Reviewerly/exHarmony huggingface dataset:
Noise-enhanced spatial-photonic Ising machine

Abstract Ising machines are novel computing devices for the energy minimization of Ising models. These combinatorial optimization problems are of paramount importance for science and technology, but remain difficult to tackle on large scale by conventional electronics. Recently, various photonics-based Ising machines demonstrated fast computing of a Ising ground state by data processing through multiple temporal or spatial optical channels. Experimental noise acts as a detrimental effect in many of these devices. On the contrary, here we demonstrate that an optimal noise level enhances the performance of spatial-photonic Ising machines on frustrated spin problems. By controlling the error rate at the detection, we introduce a noisy-feedback mechanism in an Ising machine based on spatial light modulation. We investigate the device performance on systems with hundreds of individually-addressable spins with all-to-all couplings and we found an increased success probability at a specific noise level. The optimal noise amplitude depends on graph properties and size, thus indicating an additional tunable parameter helpful in exploring complex energy landscapes and in avoiding getting stuck in local minima. Our experimental results identify noise as a potentially valuable resource for optical computing. This concept, which also holds in different nanophotonic neural networks, may be crucial in developing novel hardware with optics-enabled parallel architecture for large-scale optimizations.
Evaluating NLP Models via Contrast Sets

Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities. We propose a new annotation paradigm for NLP that helps to close systematic gaps in the test data. In particular, after a dataset is constructed, we recommend that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Contrast sets provide a local view of a model's decision boundary, which can be used to more accurately evaluate a model's true linguistic capabilities. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets (e.g., DROP reading comprehension, UD parsing, IMDb sentiment analysis). Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets---up to 25\% in some cases. We release our contrast sets as new evaluation benchmarks and encourage future dataset construction efforts to follow similar annotation processes.
A Survey on Temporal Action Localization

Temporal action localization is one of the most crucial and challenging problems for video understanding in computer vision. It has received a lot of attention in recent years because of the extensive application of daily life. Temporal action localization has made some significant progress, especially with the development of deep learning recently. And more demand is for temporal action localization in untrimmed videos. In this paper, our target is to survey the state-of–the-art techniques and models for video temporal action localization. It mainly includes the related techniques, some benchmark datasets and the evaluation metrics of temporal action localization. In addition, we summarize temporal action localization from two aspects: fully-supervised learning and weakly-supervised learning. And we list several representative works and compare their performances respectively. Finally, we make some deep analysis and propose potential research directions, and conclude the survey.
Cyberbullying Detection using Pre-Trained BERT Model

Cyberbullying is spread across various social media platforms. It is a wrong deed in which the victim is harassed by receiving the derogatory / provocative / sensitive images or text messages by the bully. Detection of such message/post in such large platforms is very difficult and may sometimes lead to false detection. Recently, deep neural network based models have shown significant improvement over traditional models in detecting cyberbullying. Also, new and more complex deep learning architectures are being developed which are proving to be useful in various NLP tasks. Google researchers has recently developed a language learning model called BERT, which is capable of generating contextual embeddings and is also able to produce task specific embeddings for classification. A new approach is proposed to cyberbullying detection in social media platforms by using the novel pre-trained BERT model with a single linear neural network layer on top as a classifier, which improves over the existing results. The model is trained and evaluated on two social media datasets of which one dataset is small size and the second dataset is relatively larger size.
Opportunities for integrated photonic neural networks

Abstract Photonics offers exciting opportunities for neuromorphic computing. This paper specifically reviews the prospects of integrated optical solutions for accelerating inference and training of artificial neural networks. Calculating the synaptic function, thereof, is computationally very expensive and does not scale well on state-of-the-art computing platforms. Analog signal processing, using linear and nonlinear properties of integrated optical devices, offers a path toward substantially improving performance and power efficiency of these artificial intelligence workloads. The ability of integrated photonics to operate at very high speeds opens opportunities for time-critical real-time applications, while chip-level integration paves the way to cost-effective manufacturing and assembly.
GREEK-BERT: The Greeks visiting Sesame Street

Transformer-based language models, such as BERT and its variants, have achieved state-of-the-art performance in several downstream natural language processing (NLP) tasks on generic benchmark datasets (e.g., GLUE, SQUAD, RACE). However, these models have mostly been applied to the resource-rich English language. In this paper, we present GREEK-BERT, a monolingual BERT -based language model for modern Greek. We evaluate its performance in three NLP tasks, i.e., part-of-speech tagging, named entity recognition, and natural language inference, obtaining state-of-the-art performance. Interestingly, in two of the benchmarks GREEK-BERT outperforms two multilingual Transformer-based models (M-BERT, XLM-R), as well as shallower neural baselines operating on pre-trained word embeddings, by a large margin (5%-10%). Most importantly, we make both GREEK-BERT and our training code publicly available, along with code illustrating how GREEK-BERT can be fine-tuned for downstream NLP tasks. We expect these resources to boost NLP research and applications for modern Greek.
Robust Image Encryption With Scanning Technology, the El-Gamal Algorithm and Chaos Theory

Digital images are the most frequently used signals to convey information in the internet era. The security of these images is the primary concern in rapidly changing networked environments. In this research, we present a novel approach to secure images by integrating a scanning technique, the El-Gamal public key cryptosystem and chaotic systems. In brief, zigzag and spiral scanning are used first to construct a permuted image. Then, the El-Gamal encryption algorithm is exploited to encrypt the permuted image. Finally, Lorenz and Rössler chaotic sequences are utilized to scramble the pixel locations in the confusion and diffusion stages. This last step that mixes two stages can fortify the entire security performance and enlarge the key size. Exhaustive analysis has been carried out on the SIPI (signal and image processing institute) dataset to assess the efficiency and security of the proposed method. Numerical and visual results indicate the capability of the proposed image cryptosystem to protect images against several known attacks. In addition, the comparative analysis results indicate that the proposed approach outperforms the compared approaches in terms of the visual quality and security criteria.
Transformation of Higher Education During the Pandemic: Pain Points

The relevance of the study is due to the contradiction between the mass transition of the Russian system of higher education to the distance learning format and the insufficient willingness of participants in the educational process to work under the new requirements caused by the COVID 19 pandemic. Teachers, students and administrators of higher educational institutions in Russia have encountered a number of difficulties. Their analysis will highlight the objective and subjective factors of their occurrence and develop recommendations for the further organization of distance learning in its combination with the traditional format. The purpose of the study was to analyze the modern work experience in the remote format of one of the leading Russian universities – Kazan Federal University. On this basis, the article analyzes successes and problems of this process, shows the contradictions between traditional and distance learning, considers the main models of educational organization that involve traditional and remote learning formats using distance educational technologies. It is concluded that in the system of basic formal education at all levels and areas of training, distance learning can be considered as a form that complements and strengthens the social, pedagogical, organizational, psychological, and didactic potential of the traditional (face-to-face) educational format. The work comprehensively used the methods of theoretical (analysis, synthesis, systematization, comparison, etc.) and empirical (study of literature, conversation, questioning, observation) research.
Driven-Dissipative Quantum Kerr Resonators: New Exact Solutions, Photon Blockade and Quantum Bistability

A new approach to describing the interplay between quantum mechanics, nonequilibrium driving, and dissipation could enable a paradigm shift in how bosonic systems are used in quantum-based technologies.
Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols

Research on temporal knowledge bases, which associate a relational fact (s, r, o) with a validity time period (or time instant), is in its early days.Our work considers predicting missing entities (link prediction) and missing time intervals (time prediction) as joint Temporal Knowledge Base Completion (TKBC) tasks, and presents TIMEPLEX, a novel TKBC method, in which entities, relations and, time are all embedded in a uniform, compatible space.TIMEPLEX exploits the recurrent nature of some facts/events and temporal interactions between pairs of relations, yielding stateof-the-art results on both prediction tasks.We also find that existing TKBC models heavily overestimate link prediction performance due to imperfect evaluation mechanisms.In response, we propose improved TKBC evaluation protocols for both link and time prediction tasks, dealing with subtle issues that arise from the partial overlap of time intervals in gold instances and system predictions.
Applying the Quantum Approximate Optimization Algorithm to the Tail-Assignment Problem

Airlines today are faced with a number of large-scale scheduling problems. One such problem is the tail-assignment problem, which is the task of assigning individual aircraft to a given set of flights, minimizing the overall cost. Each aircraft is identified by the registration number on its tail fin. In this paper, we simulate the quantum approximate optimization algorithm (QAOA) applied to instances of this problem derived from real-world data. The QAOA is a variational hybrid quantum-classical algorithm recently introduced and likely to run on near-term quantum devices. The instances are reduced to fit on quantum devices with 8, 15, and 25 qubits. The reduction procedure leaves only one feasible solution per instance, which allows us to map the tail-assignment problem onto the exact-cover problem. We find that repeated runs of the QAOA identify the feasible solution with close to unit probability for all instances. Furthermore, we observe patterns in the variational parameters such that an interpolation strategy can be employed, which significantly simplifies the classical optimization part of the QAOA. Finally, we empirically find a relation between the connectivity of the problem graph and the single-shot success probability of the algorithm.
Chain FL: Decentralized Federated Machine Learning via Blockchain

Federated learning is a collaborative machine learning mechanism that allows multiple parties to develop a model without sharing the training data. It is a promising mechanism since it empowers collaboration in fields such as medicine and banking where data sharing is not favorable due to legal, technical, ethical, or safety issues without significantly sacrificing accuracy. In centralized federated learning, there is a single central server, and hence it has a single point of failure. Unlike centralized federated learning, decentralized federated learning does not depend on a single central server for the updates. In this paper, we propose a decentralized federated learning approach named Chain FL that makes use of the blockchain to delegate the responsibility of storing the model to the nodes on the network instead of a centralized server. Chain FL produced promising results on the MNIST digit recognition task with a maximum 0.20% accuracy decrease, and on the CIFAR-10 image classification task with a maximum of 2.57% accuracy decrease as compared to non-FL counterparts.
Differentially private synthetic medical data generation using convolutional GANs

Deep learning models have demonstrated superior performance in several real-world application problems such as image classification and speech processing. However, creating these models in sensitive domains like healthcare typically requires addressing certain privacy challenges that bring unique concerns. One effective way to handle such private data concerns is to generate realistic synthetic data that can provide practically acceptable data quality as well as be used to improve model performance. To tackle this challenge, we develop a differentially private framework for synthetic data generation using Rényi differential privacy. Our approach builds on convolutional autoencoders and convolutional generative adversarial networks to preserve critical characteristics of the generated synthetic data. In addition, our model can capture the temporal information and feature correlations present in the original data. We demonstrate that our model outperforms existing state-of-the-art models under the same privacy budget using several publicly available benchmark medical datasets in both supervised and unsupervised settings. The source code of this work is available at https://github.com/astorfi/differentially-private-cgan.
A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment

With the increasing of the scale of task or request and dynamic nature of cloud resources, it gives significant issues of load balancing, resource utilization, task allocation, and system performance and so on. To solve those problems many researchers have applied different types of scheduling techniques. But meta-heuristic scheduling is the most accomplish preferred outcomes over conventional heuristics and hybrid scheduling. Among various meta-heuristics algorithms, PSO is a famous metaheuristic technique to solved optimization issue. PSO is appropriate for dynamic task scheduling, workflow scheduling and load balancing. PSO has a strong worldwide searching capability toward the start of the run and a nearby pursuit close to the furthest limit of the run. Therefore, it has been generally utilized in different applications and has made incredible progress. In this paper a systematically reviews is done on different types of particle swarm optimization (PSO) based scheduling strategy with set of challenges and future direction.
Free-space optical neural network based on thermal atomic nonlinearity

As artificial neural networks (ANNs) continue to make strides in wide-ranging and diverse fields of technology, the search for more efficient hardware implementations beyond conventional electronics is gaining traction. In particular, optical implementations potentially offer extraordinary gains in terms of speed and reduced energy consumption due to intrinsic parallelism of free-space optics. At the same time, a physical nonlinearity, a crucial ingredient of an ANN, is not easy to realize in free-space optics, which restricts the potential of this platform. This problem is further exacerbated by the need to perform the nonlinear activation also in parallel for each data point to preserve the benefit of linear free-space optics. Here, we present a free-space optical ANN with diffraction-based linear weight summation and nonlinear activation enabled by the saturable absorption of thermal atoms. We demonstrate, via both simulation and experiment, image classification of handwritten digits using only a single layer and observed 6-percent improvement in classification accuracy due to the optical nonlinearity compared to a linear model. Our platform preserves the massive parallelism of free-space optics even with physical nonlinearity, and thus opens the way for novel designs and wider deployment of optical ANNs.
The use of mobile Internet devices in the formation of ICT component of bachelors in electromechanics competency in modeling of technical objects

Computer simulation of technical objects and processes is one of the components of the system of professional training of a modern electromechanics engineer. It has been established that despite the fact that mobile Internet devices (MID) are actively used by electrical engineers, the methods of using them in the process of bachelor in electromechanics training is considered only in some domestic scientific studies. The article highlights the components of the methods of using MID in the formation of the ICT component of the competence of the bachelor in electromechanics in modeling of technical objects, providing for students to acquire basic knowledge in the field of Computer Science and modern ICT and skills to use programming systems, math packages, subroutine libraries, and the like. For processing tabular data, it is proposed to use various freely distributed tools that do not significantly differ in functionality, such as Google Sheets, Microsoft Excel, for processing text data – QuickEdit Text Editor, Google Docs, Microsoft Word. For 3D-modeling and viewing the design and technological documentation, the proposed comprehensive use of Autodesk tools in the training process.
A Comprehensive Study on Deep Learning-Based Methods for Sign Language Recognition

In this paper, a comparative experimental assessment of computer vision-based methods for sign language recognition is conducted. By implementing the most recent deep neural network methods in this field, a thorough evaluation on multiple publicly available datasets is performed. The aim of the present study is to provide insights on sign language recognition, focusing on mapping non-segmented video streams to glosses. For this task, two new sequence training criteria, known from the fields of speech and scene text recognition, are introduced. Furthermore, a plethora of pretraining schemes is thoroughly discussed. Finally, a new RGB+D dataset for the Greek sign language is created. To the best of our knowledge, this is the first sign language dataset where sentence and gloss level annotations are provided for a video capture.
Implementing decentralized auctions using blockchain smart contracts

Rapid advances in ecommerce and changing consumer preferences have contributed to the growing popularity of online auctioning platforms such as eBay. Online platforms provide some advantages to consumers such as product variety, deals on prices and mitigate geographical barriers. Nevertheless, existing auction platforms are centralized and depend on third-party intermediaries for transaction settlement. As a result, online platforms raise critical concerns regarding data integrity, security, transparency, and traceability as potential bidders have to trust the organizer for legitimacy of bids. Thus, adopting a decentralized approach using blockchain technology can transform the auction process, eliminating intermediaries, ensure transparency and reduce transaction costs. In this paper, we propose a general framework for decentralized auctions leveraging (i) Ethereum smart contracts to trace and track bids, (ii) decentralized storage systems to upload documents related to bidding and (iii) trusted timer oracles that act as gateway between smart contract and external data feeds. In the proposed solution, we develop detailed algorithms that define the working principles of the Smart contract for the auction process. We present detailed cost analysis of the solution to demonstrate economic feasibility, providing a secure, transparent and reliable approach to online auctions.
Graph Structure Estimation Neural Networks

Graph Neural Networks (GNNs) have drawn considerable attention in recent years and achieved outstanding performance in many tasks. Most empirical studies of GNNs assume that the observed graph represents a complete and accurate picture of node relationship. However, this fundamental assumption cannot always be satisfied, since the real-world graphs from complex systems are error-prone and may not be compatible with the properties of GNNs. Therefore, GNNs solely relying on original graph may cause unsatisfactory results, one typical example of which is that GNNs perform well on graphs with homophily while fail on the disassortative situation. In this paper, we propose graph estimation neural networks GEN, which estimates graph structure for GNNs. Specifically, our GEN presents a structure model to fit the mechanism of GNNs by generating graphs with community structure, and an observation model that injects multifaceted observations into calculating the posterior distribution of graphs and is the first to incorporate multi-order neighborhood information. With above two models, the estimation of graph is implemented based on Bayesian inference to maximize the posterior probability, which attains mutual optimization with GNN parameters in an iterative framework. To comprehensively evaluate the performance of GEN, we perform a set of experiments on several benchmark datasets with different homophily and a synthetic dataset, where the experimental results demonstrate the effectiveness of our GEN and rationality of the estimated graph.
Augmented Reality in Industry 4.0 and Future Innovation Programs

Augmented Reality (AR) is worldwide recognized as one of the leading technologies of the 21st century and one of the pillars of the new industrial revolution envisaged by the Industry 4.0 international program. Several papers describe, in detail, specific applications of Augmented Reality developed to test its potentiality in a variety of fields. However, there is a lack of sources detailing the current limits of this technology in the event of its introduction in a real working environment where everyday tasks could be carried out by operators using an AR-based approach. A literature analysis to detect AR strength and weakness has been carried out, and a set of case studies has been implemented by authors to find the limits of current AR technologies in industrial applications outside the laboratory-protected environment. The outcome of this paper is that, even though Augmented Reality is a well-consolidated computer graphic technique in research applications, several improvements both from a software and hardware point of view should be introduced before its introduction in industrial operations. The originality of this paper lies in the detection of guidelines to improve the Augmented Reality potentialities in factories and industries.