Reviewerly/exHarmony huggingface dataset:
Arnoldi-Lindblad time evolution: Faster-than-the-clock algorithm for the spectrum of time-independent and Floquet open quantum systems

The characterization of open quantum systems is a central and recurring problem for the development of quantum technologies. For time-independent systems, an (often unique) steady state describes the average physics once all the transient processes have faded out, but interesting quantum properties can emerge at intermediate timescales. Given a Lindblad master equation, these properties are encoded in the spectrum of the Liouvillian whose diagonalization, however, is a challenge even for small-size quantum systems. Here, we propose a new method to efficiently provide the Liouvillian spectral decomposition. We call this method an Arnoldi-Lindblad time evolution, because it exploits the algebraic properties of the Liouvillian superoperator to efficiently construct a basis for the Arnoldi iteration problem. The advantage of our method is double: (i) It provides a faster-than-the-clock method to efficiently obtain the steady state, meaning that it produces the steady state through time evolution shorter than needed for the system to reach stationarity. (ii) It retrieves the low-lying spectral properties of the Liouvillian with a minimal overhead, allowing to determine both which quantum properties emerge and for how long they can be observed in a system. This method is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">general and model-independent</mml:mtext></mml:mrow></mml:math>, and lends itself to the study of large systems where the determination of the Liouvillian spectrum can be numerically demanding but the time evolution of the density matrix is still doable. Our results can be extended to time evolution with a time-dependent Liouvillian. In particular, our method works for Floquet (i.e., periodically driven) systems, where it allows not only to construct the Floquet map for the slow-decaying processes, but also to retrieve the stroboscopic steady state and the eigenspectrum of the Floquet map. Although the method can be applied to any Lindbladian evolution (spin, fermions, bosons, …), for the sake of simplicity we demonstrate the efficiency of our method on several examples of coupled bosonic resonators (as a particular example). Our method outperforms other diagonalization techniques and retrieves the Liouvillian low-lying spectrum even for system sizes for which it would be impossible to perform exact diagonalization.
SMIR: A Transformer-Based Model for MRI super-resolution reconstruction

Down-sampling magnetic resonance imaging super-resolution reconstruction is one of the main problems in the field of accelerating magnetic resonance imaging research. The current method with better results is the traditional method of compressed sensing, but the solution requires iteration, which consumes a lot of time and only solves the reconstruction for a single image. At present, the more advanced image restoration methods are based on convolutional neural networks, but few people have tried to apply Transformer to the field of medical image reconstruction and have shown relatively good results. In this article, we propose a magnetic resonance imaging reconstruction model SMIR based on Swin Transformers, namely SMIR. SMIR consists of two modules: a multi-level feature extraction module and a reconstruction module. The model combines frequency domain and spatial domain losses to better reconstruct image details. We compared this model with some traditional image processing methods and advanced convolutional neural networks image restoration methods. The experimental results show that our method achieves the best results.
Developing Trends and Challenges of Digital Forensics

Digital forensics is concerned with identifying, reporting and responding to security breaches. It is about how to acquire, analyze and report digital evidence and using the technical skills, discovering the traces of Cyber Crime. The field of digital forensics is in high demand due to the constant threats of data breaches and information hacks. Digital Forensics is utilized in the identification and elimination of crimes in any controversy where evidence is preserved in online space. This is the use of specialized techniques for retrieval, authentication and electronic data analysis. Computer forensics deals with the identification, preservation, analysis, documentation and presentation of digital evidence. The paper has analyzed the present-day trends that includes IoT forensics, cloud forensics, network forensics and social media forensics. Recent researches have shown a wide range of threats and cyber-attacks, which requires forensic investigators and forensics scientists to simplify the digital world. Hence, all our research gives a clear view of digital forensics which could be of a great help in forensic investigation. In this research paper we have discussed about the need and way to preserve the digital evidence, so that it is not compromised at any point in time and an unalter evidence can be presented before the court of law.
Load balancing in cloud using improved gray wolf optimizer

Abstract Cloud computing allocates virtual resources dynamically on user's demand. The sudden rise of data storage and computation in the cloud computing environment may cause an imbalanced workload distribution. As a result, job completion time will be higher in overloaded servers than the underloaded servers in the same environment. Distributing load fairly in the cloud is a crucial challenge. Traditionally, load balancing is used to distribute the workload among multiple servers to overcome the overloading and underloading of servers. This article presents a novel load balancing approach for cloud computing using improved gray wolf optimization algorithm. We compare our approach with harmony search algorithm, artificial bee colony algorithm, particle swarm optimization, and gray wolf optimization algorithms. Results of simulation are encouraging with improved system performance and fair utilization of resources.
Research on Short Text Classification Based on RoBERTa-TextRCNN

Aiming at the problem of short text classification, this paper proposes a short text classification method for media data based on RoBERTa and TextRCNN. The semantic text vector representation is obtained through RoBERTa and used as the input of TextRCNN for training. After experiments on the THUCNews dataset, RoBERTa-TextRCNN’s accuracy rate reached 94.64%, which is 4.53% higher than TextRCNN and 0.62% higher than Bert+RCNN. The effect is better than other classification models, which proves its effectiveness in short text classification.
Detecting Arabic textual threats in social media using artificial intelligence: An overview

&lt;span&gt;Recent studies show that social media has become an integral part of everyone's daily routine. People often use it to convey their ideas, opinions, and critiques. Consequently, the increasing use of social media has motivated malicious users to misuse online social media anonymity. Thus, these users can exploit this advantage and engage in socially unacceptable behavior. The use of inappropriate language on social media is one of the greatest societal dangers that exist today. Therefore, there is a need to monitor and evaluate social media postings using automated methods and techniques. The majority of studies that deal with offensive language classification in texts have used English datasets. However, the enhancement of offensive language detection in Arabic has gotten less consideration. The Arabic language has different rules and structures. This article provides a thorough review of research studies that have made use of artificial intelligence (AI) for the identification of Arabic offensive language in various contexts.&lt;/span&gt;
An Efficient Blockchain-Based IoT System Using Improved KNN Machine Learning Classifier

TheClassification introduction of blockchain technologyBlockchain Technology (BT) has evolved into a distinctive, disturbing, and trendy technology in recent years. Data security and privacy are prioritized in BT's decentralized database. New security concerns raised by BT include common attacks and double-spending. To address the above-mentioned challenges, data analytics on a blockchain-based IoT network is necessary to protect data. The value of emerging technology Machine LearningMachine learning (ML) is highlighted through analytics on this data. When ML and BT are combined, very precise results may be obtained. This study, therefore, aimed to give a comprehensive study on the use of machine learning to make Blockchain-based IoT network smart applications that are more robust to handle network attacksNetwork attack. To investigate these attacks on a blockchain-based IoT network, an improved K-Nearest NeighborImproved K-Nearest Neighbor (KNN) classifier was postulated. Improved KNN (I-KNN) surpassed traditional KNNTraditional K-Nearest Neighbor (T-KNN) with an accuracy of 96.7% and 81.6% for the I-KNN classifier and T-KNN, respectively.
Piezo-phototronic spin laser based on wurtzite quantum wells

Piezo-phototronic spin lasers are studied based on wurtzite quantum wells. The strain-induced polarization changes the gain spectrum and gain asymmetry at room temperature. In this work, the gain properties of piezo-phototronic spin lasers based on InGaN/GaN quantum wells can be modulated by strain-induced polarization. This work not only paves a new way for the room-temperature applications of quantum piezotronic and piezo-phototronic devices, but also provides new method of regulation for wurtzite spin lasers by strain-induced polarization.
Comparing Machine Learning to Regression Methods for Mortality Prediction Using Veterans Affairs Electronic Health Record Clinical Data

Background: It is unclear whether machine learning methods yield more accurate electronic health record (EHR) prediction models compared with traditional regression methods. Objective: The objective of this study was to compare machine learning and traditional regression models for 10-year mortality prediction using EHR data. Design: This was a cohort study. Setting: Veterans Affairs (VA) EHR data. Participants: Veterans age above 50 with a primary care visit in 2005, divided into separate training and testing cohorts (n= 124,360 each). Measurements and Analytic Methods: The primary outcome was 10-year all-cause mortality. We considered 924 potential predictors across a wide range of EHR data elements including demographics (3), vital signs (9), medication classes (399), disease diagnoses (293), laboratory results (71), and health care utilization (149). We compared discrimination ( c -statistics), calibration metrics, and diagnostic test characteristics (sensitivity, specificity, and positive and negative predictive values) of machine learning and regression models. Results: Our cohort mean age (SD) was 68.2 (10.5), 93.9% were male; 39.4% died within 10 years. Models yielded testing cohort c -statistics between 0.827 and 0.837. Utilizing all 924 predictors, the Gradient Boosting model yielded the highest c -statistic [0.837, 95% confidence interval (CI): 0.835–0.839]. The full (unselected) logistic regression model had the highest c -statistic of regression models (0.833, 95% CI: 0.830–0.835) but showed evidence of overfitting. The discrimination of the stepwise selection logistic model (101 predictors) was similar (0.832, 95% CI: 0.830–0.834) with minimal overfitting. All models were well-calibrated and had similar diagnostic test characteristics. Limitation: Our results should be confirmed in non-VA EHRs. Conclusion: The differences in c -statistic between the best machine learning model (924-predictor Gradient Boosting) and 101-predictor stepwise logistic models for 10-year mortality prediction were modest, suggesting stepwise regression methods continue to be a reasonable method for VA EHR mortality prediction model development.
A Text-mining approach for crime tweets in Saudi Arabia: From analysis to prediction

Social networks have proven to be a massive hub for investigating contextual and individual behavior of people. Most recently micro-blogging sites like Twitter are indicating to researchers that their content can be aggregated and used to effectively predict forecast, and infer outcomes of real-world events. The crime-related tweets analysis research in Saudi Arabia set off with an ultimate goal of gathering a deeper understanding of what kinds of criminal weapons are people frequently talking about. In this paper, we aim at dealing with tweets mentioning different weapons, analyzing them to gather facts such as annual variation of percentage tweets mentioning different weapons, recognizing the impact of events such as the Covid-19 pandemic on crime social discussions. In the following step, we develop a number of classifiers to predict which weapon is mentioned in a tweet. In order to perform our tasks, the Python programming language is used in the majority of the cases.
Unsupervised domain adaptation for segmentation with black-box source model

Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain. The training of conventional solutions usually relies on the existence of both source and target domain data. However, privacy of the large-scale and well-labeled data in the source domain and trained model parameters can become the major concern of cross center/domain collaborations. In this work, to address this, we propose a practical solution to UDA for segmentation with a black-box segmentation model trained in the source domain only, rather than original source data or a white-box source model. Specifically, we resort to a knowledge distillation scheme with exponential mixup decay (EMD) to gradually learn target-specific representations. In addition, unsupervised entropy minimization is further applied to regularization of the target domain confidence. We evaluated our framework on the BraTS 2018 database, achieving performance on par with white-box source model adaptation approaches.
Cross-Lingual Text Reuse Detection at sentence level for English–Urdu language pair

In recent years, the problem of Cross-Lingual Text Reuse Detection (X-TRD) has gained the interest of researchers due to the availability of large digital repositories and automatic translation systems. These systems are promptly available and openly accessible, which makes it easier to reuse text across the languages and hard to detect. In previous studies, different corpora and techniques have been developed for X-TRD at sentence/passage and document level for the English–Urdu language pair. However, there is a lack of large benchmark corpora and standard techniques for X-TRD for the English–Urdu language pair at the sentence level. To overcome this limitation, this study presents a large benchmark sentential cross-lingual (English–Urdu) corpus of 21,669 sentence pairs with simulated cases of X-TR, which are manually annotated at three levels of rewrite (Wholly Derived (WD) = 7,655, Partially Derived (PD) = 6,461, and Non Derived (ND) = 7,553). As a second major contribution, we have applied various state-of-the-art Cross-Lingual Sentence Transformers (CLST), and Translation plus Mono-lingual Analysis (T+MA) including N-gram Overlap (lexical), WordNet based techniques (semantic), mono-lingual word embedding-based techniques, and Kullback–Leibler Distance (KLD) (probabilistic) on our proposed sentential corpus for X-TRD. For the binary classification, the best results are obtained (F1 = 0.94) using a combination of all CLST and T+MA techniques and a combination of all T+MA techniques, whereas, for the ternary classification task, the best results are obtained (F1 = 0.84) using a combination of all CLST and T+MA techniques. The corpus will be publicly available to foster and promote research for X-TRD in an under-resourced language, such as the Urdu language.
CoLoRd: compressing long reads

The cost of maintaining exabytes of data produced by sequencing experiments every year has become a major issue in today's genomic research. In spite of the increasing popularity of third-generation sequencing, the existing algorithms for compressing long reads exhibit a minor advantage over the general-purpose gzip. We present CoLoRd, an algorithm able to reduce the size of third-generation sequencing data by an order of magnitude without affecting the accuracy of downstream analyses.
aSGD: Stochastic Gradient Descent with Adaptive Batch Size for Every Parameter

In recent years, deep neural networks (DNN) have been widely used in many fields. Lots of effort has been put into training due to their numerous parameters in a deep network. Some complex optimizers with many hyperparameters have been utilized to accelerate the process of network training and improve its generalization ability. It often is a trial-and-error process to tune these hyperparameters in a complex optimizer. In this paper, we analyze the different roles of training samples on a parameter update, visually, and find that a training sample contributes differently to the parameter update. Furthermore, we present a variant of the batch stochastic gradient decedent for a neural network using the ReLU as the activation function in the hidden layers, which is called adaptive stochastic gradient descent (aSGD). Different from the existing methods, it calculates the adaptive batch size for each parameter in the model and uses the mean effective gradient as the actual gradient for parameter updates. Experimental results over MNIST show that aSGD can speed up the optimization process of DNN and achieve higher accuracy without extra hyperparameters. Experimental results over synthetic datasets show that it can find redundant nodes effectively, which is helpful for model compression.
Correlation of Agile Principles and Practices to Software Project Performance: An AHP–Delphi Analysis

Currently, Extreme Programming, Scrum, and Kanban are the three most commonly used methods in agile software development (ASD) projects. Each method has different practices and shares a set of agile principles, where quality, time, and cost are the three project performance indicators. Companies may focus on and prioritize certain indicators based on industry or project differences. Therefore, choosing appropriate practices that fit the specific performance indicator is an important decision for organizations. This study utilizes a hierarchical consensus model to examine the correlation between four agile practice groups, six agile principle categories, and three project performance indicators. The modified Delphi method was applied to collect the pairwise comparison data, and the analytic hierarchy process was utilized to analyze the data. A Delphi panel of experts from both academia and industry was established to reach a consensus on the correlation priority using pairwise comparison matrices. The principle of cooperation between customer and developer is considered the most important principle related to project time and cost performance, while the technical excellence principle is the most important principle related to project quality performance. These results can assist organizations and practitioners in adopting the ASD practices that will best enhance their competitive advantage.
Excerpt from Datasheets for Datasets *

Data plays a critical role in machine learning. Every machine learning model is trained and evaluated using data, quite often in the form of a static dataset. The characteristics of these datasets will fundamentally influence a model's behavior. Datasheets for datasets have the potential to increase transparency and accountability within the machine learning community, mitigate unwanted biases in machine learning systems, facilitate greater reproducibility of machine learning results, and help researchers and practitioners select more appropriate datasets for their chosen tasks. This chapter describes the process by which we developed the datasheets questions and the workflow for dataset creators to use when answering these questions. It concludes with a summary of the impact of datasheets for datasets and a discussion of implementation challenges and avenues for future work.
Source Code Summarization with Structural Relative Position Guided Transformer

Source code summarization aims at generating concise and clear natural language descriptions for programming languages. Well-written code summaries are beneficial for programmers to participate in the software development and maintenance process. To learn the semantic representations of source code, recent efforts focus on incorporating the syntax structure of code into neural networks such as Transformer. Such Transformer-based approaches can better capture the long-range dependencies than other neural networks including Recurrent Neural Networks (RNNs), however, most of them do not consider the structural relative correlations between tokens, e.g., relative positions in Abstract Syntax Trees (ASTs), which is beneficial for code semantics learning. To model the structural dependency, we propose a StruCtural RelatIve Position guided Transformer, named SCRIPT. SCRIPT first obtains the structural relative positions between tokens via parsing the ASTs of source code, and then passes them into two types of Transformer encoders. One Transformer directly adjusts the input according to the structural relative distance; and the other Transformer encodes the structural relative positions during computing the self-attention scores. Finally, we stack these two types of Transformer encoders to learn representations of source code. Experimental results show that the proposed SCRIPT outperforms the state-of-the-art methods by at least 1.6&#x0025;, 1.4&#x0025; and 2.8&#x0025; with respect to BLEU, ROUGE-L and METEOR on benchmark datasets, respectively. We further show that how the proposed SCRIPT captures the structural relative dependencies.
A Comparative Analysis of SMS Spam Detection employing Machine Learning Methods

In recent times, the increment of mobile phone usage has resulted in a huge number of spam messages. Spammers continuously apply more and more new tricks that cause managing or preventing spam messages a challenging task. The aim of this study is to detect spam message to prevent different cybercrimes as spam messages have become a security threat nowadays. In this paper, studies on SMS spam problems to perform a better accuracy using several different techniques such as Support Vector Machine, K-Nearest Neighbor, Na&#x00EF;ve Bayes, Random Forest, Logistic Regression and some more are performed. The result indicated that Support Vector Machine achieved the highest accuracy of 99%, indicating it might be useful as an effective machine learning system for future research.
ReSIL

Function signature recovery is important for binary analysis and security enhancement, such as bug finding and control-flow integrity enforcement. However, binary executables typically have crucial information vital for function signature recovery stripped off during compilation. To make things worse, recent studies show that many compiler optimization strategies further complicate the recovery of function signatures with intended violations to function calling conventions.