Reviewerly/exHarmony huggingface dataset:
Threat for the Secure Remote Password Protocol and a Leak in Apple’s Cryptographic Library

The Secure Remote Password protocol is a password-based authenticated key-exchange between two parties. One advantage is to prevent offline dictionary attacks from an adversary eavesdropping the communication. We present how such an attack is feasible if the modular exponentiation at the heart of the protocol is vulnerable and leaks some data related to the password.In the case of a fixed exponent, adding randomness during the execution is a classical protection mechanism, and such a mechanism is present in Apple’s cryptographic library to randomize the exponent. Despite being intended to protect against complex side-channel attacks, we show that its usage makes the implementation vulnerable to simple side-channels such as power analysis.This leakage observed in the library is mild but is useful for the attack we propose on the Secure Remote Password protocol.
EMPLOYEE JOB SATISFACTION AND ITS ANTECEDENTS

Employee job satisfaction is a very crucial area formanager in this day and age as far as managing their employees is concerned. Employee job satisfaction is a term to determine whether employees are happy and fulfilling their needs and aspirations in the organization. Essentially employee satisfaction is a gauge of the degree of worker‘s happiness with their job and working environment.Many measures establish that employee satisfaction is necessary for employee motivation, higher performance andbetter employee self-esteem in the organization. In this paper different variables accountable for employee job satisfaction has been identified and an attempt has been made to shed some light on the differences in variables among the studies. This study reveals that commonly acknowledged antecedents to build employee job satisfaction are salary, relationship with coworkers, relationship with supervisor, training and development, working environment, motivation, appreciation etc. In the end, the conceptual framework of this research is also presented.
PRISMA Hyperspectral &amp;#8211; First insights in the performance in urban surface cover and coastal seascape analysis

&amp;lt;p&amp;gt;The characterization of the Earth&amp;amp;#8217;s surface cover based on predefined classes is among the fundamental activities in the domain of satellite image analysis image since the early 70s. It was the joint NASA/ U.S. Geological Survey Landsat series of Earth Observation satellites that start to continuously acquired images of the Earth's land surface, providing uninterrupted data to help land managers and policymakers make informed decisions about natural resources and the environment. However, in 2020, the collected data even if are of continuous flow in terms volume of terrabytes per day from various optical and radar systems, are limited in terms of spectral resolution since almost all sensors are limited to a maximum of 25 spectral channels in the visible, near-and-shortwave-and-thermal infrared spectrum. The need of denser spectral information has been highlighted in early 80s and the first satellite-based hyperspectral sensor, AVIRIS, start to provide data allowing the extraction information on material composition and precise surface cover information. Since then few attempt appear but more are undergoing for launching. In 2019, the Italian Space Agency launch the PRISMA hyperspectral satellite which collect spectral data in the 400-2500nm spectrum; in total 250 spectral channels with a spectral width of ~ 12nm, at 30m pixel size. Here we present first results of the use of Level 2D PRISMA hyperspectral data in mapping the surface characteristics of the urban and periurban area of Heraklion city along with the coastal zone of the urban front aiming at the simultaneous creation of a land-and-coastal cover map along with the extraction of coastal bathymetry information using artificial intelligence approaches within open access platforms. The use of hyperspectral information allow the separation of urban surfaces based on material signatures, while the availability of dense spectral information in the blue-green spectrum allow the more accurate retrieval of coastal seascape characteristics. It is envisaged that hyperspectral missions soon to be the normal in Earth Observation, allowing the accurate creation of geospatial information for further use in several applications.&amp;lt;/p&amp;gt;
Federated Learning with Spiking Neural Networks

As neural networks get widespread adoption in resource-constrained embedded devices, there is a growing need for low-power neural systems. Spiking Neural Networks (SNNs)are emerging to be an energy-efficient alternative to the traditional Artificial Neural Networks (ANNs) which are known to be computationally intensive. From an application perspective, as federated learning involves multiple energy-constrained devices, there is a huge scope to leverage energy efficiency provided by SNNs. Despite its importance, there has been little attention on training SNNs on a large-scale distributed system like federated learning. In this paper, we bring SNNs to a more realistic federated learning scenario. Specifically, we propose a federated learning framework for decentralized and privacy-preserving training of SNNs. To validate the proposed federated learning framework, we experimentally evaluate the advantages of SNNs on various aspects of federated learning with CIFAR10 and CIFAR100 benchmarks. We observe that SNNs outperform ANNs in terms of overall accuracy by over 15% when the data is distributed across a large number of clients in the federation while providing up to5.3x energy efficiency. In addition to efficiency, we also analyze the sensitivity of the proposed federated SNN framework to data distribution among the clients, stragglers, and gradient noise and perform a comprehensive comparison with ANNs.
Gesture Based Real-time Indian Sign Language Interpreter

Hand gesture is one of the methods used in sign language for non-verbal communication. It is most commonly used by hearing &amp; speech impaired people who have hearing or speech problems to communicate among themselves or with normal people. Developing sign language applications for hearing impaired people can be very important, as hearing &amp; speech impaired people will be able to communicate easily with even those who don’t understand sign language. This project aims at taking the basic step in bridging the communication gap between normal people, deaf and dumb people using sign language. The main focus of this work is to create a vision based system to identify sign language gestures from the video sequences. The reason for choosing a system based on vision relates to the fact that it provides a simpler and more intuitive way of communication between a human and a computer. Video sequences contain both temporal as well as spatial features. In this project, two different models are used to train the temporal as well as spatial features. To train the model on the spatial features of the video sequences a deep Convolutional Neural Network. Convolutional Neural Network was trained on the frames obtained from the video sequences of train data. To train the model on the temporal features Recurrent Neural Network is used. The Trained Convolutional Neural Network model was used to make predictions for individual frames to obtain a sequence of predictions. Now this sequence of prediction outputs was given to the Recurrent Neural Network to train on the temporal features. Collectively both the trained models i.e. Convolutional Neural Network and Recurrent Neural Network will produce the text output of the respective gesture.
Hybrid Data Decomposition-Based Deep Learning for Bitcoin Prediction and Algorithm Trading

Although Bitcoin has attracted significant attention from investors and policy makers, the empirical works in the Bitcoin forecasting and trading support systems are still at an early stage. This study proposes a novel data decomposition based hybrid bidirectional deep learning model, namely VMD-LMH-BiGRU, in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market. Two main steps are involved in our methodology framework, i.e., data-decomposition for inner factors extraction, and bidirectional deep learning for forecasting the Bitcoin price. The results demonstrate that the proposed model outperforms four other benchmark models, including econometric models, machine learning models and deep learning models. Furthermore, the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in the trading simulation. The robustness of the model is verified through multiple forecasting periods and testing intervals.
Evaluating Cloud Auto-Scaler Resource Allocation Planning Under High-Performance Computing Workloads

In this paper, we analyze the effects of changing the workload mix and adjusting the average job runtime (as represented by the service rate) between one representative of high-performance computing workloads and one representative of web serving workloads on the performance of three cloud auto-scalers from the literature in an effort to better model the behavior of cloud auto-scalers under high-performance computing scenarios involving long-running jobs rather than short-lived jobs as in previous studies. The simulation and analysis was carried out using the Performance Evaluation framework for Auto-Scaling (PEAS), a cloud simulator framework for modeling the performance of cloud auto-scaling algorithms using scenario theory.
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation

Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire. This paper describes Diffusion-Decoding models with Contrastive representations (D2C), a paradigm for training unconditional variational autoencoders (VAEs) for few-shot conditional image generation. D2C uses a learned diffusion-based prior over the latent representations to improve generation and contrastive self-supervised learning to improve representation quality. D2C can adapt to novel generation tasks conditioned on labels or manipulation constraints, by learning from as few as 100 labeled examples. On conditional generation from new labels, D2C achieves superior performance over state-of-the-art VAEs and diffusion models. On conditional image manipulation, D2C generations are two orders of magnitude faster to produce over StyleGAN2 ones and are preferred by 50% - 60% of the human evaluators in a double-blind study.
The Effect of Millennial Transformational Leadership on IT Capability, Organizational Agility and Organizational Performance in the Pandemic Era: an Empirical Evidence of Fishery Startups in Indonesia

This study aims to understand: Does millennial transformational leadership influence organizational performance, organizational agility and IT capability? Does IT capability influence organizational agility? Does organizational agility influence organizational performance? The population of this study included selected fishery startups in Indonesia based on the data released by the Ministry of Marine Affairs and Fisheries of Republic of Indonesia and Digital Fishery Network. This study used the statistical method of PLS-SEM to analyze the data. The findings show that there is a positive relationship between millennial transformational leadership and organizational agility, millennial transformational leadership and IT capability, IT capability and organizational agility, and organizational agility and organizational performance. Future researches are suggested to examine the effects of several mediating variables in the proposed model.
Theoretical basis of mobile learning and use of mobile platforms

The article describes mobile education, pedagogical foundations of mobile education, development history, mobile platforms and their analysis
Aesthetic ideal and aesthetic criterion

The question of whether aesthetics determines the criteria of art, artistic creation, or when creating laws, new criteria, norms, aesthetics follows art has always worried thinkers. The theorist of classicism Boileau, from his point of view, recognized aesthetics as a science that presents its canons and laws to the artist, while others have shown that art is primary in the emergence of aesthetic norms. Of course, in the creative process it is impossible to adhere to only a scheme, a formula, but this does not mean that there are no normative foundations in aesthetics. In our opinion, in art and the creative process, adhering to aesthetic laws, norms and criteria, first of all, means creating artistry in works of art and ensuring that the works meet the highest aesthetic requirements. Thus, aesthetic norms are recognized boundaries, the crossing of which leads to a violation of the aesthetic level and perception. The ancient Greeks denoted disorderly and ugly by the concept of Chaos, order, harmony, harmony of the universe and the ideal were denoted by the word Cosmos. Where the lawful principle of beauty - the aesthetic norm - is preserved, there will always be beauty, while the ugly appears on the basis of a violation of aesthetic norms. For the art form in a work of art to become beautiful, order, high organization, perfect harmony, color, plastic and verbal construction are necessary. In the works of the classics, the logical development of the content requires harmony, balance of composition, a clear image, an accurate representation of volume, generalization of lines. Today, aesthetic criteria and norms play an important role not only in art, in all types of artistic creation, and even in the beauty industry, sports and medicine.
Comparing Test Sets with Item Response Theory

Recent years have seen numerous NLP datasets introduced to evaluate the performance of fine-tuned models on natural language understanding tasks. Recent results from large pretrained models, though, show that many of these datasets are largely saturated and unlikely to be able to detect further progress. What kind of datasets are still effective at discriminating among strong models, and what kind of datasets should we expect to be able to detect future improvements? To measure this uniformly across datasets, we draw on Item Response Theory and evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. We find that Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models, while SNLI, MNLI, and CommitmentBank seem to be saturated for current strong models. We also observe span selection task format, which is used for QA datasets like QAMR or SQuAD2.0, is effective in differentiating between strong and weak models.
Defending Against Backdoor Attacks in Natural Language Generation

The frustratingly fragile nature of neural network models make current natural language generation (NLG) systems prone to backdoor attacks and generate malicious sequences that could be sexist or offensive. Unfortunately, little effort has been invested to how backdoor attacks can affect current NLG models and how to defend against these attacks. In this work, by giving a formal definition of backdoor attack and defense, we investigate this problem on two important NLG tasks, machine translation and dialog generation. Tailored to the inherent nature of NLG models (e.g., producing a sequence of coherent words given contexts), we design defending strategies against attacks. We find that testing the backward probability of generating sources given targets yields effective defense performance against all different types of attacks, and is able to handle the {\it one-to-many} issue in many NLG tasks such as dialog generation. We hope that this work can raise the awareness of backdoor risks concealed in deep NLG systems and inspire more future work (both attack and defense) towards this direction.
Improved Algorithms for Agnostic Pool-based Active Classification

We consider active learning for binary classification in the agnostic pool-based setting. The vast majority of works in active learning in the agnostic setting are inspired by the CAL algorithm where each query is uniformly sampled from the disagreement region of the current version space. The sample complexity of such algorithms is described by a quantity known as the disagreement coefficient which captures both the geometry of the hypothesis space as well as the underlying probability space. To date, the disagreement coefficient has been justified by minimax lower bounds only, leaving the door open for superior instance dependent sample complexities. In this work we propose an algorithm that, in contrast to uniform sampling over the disagreement region, solves an experimental design problem to determine a distribution over examples from which to request labels. We show that the new approach achieves sample complexity bounds that are never worse than the best disagreement coefficient-based bounds, but in specific cases can be dramatically smaller. From a practical perspective, the proposed algorithm requires no hyperparameters to tune (e.g., to control the aggressiveness of sampling), and is computationally efficient by means of assuming access to an empirical risk minimization oracle (without any constraints). Empirically, we demonstrate that our algorithm is superior to state of the art agnostic active learning algorithms on image classification datasets.
Performance Improvement on <i>k</i>²-Raster Compact Data Structure for Hyperspectral Scenes

This letter proposes methods to improve data size and access time for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k^{2}$ </tex-math></inline-formula> -raster, a losslessly compressed data structure that provides efficient storage and real-time processing. Hyperspectral scenes from real missions are used as our testing data. In previous studies, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k^{2}$ </tex-math></inline-formula> -raster, the size of the hyperspectral data was reduced by up to 52% compared with the uncompressed data. In this letter, we continue to explore novel ways of further reducing the data size and access time. First, we examine the possibility of using the raster matrix of hyperspectral data without any padding (unpadded matrix) while still being able to compress the structure and access the data. Second, we examine some integer encoders, more specifically the Simple family. We discuss their ability to provide random element access and compare them with directly addressable codes (DACs), the integer encoder used in the original description for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k^{2}$ </tex-math></inline-formula> -raster. Experiments show that the use of unpadded matrices has improved the storage size up to 6% while the use of a different integer encoder reduces the storage size up to 6% and element access time up to 20%.
Using Blockchain to Secure IoT

Blockchain is an emerging technology that can radically improve transactions security at banking, supply chain, and other transaction networks. It's estimated that Blockchain will generate $3.1 trillion in new business value by 2030. Essentially, it provides the basis for a dynamic distributed ledger that can be applied to save time when recording transactions between parties, remove costs associated with intermediaries, and reduce risks of fraud and tampering. This book explores the fundamentals and applications of Blockchain technology. Readers will learn about the decentralized peer-to-peer network, distributed ledger, and the trust model that defines Blockchain technology. They will also be introduced to the basic components of Blockchain (transaction, block, block header, and the chain), its operations (hashing, verification, validation, and consensus model), underlying algorithms, and essentials of trust (hard fork and soft fork). Private and public Blockchain networks similar to Bitcoin and Ethereum will be introduced, as will concepts of Smart Contracts, Proof of Work and Proof of Stack, and cryptocurrency including Facebook's Libra will be elucidated. Also, the book will address the relationship between Blockchain technology, Internet of Things (IoT), Artificial Intelligence (AI), Cybersecurity, Digital Transformation and Quantum Computing. Readers will understand the inner workings and applications of this disruptive technology and its potential impact on all aspects of the business world and society. A look at the future trends of Blockchain Technology will be presented in the book.
Dual Prototype Relaxation for Generalized Zero Shot Learning

Generalized Zero Shot Learning (GZSL) is proposed to solve the training data missing problem by transferring the knowledge learned in seen classes to unseen classes. Many methods project the visual features into semantic space and find their nearest neighbours among the pre-defined attributes, which has achieved significant success. However, there are two problems involved in this type of methods, one is that the projection is a many-to-one mapping, which cannot maintain the diversity of features in semantic space, the other is that searching within all classes cannot well utilize the knowledge learned in the seen classes. In this paper, we propose a novel method named Dual Prototype Relaxation (DPR) by relaxing the projection from many-to-one to many-to-many. Specifically, we add noise to the semantic prototype in response to the projection of multiple features within a class, and reconstruct the visual features with the same relaxed prototype. Besides, in order to make better use of the knowledge learned in the seen classes, an Out-of-Domain (OoD) based method is employed to first classify the feature to seen or unseen domains, and then the same DPR model is applied to recognize its category within each domain. Extensive experiments on four popular datasets are conducted and the results show that our method can outperform many linear and deep state-of-the-art methods although our method is a linear one.
Identification of profession &amp; occupation in Health-related Social Media using tweets in Spanish

In this paper we present our approach and system description on Task 7a in ProfNer-ST: Identification of profession & occupation in Health related Social Media. Our main contribution is to show the effectiveness of using BETO-Spanish BERT as a model based on transformers pretrained with a Spanish Corpus for classification tasks. In our experiments we compared several architectures based on transformers with others based on classical machine learning algorithms. With this approach, we achieved an F1-score of 0.92 in the evaluation process.
Secret key extraction in direct reconciliation CV-QKD systems

Quantum Key Distribution (QKD) is, nowadays, the dominant practical application in the quantum technologies field. Nevertheless, extracting secure keys from a physical implementation of a QKD system remains a complicated procedure. In this paper, post-processing algorithms were implemented to extract secret keys from a Continuous-Variable QKD (CV-QKD) system. The Discrete Modulated Four State CV-QKD protocol was considered due to lower complexity in the reconciliation procedure. The secret keys were extracted considering Maximum-Likelihood Estimators (MLEs) for parameter estimation, Iterative Low-Density Parity Check (LDPC) codes complemented with the Sum-Product Algorithm and error groups iterative reconciliation for information reconciliation, and the Toeplitz matrix as the universal hash function for privacy amplification. The post-processing algorithms were applied to extract secret keys from a simulated implementation of the Discrete Modulated Four State CV-QKD protocol, with double homodyne detection.