. In this paper, we present the INESC-ID contribution to the ComParE 2021 COVID-19 Cough Sub-challenge. We leverage transfer learning to develop a set of three expert classifiers based on deep cough representation extractors. A calibrated decision-level fusion system provides the final classification of coughs recordings as either COVID-19 positive or negative. Results show unweighted average recalls of 72.3% and 69.3% in the development and test sets, respectively. Overall, the experimental assessment shows the potential of this approach although much more research on extended respiratory sounds datasets is needed. Copyright © 2021 ISCA.
rec-Image Patching for Image Denoising: A Review

: Usually we have a large number of devices by which we can take pictures, from a digital camera to a cell phone. Problem is that usually we take the picture we don’t see some errors that can be present in the image until we observe it in a computer or printed image. Now the real problem arises because it is almost impossible to recapture the same moment in a new photo. Hence to find a solution for this problem comes the need to implement a method to correct some of the defects that appear. In this paper we compare different methods, problems and their proposed solutions algorithms to patch an original image, using different texture image techniques. Traditional match matching algorithms treat each pixel/patch as an independent sample and build a hierarchical data structure, such as the kd-tree, to accelerate nearest patch finding. However, most of these approaches can only find approximate nearest patch and do not explore the sequential overlap between patches. Hence, they are neither accurate in quality nor optimal in speed.
Identification of Highly Frequent TP53 Germline Variations in a Cohort of Head and Neck Cancer Cases

TP53 tumour suppressor gene inactivation plays a significant role in molecular pathology of cancers. TP53 germline mutations/variations increase the risk of developing multiple primary cancers. However, the role of such TP53 genetic alterations in head and neck cancers (HNCs) is not well-established. HNCs comprise one of the most frequent cancers in Karachi, Pakistan. The pilot study reports the investigation of germline mutations in TP53 gene in a cohort of 30 HNC patients from Pakistan. Blood samples were collected and genomic DNA was extracted from white blood cells. After quality control, amplification of seven selected exons along with their splice sites, two intronic regions (introns 2-3 and 3-4), and 3'UTR were carried out. Sanger sequencing was carried out in order to identify germline variations. Comparison with wild type sequence revealed 11099C>G (intron 2-3) variation in 90%, PIN3 duplication (intron 3-4) in 95%, and exon 4 C>G (rs1042522) alteration in 93.4% of the cases. In 3'UTR, gDNAdelCC1558 was found in 13.4% of the analyzed cases. In conclusion, we report three highly frequent germline variations and a newly discovered variation in 3'UTR in TP53 germline DNA of head and neck cancer patients from Pakistan. These results shall contribute in delineating the genetic component of head and neck cancers.
Blood Pressure Measurement among Women of Reproductive Age in Southern Nepal

Background and objectives: Accurate measurement of Blood Pressure (BP) is crucial to the diagnosis and management of hypertensive disorders in pregnancy. Weight gain and higher Body Mass Index (BMI) is believed to increase maternal risk during pregnancy. Therefore, the present study was designed to measure the Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) among pregnant women and its association with BMI and age in Southern Nepal.
Genaro Network

The Genaro Network is a new public blockchain platform based on peer-to-peer encryption and sharing. The platform aims to realize highly efficient node management on the public chain based on PoS (Proof of Stake) and SPoR (Sentinel Proof of Retrievability). The vision of Genaro is to establish a new medium of distributed and encrypted storage, and to enable each user to use and share data, and to establish abundant distributed applications (DApps) on the blockchain and provide stable support for these. Compared with other public chains, Genaro has the following advantages: (1) Genaro modified the use of file sentinels to better suit distributed systems through the combination of PoS and SPoR, thus enhancing the ability to defend against replay attacks; (2) in the design of chain-style PoS, Genaro studied famous PoS methods such as Casper (CFFG, CTFG) ， Tendermint, and Ouroboros, analyzed the major ways of attacking PoS and proposed relevant schemes; and (3) in terms of management structure, Genaro combines the proof of data integrity and PoS, and provides effective methods of defense against potential problems in PoS. In addition, in terms of the data structure in the public chain, Genaro has developed the GSIOP protocol in line with up-to-date methods of storage encryption, so as to settle different layers of data usage. Finally, in terms of adding data, Genaro has also added relevant VM order sets.
Development of a novel startle response task in Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD), an X-linked childhood-onset muscular dystrophy caused by loss of the protein dystrophin, can be associated with neurodevelopmental, emotional and behavioural problems. A DMD mouse model also displays a neuropsychiatric phenotype, including increased startle responses to threat which normalise when dystrophin is restored in the brain. We hypothesised that startle responses may also be increased in humans with DMD, which would have potential translational therapeutic implications. To investigate this, we first designed a novel discrimination fear-conditioning task and tested it in six healthy volunteers, followed by male DMD (n=11) and Control (n=9) participants aged 7-12 years. The aims of this methodological task development study were to: i) confirm the task efficacy; ii) optimise data processing procedures; iii) determine the most appropriate outcome measures. In the task, two neutral visual stimuli were presented: one safe cue presented alone; one threat cue paired with a threat stimulus (aversive noise) to enable conditioning of physiological startle responses (skin conductance response, SCR, and heart rate). Outcomes were the unconditioned physiological startle responses to the initial threat, and retention of conditioned responses in the absence of the threat stimulus. We present the protocol development and optimisation of data processing methods based on empirical data. We found that the task was effective in producing significantly higher physiological startle SCR in reinforced threat trials compared to safe trials (P<.001). Different data extraction methods were compared and optimised, and the optimal sampling window was derived empirically. SCR amplitude was the most effective physiological outcome measure when compared to SCR area and change in heart rate, with the best profile on data processing, the least variance, successful conditioned response retention (P=.01) and reliability assessment in test-retest analysis (rho=.86). The definition of this novel outcome will allow us to study this response in a DMD population.
Randomized Substitution and Vote for Textual Adversarial Example Detection

A line of work has shown that natural text processing models are vulnerable to adversarial examples. Correspondingly, various defense methods are proposed to mitigate the threat of textual adversarial examples, e.g . adversarial training, cer-tiﬁed defense, input pre-processing, detection, etc . In this work, we treat the optimization process for synonym substi-tution based textual adversarial attacks as a speciﬁc sequence of word replacement, in which each word mutually inﬂu-ences other words. We identify that we could destroy such mutual interaction and eliminate the adversarial perturbation by randomly substituting a word with its synonyms. Based on this observation, we propose a novel textual adversarial example detection method, termed Randomized Substitution and Vote (RS&V), which votes the prediction label by accumulating the logits of k samples generated by randomly substituting the words in the input text with synonyms. The proposed RS&V is generally applicable to any existing neural networks without modiﬁcation on the architecture or extra training, and it is orthogonal to prior work on making the classiﬁcation network itself more robust. Empirical evaluations on three benchmark datasets demonstrate that RS&V could detect the textual adversarial examples more successfully than the existing detection methods while maintaining the high classiﬁcation accuracy on benign samples.
Towards a Rigorous Evaluation of Time-series Anomaly Detection

In recent years, proposed studies on time-series anomaly detection (TAD) report high F1 scores on benchmark TAD datasets, giving the impression of clear improvements in TAD. However, most studies apply a peculiar evaluation protocol called point adjustment (PA) before scoring. In this paper, we theoretically and experimentally reveal that the PA protocol has a great possibility of overestimating the detection performance; even a random anomaly score can easily turn into a state-of-the-art TAD method. Therefore, the comparison of TAD methods after applying the PA protocol can lead to misguided rankings. Furthermore, we question the potential of existing TAD methods by showing that an untrained model obtains comparable detection performance to the existing methods even when PA is forbidden. Based on our findings, we propose a new baseline and an evaluation protocol. We expect that our study will help a rigorous evaluation of TAD and lead to further improvement in future researches.
An Improved Hybrid Recommender System: Integrating Document Context-Based and Behavior-Based Methods

One of the main challenges in recommender systems is data sparsity, which leads to high variance. Several attempts have been made to improve the bias-variance trade-off using auxiliary information. In particular, document modeling-based methods have improved the model's accuracy by using textual data such as reviews, abstracts, and storylines when the user-to-item rating matrix is sparse. However, such models are insufficient to learn optimal representation for users and items. For building recommender systems, user-based and item-based collaborative filtering have long been used due to their efficiency. A user and item profile are created based on their historically interacted items and the users who interacted with the target item. In spite of the fact that these two approaches have been studied separately, there has been little research into combining them. The purpose of this study is to combine these two approaches by considering the opinions of users on these items. Each user is represented by their historical behavior, while each item is represented by the users who have interacted with it before, combined with contextual information, which is processed with NLP. The proposed algorithm is implemented and tested on three real-world datasets that demonstrate our model's effectiveness over the baseline methods.
Conditional Generation of Synthetic Geospatial Images from Pixel-level and Feature-level Inputs

Training robust supervised deep learning models for many geospatial applications of computer vision is difficult due to dearth of class-balanced and diverse training data. Conversely, obtaining enough training data for many applications is financially prohibitive or may be infeasible, especially when the application involves modeling rare or extreme events. Synthetically generating data (and labels) using a generative model that can sample from a target distribution and exploit the multi-scale nature of images can be an inexpensive solution to address scarcity of labeled data. Towards this goal, we present a deep conditional generative model, called VAE-Info-cGAN, that combines a Variational Autoencoder (VAE) with a conditional Information Maximizing Generative Adversarial Network (InfoGAN), for synthesizing semantically rich images simultaneously conditioned on a pixel-level condition (PLC) and a macroscopic feature-level condition (FLC). Dimensionally, the PLC can only vary in the channel dimension from the synthesized image and is meant to be a task-specific input. The FLC is modeled as an attribute vector in the latent space of the generated image which controls the contributions of various characteristic attributes germane to the target distribution. Experiments on a GPS trajectories dataset show that the proposed model can accurately generate various forms of spatiotemporal aggregates across different geographic locations while conditioned only on a raster representation of the road network. The primary intended application of the VAE-Info-cGAN is synthetic data (and label) generation for targeted data augmentation for computer vision-based modeling of problems relevant to geospatial analysis and remote sensing.
Generation of optical potentials for ultracold atoms using a superluminescent diode

We report on the realization and characterisation of optical potentials for ultracold atoms using a superluminescent diode. The light emitted by this class of diodes is characterised by high spatial coherence but low temporal coherence. On the one hand, this implies that it follows Gaussian propagation similar to lasers, allowing for high intensities and well-collimated beams. On the other, it significantly reduces those interference effects that lead to severe distortions in imaging. By using a high-resolution optical setup, we produce patterned optical potentials with a digital micromirror device and demonstrate that the quality of the patterns produced by our superluminescent diode is consistently and substantially higher than those produced by our laser. We show that the resulting optical potentials can be used to arrange the atoms in arbitrary structures and manipulate them dynamically. Our results can open new opportunities in the fields of quantum simulations and atomtronics.
Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise

Resistive random-access memory (ReRAM) is a promising technology for designing hardware accelerators for deep neural network (DNN) inferencing. However, stochastic noise in ReRAM crossbars can degrade the DNN inferencing accuracy. We propose the design and optimization of a high-performance, area-and energy-efficient ReRAM-based hardware accelerator to achieve robust DNN inferencing in the presence of stochastic noise. We make two key technical contributions. First, we propose a stochastic-noise-aware training method, referred to as ReSNA, to improve the accuracy of DNN inferencing on ReRAM crossbars with stochastic noise. Second, we propose an information-theoretic algorithm, referred to as CF-MESMO, to identify the Pareto set of solutions to trade-off multiple objectives, including inferencing accuracy, area overhead, execution time, and energy consumption. The main challenge in this context is that executing the ReSNA method to evaluate each candidate ReRAM design is prohibitive. To address this challenge, we utilize the continuous-fidelity evaluation of ReRAM designs associated with prohibitive high computation cost by varying the number of training epochs to trade-off accuracy and cost. CF-MESMO iteratively selects the candidate ReRAM design and fidelity pair that maximizes the information gained per unit computation cost about the optimal Pareto front. Our experiments on benchmark DNNs show that the proposed algorithms efficiently uncover high-quality Pareto fronts. On average, ReSNA achieves 2.57% inferencing accuracy improvement for ResNet20 on the CIFAR-10 dataset with respect to the baseline configuration. Moreover, CF-MESMO algorithm achieves 90.91% reduction in computation cost compared to the popular multi-objective optimization algorithm NSGA-II to reach the best solution from NSGA-II.
Hot electron cooling in InSb probed by ultrafast time-resolved terahertz cyclotron resonance

Chelsea Q. Xia ,1 Maurizio Monti ,2 Jessica L. Boland ,1,3 Laura M. Herz ,1 James Lloyd-Hughes ,2 Marina R. Filip ,1 and Michael B. Johnston 1,* 1Clarendon Laboratory, Department of Physics, University of Oxford, Parks Road, OX1 3PU Oxford, United Kingdom 2Department of Physics, University of Warwick, Gibbet Hill Road, CV4 7AL Coventry, United Kingdom 3Photon Science Institute, Department of Electrical and Electronic Engineering, University of Manchester, Oxford Road, M13 9PL Manchester, United Kingdom
Zero surface tension limit of the free-boundary problem in incompressible magnetohydrodynamics

We show that the solution of the free-boundary incompressible ideal magnetohydrodynamic (MHD) equations with surface tension converges to that of the free-boundary incompressible ideal MHD equations without surface tension given the Rayleigh–Taylor sign condition holds initially. This result is a continuation of the authors’ previous works (Gu et al 2020 arXiv:2105.00596; Gu and Wang 2019 J. Math. Pures Appl. 128 1–41; Luo and Zhang 2021 SIAM J. Math. Anal. 53 2595–630). Our proof is based on the combination of the techniques developed in our previous works (Gu et al 2020 arXiv:2105.00596; Gu and Wang 2019 J. Math. Pures Appl. 128 1–41; Luo and Zhang 2021 SIAM J. Math. Anal. 53 2595–630), Alinhac good unknowns, and a crucial anti-symmetric structure on the boundary.
Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions

In the future, extraterrestrial expeditions will not only be conducted by rovers but also by flying robots. The technical demonstration drone Ingenuity, that just landed on Mars, will mark the beginning of a new era of exploration unhindered by terrain traversability. Robust self-localization is crucial for that. Cameras that are lightweight, cheap and information-rich sensors are already used to estimate the ego-motion of vehicles. However, methods proven to work in man-made environments cannot simply be deployed on other planets. The highly repetitive textures present in the wastelands of Mars pose a huge challenge to descriptor matching based approaches.In this paper, we present an advanced robust monocular odometry algorithm that uses efficient optical flow tracking to obtain feature correspondences between images and a refined keyframe selection criterion. In contrast to most other approaches, our framework can also handle rotation-only motions that are particularly challenging for monocular odometry systems. Furthermore, we present a novel approach to estimate the current risk of scale drift based on a principal component analysis of the relative translation information matrix. This way we obtain an implicit measure of uncertainty. We evaluate the validity of our approach on all sequences of a challenging real-world dataset captured in a Mars-like environment and show that it outperforms state-of-the-art approaches. The source code is publicly available at: https://github.com/DLR-RM/granit.
Robot Navigation in Irregular Environments with Local Elevation Estimation using Deep Reinforcement Learning

—We present a novel method for safely navigating a robot in unknown and uneven outdoor terrains. Our approach trains a novel Deep Reinforcement Learning (DRL)- based network with channel and spatial attention modules using a novel reward function to compute an attention map of the environment. The attention map identiﬁes regions in the environment’s elevation map with high elevation gradients where the robot could have reduced stability or even ﬂip over. We transform this attention map into a 2D navigation cost-map, which encodes the planarity (level of ﬂatness) of the terrain. Using the cost-map, we formulate a novel method for computing local least-cost waypoints leading to the robot’s goal and integrate our approach with DWA-RL, a state-of-the-art navigation method. Our approach guarantees safe, locally least-cost paths and dynamically feasible robot velocities in highly uneven terrains. Our hybrid approach also leads to a low sim-to-real gap, which arises while training DRL networks. We observe an improvement of X% in terms of success rate, the cumulative elevation gradient of the robot’s trajectory, and the safety of the robot’s velocity. We evaluate our method on a real Husky robot in highly uneven real-world terrains and demonstrate its beneﬁts.
Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation

Have you ever looked at a painting and wondered what is the story behind it? This work presents a framework to bring art closer to people by generating comprehensive descriptions of fine-art paintings. Generating informative descriptions for artworks, however, is extremely challenging, as it requires to 1) describe multiple aspects of the image such as its style, content, or composition, and 2) provide background and contextual knowledge about the artist, their influences, or the historical period. To address these challenges, we introduce a multi-topic and knowledgeable art description framework, which modules the generated sentences according to three artistic topics and, additionally, enhances each description with external knowledge. The framework is validated through an exhaustive analysis, both quantitative and qualitative, as well as a comparative human evaluation, demonstrating outstanding results in terms of both topic diversity and information veracity.