Reviewerly/exHarmony huggingface dataset:
Feature Extraction and Target Recognition of Moving Image Sequences

The detection and recognition of moving objects in image sequence images involve many aspects, such as pattern recognition, image processing, and computer vision. The main difficulties of target detection and recognition are complex background interference, local occlusion, real-time recognition, illumination changes, target size type changes, etc. However, it is very difficult to solve these problems in practical applications. This article introduces image pre-processing for the pre-processing of image sequences. Selectively we highlight the visually obvious features that are helpful for target detection in the image, weaken the image background and features that are not related to the target, and improve the quality of the image sequence. A multi-information integrated probability density estimation kernel integrating gray scale, spatial relationship and local standard deviation information is designed, and the multi-information integrated kernel is used to extract the feature of the moving target. In terms of moving target recognition, Naive Bayes is used as a weak learner. In order to avoid the over-fitting of the classifier caused by high-noise moving image sequence features, the regularized Adaboost recognition model is introduced as a moving target recognition classifier. In order to completely separate the target and the background, we propose a moving target extraction method based on multi-information kernel density estimation, and input relevant target feature description vectors into the regularized Adaboost-based moving target recognition framework. Robust target recognition performance is obtained, and the reliability of target recognition under high noise data is improved.
Big-Five Personality Traits Mapped with Software Development Tasks to Find Most Productive Software Development Teams

The software development lifecycle defines a systematic way of developing good quality software to meet customer expectations about scope, time and budget. Each phase of this development lifecycle is distinct and rigorous which defines an independent body-of-knowledge within the domain of software engineering. Consequently, each phase of development lifecycle demands a different set of skills and personality traits for software engineers to cope-up their work effectiveness to improve the software development process. Hence, it is obligatory to highlight the most relevant personality traits of software engineers working for different phases of development lifecycle. In this regard, this article presents a comprehensive study in which the most relevant personality traits of software engineers have been highlighted for different tasks/phases of software development lifecycle. The big-five personality model has been used to evaluate and correlate the personality of software engineers for each phase due to its wide acceptance in the research and development community.
Efficient and Privacy-Preserving Ride Matching Using Exact Road Distance in Online Ride Hailing Services

Online Ride Hailing (ORH) services enable a rider to request a taxi via a smartphone app in real time. When using ORH services, users (including riders and taxis) have to submit their locations to the ORH server. With received locations, the ORH server makes online ride matching between riders and taxis. There are serious privacy concerns for users to reveal location information to ORH servers. In this article, we propose an efficient and privacy-preserving ride matching scheme for ORH services, named EPRide. EPRide can find the taxi with the minimum road distance to serve an incoming rider, while protecting the location information of both taxis and riders against ORH servers or other curious servers. In EPRide, we propose an efficient exact shortest road distance computation approach over encrypted data, which converts road distance computation into Hamming distance computation over packed ciphertexts by using road network hypercube embedding and somewhat homomorphic encryption. Meanwhile, we design a secure comparison protocol, which efficiently compares encrypted distances in parallel by using ciphertexts blinding and packing, without leaking any distance. Theoretical analysis and experimental evaluations show that EPRide is secure, accurate and efficient.
Uncertainty based active learning with deep neural networks for inertial gait analysis

Inertial measurement units (IMUs) enable the capture of human motion in-field. This can be used in various analysis applications ranging from the medical domain over sports to daily activities. Manual data labelling for classification or regression tasks is often time-consuming and cumbersome, in particular when it comes to larger datasets. Active learning algorithms try to reduce the labelling cost, for instance via suggesting samples with high prediction uncertainty that should be explicitly labelled. In this work, we apply two probabilistic deep learning approaches on different state-of-the-art deep neural network structures for timeseries data, with uncertainty based measures to actively query sample labels. This is applied to gait phase classification using IMU data as inputs. We demonstrate the performance on a newly captured dataset, where we obtained high accuracy (up to 96%) with up to 43% fewer samples as compared to random sampling in an online setting. In an offline setting, we could extract heel strike and toe-off foot events with an accuracy of 99.9% using active learning strategies with up to 58% fewer samples.
Modular approach for optimal pipeline layout

Oil and gas production systems are getting deeper, more complex, and far away offshore where energy companies are targeting further resources. These complexities are transforming the problem of field layout design optimization into a much more pressing need. Considering the various pipeline design and layout constraints and associated investment costs, planning and development of production gathering and transmission pipeline networks for oil and gas fields is gaining further importance in field development planning. The optimization of transmission and gathering pipeline networks is conducted to accommodate the encountered topological complexities and significantly reduce total investment cost for the corresponding fields. Although many optimization schemes are developed and widely available in literature, these methods are either prohibitively slow with exhaustive search required, or they don't account for the various topological complexities typically encountered in real scenarios. Thus, the deficiency associated to these optimization schemes becomes drastically more limiting in the case of a concept-select phase of field development planning where many scenarios need to be assessed in a relatively short timeframe. In this study, the optimal shortest path A* algorithm is introduced to the field of pipeline placement optimization. A* is successfully used in other applications such as unmanned aerial vehicle (UAV) and robots motion planning. A benchmark comparison is presented with the Dijkstra algorithm; another algorithm that assures optimal shortest path solution that was recently introduced into this field. This comparison is performed on a varied set of pipeline layout scenarios accounting for different topological complexities and dynamic conditions. The conducted tests show the superiority of the A* algorithm in terms of accounting for the application heuristics and assuring an optimal and efficient solution.
Computer Assisted Classification Framework for Detection of Acute Myeloid Leukemia in Peripheral Blood Smear Images

Acute Myeloid Leukemia (AML) affects myeloma cells in human blood. The manual detection of AML cells in peripheral blood smear image is a difficult task and also needs more time. The computer assisted classification framework for AML cell is proposed in this work. AML causes changes in nu- clues of myeloma cells. The microscopic images of myeloid cell are obtained from online database. Pre-processing and segmentation process are performed to obtain nucleus and cell mask. The shape of nucleus is irregular and its texture is also changed when the human is affected by acute myeloid leukemia. Separation of nucleus and cytoplasm is achieved through k-means clustering algorithm. The morphological feature vectors are used as input for classifiers. Classification framework is done by using different machine learning classifiers and found that Random forest classifier gives an accuracy of 95.89%.
Benefit Evaluation of PV Orientation for Individual Residential Consumers

Photovoltaic (PV) installations located in the northern hemisphere must be oriented to the south in order to obtain maximal annual yield. This is mainly driven by the remuneration mechanisms which incentivize maximal energy production to a certain extent. Nowadays, such support mechanisms are declining or even phased out in many countries. Hence, self-consuming the produced energy is getting more viable. In order to match better the load demand pattern, the azimuth angle of a PV installation could be changed or oriented towards multiple directions. This article investigates the benefits of PV installations facing other directions than the south. Therefore, the Hay &amp; Davies transposition model has been used to calculate the in-plane irradiance, as it is found in the literature to be the most accurate for non-south faced PV installations. In order to determine the benefit, a large dataset of real measured consumption profiles has been used and then divided according to their annual consumption. Large consumers with an oversized east/west-oriented PV installation especially take advantage. The self-sufficiency index (SSI) is found to increase with almost 0.94 percent points, while the self-consumption index (SCI) increases with 6.46 percent points. The peak reduction is assessed by calculating the annual moving average of the month peaks. It is found that this moving average month peak reduction is marginal. Lastly, the reduction in storage capacity is found to be not that significant, although in terms of battery utilization it is found that the number of discharge cycles is reduced with 6%.
Automatic Electronic Invoice Classification Using Machine Learning Models

Electronic invoicing has been mandatory for Italian companies since January 2019. All the invoices are structured in a predefined xml template which facilitates the extraction of the information. The main aim of this paper is to exploit the information contained in electronic invoices to build an intelligent system which can simplify accountants’ work. More precisely, this contribution shows how it is possible to automate part of the accounting process: all the invoices of a company are classified into specific codes which represent the economic nature of the financial transactions. To accomplish this classification task, a multiclass classification algorithm is proposed to predict two different target variables, the account and the VAT codes, which are part of the general ledger entry. To apply this model to real datasets, a multi-step procedure is proposed: first, a matching algorithm is used for the reconstruction of the training set, then input data are elaborated and prepared for the training phase, and finally a classification algorithm is trained. Different classification algorithms are compared in terms of prediction accuracy, including ensemble models and neural networks. The models under comparison show optimal results in the prediction of the target variables, meaning that machine learning classifiers succeed in translating the complex rules of the accounting process into an automated model. A final study suggests that best performances can be achieved considering the hierarchical structure of the account codes, splitting the classification task into smaller sub-problems.
Generating Multi-label Adversarial Examples by Linear Programming

Deep neural networks (DNNs) are used in various domains, such as image classification, natural language processing and face recognition, etc. However, the presence of malicious examples, generated by specific methods, could result in DNNs misclassification. Such maliciously modified examples are called adversarial examples. So far, most work about adversarial examples mainly focuses on the multi-class classification tasks, and only a little work has been done in the field of multi-label classification.In this study, we have proposed a novel algorithm that generates effective multi-label adversarial examples by solving a linear programming problem (MLA-LP). We minimize the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> norm of distortion while constraining the changes in the label loss of the example after being perturbed. Then, we transform this constrained optimization problem into a linear programming problem for reducing the time cost. In comparison to the existing multi-label classification model attack algorithms, the attack performance of the proposed MLA-LP is found to be competitive, and the adversarial examples generated by MLA-LP have significantly smaller distortions.
Long-Tail Zero and Few-Shot Learning via Contrastive Pretraining on and for Small Data

Preserving long-tail, minority information during model compression has been linked to algorithmic fairness considerations. However, this assumes that large models capture long-tail information and smaller ones do not, which raises two questions. One, how well do large pretrained language models encode long-tail information? Two, how can small language models be made to better capture long-tail information, without requiring a compression step? First, we study the performance of pretrained Transformers on a challenging new long-tail, web text classification task. Second, to train small long-tail capture models we propose a contrastive training objective that unifies self-supervised pretraining, and supervised long-tail fine-tuning, which markedly increases tail data-efficiency and tail prediction performance. Third, we analyze the resulting long-tail learning capabilities under zero-shot, few-shot and full supervision conditions, and study the performance impact of model size and self-supervision signal amount. We find that large pretrained language models do not guarantee long-tail retention and that much smaller, contrastively pretrained models better retain long-tail information while gaining data and compute efficiency. This demonstrates that model compression may not be the go-to method for obtaining good long-tail performance from compact models.
FleetChain: A Secure Scalable and Responsive Blockchain Achieving Optimal Sharding

Sharding blockchains are promising in improving transaction throughput and achieving network scalability. Intra-shard consensus and cross-shard communication are two essential parts for almost every kind of sharding blockchain. However, some security problems still exist in current sharding solutions such as replay attacks, and there is still room for improvement in efficiency.
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison

We prove performance guarantees of two algorithms for approximating $Q^\star$ in batch reinforcement learning. Compared to classical iterative methods such as Fitted Q-Iteration---whose performance loss incurs quadratic dependence on horizon---these methods estimate (some forms of) the Bellman error and enjoy linear-in-horizon error propagation, a property established for the first time for algorithms that rely solely on batch data and output stationary policies. One of the algorithms uses a novel and explicit importance-weighting correction to overcome the infamous double sampling difficulty in Bellman error estimation, and does not use any squared losses. Our analyses reveal its distinct characteristics and potential advantages compared to classical algorithms.
Privacy Preservation Between Privacy and Utility Using ECC-based PSO Algorithm

In this paper, we propose a new privacy utility method, where privacy is maintained by lightweight Elliptical Curve Cryptography (ECC) and utility is maintained through Particle Swarm Optimization (PSO) clustering. Initially, the datasets are clustered using PSO, and then the privacy of clustered datasets is maintained using ECC. The proposed method is experimented over medical datasets, and it is compared with existing methods through several performance metrics like clustering accuracy, F-measure, and data utility and privacy metrics. The evaluation shows that proposed method obtains improved privacy preservation using clustering algorithm than existing methods.
Ontology for preserving the knowledge base of traditional dances (OTD)

Purpose Systematic organization of domain knowledge has many advantages in archiving, sharing and retrieval of information. Ontologies provide a cushion for such practices in the semantic Web environment. This study aims to develop an ontology that can preserve the knowledge base of traditional dance practices. Design/methodology/approach It is hypothesized that an ontology-based approach for the chosen domain might boost collaborative research prospects in the domain. A systematic methodology was developed for modeling the ontology based on the analytico-synthetic rule of library classification. Protégé 5.2 was used as an editor for the ontology using the Web ontology language combined with description logic axioms. Ontology was later implemented in a local GraphDB repository to run queries over it. Findings The developed ontology on traditional dances (OTD) was tested using the dances of the Rabha tribes of North East India. Rabha tribes are from an indigenous mongoloid community and have a robust presence in Southeast Asian countries, such as Myanmar, Thailand, Bangladesh, Bhutan and Nepal. The result from HermiT reasoner found the presence of no logical inconsistency in the ontology, while the OOPS! pitfall checker tool reported no major internal inconsistency. The induced knowledge base of traditional dances of the Rabha’s in the developed OTD was further validated based on some competency questions. Research limitations/implications In the growing trend of globalization, preservation of the cultural knowledge base of human societies is an important issue. Traditional dances reflect a strong base of the cultural heritage of human societies as they are closely related to the lifestyle, habitat, religious practices and festivals of a specific community. Originality/value The current study is exclusively designed, keeping in mind the variables of traditional dance domain based on a survey of the user- and domain-specific needs. The ontology finds probable uses in traditional knowledge information systems, lifestyle-based e-commerce sites and e-learning platforms.
Photo Sleuth

Identifying people in historical photographs is important for preserving material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this article, we focus on identifying portraits of soldiers who participated in the American Civil War (1861--65), the first widely photographed conflict. Many thousands of these portraits survive, but only 10%--20% are identified. We created Photo Sleuth, a web-based platform that combines crowdsourced human expertise and automated face recognition to support Civil War portrait identification. Our mixed-methods evaluations of Photo Sleuth one month and 11 months after its public launch showed that it helped users successfully identify unknown portraits and provided a sustainable model for volunteer contribution. We also discuss implications for crowd-AI interaction and person identification pipelines.
WITHDRAWN: Inception ResNet deep transfer learning model for human action recognition using LSTM

In recent days, variety of approaches using deep learning features have been proposed for human action recognition due to the worth of deep neural networks. In this work, we put forward a new deep neural network architecture based on transfer learning [TL] for human action recognition. We illustrate how to progress the recognition of human activities with a video data set of small size using transfer learning. The model constructed is based on Inception ResNet convolutional neural networks (CNN) and long short term model (LSTM).We train the model by extracting feature vectors from Inception_ResNet_v2 and then the output feature vectors from CNN are applied onto the RNN for learning the action sequence. Then we attempt to classify the input videos according to the model trained. He accuracy score of the model was compared against VGG16, ResNet152 and Inception_v3 models studied on. The results prove the LSTM architecture using Inception_ ResNet_v2 provide the best accuracy score of 92%, 91% on UCI 101 and HMDB 51 data sets.
Insight Beyond Numbers: The Impact of Qualitative Factors on Visual Data Analysis

As of today, data analysis focuses primarily on the findings to be made inside the data and concentrates less on how those findings relate to the domain of investigation. Contemporary visualization as a field of research shows a strong tendency to adopt this data-centrism. Despite their decisive influence on the analysis result, qualitative aspects of the analysis process such as the structure, soundness, and complexity of the applied reasoning strategy are rarely discussed explicitly. We argue that if the purpose of visualization is the provision of domain insight rather than the depiction of data analysis results, a holistic perspective requires a qualitative component to to be added to the discussion of quantitative and human factors. To support this point, we demonstrate how considerations of qualitative factors in visual analysis can be applied to obtain explanations and possible solutions for a number of practical limitations inherent to the data-centric perspective on analysis. Based on this discussion of what we call qualitative visual analysis, we develop an inside-outside principle of nested levels of context that can serve as a conceptual basis for the development of visualization systems that optimally support the emergence of insight during analysis.
Functional properties of extended body representations in the context of kinesthesia

A person’s internal representation of his/her body is not fixed. It can be substantially modified by neurological injuries and can also be extended (in healthy participants) to incorporate objects that have a corporeal appearance (such as fake body segments, e.g. a rubber hand), virtual whole bodies (e.g. avatars), and even objects that do not have a corporeal appearance (e.g. tools). Here, we report data from patients and healthy participants that emphasize the flexible nature of body representation and question the extent to which incorporated objects have the same functional properties as biological body parts. Our data shed new light by highlighting the involvement of visual motion information from incorporated objects (rubber hands, full body avatars and hand-held tools) in the perception of one's own movement (kinesthesia). On the basis of these findings, we argue that incorporated objects can be treated as body parts, especially when kinesthesia is involved.