Reviewerly/exHarmony huggingface dataset:
Bag-of-Event-Models based embeddings for detecting anomalies in surveillance videos

Automated monitoring of unconstrained videos is becoming mandatory due to its widespread applications over public and private domains. Especially, research over detecting anomalous human behaviors in surveillance videos has created much attention. Understanding patterns in surveillance videos are always challenging due to the rapid movement of the crowd, occlusions, and cluttered backgrounds. The intra-class variations existing among normal and abnormal events lead to poor performance of anomaly detection system. These issues can be addressed by learning discriminative embeddings for video segments of surveillance videos. We propose an efficient Bag-of-Event-Models (BoEM) based embedding to represent video segments of normal and abnormal behaviors. Proposed BoEM can also be formed using training data of normal events only and the embeddings can be given as input to one-class classifier such as OC-SVM in an outlier detection fashion. The proposed embeddings handle intra-class variations and provide improved discrimination with much reduced dimension. Results over benchmark datasets namely Live Videos (LV), UCF-Crime and Crowd Violence demonstrate that the proposed BoEM based event embeddings in conjunction with SVM Classifier give significantly better performance than the other state-of-the-art methods. In addition, studies prove that the proposed embeddings are appropriate even for imbalanced sequential data such as video segments.
Efficient Residual Shrinkage CNN Denoiser Design for Intelligent Signal Processing: Modulation Recognition, Detection, and Decoding

The noises embedded in signals will degrade the signal processing quality. Traditional denoising algorithms might not work in practical systems since the statistical characteristics of noises might not be learned. To address this issue, we propose an efficient residual shrinkage convolutional neural network (RSCNN) aided denoiser based on the principle of the domain transformation, shrinking and inverse transforming operations conducted by the traditional denoiser. The proposed RSCNN is composed by the batch normalization layer, domain transformation layers, the shrinkage module and inverse transformation layers, wherein transformation layers consist of convolutional layers and the nonlinear activation function. Moreover, we propose a thresholds learning subnetwork to automatically determine the thresholds, so as to enhance noise suppressing performances. Furthermore, we compose the data set by preprocessing the received signals, and design the loss function according to different denoising requirements. To validate the efficiency and universality of the RSCNN aided denoiser, we apply the proposed RSCNN denoiser to three different application scenarios, including the modulation recognition, detection and decoding. After the offline training, at the online deployment stage, we utilize the RSCNN denoiser to reduce the noise power and improve the signal to noise ratios. Simulation results demonstrate that the proposed intelligent denoiser can efficiently improve the signal processing capabilities to achieve higher modulation recognition accuracy, better detection and decoding performances with lower complexity than benchmark schemes.
Power Allocation for Wireless Federated Learning Using Graph Neural Networks

We propose a data-driven approach for power allocation in the context of federated learning (FL) over interference-limited wireless networks. The power policy is designed to maximize the transmitted information during the FL process under communication constraints, with the ultimate objective of improving the accuracy and efficiency of the global FL model being trained. The proposed power allocation policy is parameterized using a graph convolutional network and the associated constrained optimization problem is solved through a primal-dual algorithm. Numerical experiments show that the proposed method outperforms three baseline methods in both transmission success rate and FL global performance.
A Survey on Multimedia Analytics in Security Systems of Cyber Physical Systems and IoT

With the advancement of software systems, the reliance on humans and their judgments has decreased, resulting in an increase in the use of computational intelligence. As a result of this drift and the available technology, an enhanced method of sensing and controlling as well as measuring and exchanging data has emerged, leading to the emergence of a new area known as Cyber-Physical Systems (CPS). Despite the fact that these systems exhibit remarkable features, the increasing complexity imposed by their components and services makes their design extremely challenging to achieve. This article provides an overview of what a cyber-physical system (CPS) is, its design, and how multimedia analytics and the Internet of Things (IoT) play a significant part in CPS. The importance of CPS in the medical profession cannot be overstated. In this section, a case study is provided to demonstrate the importance of CPS in the medical profession.
Image Data Augmentation techniques for Deep Learning -A Mirror Review

Making a computer understand the images is the task of computer vision. However, to familiarize it and learn to analyze what the computer is able to perceive is a tedious task. To address this challenge, Convolutional Neural Networks using deep learning frameworks could be performed and our goal could be achieved. For enhancing this task and making a computer learn more accurately and precisely about the variations in the real-world images, augmentation is an excellent way for making it possible. In this paper different types of normal augmentations, using deep learning methods for augmentation and other important works on this technique are discussed. This paper tries to cover the critical features of the augmentation, which will thus not only aid the new researchers but also other experienced researchers to interpret the latest trends going on in this technology, this would enable us to work together to make the computers learn more meticulously by utilizing various deep learning models that are trained on the images, making them more efficient and robust.
Performance characterization of containerization for HPC workloads on InfiniBand clusters: an empirical study

Abstract Containerization technology offers an appealing alternative for encapsulating and operating applications (and all their dependencies) without being constrained by the performance penalties of using Virtual Machines and, as a result, has got the interest of the High-Performance Computing (HPC) community to obtain fast, customized, portable, flexible, and reproducible deployments of their workloads. Previous work on this area has demonstrated that containerized HPC applications can exploit InfiniBand networks, but has ignored the potential of multi-container deployments which partition the processes that belong to each application into multiple containers in each host. Partitioning HPC applications has demonstrated to be useful when using virtual machines by constraining them to a single NUMA (Non-Uniform Memory Access) domain. This paper conducts a systematical study on the performance of multi-container deployments with different network fabrics and protocols, focusing especially on Infiniband networks. We analyze the impact of container granularity and its potential to exploit processor and memory affinity to improve applications’ performance. Our results show that default Singularity can achieve near bare-metal performance but does not support fine-grain multi-container deployments. Docker and Singularity-instance have similar behavior in terms of the performance of deployment schemes with different container granularity and affinity. This behavior differs for the several network fabrics and protocols, and depends as well on the application communication patterns and the message size. Moreover, deployments on Infiniband are also more impacted by the computation and memory allocation, and because of that, they can exploit the affinity better.
On a Sparse Shortcut Topology of Artificial Neural Networks

In established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effectiveness of shortcuts, there remain open questions on the mechanism and characteristics. For example, why are shortcuts powerful? Why do shortcuts generalize well? In this article, we investigate the expressivity and generalizability of a novel sparse shortcut topology. First, we demonstrate that this topology can empower a one-neuron-wide deep network to approximate any univariate continuous function. Then, we present a novel width-bounded universal approximator in contrast to depth-bounded universal approximators and extend the approximation result to a family of equally competent networks. Furthermore, with generalization bound theory, we show that the proposed shortcut topology enjoys excellent generalizability. Finally, we corroborate our theoretical analyses by comparing the proposed topology with popular architectures, including ResNet and DenseNet, on well-known benchmarks and perform a saliency map analysis to interpret the proposed topology. Our work helps understand the role of shortcuts and suggests further opportunities to innovate neural architectures.
Automatic modulation classification of noise‐like radar intrapulse signals using cascade classifier

Automatic modulation classification is essential in radar emitter identification. We propose a cascade classifier by combining a support vector machine (SVM) and convolutional neural network (CNN), considering that noise might be taken as radar signals. First, the SVM distinguishes noise signals by the main ridge slice feature of signals. Second, the complex envelope features of the predicted radar signals are extracted and placed into a designed CNN, where a modulation classification task is performed. Simulation results show that the SVM-CNN can effectively distinguish radar signals from noise. The overall probability of successful recognition (PSR) of modulation is 98.52% at 20 dB and 82.27% at −2 dB with low computation costs. Furthermore, we found that the accuracy of intermediate frequency estimation significantly affects the PSR. This study shows the possibility of training a classifier using complex envelope features. What the proposed CNN has learned can be interpreted as an equivalent matched filter consisting of a series of small filters that can provide different responses determined by envelope features.
Benchmarking clear sky and transposition models for solar irradiance estimation on vertical planes to facilitate glazed facade design

The present study aims to benchmark the models utilized for estimating the solar irradiance on vertical planes that can facilitate the determination of critical conditions, which are utilized in the glazed facade design. Accordingly, in the first part of the study, the performance of PVGIS databases for Europe for estimating the solar irradiance components on the horizontal surfaces is compared with the on-site measurements at Bolzano airport (Italy). It is demonstrated that PVGIS-SARAH estimates both the global and diffuse solar irradiance components, compared to on-site measurements, with an acceptable error range (± 2–5%). The accuracy of isotropic and anisotropic transposition models is then compared for the same location, which demonstrates that the isotropic model tends to underestimate the global vertical incident irradiance by 6–12% of the daily peak value, while an agreement between the estimations of the anisotropic Perez model and the measurements is observed. Next, the performance of Ineichen, Solis and SPECMAGIC clear sky models, transposed with anisotropic Perez model, have been assessed utilizing field measurements on vertical facades at four different locations in Europe. The broadband version of Solis clear sky model, transposed with anisotropic Perez model, demonstrates R2 scores between 0.85 to 0.95 for the considered locations, indicating that this approach can be utilized as an alternative to open-source/commercially available data sources to estimate the solar irradiance on vertical facades. Finally, for the case study of a triple glazing system, comparing the surface temperatures obtained employing the latter approach with those determined using other commonly utilized irradiance data sources shows a discrepancy of up to 7 °C.
Consumer communications and current events: a cross-cultural study of the change in consumer response to company social media posts due to the COVID-19 pandemic

The COVID-19 pandemic has changed the lives of consumers in virtually every nation. Based upon the theory of psychological reactance and psychoevolutionary theory of emotion, we hypothesize how such lifestyle changes affect consumers perceiving and responding to companies' communications messages. The theories also suggest that consumers in different cultures may respond to COVID-19 differently. To test our hypotheses, we implemented a Python scraper to collect companies' Instagram posts pre- and during the COVID-19 lockdown. A machine learning algorithm was applied on the collected post photos to automatically identify certain photo characteristics, such as indoor versus outdoor, and with a single person versus many people; a text mining and sentiment analysis was implemented on the collected post captions to identify the salient emotion each caption exhibited, such as joy and anticipation. After that, we conducted a regression discontinuity analysis of photo characteristics or caption emotion on number of likes or comments to identify consumers' response change due to the COVID-19 pandemic. The estimation results supported our hypotheses and suggested tactics that could improve consumer communications effectiveness in this changed time. Viewing COVID-19 as an example of a current event in the ever-changing world, this paper suggests that such events could impact consumer response and behavior, and that companies' marketing and advertising strategies should be responsive to such events.
Optimal in-place suffix sorting

The suffix array is a fundamental data structure for many applications that involve string searching and data compression. We obtain the first in-place suffix array construction algorithms that are optimal both in time and space for (read-only) integer alphabets. We make the following contributions: For integer alphabets, we obtain the first linear time suffix sorting algorithm which uses only O(1) workspace. The input string may be modified during the execution of the algorithm, but should be restored upon termination of the algorithm. We strengthen the first result by providing the first in-place linear time algorithm for read-only integer alphabets with |Σ|=O(n) (i.e., the input string cannot be modified). This algorithm settles the open problem posed by Franceschini and Muthukrishnan in ICALP 2007. Besides, for the read-only general alphabets (i.e., only comparisons are allowed), we present an optimal in-place O(nlog⁡n) time suffix sorting algorithm.
Three-Dimensional Bearings-Only Target Tracking: Comparison of Few Sigma Point Kalman Filters

Three-dimensional bearings-only target tracking problem is considered in this work. To solve this problem, highly accurate nonlinear filtering algorithms are required. This is because the nonlinearities in the noise corrupted measurements, combined with the other uncertainties, make the estimation problem highly challenging. In this work, the extended Kalman filter (EKF), the cubature Kalman filter (CKF), the unscented Kalman filter (UKF), and the new sigma point Kalman filter (NSKF) are used to solve this state estimation problem. The performance criteria chosen are root mean square error (RMSE) in position and velocity. To have a more meaningful study, RMSE at the end of the observation period and time-averaged RMSE after the observer manoeuvre is calculated, for varying measurement noise and initial uncertainty.
Privacy Protection Data Retrieval Scheme With Inverted Index for IoT Based on Blockchain

In the 6G era, Internet of Things (IoT) devices can form a blockchain network, which also faces the problems of data sharing. The data transmitted and stored through the network have the risk of privacy leaking. Encrypting the shared data can satisfy the need of the privacy, and retrieving the encrypted data can make the data used efficiently. However, to enable users to retrieve encrypted data and perform fine-grained authorization on their encrypted files is still a great challenge. Although attribute-based keyword search (ABKS) is a well-received solution to the challenge, there are still privacy and efficiency issues if the traditional ABKS schemes are directly used in blockchain data sharing. In order to solve the problems, this article proposes privacy protection data retrieval scheme with an inverted index, which is an application of attribute-based encryption. First, our scheme is proved secure against the outside keyword guessing attack (KGA) and chosen keyword attack (CKA) under the semitrusted model. Second, the scheme returns a multikeywords ranked result. Third, we analyze the efficiency of our scheme and verify it by simulation. The results show that our scheme has improvement in efficiency and can meet the data sharing needs of the blockchain network composed of IoT devices.
Membership Inference Attacks against GANs by Leveraging Over-representation Regions

Generative adversarial networks (GANs) have made unprecedented performance in image synthesis and play a key role in various downstream applications of computer vision. However, GAN models trained on sensitive data also pose a distinct threat to privacy. In this poster, we present a novel over-representation based membership inference attack. Unlike prior attacks against GANs which focus on the overall metrics, such as the attack accuracy, our attack aims to make inference from the high-precision perspective, which allows the adversary to concentrate on inferring a sample as a member confidently. Initial experimental results demonstrate that the adversary can achieve a high precision attack even if the overall attack accuracy is about 50% for a well-trained GAN model. Our work will raise awareness of the importance of precision when GAN owners evaluate the privacy risks of their models.
TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized Tabular Data Releasing

Generative Adversarial Networks (GAN)-synthesized table publishing lets people privately learn insights without access to the private table. However, existing studies on Membership Inference (MI) Attacks show promising results on disclosing membership of training datasets of GAN-synthesized tables. Different from those works focusing on discovering membership of a given data point, in this paper, we propose a novel Membership Collision Attack against GANs (TableGAN-MCA), which allows an adversary given only synthetic entries randomly sampled from a black-box generator to recover partial GAN training data. Namely, a GAN-synthesized table immune to state-of-the-art MI attacks is vulnerable to the TableGAN-MCA. The success of TableGAN-MCA is boosted by an observation that GAN-synthesized tables potentially collide with the training data of the generator.
Augmented Reality Based Machine Monitoring for Legacy Machines: a retrofitting use case

Monitoring a machine and the insight it provides for appropriate maintenance is of prime importance for the modern industry. While this is fully supported in Industry 4.0, many manufacturing units today are unable to use its technologies. One of the major reasons behind that is the lack of onboard sensors to capture and communicate appropriate data from a legacy machine. Retrofitting is considered an efficient solution to include legacy machines in an industry 4.0 ecosystem by adding external sensing and connectivity. As a proof of concept, in this paper we consider an induction motor for retrofitting, a major component of any manufacturing unit. We further show how retrofitting can help in making legacy machines compliant to the new technologies from Industry 4.0. Finally, visualization of the collected data is performed over a web based Augmented Reality application. The choice for a web-based Augmented Reality application was made to enable monitoring of the sensor data over a multitude of devices.
Epoque: Practical End-to-End Verifiable Post-Quantum-Secure E-Voting

The ultimate goal in modern secure e-voting is to enable everyone to verify whether the final election result correctly reflects the votes chosen by the (human) voters, without exposing how each individual voted. These fundamental security properties are called end-to-end verifiability and voter privacy. Unfortunately, it turns out to be very challenging to pursue these properties simultaneously, especially when the latter must be future-proofed against the rise of quantum computers. In this work, we show, for the first time, a practical approach to do this. We present Epoque, the first end-to-end verifiable, voter-private, post-quantum-secure homomorphic e-voting protocol. It achieves its properties through the combination of practical lattice-based cryptographic primitives only, in a novel way. We formally prove all our security claims under common trust and hardness assumptions. At the core of Epoque lies an efficient identity-based encryption (IBE) scheme with blazingly fast master-key decryption. It is the component that makes the efficient tallying of thousands or millions of ballots a practical possibility. In order to demonstrate its practicality, we fully implemented it and provide detailed benchmarks; we believe this latter contribution is of independent interest beyond the specific e-voting application.
Digital Educational Platforms: Advantages and Disadvantages

The relevance of this article is justified by the authors due to the importance of digitalization of the educational process in the modern world. Digital platforms take a significant position in the issues of digitalization not only in the educational environment, but also in many other areas of human activity. Therefore, it was decided to analyze the main advantages and disadvantages of using digital educational platforms. The purpose of the article is to study the classification of existing digital educational platforms and reflect their advantages and disadvantages. The study uses classical general scientific research methods: analysis and synthesis of information; comparison; grouping and generalization. The authors suggest three classification features of digital educational platforms. It is concluded that digital educational platforms have their advantages and disadvantages, while only what is more important for a particular person remains significant.
Amortizing Rate-1 OT and Applications to PIR and PSI

Recent new constructions of rate-1 OT [Döttling, Garg, Ishai, Malavolta, Mour, and Ostrovsky, CRYPTO 2019] have brought this primitive under the spotlight and the techniques have led to new feasibility results for private-information retrieval, and homomorphic encryption for branching programs. The receiver communication of this construction consists of a quadratic (in the sender's input size) number of group elements for a single instance of rate-1 OT. Recently [Garg, Hajiabadi, Ostrovsky, TCC 2020] improved the receiver communication to a linear number of group elements for a single string-OT. However, most applications of rate-1 OT require executing it multiple times, resulting in large communication costs for the receiver.