Reviewerly/exHarmony huggingface dataset:
Blockchain Technology: Application in the Financial Industry

Cryptocurrencies and their underlying blockchain technology are being out as the next big thing after the creation of the internet. The Blockchain is an encrypted, distributed database that records data, or in other words, it is a digital ledger of any transactions, contracts - that needs to be independently recorded. Blockchain has been initially launched as an approach to payment transactions based on cryptography to provide an alternative mechanism for the trust between two transacting parties. Blockchain technology has sparked a lively debate among researchers. This article tried to study the applications of blockchain technology in the financial industry.
ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations

Modelling persuasion strategies as predictors of task outcome has several real-world applications and has received considerable attention from the computational linguistics community.However, previous research has failed to account for the resisting strategies employed by an individual to foil such persuasion attempts.Grounded in prior literature in cognitive and social psychology, we propose a generalised framework for identifying resisting strategies in persuasive conversations.We instantiate our framework on two distinct datasets comprising persuasion and negotiation conversations.We also leverage a hierarchical sequence-labelling neural architecture to infer the aforementioned resisting strategies automatically.Our experiments reveal the asymmetry of power roles in non-collaborative goal-directed conversations and the benefits accrued from incorporating resisting strategies on the final conversation outcome.We also investigate the role of different resisting strategies on the conversation outcome and glean insights that corroborate with past findings.We also make the code and the dataset of this work publicly available at https://github.com/americast/resper.
Reflection on the center for digital scholarship in China: a case study on space redesign

Purpose Focusing on two particularly challenging issues facing Chinese academic libraries – space constraints and the trending of digital scholarship services, this paper aims to explore which spaces students and faculty wanted and how to leverage low-use spaces and growing digital scholarship services to build the Center for Digital Scholarship (CDS) to meet their demands. Design/methodology/approach The participant observation method was used in the launch stage of the space redesign from May 2016 to October 2018. The usage analysis method was used to reveal the use of the renovated spaces and assess the success of the space redesign when CDS was open to users between October 1, 2017 and September 30, 2018. The usage was gathered from the space reservation system. Findings A hybrid academic service center combining information commons, a collaborative workplace, social spaces and digital scholarship services, the CDS is able to meet the complexity and diversity of users’ needs and fulfill the mission of its university in the context of insufficient funds, space and specialists. While it approaches the goal of the space redesign project, some deficiencies remain to be addressed in the future design and service plan, including separating quiet and noisy areas, flexible arrangements and business process reengineering. Practical implications This study shows a hybrid academic service center can meet the complexity and diversity of users’ needs, despite insufficient funds, space and specialists. To ensure sustainability, digital scholarship services should adapt to local users’ needs and expectations. While the author’s patent service and subject development analysis are local and popular with the users and sectors in his university, they make their services somewhat different from those of the Association of Research Libraries (ARL) members. Originality/value This is one of the few, recent studies on space redesign incorporating digital scholarship services in a well-known academic library in China.
Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell $$\hbox {Ca}^{2+}$$ fluorescence microscopy

Advances in high-resolution live-cell [Formula: see text] imaging enabled subcellular localization of early [Formula: see text] signaling events in T-cells and paved the way to investigate the interplay between receptors and potential target channels in [Formula: see text] release events. The huge amount of acquired data requires efficient, ideally automated image processing pipelines, with cell localization/segmentation as central tasks. Automated segmentation in live-cell cytosolic [Formula: see text] imaging data is, however, challenging due to temporal image intensity fluctuations, low signal-to-noise ratio, and photo-bleaching. Here, we propose a reservoir computing (RC) framework for efficient and temporally consistent segmentation. Experiments were conducted with Jurkat T-cells and anti-CD3 coated beads used for T-cell activation. We compared the RC performance with a standard U-Net and a convolutional long short-term memory (LSTM) model. The RC-based models (1) perform on par in terms of segmentation accuracy with the deep learning models for cell-only segmentation, but show improved temporal segmentation consistency compared to the U-Net; (2) outperform the U-Net for two-emission wavelengths image segmentation and differentiation of T-cells and beads; and (3) perform on par with the convolutional LSTM for single-emission wavelength T-cell/bead segmentation and differentiation. In turn, RC models contain only a fraction of the parameters of the baseline models and reduce the training time considerably.
An Integration of Cardiovascular Event Data and Machine Learning Models for Cardiac Arrest Predictions

Purpose: Predicting and then preventing cardiac arrest of a patient in ICU is the most challenging phase even for a most highly skilled professional. The data been collected in ICU for a patient are huge, and the selection of a portion of data for preventing cardiac arrest in a quantum of time is highly decisive, analysing and predicting that large data require an effective system. An effective integration of computer applications and cardiovascular data is necessary to predict the cardiovascular risks. A machine learning technique is the right choice in the advent of technology to manage patients with cardiac arrest. Methodology: In this work we have collected and merged three data sets, Cleveland Dataset of US patients with total 303 records, Statlog Dataset of UK patients with 270 records, and Hungarian dataset of Hungary, Switzerland with 617 records. These data are the most comprehensive data set with a combination of all three data sets consisting of 11 common features with 1190 records. Findings/Results: Feature extraction phase extracts 7 features, which contribute to the event. In addition, extracted features are used to train the selected machine learning classifier models, and results are obtained and obtained results are then evaluated using test data and final results are drawn. Extra Tree Classifier has the highest value of 0.957 for average area under the curve (AUC). Originality: The originality of this combined Dataset analysis using machine learning classifier model results Extra Tree Classifier with highest value of 0.957 for average area under the curve (AUC). Paper Type: Experimental Research Keywords: Cardiac, Machine Learning, Random Forest, XBOOST, ROC AUC, ST Slope.
QuicK-means: accelerating inference for K-means by learning fast transforms

K-means—and the celebrated Lloyd’s algorithm—is more than the clustering method it was originally designed to be. It has indeed proven pivotal to help increase the speed of many machine learning, data analysis techniques such as indexing, nearest-neighbor search and prediction, data compression and, lately, inference with kernel machines. Here, we introduce an efficient extension of K-means, dubbed QuicK-means, that rests on the idea of expressing the matrix of the $$K$$ cluster centroids as a product of sparse matrices, a feat made possible by recent results devoted to find approximations of matrices as a product of sparse factors. Using such a decomposition squashes the complexity of the matrix-vector product between the factorized $$K\times D$$ centroid matrix $${\mathbf {U}}$$ and any vector from $${\mathcal {O}}\left( KD\right)$$ to $${\mathcal {O}}\left( A \log B~ +B\right)$$ , with $$A=\min \left( K,D\right)$$ and $$B=\max \left( K,D\right)$$ , where $$D$$ is the dimension of the data. This drastic computational saving has a direct impact in the assignment process of a point to a cluster. We propose to learn such a factorization during the Lloyd’s training procedure. We show that resorting to a factorization step at each iteration does not impair the convergence of the optimization scheme, and demonstrate the benefits of our approach experimentally.
A Fuzzy AHP Approach to Evaluate the Strategic Design Criteria of a Smart Robotic Powered Wheelchair Prototype

In the product development process of a prototype, the most noteworthy task is to select important engineering characteristics (ECs) to avoid early stage failures such as design manufacturing, assembly, and operating errors. Hence, it is very essential to identify and reveal the conflicting design criteria with the various labels of importance associated with it. In this work, an investigation on the prototype design of a smart robotic powered wheelchair (SRPW) is proposed to select and rank the design criteria based on multi-criteria decision-making (MCDM) methods. We have introduced two new additional design criteria, namely functional performance of wheelchair (FPW) and user immediate environment (UIE) to the existing attributes. Then, we apply analytical hierarchy process (AHP) to analyze the importance of various criteria. Furthermore, a fuzzy analytic hierarchy process (FAHP) is used to validate the results of AHP as the various conflicting criteria’s rating provided by expert’s acumen on the presence of these engineering characteristics could be personalized, unclear, and uncertain. In the present analysis, FPW design criteria turn out to be an important criterion for designing a SRPW, as it comes in second position in the priority level following the manufacturing cost criterion out of seven nos. of design criteria that are identified, selected, and analyzed for the proposed wheelchair. Whereas UIE placed fifth rank in this analysis and manufacturing cost as a criterion has the highest weight of 0.3657 corresponding to other ECs. It was found that both AHP and FAHP give similar result regarding the importance of calculated weights assigned to the attributes. So, this study will assist the decision maker in a better way while evaluating different factors required for the early stage design of the product as it analyzes a more number of important criterions.
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability

Abuse on the Internet is an important societal problem of our time.Millions of Internet users face harassment, racism, personal attacks, and other types of abuse across various platforms.The psychological effects of abuse on individuals can be profound and lasting.Consequently, over the past few years, there has been a substantial research effort towards automated abusive language detection in the field of NLP.In this position paper, we discuss the role that modeling of users and online communities plays in abuse detection.Specifically, we review and analyze the state of the art methods that leverage user or community information to enhance the understanding and detection of abusive language.We then explore the ethical challenges of incorporating user and community information, laying out considerations to guide future research.Finally, we address the topic of explainability in abusive language detection, proposing properties that an explainable method should aim to exhibit.We describe how user and community information can facilitate the realization of these properties and discuss the effective operationalization of explainability in view of the properties.
A genre trust model for defending shilling attacks in recommender systems

Abstract Shilling attacks have been a significant vulnerability of collaborative filtering (CF) recommender systems, and trust in CF recommender algorithms has been proven to be helpful for improving the accuracy of system recommendations. As a few studies have been devoted to trust in this area, we explore the benefits of using trust to resist shilling attacks. Rather than simply using user-generated trust values, we propose the genre trust degree, which differ in terms of the genres of items and take both trust value and user credibility into consideration. This paper introduces different types of shilling attack methods in an attempt to study the impact of users’ trust values and behavior features on defending against shilling attacks. Meanwhile, it improves the approach used to calculate user similarities to form a recommendation model based on genre trust degrees. The performance of the genre trust-based recommender system is evaluated on the Ciao dataset. Experimental results demonstrated the superior and comparable genre trust degrees recommended for defending against different types of shilling attacks.
CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

This Project is focused on credit card fraud detection in real world scenarios. Nowadays credit card frauds are drastically increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this proposed project we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming difficult to track the behavior and pattern of criminal transactions. To come up with the solution one can make use of technologies with the increase of machine learning, artificial intelligence and other relevant fields of information technology; it becomes feasible to automate this process and to save some of the intensive amounts of labor that is put into detecting credit card fraud. Initially, we will collect the credit card usage data-set by users and classify it as trained and testing dataset using a random forest algorithm and decision trees. Using this feasible algorithm, we can analyze the larger data-set and user provided current data-set. Then augment the accuracy of the result data. Proceeded with the application of processing of some of the attributes provided which can find affected fraud detection in viewing the graphical model of data visualization. The performance of the techniques is gauged based on accuracy, sensitivity, and specificity, precision. The results is indicatedconcerning the best accuracy for Random Forest are unit 98.6% respectively.
Multilingual and cross-lingual document classification: A meta-learning approach

The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods.In this work, we propose a meta-learning approach to document classification in limitedresource setting and demonstrate its effectiveness in two different settings: few-shot, crosslingual adaptation to previously unseen languages; and multilingual joint training when limited target-language data is available during training.We conduct a systematic comparison of several meta-learning methods, investigate multiple settings in terms of data availability and show that meta-learning thrives in settings with a heterogeneous task distribution.We propose a simple, yet effective adjustment to existing meta-learning methods which allows for better and more stable learning, and set a new state of the art on several languages while performing on-par on others, using only a small amount of labeled data.
DAPter: Preventing User Data Abuse in Deep Learning Inference Services

The data abuse issue has risen along with the widespread development of the deep learning inference service (DLIS). Specifically, mobile users worry about their input data being labeled to secretly train new deep learning models that are unrelated to the DLIS they subscribe to. This unique issue, unlike the privacy problem, is about the rights of data owners in the context of deep learning. However, preventing data abuse is demanding when considering the usability and generality in the mobile scenario. In this work, we propose, to our best knowledge, the first data abuse prevention mechanism called DAPter. DAPter is a user-side DLIS-input converter, which removes unnecessary information with respect to the targeted DLIS. The converted input data by DAPter maintains good inference accuracy and is difficult to be labeled manually or automatically for the new model training. DAPter’s conversion is empowered by our lightweight generative model trained with a novel loss function to minimize abusable information in the input data. Furthermore, adapting DAPter requires no change in the existing DLIS backend and models. We conduct comprehensive experiments with our DAPter prototype on mobile devices and demonstrate that DAPter can substantially raise the bar of the data abuse difficulty with little impact on the service quality and overhead.
Intelligent Feature Selection Methods: A Survey

Consider feature selection is the main in intelligent algorithms and machine learning to select the subset of data to help acquire the optimal solution. Feature selection used an extract the relevance of the data and discarding the irrelevance of the data with increment fast to select it and to reduce the dimensional of dataset. In the past, it used traditional methods, but these methods are slow of fast and accuracy. In modern times, however, it uses the intelligent methods, Genetic algorithm and swarm optimization methods Ant colony, Bees colony, Cuckoo search, Particle optimization, fish algorithm, cat algorithm, Genetic algorithm ...etc. In feature selection because to increment fast, high accuracy and easy to use of user. In this paper survey it used the Some the swarm intelligent method: Ant colony, Bees colony, Cuckoo search, Particle optimization and Genetic algorithm (GA). It done take the related work for each algorithms the swarm intelligent the ideas, dataset and accuracy of the results after that was done to compare the result in the table among the algorithms and learning the better algorithm is discuses in the discussion and why it is better. Finally, it learning who is the advantage and disadvantage for each algorithms of swarm intelligent in feature selection.
Finding and analysing energy research funding data: The EnArgus system

This paper presents the concept, a system-overview, and the evaluation of EnArgus, the central information system for energy research funding in Germany. Initiated by the German Federal Ministry for Economic Affairs and Energy (BMWi), EnArgus establishes a one-stop information system about all recent and ongoing energy research funding projects in Germany. Participants ranging from laypersons to experts were surveyed in three workshops to evaluate both the public and expert interfaces of the EnArgus system in comparison to peer systems. The results showed that the EnArgus system was predominantly evaluated positively by the various participants. It contributes to making the energy sector more transparent and offers clear advantages for professional use compared to similar systems. The system’s semantic processing enables more precise hits and better coverage by including semantically related terms in search results; its intelligence makes it fail-safe, rendering it suitable for areas where poor results can have dire consequences. Reporting on an actual real-world system, the paper also provides a roadmap-view of how electronic filing of administrative project data can be semantically enhanced and opened-up to provide the basis for new ways into the data that are key for future breakthrough AI interfaces.
Generalized quantum process discrimination problems

We study a broad class of quantum process discrimination problems that can handle many optimization strategies such as the Bayes, Neyman-Pearson, and unambiguous strategies, where each process can consist of multiple time steps and can have an internal memory. Given a collection of candidate processes, our task is to find a discrimination strategy, which may be adaptive and/or entanglement assisted, that maximizes a given objective function subject to given constraints. Our problem can be formulated as a convex problem. Its Lagrange dual problem with no duality gap and necessary and sufficient conditions for an optimal solution are derived. We also show that if a problem has a certain symmetry and at least one optimal solution exists, then there also exists an optimal solution with the same type of symmetry. A minimax strategy for a process discrimination problem is also discussed. As applications of our results, we provide some problems in which an adaptive strategy is not necessary for optimal discrimination. We also present an example of single-shot channel discrimination for which an analytical solution can be obtained.
The Effect of Human Resource Management Practices on Employee Performance

The present study examines the relationship between HRM practices and performance of employee to be tested in Punjab police, Pakistan. The sample size in this study is 368 respondents that consists of Inspectors and Sub-inspectors of Punjab Police Department in Pakistan. We collected data using questionnaire and analysis using Smart-PLS. Our result discovered that job rotation, training and development, compensation, career planning, and performance appraisal have a positive affect to Employee Performance in Punjab Policy department, Pakistan. Based on our findings, we suggest the policymakers to provide more intensive compensation to employees, optimizing job rotation, implementing training and development, provide appropriate career planning, and making advance performance appraisal in order to improve the performance of Punjab police employees in Pakistan. This study extends existing literature of Employee Performance, it delivers new understandings on the conception and role of job rotation, training and development, compensation, career planning, and performance appraisal to stimulate the reformation of Employee Performance in the government institution such as the Punjab police department.