Reviewerly/exHarmony huggingface dataset:
A Public Repository to Improve Replicability and Collaboration in Deep Learning for HAR

Deep learning methods have become an almost default choice of machine learning approach for human activity recognition (HAR) systems that operate on time series data, such as those of wearable sensors. However, the implementations of such methods suffer from complex package dependencies, obsolescence, and subtleties in the implementation which are sometimes not well documented. In order to accelerate research and minimise any discrepancies between (re-)implementations, we introduce a curated, open-source repository which (1) contains complete data loading and preprocessing pipelines for 6 well-established HAR datasets, (2) supports several popular HAR deep learning architectures, and (3) provides necessary functionalities to train and evaluate said models. We welcome contributions from the fellow researcher to this repository, made available through: https://github.com/STRCSussex-UbiCompSiegen/dl_har_public
A remark about a learning risk lower bound

In this paper, we correct the learning risk lower bound in the non-realizable case provided by Anthony and Bartlett in 1999 Anthony, M, and P. L. Bartlett. 1999. Neural Network Learning: Theoretical Foundations. Cambridge, England: Cambridge University Press.[Crossref] , [Google Scholar]. Our contribution is mainly technical as we follow closely the proof of Anthony and Bartlett by correcting first the lemma they use and adapting then the lower bound proof itself.
Optimal trained long short-term memory for opinion mining: A hybrid semantic knowledgebase approach

This study describes a Hybrid Semantic Knowledgebase-Machine Learning strategy for mining the domain feature-level opinions and categorizing them on a multi-point scale. The proposed work is constructed by following the six major stages: “(a) Pre-processing, (b) Domain Feature Extraction, (c) Sentiment Extraction, (d) Domain Feature-Sentiment Association, (e) Domain Feature Polarity, (f) Knowledgebase Enrichment and (g) Opinion Classification with optimized deep learning model”. The proposed method is divided into several levels, each of which is designed to solve the opinion mining challenges at “domain feature level”. Initially, from the collected reviews, the domain features as well as semantics are extracted. Subsequently, in the Domain feature-sentiment association stage, the extracted filtered domain features are associated with their corresponding extracted sentiments acquired from the Sentiment Extraction phase. Once, the sentiments and the features are associated, the polarity of the feature sentiment pairs are computed using the sentiment aggregation function, which assigns a score to the features based on its proximity. Then, in the Knowledgebase enrichment stage, the initial review knowledgebase was used to bootstrap the domain feature extraction process, which is further enriched with new semantic information related to the analyzed review and the corresponding extracted domain features. Eventually, in the Opinion classification phase, a new optimized deep learning classifier, the optimized Long Short-Term Memory (LSTM) is introduced. The optimized LSTM is trained with the semantic information acquired from the enriched knowledge base and the statistical features created by Vector Space Model. Moreover, to enhance the classification accuracy of the LSTM, an Improved Butterfly Optimization Algorithm is introduced to fine-tune the weight of the model. Finally, the performance of the proposed work is evaluated over the conventional models.
Between Aesthetics and Culture Studies: To the Methodological Problems of the Modern Science of Art

This article discusses the methodology of interdisciplinarity in scientific cognition by the example of the relationship between art studies, cultural studies, and aesthetics. The author focuses on art studies currently functioning in its mature form, usually referred to as “normal science” in the philosophy of science. It is in need of development and improvement through the assimilation of methods of other humanities and philosophical areas, such as hermeneutics, phenomenology, and ontology. The attitude of art historians towards this trend is highly ambiguous. Their priority task is connected with maintaining the status of the individual and subjective element in art. Meanwhile, the assimilation of methods of related sciences often shifts the scientists’ interest to the sphere of non-authorial art, in which the role of clichés and genre techniques is great, and the skill of the creators and the artistic perfection of the work are no longer considered the highest values. The relevance of this article is related to the modern processes of integration of various areas of the art research. As the history of science develops, it becomes characterized by the processes of differentiation of scientific disciplines, trends, paradigms, and concepts, and the processes of integration. Integration corresponds to those states in the history of science when a new paradigm develops into a universal and authoritative one, preventing the pluralism of approaches. Differentiation is consonant with those states in science when the paradigm is still unstable and the universal paradigm has not yet emerged. Analyzing the external and internal factors of the development of the science of art, the author comes to the conclusion about the primacy of the internal ones. This implies the need to promote the theory of art, which still remains less developed than other subdisciplines of art studies.
“Short on time and big on ideas”: Perspectives from Lab Members on DIYBio Work in Community Biolabs

DIYbio challenges the status quo by positioning laboratory biology work outside of traditional institutions. HCI has increasingly explored the DIYbio movement, but we lack insight into sites of practice such as community biolabs. Therefore, we gathered data on eleven community biolabs by interviewing sixteen lab managers and members. These labs represent half of identified organizations in scope worldwide. Participants detailed their practices and motivations, outlining the constraints and opportunities of their community biolabs. We found that lab members conducted technically challenging project work with access to high-end equipment and professional expertise. We found that the unique nature of biowork exacerbated challenges for cooperative work, partially due to the particular time sensitivities of work with living organisms. Building on our findings, we discuss how community biolab members are creating new approaches to laboratory biology and how this has design implications for systems that support non-traditional settings for scientific practice.
Mutual information maximization for amortized likelihood inference from sampled trajectories: MINIMALIST

Simulation-based inference enables learning the parameters of a model even when its likelihood cannot be computed in practice. One class of methods uses data simulated with different parameters to infer models of the likelihood-to-evidence ratio, or equivalently the posterior function. Here we frame the inference task as an estimation of an energy function parametrized with an artificial neural network. We present an intuitive approach, named MINIMALIST, in which the optimal model of the likelihood-to-evidence ratio is found by maximizing the likelihood of simulated data. Within this framework, the connection between the task of simulation-based inference and mutual information maximization is clear, and we show how several known methods of posterior estimation relate to alternative lower bounds to mutual information. These distinct objective functions aim at the same optimal energy form and therefore can be directly benchmarked. We compare their accuracy in the inference of model parameters, focusing on four dynamical systems that encompass common challenges in time series analysis: dynamics driven by multiplicative noise, nonlinear interactions, chaotic behavior, and high-dimensional parameter space.
Novel Approach to Predict the Chances of Breast Cancer by Demographic Data using Machine Learning Algorithms

Breast cancer is one of the major problems faced by women. Generally, breast cancer is detected using mammography, but many women are not aware of this technique and symptoms because breast cancer is a type of cancer in which one cannot identify symptoms at an early stage, which leads to a high mortality rate. If breast cancer is detected at an earlier stage, mortality will be less. So, in this paper an approach to predict the chance of occurrence of breast cancer using demographic data is presented. Demographic data collected from users, such as age, family history , BMI etc., are implemented using various machine learning algorithms such as KNN(K Nearest Neighbour), K Means, Random Forest and Boosting algorithms to determine the chance of occurrence of breast cancer. If there is a chance of occurrence, then a message will be sent to their registered phone number, as "Please, once you take a Mammography test you have a chance of occurrence". Else send as "You are Safe". Random forest algorithm provided best and accurate result.
A multi-modal fusion framework for continuous sign language recognition based on multi-layer self-attention mechanism

Some of the existing continuous sign language recognition (CSLR) methods require alignment. However, this is time-consuming, and breaks the continuity of the frame sequence, and also affects the subsequent process of CSLR. In this paper, we propose a multi-modal network framework for CSLR based on a multi-layer self-attention mechanism. We propose a 3D convolution residual neural network (CR3D) and a multi-layer self-attention network (ML-SAN) for the feature extraction stage. The CR3D obtains the short-term spatiotemporal features of the RGB and optical flow image streams, whereas the ML-SAN uses a bi-gated recurrent unit (BGRU) to model the long-term sequence relationship and a multi-layer self-attention mechanism to learn the internal relationships between sign language sequences. For the performance optimization stage, we propose a cross-modal spatial mapping loss function, which improves the precision of CSLR by studying the spatial similarity between the video and text domains. Experiments were conducted on two test datasets: the RWTH-PHOENIX-Weather multi-signer dataset, and a Chinese SL (CSL) dataset. The results show that the proposed method can obtain state-of-the-art recognition performance on the two datasets, with word error rate (WER) value of 24.4% and accuracy value of 14.42%, respectively.
Secure storage and accessing the data in cloud using optimized homomorphic encryption

Different efforts have been undertaken to customizing a security and privacy concern in cloud data access. Therefore, the security measures are reliable and the data access was verified as the major problem in the cloud environment. To overcome this problem, we proposed an efficient data access control using optimized homomorphic encryption (HE). Because users outsource their sensitive information to cloud providers, data security and access control is one of the most difficult ongoing cloud computing research projects. Existing solutions that rely on cryptographic technologies to address these security issues result in significant complexity for both data and cloud service providers. The experimental results show that the key generation is 7.6% decreased by HE and 14.14% less than the proposed method. The encryption time is 11.34% less than the optimized HE and 23.28% decreased by ECC. The decryption time is 13.18% and 24.07% when compared with HE and ECC respectively.
Employees Turnover Rate with Pivoted Length Normalization

Continual evaluation of performance is crucial to the success of any business. These evaluations are measured using key performance indicators (KPIs), and different kinds of KPIs are widely used in various business fields. A company or business with multiple departments needs to evaluate the performance of each department manager because it affects the entire company. Turnover rate is a metric that shows the ratio of employees leaving a company, and a typical formula for it exists, but the formula is not fair when there are multiple departments with different sizes in a company. In this paper we have introduced, Pivoted Length Normalization to be used to normalize the turnover rate. Additionally, the normalized turnover rate depends on both company and department sizes, and it is more equitable. We have examined the normalized value of turnover rate with its initial value in departments of different sizes to show the fairness of the proposed method.
C-MIFS: Cellucci’s Mutual Information For Feature Selection

Data preparation, especially feature selection, is the fundamental step taken in any data analytic project. Considering the exponential growth of generated data, dataset’s dimensions should be reduced properly to facilitate the knowledge extraction procedure. This study proposes a linear feature selection method based on information theory, named C-MIFS. An optimized criteria for mutual information estimation has utilized for selecting features with high relevancy and less redundancy. To performance evaluation, a 3-NN classifier is applied to compare C-MIFS with MI-based feature selection algorithms through benchmark datasets available in UCI repository. Results show satisfactory performance of the proposed method in term of classification accuracy.
Konsep Sistem Customer Relationship Management Pada Toko Penjualan Produk

The product sales system in the store still uses a manual system in marketing its products, so only certain people know about the product. Stores cannot know their customers and who are the best customers, nor can they know when and how customers buy. In this case the author uses the method of Customer Relationship Management (CRM) which allows the correct and effective information culture and relationship technology. With the construction of this system, it is hoped that later they will be able to find out who the best and profitable customers are, be able to predict what customers can get or which products they will buy in the future and can interact in real time by integrating web technology developments in improving the quality of product sales. at the gift shop of the range. Customer Relationship Management (CRM) is a company strategy used to please customers or customers so they don't turn to competitors. The product sales system at Anugrah Kisaran Store is built using the PHP and MySQL programming languages ​​as its database, as well as tools used to describe the system model. is the Unified Modeling Language (UML). This system will only be intended for Anugrah Kisaran Stores. Customer Relationship Management (CRM) will provide the best services to customers such as product information, payments will be easier and faster to access
Challenges of Secure Software Deployment: An Empirical Study

research-article Share on Challenges of Secure Software Deployment: An Empirical Study Authors: Azzah A. Alghamdi King Fahd University of Petroleum and Minerals, SA and Imam Abdulrahman Bin Faisal University, Saudi Arabia King Fahd University of Petroleum and Minerals, SA and Imam Abdulrahman Bin Faisal University, Saudi ArabiaView Profile , Mahmood Niazi King Fahd University of Petroleum and Minerals, SA King Fahd University of Petroleum and Minerals, SAView Profile Authors Info & Claims EASE '22: Proceedings of the International Conference on Evaluation and Assessment in Software Engineering 2022June 2022 Pages 440–445https://doi.org/10.1145/3530019.3531337Online:13 June 2022Publication History 0citation39DownloadsMetricsTotal Citations0Total Downloads39Last 12 Months39Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Characterization of <i>α</i>-open set and <i>α</i>-closure operator via digraph

In this paper, authors defined a conical open set’s characterization. As a consequence, they introduced the (- space and - space)-open subdigraph in graph theory. Their interrelationships were investigated. As a result, several theorems have been proven.The aim of this paper, is to find graph theory applications due to the difficulty in finding applications in topological spaces, which are pure mathematics branches. We defined a conical open set’s characterization. As a consequence, we introduced the ( space and - space )-open subdigraph in graph theory. Their interrelationships were investigated. As a result, several theorems have been proven.
On the efficacy of Pedestrian Detection in Indian Road Scenario

Pedestrian detection has been a popular computer vision problem for the better part of two decades. With many available datasets in this context, the problem has seen a lot of attention with a variety of approaches developed for it over the years. However, the majority of the research in this context has been done in developed nations with structured roads and highly ordered pedestrian movement. The applicability of such context and the solutions working over it, in chaotic scenarios like that of Indian roads, is limited. The creation of such context is also usually challenging as data collection and annotation are both demanding tasks. In this work, we present the performance comparison of a widely used object detection framework, Faster RCNN, on roads of New Delhi and demonstrate the limitations of the training done using western datasets. We also give our analysis some relevant context by demonstrating the performance improvement after transfer learning on a dataset self-recorded on the busy roads of New Delhi and annotated using a novel video annotation tool LabelVDOS.
A note on hidden quantum discord in two- and three-qubit systems under local filtering operations

Abstract One of the primary goal of quantum information theory is to understand and quantify the various correlations that exist in quantum systems. Quantum correlation is broader than quantum entanglement, and quantum discord has always attracted more attention. In this paper, we investigate the hidden properties of quantum discord. In particular, for a class of two-qubit states, we prove the existence of hidden quantum discord under local filtering operations and give sufficient conditions for the absence of hidden quantum discord. For a class of three-qubit states, we investigate hidden geometric discord and analyze its dynamic behavior under several conditions. Furthermore, by comparing two- and three-qubit systems, we discover the inconsistency of hidden discord for <?CDATA $\rho = \frac{1}{2^{N}}(I+\sum_{j = 1}^{3}c_{j}\sigma _{j}\otimes{\ldots}\otimes \sigma_{j} ) (c_{3} = 1,N = 2,3)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>ρ</mml:mi> <mml:mo>=</mml:mo> <mml:mfrac> <mml:mn>1</mml:mn> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:msup> </mml:mfrac> <mml:mo stretchy="false">(</mml:mo> <mml:mi>I</mml:mi> <mml:mo>+</mml:mo> <mml:mrow> <mml:munderover> <mml:mo>∑</mml:mo> <mml:mrow> <mml:mi>j</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:munderover> </mml:mrow> <mml:msub> <mml:mi>c</mml:mi> <mml:mrow> <mml:mi>j</mml:mi> </mml:mrow> </mml:msub> <mml:msub> <mml:mi>σ</mml:mi> <mml:mrow> <mml:mi>j</mml:mi> </mml:mrow> </mml:msub> <mml:mo>⊗</mml:mo> <mml:mrow> <mml:mo>…</mml:mo> </mml:mrow> <mml:mo>⊗</mml:mo> <mml:msub> <mml:mi>σ</mml:mi> <mml:mrow> <mml:mi>j</mml:mi> </mml:mrow> </mml:msub> <mml:mo stretchy="false">)</mml:mo> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mi>c</mml:mi> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mn>3</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> .
Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning

While annotating decent amounts of data to satisfy sophisticated learning models can be cost-prohibitive for many real-world applications. Active learning (AL) and semi-supervised learning (SSL) are two effective, but often isolated, means to alleviate the data-hungry problem. Some recent studies explored the potential of combining AL and SSL to better probe the unlabeled data. However, almost all these contemporary SSL-AL works use a simple combination strategy, ignoring SSL and AL's inherent relation. Further, other methods suffer from high computational costs when dealing with large-scale, high-dimensional datasets. Motivated by the industry practice of labeling data, we propose an innovative Inconsistency-based virtual aDvErsarial Active Learning (IDEAL) algorithm to further investigate SSL-AL's potential superiority and achieve mutual enhancement of AL and SSL, i.e., SSL propagates label information to unlabeled samples and provides smoothed embeddings for AL, while AL excludes samples with inconsistent predictions and considerable uncertainty for SSL. We estimate unlabeled samples' inconsistency by augmentation strategies of different granularities, including fine-grained continuous perturbation exploration and coarse-grained data transformations. Extensive experiments, in both text and image domains, validate the effectiveness of the proposed algorithm, comparing it against state-of-the-art baselines. Two real-world case studies visualize the practical industrial value of applying and deploying the proposed data sampling algorithm.
Tutorial 3: Pitfalls in the Measurement Methods Applied in Experimental Software Engineering - Assessment and Suggestions for Improvement

Measurement is an essential issue in empirical software engineering. It is subject to different sources of error that must be kept as small as possible. Measuring instruments is one of these sources of error. In this tutorial, we provide awareness of potential pitfalls in the measurement methods—specifically measuring instruments—applied in empirical software engineering, describing statistical techniques that can be used for measures assessment, and making recommendations to improve the measurement practice in software engineering. Test suites are used as measuring instruments in many software engineering experiments. We will use the case of test suites when measuring the external quality of the code developed by participants of TDD-related experiments.
Privacy Preserving Data Sharing in Cloud Using EAE Technique

Accessing and controlling data on the cloud is a low-cost method of ensuring data security. However, in cloud storage systems with untrusted cloud servers, data storage and retrieval management become a challenge. Because this new computing technology encourages users to leave their sensitive data to cloud providers, security and privacy issues over outsourced data are growing. To protect data privacy, attribute-based encryption (ABE) systems and variations are frequently used. ABE scheme are resistant to a variety of assaults. Which includes data access, interruption, interception alteration, illegal authentication, and fabrication? Existing encryption approaches, on the other hand, necessitate greater processing time and secure communications of sensitive information transport, both of which remain unsolved. The proposed scheme uses enhanced elliptic curve Attribute-based Encryption (EAE) to address this problem. EAE encrypts user data during transmission to ensure privacy. EAE generates the hash value for the encrypted data and stores it in the cloud server after completing the encryption process. To strengthen data transfer security, this produced hash value is sent to the associated users in the cloud. We deployed our scheme and demonstrated that this is both effective and versatile for outsourcing data in the cloud.