Reviewerly/exHarmony huggingface dataset:
Comprehensive Analysis of Privacy and Data Mining Techniques

Data analytics, also referred to as big data, is a technique for analyzing massive amounts of information in both organized and unstructured forms in order to uncover relevant patterns and trends. It is necessary to design a proper framework for the use of sensitive data, as well as assurances that sensitive data will not be breached or altered, in order to protect big data privacy. Consequently, because big da-ta systems are substantially larger in terms of both scale and velocity than regular big data systems, the privacy rules for traditional database systems do not apply to big data systems, and vice versa. The delivery of new models and algorithms to large-scale data repositories that handle developing distributed contexts such as clouds, social networks, and other similar environments is essential for data privacy preservation and data security in big data environments. The current research is aimed towards achieving this goal. As a result of the usage of data for data analysis, businesses must develop a data-sharing plan that ensures data security while also protecting individual privacy. Data sharing governance is the term used to describe this process. Because of this research, the Chain-PPDM architecture for privacy-preserving massive data sets, which makes use of block chain technology and the Inter Planetary File System, has been developed and is now in use (IPFS). For the purpose of better understanding ChainPPDM, this research adopts a data mining scenario in which a large number of people are participating in examining and detecting patterns from vast volumes of data. We used data from healthcare organizations and online food delivery firms to test and validate our method.
Comparison of Machine Learning Methods for Short-Term Solar Irradiation Forecast

The transitory character of solar power has led researchers to forecast solar irradiance to exploit this green energy befitting the electrical demand. This can help the photovoltaic (PV) generators to participate in energy markets generating revenue without stressing the power system as a whole. Accurate forecasting based on weather inputs can reduce large-scale backup requirements. Here, three types of regression: random forest regression, gradient boosting regression, and adaptive boosting regression method are studied on hourly weather data of Singapore for one year to forecast solar irradiance in the short-term. The hyperparameters are set after a thorough trial. The effectiveness of the methods which are being proposed is evaluated based on Mean Square Error (MSE), Root Mean Square Error (RMSE), and regression score indices that indicate random forest regression as a better method than the other two.
Enabling Autonomous Teams and Continuous Deployment at Scale

In this article, we advise on transitioning to a more agile delivery model for large-scale agile development projects based on experience from the Parental Benefit Project of the Norwegian Labour and Welfare Administration. The project modernized a central part of the organization's IT portfolio and included up to ten development teams working in parallel. The project successfully changed from using a delivery model that combined traditional project management elements and agile methods to a more agile delivery model with autonomous teams and continuous deployment. This transition was completed in tandem with the project execution. We identify key lessons learned, which will be useful for other organizations considering similar changes and report how the new delivery model reduced risk and opened up a range of new possibilities for delivering the benefits of digitalization.
Symbolic Abstract Heaps for Polymorphic Information-Flow Guard Inference

In the realm of sound object-oriented program analyses for information-flow control, very few approaches adopt flow-sensitive abstractions of the heap that enable a precise modeling of implicit flows. To tackle this challenge, we advance a new symbolic abstraction approach for modeling the heap in Java-like programs. We use a store-less representation that is parameterized with a family of relations among references to offer various levels of precision based on user preferences. This enables us to automatically infer polymorphic information-flow guards for methods via a co-reachability analysis of a symbolic finite-state system. We instantiate the heap abstraction with three different families of relations. We prove the soundness of our approach and compare the precision and scalability obtained with each instantiated heap domain by using the IFSpec benchmarks and real-life applications.
End-to-End Object Detection with Enhanced Positive Sample Filter

Discarding Non-Maximum Suppression (NMS) post-processing and realizing fully end-to-end object detection is a recent research focus. Previous works have proved that the one-to-one label assignment strategy provides the chance to eliminate NMS during inference. However, this strategy might also result in multiple predictions with high scores due to the inconsistency of label assignment during training. Thus, how to adaptively identify only one positive sample as a final prediction for each Ground-Truth instance remains important. In this paper, we propose an Enhanced Positive Sample Filter (EPSF) to filter out the single positive sample for each Ground-Truth instance and lower the confidence of other negative samples. This is mainly achieved with two components: a Dual-stream Feature Enhancement module (DsFE) and a Disentangled Max Pooling Filter (DeMF). DsFE makes full use of representations trained with different targets so as to provide rich information clues for positive sample selection, while DeMF enhances the feature discriminability in potential foreground regions with disentangled pooling. With the proposed methods, our end-to-end detector achieves a better performances against existing NMS-free object detectors on COCO, PASCAL VOC, CrowdHuman and Caltech datasets.
Towards Intelligent Processing of Electronic Invoices: The General Framework and Case Study of Short Text Deep Learning in Brazil

An electronic invoice (E-invoice) is a kind of document that records the transactions of goods or services and then stores and exchanges them electronically. E-invoice is an emerging practice and presents a valuable source of information for many areas. Dealing with these invoices is usually a very challenging task. Information reported is often incomplete or presents mistakes. Before any meaningful treatment of these invoices, it is necessary to evaluate the product represented in each file. This research puts forward a conceptual framework to explain how to apply machine learning technology to extract meaningful information from invoices at different levels of aggregation. Related work in the field is contextualized within a given framework. A study case based on real data from Electronic invoice (NF-e) and Electronic Consumer Invoice (NFC-e) documents in Brazil, related to B2B and retail transactions. We compared traditional term frequency models with the Convolutions sentence classification models. Our experiments show that even if invoice text descriptions are short and there are a lot of errors and typos, simple term frequency models can achieve high baseline results on product code assignment.
Verification of Static Signatures using Dynamic Time Warping on Features from High Pressure Points

In this paper, we present a novel methodology for the classification of static handwritten signatures. We extract features from each user’s high-pressure points by filtering the skeletonized-binarized digital signatures at seven different thresholds. We compare the sequences obtained using the Dynamic Time Warping (DTW) algorithm with five metrics for the x and y axes. The resulting distances are used as features in a binary dataset of genuine-genuine and genuine-forgery signatures with fourteen features. We train and compare three classifiers: Random Forest (RF), extreme gradient boosting (XGBoost), and Extreme Learning Machines (ELM) on the processed data from the MCYT-75 dataset [12]. Our results show that all models perform with similar accuracy, where the Euclidean and Manhattan metrics perform the best.
Effect of herding behavior and overconfidence bias on investor’s financial decisions: A case of investment in crypto currency in Pakistan

There are various behavioral aspects that influence the financial decisions of individual investors, such as overconfidence, herding behavior, loss aversion, and regret aversion. Other psychological elements may play a substantial influence in deciding the success or failure of any financial action. The primary aims of the present research article are to examine the effects of overconfidence bias and herding behavior on individual financial decisions regarding crypto currency investment. The markets for crypto currencies are extremely sensitive to any minute or significant changes in the economy, as well as to any changes in the individual behaviors of investors. To achieve these aims, 650 questionnaires were distributed to individual investors employed by various financial institutions, multinational corporations, including oil and gas exploration and production companies, operating in Pakistan, individual investors dealing in stocks, and students regardless of investment size. Purposive sampling was used to choose 311 valid responses from a total of 593. Those investors that engage in crypto currency investments were deemed valid responses. Classifications of investments were outlined, and demographic information was requested from respondents. SPSS-based empirical analysis provided the basis for the conclusions. The primary findings indicate that both overconfidence bias and herding tendency have a favorable and statistically significant impact on investors' financial decisions when investing in cryptocurrencies. In this study's sample, however, the majority of investors were overconfident in their crypto currency investing decisions.
Full Utilization of a Single Image by Characterizing Multiple Regions of Interest for Line Tracing

This paper presents a new method of image processing for the line tracing task, which is one of the simple and fundamental tasks that has been applied to an unmanned system, utilizing multiple regions of interest to draw information from the entire image which was discarded in traditional image processing method for more accurate and flexible line trace. This new method divides the acquired image by machine vision into 3 regions: feedback region, prediction region, and inspection region. And different process was applied to each region to acquire parameters depending on the characteristics of each region that can enhance line tracing performance. In this paper, parameters of the new method are applied to the proportional control method and implemented to the robot arm and the camera and evaluated with the basic proportional control by comparing adaptability to a sharp curve. Consequently, the new method provided more adaptability in line tracing compared to the traditional single region of interest method.
Data abnormal fluctuation detection and early warning

This paper focuses on abnormal data evaluation index, tries to use quantitative abnormal data evaluation index and abnormal degree, so as to realize abnormal fluctuation detection and early warning.firstly analyzes whether the sensor data have fluctuation or not. By comparing the time series data difference and the average value of the difference, the fluctuating data, i. e. the abnormal data, can be filtered out. According to the five definitions and measurement standards of volatility characteristics, the risk assessment model of abnormal data is established, and the judgment of non-risk and risk is realized.And we set the two risk volatility characteristics as two quantitative indicators of abnormal degree of risk anomaly data, so we focus on the quantitative analysis of abnormal degree. By using entropy weight method, the weight of these two indexes is assigned, and then the abnormal degree is quantified by mechanism analysis.
User Identification Based on Mouse Operation Toward Automatic Home Appliance Configuration

Smart home appliances such as coffee machines with precise taste control are prevalent nowadays. These home appliances are often shared with family members, while the individual has a preferred configuration. In this paper, we propose an automatic home-appliance configurator that identifies a user and automatically configures the home appliance based on the user’s preference. As a first step of the proposed system, we present a user identification system based on mouse operations to wake a computer from a sleep state, which is an example of daily home appliance control operations. The key idea is to extract the features of user-specific mouse operations and identify the user by supervised learning. We collected mouse operation logs from 24 subjects and confirmed that the proposed system identified users with an accuracy of 93.5%.
Radio Frequency Fingerprints Extraction for LTE-V2X: A Channel Estimation Based Methodology

The vehicle-to-everything (V2X) technology has recently drawn attention from both academic and industrial areas. However, the openness of the wireless communication system makes it more vulnerable to identity impersonation and information tampering. How to employ the powerful radio frequency fingerprint (RFF) identification technology in V2X systems turns out to be a vital and challenging task. In this paper, we propose a novel RFF extraction method for Long Term Evolution-V2X (LTE-V2X) systems. In order to conquer the difficulty of extracting transmitter RFF in the presence of wireless channel and receiver noise, we first estimate the wireless channel which excludes the RFF. Then, we remove the impact of the wireless channel based on the channel estimate and obtain initial RFF features. Finally, we conduct RFF denoising to enhance the quality of the initial RFF. Simulation and experiment results both demonstrate that our proposed RFF extraction scheme achieves a high identification accuracy. Furthermore, the performance is also robust to the vehicle speed.
Exploring team collective voice: the case of software development teams in Vietnam

Purpose The team voice (TV) concept has been largely understudied, with different definitions and understandings among prior research creating confusion for readers and future researchers. This study proposes a unified definition and connotation of TV that captures TV's collective meaning and highlights TV's vital role in Eastern contexts. Design/methodology/approach This study applied the constructivist grounded theory (CGT) methodology to collect and analyze qualitative data from Vietnam software companies. A total of 32 software development managers and employees were interviewed regarding TV behavior of the managers and employees. Findings The findings emphasize that TV should not be understood as team members' average or aggregate voice. Rather, TV should be understood as the shared voice of team members toward higher management, other teams or individuals in the organization in an attempt to challenge/change the status quo [team collective voice (TCV)]. The findings also reveal the characteristics of TCV (purpose, voicing and consensus mechanisms), TCV's different types and important roles in the context of an Eastern country operating under weak institutions. Originality/value This exploratory study was able to clarify different connotations of employee voice at the team level, which helps raise awareness among scholars on the collective nature of TV and guides successive researchers away from inconsistent understandings of the term. The study also reveals certain institutional conditions that foster this type of voice and suggests the employee voice concept should not be examined independently from the concept's institutional context. The proposed typology contributes comprehensively to this conceptual work of TCV as the topology reveals the concept's multidimensionality and aids future research on measurement construction.
Cluster quality analysis based on SVD, PCA-based k-means and NMF techniques: an online survey data

With the increase in computerisation in every field, a huge amount of data is collected from everywhere. Therefore, extracting useful information has become a necessary task in the present era. Data mining helps to extract the information and uncover the relationship among the data. Clustering is an unsupervised technique used for partitioning objects into several groups and discover the hidden relationship among the data. There are many techniques used for clustering. In this article, a comparative study and analysis of three famous clustering techniques are done: principal component analysis (PCA), singular value decomposition (SVD) and non-negative matrix factorisation (NMF) for the clustering of a database. The database collected through a set of questionnaire surveys related to day-to-day activities. Then a comparison of their natural clustering ability is being done. Also, the use of normalised mutual information (NMI) and purity as two-cluster quality evaluation measures are explored. Then an attempt is made to show the amount of information from the original data matrix that the approximated data matrix contains. Next, to verify the accuracy of the variance covered by the approximated data matrix, the Frobenius norm is used. At last, the results are compared with the variance covered by using singular values, and a detailed analysis of each data matrix is explained.
Embedding teacher scaffolding in a mobile technology supported collaborative learning environment in English reading class: students' learning outcomes, engagement, and attitudes

As one of the effective strategies, teacher scaffolding in technology supported language learning needs more exploration considering the advance of technologies and the needs of students. This study implemented an innovative reading program that integrated teacher scaffolding into a mobile technology supported collaborative learning environment to engage students of English as a foreign language in reading activities and to develop motivations in reading. The program was supported by a learning management system of Edmodo, and was conducted in a local secondary school. During implementation, multiple data such as reading test results, log data and interview data were collected to examine student learning outcomes, engagement, and attitudes. The findings confirmed the positive impacts the program made on students' reading performance. The study also identified the effective features of the learning system and the provision of teacher scaffoldings, which will inform future design of school-based language learning programs supported by mobile technologies.
Source-Free Unsupervised Domain Adaptation via Denoising Mutual Learning

Unsupervised domain adaptation (UDA) aims to transfer knowledge from related but different labeled source domains to new unlabeled target domains. The existing UDA methods need to access the source domain data in the adaptation process. In some practical application scenarios, this assumption is no longer applicable due to the time-consuming data transmission and data privacy. In this paper, we are committed to solving the problem of source-free unsupervised domain adaptation (SFUDA). In this process, we only use the trained source domain model without accessing the source domain data. Many SFUDA methods use the pseudo labeling approach for selfsupervised learning. However, there is a lot of noise in the pseudo labels due to the domain shift, which will optimize the model in the wrong direction and affect the performance of the model in the target domain. In order to solve this problem, we propose a method for source-free unsupervised Domain Adaptation via denoising Mutual learning, dubbed as MutualDA. In self-training route, it is believed that the model can capture the distribution of target data in the training process, thus its output can be used as an effective supervision, but it is not advisable to use the output of the model to supervise itself, which will enlarge the error. Therefore, our method trains two symmetrical parallel models to supervise their learning with each other’s outputs, and the two models constantly transfer their learned knowledge to each other, which effectively reduces the impact of noise in pseudo labels and optimizes each model in a more correct direction. Experimental results show that compared with the existing UDA and SFUDA methods, our proposed method achieves the state-of-the-art performance on multiple datasets.
An Ethereum Blockchain-Based Healthcare System Using Smart Contract

One of the prerequisites for the medical service industry is interoperability, handled by humans or machines to exchange medical records that contain confidential information of patients effectively and reliably. The fast take-up of digitization in the medical sector has increased the generation of massive records of healthcare information. Different organizations manage medical records in several ways and there is no standard procedure for maintaining medical records. Considering the confidentiality and integrity of patient information, this research work introduces a secure, transparent, and authentication approach for storing, sharing as well as authorized access of health records in the ethereum platform using decentralized technology blockchain. Each healthcare stakeholder (Patient, Doctor, and Pharmacy) is authenticated with their cryptocurrency wallet address by Regulatory Authority (RA). Patients can migrate to other doctors without re-authentication of their distributed identity which minimizes the delay of information exchange throughout the same network. Pharmacy can generate a new ledger of the sold medicine list from recommendations. JavaScript VM and injected web3 environment of Ethereum Virtual Machine (EVM) platforms are used for the implementation of smart contracts in solidity. The experimental outcome shows, with the use of the Markle tree and hash in the smart contract, integrity, verifiability, privacy, security of the patient records have been achieved, and finally manifest comparison with the proposed and existing.
Emotion Detection from Natural Text Using Embedding Techniques

Automatic emotion detection from natural text or reviews or blog posts is a part of affective computing and a challenging area of research that has attracted growing attention due to its potentially useful applications, especially for computational social systems. Though there has been ample research on the topic of determining sentiment based on `positivity’ or `negativity’ from texts, detecting emotions such as `anger’, `happiness’, `sadness’, or `fear’ has yet to meet a lot of advances. Emotion detection from texts such as posts and feedback can provide a better view of the public sentiment and richer feature space for neural networks. Recent text embedding techniques have achieved promising results in different natural language processing tasks. Thus, this paper has mulled over the current state-of-the-art embedding techniques and suggested emotion detection models that identify emotions such as `anger’, `happiness’, `sadness’, or `fear’ from texts using them. We have also introduced a new hybrid text embedding approach for emotion detection named DocDict. The proposed approach effectively detects emotions from the texts using the combined effects of the popular Doc2Vec embedding technique and the Keyword-based technique. From experimental results, we have observed that the proposed DocDict method can significantly outperform other approaches for the same benchmark datasets.