Reviewerly/exHarmony huggingface dataset:
A Computer Security System for Cloud Computing Based on Encryption Technique

In recent years, progressively data proprietors have embraced cloud storage service, by which they will subcontract their data to the cloud server to significantly reduce the local storage overhead, due to the rapid growth in the cloud computing market and development. Cloud computing is the delivery of hosting services that are provided to clients over the web. It is quite common, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that may be rapidly provisioned and released with minimal management effort or service provider interaction. Sensitive information on the cloud is developing unexpectedly and bringing up several challenges and massive security concerns of the modern-day world. The cloud data and services reside in massively scalable data centers and may be accessed ubiquitously. Some issues concern in accessing this data is the security and confidentiality of consumer data in phrases of its location, relocation, availability, and security. Numerous users are surfing the Cloud for various purposes, therefore, they have highly safe and protracted services. The long run of the cloud, especially in expanding the range of applications, involves away the deeper degree of privacy, and authentication. Because of the safety concern associated with cloud computing, this paper presents a Computer Security System for Cloud Computing by employing a simple data protection model where data is encrypted using Advanced Encryption Standard (AES) technique before it is launched to the cloud, thus ensuring data confidentiality and security which is implemented with packet tracer.
Evaluation comparison of wave amount measurement results in brass-plated tire steel cord using RMSE and cosine similarity

&lt;span lang="IN"&gt;In the production process, quality checking is very important, one of which is on the wire. In the process of making brass-coated steel tire straps sometimes produce quality goods not in accordance with the desired standard values. Checks that are carried out manually have low efficiency and quite high errors occur. So it is necessary to check by measuring the wavelength on the brass plated steel cord automatically. In this study, used 3 automatic measurement methods using 2 evaluations, namely RMSE and Cosine Similarity. The results showed the best measurement using RMSE with method 2. Whereas the worst method uses RMSE with method 1. The smallest RMSE value is 0.0098 and the largest RMSE is 0.0966. The lowest Cosine Similarity value is 0.1253, while the highest Cosine Similarity value is 0.2079.&lt;/span&gt;
Lossless Decompression Accelerator for Embedded Processor with GUI

The development of the mobile industry brings about the demand for high-performance embedded systems in order to meet the requirement of user-centered application. Because of the limitation of memory resource, employing compressed data is efficient for an embedded system. However, the workload for data decompression causes an extreme bottleneck to the embedded processor. One of the ways to alleviate the bottleneck is to integrate a hardware accelerator along with the processor, constructing a system-on-chip (SoC) for the embedded system. In this paper, we propose a lossless decompression accelerator for an embedded processor, which supports LZ77 decompression and static Huffman decoding for an inflate algorithm. The accelerator is implemented on a field programmable gate array (FPGA) to verify the functional suitability and fabricated in a Samsung 65 nm complementary metal oxide semiconductor (CMOS) process. The performance of the accelerator is evaluated by the Canterbury corpus benchmark and achieved throughput up to 20.7 MB/s at 50 MHz system clock frequency.
Property Checking with Interpretable Error Characterization for Recurrent Neural Networks

This paper presents a novel on-the-fly, black-box, property-checking through learning approach as a means for verifying requirements of recurrent neural networks (RNN) in the context of sequence classification. Our technique steps on a tool for learning probably approximately correct (PAC) deterministic finite automata (DFA). The sequence classifier inside the black-box consists of a Boolean combination of several components, including the RNN under analysis together with requirements to be checked, possibly modeled as RNN themselves. On one hand, if the output of the algorithm is an empty DFA, there is a proven upper bound (as a function of the algorithm parameters) on the probability of the language of the black-box to be nonempty. This implies the property probably holds on the RNN with probabilistic guarantees. On the other, if the DFA is nonempty, it is certain that the language of the black-box is nonempty. This entails the RNN does not satisfy the requirement for sure. In this case, the output automaton serves as an explicit and interpretable characterization of the error. Our approach does not rely on a specific property specification formalism and is capable of handling nonregular languages as well. Besides, it neither explicitly builds individual representations of any of the components of the black-box nor resorts to any external decision procedure for verification. This paper also improves previous theoretical results regarding the probabilistic guarantees of the underlying learning algorithm.
Byzantine-Resilient Non-Convex Stochastic Gradient Descent

We study adversary-resilient stochastic distributed optimization, in which $m$ machines can independently compute stochastic gradients, and cooperate to jointly optimize over their local objective functions. However, an $\alpha$-fraction of the machines are $\textit{Byzantine}$, in that they may behave in arbitrary, adversarial ways. We consider a variant of this procedure in the challenging $\textit{non-convex}$ case. Our main result is a new algorithm SafeguardSGD which can provably escape saddle points and find approximate local minima of the non-convex objective. The algorithm is based on a new concentration filtering technique, and its sample and time complexity bounds match the best known theoretical bounds in the stochastic, distributed setting when no Byzantine machines are present. Our algorithm is very practical: it improves upon the performance of all prior methods when training deep neural networks, it is relatively lightweight, and it is the first method to withstand two recently-proposed Byzantine attacks.
Long-Term IaaS Provider Selection Using Short-Term Trial Experience

We propose a novel approach to select privacy-sensitive IaaS providers for a long-term period. The proposed approach leverages a consumer's short-term trial experiences for long-term selection. We design a novel equivalence partitioning based trial strategy to discover the temporal and unknown QoS performance variability of an IaaS provider. The consumer's long-term workloads are partitioned into multiple Virtual Machines in the short-term trial. We propose a performance fingerprint matching approach to ascertain the confidence of the consumer's trial experience. A trial experience transformation method is proposed to estimate the actual long-term performance of the provider. Experimental results with real-world datasets demonstrate the efficiency of the proposed approach.
The Oribatida v1.3 Family of Lightweight Authenticated Encryption Schemes

Abstract Permutation-based modes have been established for lightweight authenticated encryption, as can be seen from the high interest in the ongoing NIST lightweight competition. However, their security is upper bounded by O ( σ 2 /2 c ) bits, where σ are the number of calls and c is the hidden capacity of the state. The development of more schemes that provide higher security bounds led to the CHES’18 proposal Beetle that raised the bound to O ( rσ /2 c ), where r is the public rate of the state. While authenticated encryption can be performed in an on-line manner, authenticated decryption assumes that the resulting plaintext is buffered and never released if the corresponding tag is incorrect. Since lightweight devices may lack the resources for buffering, additional robustness guarantees, such as integrity under release of unverified plaintexts (I nt -RUP), are desirable. In this stronger setting, the security of the established schemes, including Beetle, is limited by O ( q p q d /2 c ), where q d is the maximal number of decryption queries, and q p that of off-line primitive queries, which motivates novel approaches. This work proposes Oribatida, a permutation-based AE scheme that derives s -bit masks from previous permutation outputs to mask ciphertext blocks. Oribatida can provide a security bound of O ( rσ 2 / c + s ), which allows smaller permutations for the same level of security. It provides a security level dominated by <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mrow> <m:mi>O</m:mi> <m:mo stretchy="false">(</m:mo> <m:msubsup> <m:mo>σ</m:mo> <m:mi>d</m:mi> <m:mn>2</m:mn> </m:msubsup> <m:msup> <m:mrow> <m:mo>/</m:mo> <m:mn>2</m:mn> </m:mrow> <m:mi>c</m:mi> </m:msup> <m:mo stretchy="false">)</m:mo> </m:mrow> </m:math> O(\sigma_d^2{/2^c}) under I nt -RUP adversaries, which eliminates the dependency on primitive queries. We prove its security under nonce-respecting and I nt -RUP adversaries. We show that our I nt -RUP bound is tight and show general attacks on previous constructions.
Ideal pairing of the Stokes and anti-Stokes photons in the Raman process

A quantum model of the Raman process with the independent Stokes and anti-Stokes nonlinear interactions is developed to study nonclassical correlations between the photons in the Stokes and anti-Stokes fields. The role of the laser pump amplitude, the ratio of the Stokes and anti-Stokes coupling constants, and the population and losses of the vibrational mode in forming the correlations is elucidated. The ${g}^{(2)}$ intensity cross-correlation function, noise-reduction factor, two-mode principal squeezing variance, logarithmic negativity, nonclassicality depth, steering parameter, and the Bell parameter are analyzed side by side to shed light to the correlations between the Stokes and anti-Stokes fields. Conditions for having the Stokes and anti-Stokes fields composed of only photon pairs, similarly as it occurs in twin beams in parametric down-conversion, are revealed. They allow for nonzero mean thermal phonon numbers.
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning

Federated learning (FL) enables a neural network (NN) to be trained using privacy-sensitive data on mobile devices while retaining all the data on their local storages. However, FL asks the mobile devices to perform heavy communication and computation tasks, i.e., devices are requested to upload and download large-volume NN models and train them. This paper proposes a novel unsupervised pre-training method adapted for FL, which aims to reduce both the communication and computation costs through model compression. Since the communication and computation costs are highly dependent on the volume of NN models, reducing the volume without decreasing model performance can reduce these costs. The proposed pretraining method leverages unlabeled data, which is expected to be obtained from the Internet or data repository much more easily than labeled data. The key idea of the proposed method is to obtain a "good" subnetwork from the original NN using the unlabeled data based on the lottery hypothesis. The proposed method trains an original model using a denoising auto encoder with the unlabeled data and then prunes small-magnitude parameters of the original model to generate a small but good subnetwork. The proposed method is evaluated using an image classification task. The results show that the proposed method requires 35% less traffic and computation time than previous methods when achieving a certain test accuracy.
Local Representation and Voter Mobilization in Closed-list Proportional Representation Systems

Local Representation and Voter Mobilization in Closed-list Proportional Representation Systems
Evaluation Model of College Students’ Mental Health Based on Neural Network

Abstract With the constant growth of China’s higher education from elite education to popular education, a series of vicious events such as college students’ recess, withdrawal from school and even suicide caused by mental health problems are increasing, which not only brings great pressure and grief to the family, but also has a negative impact on the normal teaching and management of schools, but also causes great losses to society and arouses the concern of the state, society and schools. And although psychological testing is widely used as a standardized evaluation tool in the investigation and individual evaluation of college students’ mental health, the results are not obvious. Many psychological problems are still not detected early. There are many reasons for this outcome, such as students’ failure to provide true and reliable information, the poor validity of the test and the complexity of the psychological problem itself, among which the failure to make effective use of the information inherent in the test is also an important reason. Therefore, in the individual psychological evaluation, how to use the information provided by the test accurately and effectively by mathematical means to improve the recognition rate of psychological problems is an important problem facing the mental health care and promotion of college students.
A novel memory-efficient deep learning training framework via error-bounded lossy compression

DNNs are becoming increasingly deeper, wider, and nonlinear due to the growing demands on prediction accuracy and analysis quality. When training a DNN model, the intermediate activation data must be saved in the memory during forward propagation and then restored for backward propagation. Traditional memory saving techniques such as data recomputation and migration either suffers from a high performance overhead or is constrained by specific interconnect technology and limited bandwidth. In this paper, we propose a novel memory-driven high performance CNN training framework that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger neural networks. Specifically, we provide theoretical analysis and then propose an improved lossy compressor and an adaptive scheme to dynamically configure the lossy compression error-bound and adjust the training batch size to further utilize the saved memory space for additional speedup. We evaluate our design against state-of-the-art solutions with four widely-adopted CNNs and the ImangeNet dataset. Results demonstrate that our proposed framework can significantly reduce the training memory consumption by up to 13.5× and 1.8× over the baseline training and state-of-the-art framework with compression, respectively, with little or no accuracy loss. The full paper can be referred to at https://arxiv.org/abs/2011.09017.
Study of Coronavirus Impact on Parisian Population from April to June using Twitter and Text Mining Approach

The fast spreading of coronavirus name covid19, generated the actual pandemic forcing to change daily activities.Health Councils of each country promote health policies, close borders and start a partial or total lockdown.One of the first countries in Europe with high impact was Italy.Besides at the end of April, one country with a shared border was on the top of 10 countries with more total cases, then France started with its own battle to beat coronavirus.This paper studies the impact of coronavirus in the poopulation of Paris, France from April 23 to June 18, using Text Mining approach, processing data collected from Social Network and using trends related of searching.First finding is a decreasing pattern of publications/interest, and second is related to health crisis and economical impact generated by coronavirus.
ScenarIoT: Support for Scenario Specification of Internet of Things-based Software Systems

As well as any paradigm shift, the Internet of Things (IoT) brings up challenges related to several areas of research, including Software Engineering at different stages of development. This work proposes the ScenarIoT approach aiming to support the specification of scenarios when developing software systems on the IoT background. This approach is grounded on Interaction Arrangements - designed by applying an evidence-based approach – which represents recurrent flows of interaction between abstract elements of the IoT domain. These results presented can contribute to the progress of research on IoT from the lens of the Software Engineering research field.
Quantitative Expression of the Burial Phenomenon of Deep Seafloor Manganese Nodules

Manganese (polymetallic) nodules on the deep seafloor in the open ocean have attracted great interest because of their economic potential. Visual data on nodules found on the deep seafloor such as photographs and videos have increased exponentially with the recent progress of related technologies. These data are expected to reflect useful information for estimating these mineral resources, as well as understanding their geological origin. Although the size, number, and coverage of manganese nodules have been measured in seafloor images, the burial of such nodules has not been sufficiently examined. This paper focuses on mathematical expression of the burial of the manganese nodules and attempts to quantitatively elucidate relations among burial degree and nodule geological parameters. The results, that is, a dataset obtained by calculations of relations among parameters, are also utilized for considerations of quantitative expression of burial. These considerations are expected to contribute to a better understanding of the geological origin of manganese nodules.
Pronunciation-Enhanced Chinese Word Embedding

Abstract Chinese word embeddings have recently garnered considerable attention. Chinese characters and their sub-character components, which contain rich semantic information, are incorporated to learn Chinese word embeddings. Chinese characters can represent a combination of meaning, structure, and pronunciation. However, existing embedding learning methods focus on the structure and meaning of Chinese characters. In this study, we aim to develop an embedding learning method that can make complete use of the information represented by Chinese characters, including phonology, morphology, and semantics. Specifically, we propose a pronunciation-enhanced Chinese word embedding learning method, where the pronunciations of context characters and target characters are simultaneously encoded into the embeddings. Evaluation of word similarity, word analogy reasoning, text classification, and sentiment analysis validate the effectiveness of our proposed method.
Compiling single-qubit braiding gate for Fibonacci anyons topological quantum computation

Abstract Topological quantum computation is an implementation of a quantum computer in a way that radically reduces decoherence. Topological qubits are encoded in the topological evolution of two-dimensional quasi-particles called anyons and universal set of quantum gates can be constructed by braiding these anyons yielding to a topologically protected circuit model. In the present study we remind the basics of this emerging quantum computation scheme and illustrate how a topological qubit built with three Fibonacci anyons might be adopted to achieve leakage free braiding gate by exchanging the anyons composing it. A single-qubit braiding gate that approximates the Hadamard quantum gate to a certain accuracy is numerically implemented using a brute force search method. The algorithms utilized for that purpose are explained and the numerical programs are publicly shared for reproduction and further use.
Regular Polytope Networks

Neural networks are widely used as a model for classification in a large variety of tasks. Typically, a learnable transformation (i.e. the classifier) is placed at the end of such models returning a value for each class used for classification. This transformation plays an important role in determining how the generated features change during the learning process. In this work, we argue that this transformation not only can be fixed (i.e. set as non-trainable) with no loss of accuracy and with a reduction in memory usage, but it can also be used to learn stationary and maximally separated embeddings. We show that the stationarity of the embedding and its maximal separated representation can be theoretically justified by setting the weights of the fixed classifier to values taken from the coordinate vertices of the three regular polytopes available in $\mathbb{R}^d$, namely: the $d$-Simplex, the $d$-Cube and the $d$-Orthoplex. These regular polytopes have the maximal amount of symmetry that can be exploited to generate stationary features angularly centered around their corresponding fixed weights. Our approach improves and broadens the concept of a fixed classifier, recently proposed in \cite{hoffer2018fix}, to a larger class of fixed classifier models. Experimental results confirm the theoretical analysis, the generalization capability, the faster convergence and the improved performance of the proposed method. Code will be publicly available.
Addressing Communication, Coordnation and Cultural Issues in Global Software Development Projects

The field of Global Software Development has been an active area of research for the last two decades due to its enormous benefits such as lower labor cost, faster development and easy access to the skilled labor pool. Apart from these benefits, it faces some challenges like communication, coordination, trust and configuration management etc. These challenges arise primarily due to physical, cultural and time zone differences. The empirical studies highlight that the existing Global Software Development solutions do not fully meet the user needs as there are still several gaps in these solutions. Therefore, to fulfill these gaps, there is a need to develop novel frameworks that address outstanding issues. In this paper, we have attempted to address the aforesaid GSD challenges. The practitioners can benefit from our proposed framework during the execution of GSD projects. The proposed framework mainly focuses on the root causes of the two principal challenges namely the communication and cultural differences. We believe that if the team members of a software project can communicate effectively and show considerations for others by imparting due reverence to the cultural norms, then the other residual issues can easily be reduced and minimized.
A simulation-based study to calculate all the possible trajectories of differential drive mobile robot

Deferential Drive Mobile Robot (DDMR) is being used in many applications as it is easy to be modeled and controlled.This research presents the idea of using DDMR turning motion behavior to develop an algorithm that calculate all the circular trajectories.This can be used to navigate DDMR in a curvature paths instead of linear ones.In this research we design and simulate Differential Drive Mobile Robot (DDMR) model.Then we use the simulated model to calculate all the possible trajectories that DDMR can follow with each left and right wheel velocity configurations.Results are saved in a navigation look-up table that can be implemented in DDMR navigation's approach.