Reviewerly/exHarmony huggingface dataset:
State-of-the-Art: Offline Writer Identification Methodologies

This paper reviews various text independent writer identification strategies using offline documents. Various features extraction methodologies are discussed. Handcrafted and Deep Learning based methodologies for classification that are mainly used for offline writer recognition by the researchers and verification by different groups and individuals are presented. Accuracies on identification achieved by the reviewed papers are tabulated and analysed. A survey of different databases used in the reviewed papers is performed. Application of writer identification in different language domains is also discussed. Experimental results of reviewed papers demonstrate that the methodologies get highest accuracy rates till 99.80%.
Business email compromise (BEC) attacks

Cybercrime is a daily threat against organizations and partners of all sizes and with the rapid development of technology and the great dependence on it in some transactions, a type of serious threats has emerged that pose a high degree of risk to companies and organizations that rely on financial transactions in their work. This type of threat is called Business Email Compromise (BEC), which is a type of email phishing for financial purposes. This attack increased dramatically and caused very high financial losses to companies, especially in the period of remote work and in the Corona crisis, as it increased in the third quarter of the century of this year by 94%. This type of threat does not require a high percentage of knowledge, experience, or skills in deception and fraud, it requires only a reasonable level of social engineering. In this paper, we contribute to analyze this threat is, how it occurs, ways to avoid it or reduce its incidence, and impact on the organization.
Towards priority-awareness in autonomous intelligent systems

In Autonomous and Intelligent systems (AIS), the decision-making process can be divided into two parts: (i) the priorities of the requirements are determined at design-time; (ii) design selection follows where alternatives are compared, and the preferred alternatives are chosen autonomously by the AIS. Runtime design selection is a trade-off analysis between non-functional requirements (NFRs) that uses optimisation methods, including decision-analysis and utility theory. The aim is to select the design option yielding the highest expected utility. A problem with these techniques is that they use a uni-scalar cumulative utility value to represent a combined priority for all the NFRs. However, this uni-scalar value doesn't give information about the varying impacts of actions under uncertain environmental contexts on the satisfaction priorities of individual NFRs. In this paper, we present a novel use of Multi-Reward Partially Observable Markov Decision Process (MR-POMDP) to support reasoning of separate NFR priorities. We discuss the use of rewards in MR-POMDPs as a way to support AIS with (a) priority-aware decision-making; and (b) maintain service-level agreement, by autonomously tuning NFRs' priorities to new contexts and based on data gathered at runtime. We evaluate our approach by applying it to a substantial Network case.
Characterizing quantum simulations with quantum tomography on a spin quantum simulator

Developments of quantum techniques have drastically improved the control ability of current quantum processors. Characterizing quantum simulations performed on these processors has become a vital topic in quantum information processing. Traditional characterizing techniques such as quantum process and state tomography fully characterize quantum simulations, but always with the exponentially increased resource cost, unfortunately. It may place their applications only in small-scale quantum systems. Meanwhile, many potential characterizing methods have been put forward in the past decades. In this work we experimentally test and compare different methods for characterizing quantum simulations of Ising Hamiltonians on the nuclear magnetic resonance (NMR) platform, including the recent quantum Hamiltonian tomography with polynomial complexity to estimate the generator of quantum simulations and nonrepetitive direct fidelity estimation with the Monte Carlo method to estimate the fidelity of dynamical states. We also experimentally estimate the parameters of Hamiltonian via full quantum process tomography and quantum state tomography for the comparison. These methods have merits and demerits in accuracy, the required resource, and prior knowledge. Our experiments test the performance of different methods while providing a beneficial reference for characterizing quantum simulations in the future.
Harmonizing heterogeneous multi-proxy data from lake systems

When performing spatial-temporal investigations of multiple lake systems, geoscientists face the challenge of dealing with complex and heterogeneous data of different types, structure, and format. To support comparability, it is necessary to transform such data into a uniform format that ensures syntactic and semantic comparability. This paper presents a data science approach for transforming research data from different lake sediment cores into a coherent framework. For this purpose, we collected published and unpublished data from paleolimnological investigations of Arctic lake systems. Our approach adapted methods from the database field, such as developing entity-relationship (ER) diagrams, to understand the conceptual structure of the data independently of the source. We demonstrated the feasibility of our approach by transforming our ER diagram into a database schema for PostgreSQL, a popular database management system (DBMS). We validated our approach by conducting a comparative analysis on a set of acquired data, hereby focusing on the comparison of total organic carbon and bromine content in eight selected sediment cores. Still, we encountered serious obstacles in the development of the ER model. Heterogeneous structures within collected data made an automatic data integration impossible. Additionally, we realized that missing error information hampers the development of a conceptual model. Despite the strong initial heterogeneity of the original data, our harmonized dataset leads to comparable datasets, enabling numerical inter-proxy and inter-lake comparison.
Emotion Label Enhancement via Emotion Wheel and Lexicon

Emotion Distribution Learning (EDL) is a recently proposed multiemotion analysis paradigm, which identifies basic emotions with different degrees of expression in a sentence. Different from traditional methods, EDL quantitatively models the expression degree of the corresponding emotion on the given instance in an emotion distribution. However, emotion labels are crisp in most existing emotion datasets. To utilize traditional emotion datasets in EDL, label enhancement aims to convert logical emotion labels into emotion distributions. This paper proposed a novel label enhancement method, called Emotion Wheel and Lexicon-based emotion distribution Label Enhancement (EWLLE), utilizing the affective words’ linguistic emotional information and the psychological knowledge of Plutchik’s emotion wheel. The EWLLE method generates separate discrete Gaussian distributions for the emotion label of sentence and the emotion labels of sentiment words based on the psychological emotion distance and combines the two types of information into a unified emotion distribution by superposition of the distributions. The extensive experiments on 4 commonly used text emotion datasets showed that the proposed EWLLE method has a distinct advantage over the existing EDL label enhancement methods in the emotion classification task.
A language-independent authorship attribution approach for author identification of text documents

In the Authorship Attribution (AA) task, the most likely author of textual documents, such as books, papers, news, and text messages and posts are identified using statistical and computational methods. In this paper, a new computational approach is presented for identifying the most likely author of text documents. The proposed solution emphasizes lazy profile-based classification and, by using the Term Frequency-Inverse Document Frequency (TF_IDF) scheme, introduces a new measure for identifying important terms of documents. The importance of the terms is then used to calculate the similarity between an anonymous document and known documents. The proposed solution works with raw text documents and does not require any NLP tools for preprocessing, which makes it language-independent. The efficiency of the proposed solution has been evaluated by conducting several experiments on two English and Persian datasets, each of which contains six corpora with different number of authors. The obtained results demonstrate that the proposed solution outperforms state-of-the-art stylometric features, employed by seven well-known classifiers, by obtaining 0.902 accuracy for the English dataset and 0.931 accuracy for the Persian dataset. In addition, supplementary experiments have been conducted to evaluate the effects of documents' length on the accuracy of the proposed solution, to examine the computation time of the proposed solution and competitive classifiers, and to identify the most effective stylometric features and classifiers.
The Patterns of School Improvement in Digitally Innovative Schools

Abstract This study explores schools’ digital maturity self-evaluation reports’ data from Estonia. Based on quantitative data (N = 499) the schools that attempt digital transformation were clustered into three successive digital improvement types. The paper describes 3 main patterns of school improvement in different phases of innovative change: classroom innovation practices’ driven schools, participatory led structural change driven schools; and inclusive and evidence based change management type of schools. The defining variables for digital transformation towards new levels of digital maturity were teachers’ role, digital competences, learning organization culture, participatory management, inclusive leadership, structural changes and network, and IT-manager involvement to structural changes.
Gradient-based fly immune visual recurrent neural network solving large-scale global optimization

The recent achievements claimed that the fly visual neural system could serve a type of artificial computation model for revealing the properties of fly’s learning, memory and decision-making. It, however, still keeps open how to borrow the properties to develop artificial visual neural network optimization models. Hereby, inspired by gradient descent, fly’s visual information-processing and innate immunity mechanisms, this work probes into a fly immune visual recurrent neural network to solve large-scale global optimization (LSGO). As a two-step recurrent network, the neural network updates the output state matrix with the same size as that in the input layer through the gradient descent approach, where the learning rate of state transition is dynamically adjusted by a forward feedback fly immune visual neural network. Also, it is integrated with the conventional convolutional neural network to optimize the ultra-high dimensional weight and threshold parameters in order to acquire a prediction model of visual scene recognition. The theoretical results indicate that the recurrent neural network can converge to an equilibrium point under certain assumptions while the computational complexity is determined by the size of state matrix and the dimension of LSGO. The comparative experiments have confirmed that the neural network is an alternative and potential optimizer for LSGO problems, and in particular the acquired prediction model is a competitive tool for visual scene recognition.
Quantum-walk-based state-transfer algorithms on the complete <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>M</mml:mi></mml:math> -partite graph

We investigate coined quantum-walk search and state-transfer algorithms, focusing on the complete $M$-partite graph with $N$ vertices in each partition. First, it is shown that by adding a loop to each vertex, the search algorithm finds the marked vertex with unit probability in the limit of a large graph. Next, we employ the evolution operator of the search with two marked vertices to perform a state transfer between the sender and the receiver. We show that when the sender and the receiver are in different partitions, the algorithm succeeds with fidelity approaching unity for a large graph. However, when the sender and the receiver are in the same partition, the fidelity does not reach exactly 1. To solve this problem, we propose a state-transfer algorithm with an active switch, whose fidelity can be estimated based on the single vertex search alone.
Processes, challenges and recommendations of Gray Literature Review: An experience report

Systematic Literature Review (SLR), as a tool of Evidence-Based Software Engineering (EBSE), has been widely used in Software Engineering (SE). However, for certain topics in SE, especially those that are trendy or industry driven, academic literature is generally scarce and consequently Gray Literature (GL) becomes a major source of evidence. In recent years, the adoption of Gray Literature Review (GLR) or Multivocal Literature Review (MLR) is rising steadily to provide the state-of-the-practice of a specific topic where SLR is not a viable option. Although some SLR guidelines recommend the use of GL and several MLR guidelines have already been proposed in SE, researchers still have conflicting views on the value of GL and commonly accepted GLR or MLR studies are generally lacking in terms of publication. This experience report aims to shed some light on GLR through a case study that uses SLR and MLR guidelines to conduct a GLR on an emerging topic in SE to specifically answer the questions related to the reasons of using GL, the processes of conducting GL, and the impacts of GL on review results. We retrospect the review process of conducting a GLR on the topic of DevSecOps with reference to Kitchenham’s SLR and Garousi’s MLR guidelines. We specifically reflect on the processes we had to adapt in order to tackle the challenges we faced. We also compare and contrast our GLR with existing MLRs or GLRs in SE to contextualize our reflections. We distill ten challenges in nine activities of a GLR process. We provide reasons for these challenges and further suggest ways to tackle them during a GLR process. We also discuss the decision process of selecting a suitable review methodology among SLR, MLR and GLR and elaborate the impacts of GL on our review results. Although our experience on GLR is mainly derived from a specific case study on DevSecOps, we conjecture that it is relevant and would be beneficial to other GLR or MLR studies. We also expect our experience would contribute to future GLR or MLR guidelines, in a way similar to how SLR guidelines learned from the SLR experience report a dozen years ago. In addition, other researchers may find our decision making process useful before they conduct their own reviews.
Self-Supervised Learning with Graph Neural Networks for Region of Interest Retrieval in Histopathology

Deep learning has achieved successful performance in representation learning and content-based retrieval of histopathology images. The commonly used setting in deep learning-based approaches is supervised training of deep neural networks for classification, and using the trained model to extract representations that are used for computing and ranking the distances between images. However, there are two remaining major challenges. First, supervised training of deep neural networks requires large amount of manually labeled data which is often limited in the medical field. Transfer learning has been used to overcome this challenge, but its success remained limited. Second, the clinical practice in histopathology necessitates working with regions of interest (ROI) of multiple diagnostic classes with arbitrary shapes and sizes. The typical solution to this problem is to aggregate the representations of fixed-sized patches cropped from these regions to obtain region-level representations. However, naive methods cannot sufficiently exploit the rich contextual information in the complex tissue structures. To tackle these two challenges, we propose a generic method that utilizes graph neural networks (GNN), combined with a self-supervised training method using a contrastive loss. GNN enables representing arbitrarily-shaped ROIs as graphs and encoding contextual information. Self-supervised contrastive learning improves quality of learned representations without requiring labeled data. The experiments using a challenging breast histopathology data set show that the proposed method achieves better performance than the state-of-the-art.
Classification of COVID19 Tweets based on Sentimental Analysis

The year 2020, has seen the advent of a pandemic that has affected the world as we know it globally. The origin reportedly from Wuhan, China, this pandemic is caused by COVID-19 which belongs to the family of Coronavirus. The increase of infection and mortality has shot up exponentially and has left mankind bewildered amongst the remains of the unseen disaster. During these times of hardship mankind has to face with a series of emotions. Analysis of all these emotions becomes a primary target for the well-being of an individual and mankind as a whole. The main motive of our study is to analyze these emotions correctly. Gathering these big chunks of data about this study from different social platforms like Twitter, Facebook, Instagram, etc. plays a major role. For this study we will be considering only the corona virus related tweets from Twitter. Analysis of all these tweets will give us a proper insight about the real emotions that the people has to face during these COVID-19 times. The main objective is to work with multinomial attributed to assess the sentiments more precisely. The next step is cleaning the data and labelling them for further processing. Hereafter a model is developed which is used to access the data and then predict the actual sentiment behind the tweet. The data is assessed using the binary-class and multi-class property with the cross-data evaluation of various machine learning algorithms to form the model. After tedious training of models, it is seen that the proposed model gives us a 96.58% accuracy with Support Vector Machine algorithm.
Smart Trolley

On holidays and weekends, usually we see a huge rush especially in the metro cities. Huge offers and discounts make more rush in markets. In shopping malls, normally we get a trolley in which we put all the items intended to be purchased from different shelves.After putting all items in trolley, we approach the counter for billing and it results in long queues at counter. This project presents a solution to these long queues. To fulfill this, a barcode reader and a LCD screen are attached to the trolley. The barcode automatically detects the barcode on the product when we put the item in the trolley and with the help of a screen, the item details will be displayed. In this way the cost of the item gets added to bill. By this practice trolley will itself do all the billing and problem of long queue on counters will be solved.
Threshold-Based Hierarchical Clustering for Person Re-Identification

Unsupervised domain adaptation is a challenging task in person re-identification (re-ID). Recently, cluster-based methods achieve good performance; clustering and training are two important phases in these methods. For clustering, one major issue of existing methods is that they do not fully exploit the information in outliers by either discarding outliers in clusters or simply merging outliers. For training, existing methods only use source features for pretraining and target features for fine-tuning and do not make full use of all valuable information in source datasets and target datasets. To solve these problems, we propose a Threshold-based Hierarchical clustering method with Contrastive loss (THC). There are two features of THC: (1) it regards outliers as single-sample clusters to participate in training. It well preserves the information in outliers without setting cluster number and combines advantages of existing clustering methods; (2) it uses contrastive loss to make full use of all valuable information, including source-class centroids, target-cluster centroids and single-sample clusters, thus achieving better performance. We conduct extensive experiments on Market-1501, DukeMTMC-reID and MSMT17. Results show our method achieves state of the art.
ELMo and BERT in Semantic Change Detection for Russian

We study the effectiveness of contextualized embeddings for the task of diachronic semantic change detection for Russian language data. Evaluation test sets consist of Russian nouns and adjectives annotated based on their occurrences in texts created in pre-Soviet, Soviet and post-Soviet time periods. ELMo and BERT architectures are compared on the task of ranking Russian words according to the degree of their semantic change over time. We use several methods for aggregation of contextualized embeddings from these architectures and evaluate their performance. Finally, we compare unsupervised and supervised techniques in this task.
EXMA: A Genomics Accelerator for Exact-Matching

Genomics is the foundation of precision medicine, global food security and virus surveillance. Exact-match is one of the most essential operations widely used in almost every step of genomics such as alignment, assembly, annotation, and compression. Modern genomics adopts Ferragina-Manzini Index (FMIndex) augmenting space-efficient Burrows-Wheeler transform (BWT) with additional data structures to permit ultra-fast exact-match operations. However, FM-Index is notorious for its poor spatial locality and random memory access pattern. Prior works create GPU-, FPGA-, ASIC- and even process-in-memory (PIM)based accelerators to boost FM-Index search throughput. Though they achieve the state-of-the-art FM-Index search throughput, the same as all prior conventional accelerators, FM-Index PIMs process only one DNA symbol after each DRAM row activation, thereby suffering from poor memory bandwidth utilization. In this paper, we propose a hardware accelerator, EXMA, to enhance FM-Index search throughput. We first create a novel EXMA table with a multi-task-learning (MTL)-based index to process multiple DNA symbols with each DRAM row activation. We then build an accelerator to search over an EXMA table. We propose 2-stage scheduling to increase the cache hit rate of our accelerator. We introduce dynamic page policy to improve the row buffer hit rate of DRAM main memory. We also present CHAIN compression to reduce the data structure size of EXMA tables. Compared to state-of-the-art FM-Index PIMs, EXMA improves search throughput by 4.9 ×, and enhances search throughput per Watt by 4.8×.