Reviewerly/exHarmony huggingface dataset:
reval: A Python package to determine best clustering solutions with stability-based relative clustering validation

Determining the best partition for a dataset can be a challenging task because of 1) the lack of a priori information within an unsupervised learning framework; and 2) the absence of a unique clustering validation approach to evaluate clustering solutions. Here we present reval: a Python package that leverages stability-based relative clustering validation methods to determine best clustering solutions as the ones that best generalize to unseen data. Statistical software, both in R and Python, usually rely on internal validation metrics, such as silhouette, to select the number of clusters that best fits the data. Meanwhile, open-source software solutions that easily implement relative clustering techniques are lacking. Internal validation methods exploit characteristics of the data itself to produce a result, whereas relative approaches attempt to leverage the unknown underlying distribution of data points looking for generalizable and replicable results. The implementation of relative validation methods can further the theory of clustering by enriching the already available methods that can be used to investigate clustering results in different situations and for different data distributions. This work aims at contributing to this effort by developing a stability-based method that selects the best clustering solution as the one that replicates, via supervised learning, on unseen subsets of data. The package works with multiple clustering and classification algorithms, hence allowing both the automatization of the labeling process and the assessment of the stability of different clustering mechanisms.
Training students in evidence-based software engineering and systematic reviews: a systematic review and empirical study

Although influential in academia, evidence-based software engineering (EBSE) has had little impact on industry practice. We found that other disciplines have identified lack of training as a significant barrier to Evidence-Based Practice. To build and assess an EBSE training proposal suitable for students with more than 3 years of computer science/software engineering university-level training. We performed a systematic literature review (SLR) of EBSE teaching initiatives and used the SLR results to help us to develop and evaluate an EBSE training proposal. The course was based on the theory of learning outcomes and incorporated a large practical content related to performing an SLR. We ran the course with 10 students and based course evaluation on student performance and opinions of both students and teachers. We assessed knowledge of EBSE principles from the mid-term and final tests, as well as evaluating the SLRs produced by the student teams. We solicited student opinions about the course and its value via a student survey, a team survey, and a focus group. The teachers' viewpoint was collected in a debriefing meeting. Our SLR identified 14 relevant primary studies. The primary studies emphasized the importance of practical examples (usually based on the SLR process) and used a variety of evaluation methods, but lacked any formal education methodology. We identified 54 learning outcomes covering aspects of EBSE and the SLR method. All 10 students passed the course. Our course evaluation showed that a large percentage of the learning outcomes established for training were accomplished. The course proved suitable for students to understand the EBSE paradigm and to be able to apply it to a limited-scope practical assignment. Our learning outcomes, course structure, and course evaluation process should help to improve the effectiveness and comparability of future studies of EBSE training. However, future courses should increase EBSE training related to the use of SLR results.
An Empirical Comparison of Web Page Segmentation Algorithms

Over the past two decades, several algorithms have been developed to segment a web page into semantically coherent units, a task with several applications in web content analysis. However, these algorithms have hardly been compared empirically and it thus remains unclear which of them—or rather, which of their underlying paradigms—performs best. To contribute to closing this gap, we report on the reproduction and comparative evaluation of five segmentation algorithms on a large, standardized benchmark dataset for web page segmentation: Three of the algorithms have been specifically developed for web pages and have been selected to represent paradigmatically different approaches to the task, whereas the other two approaches originate from the segmentation of photos and print documents, respectively. For a fair comparison, we tuned each algorithm's parameters, if applicable, to the dataset. Altogether, the classic rule-based VIPS algorithm achieved the highest performance, closely followed by the purely visual approach of Cormier et al. For reproducibility, we provide our reimplementations of the algorithms along with detailed instructions.
HPMC: A Multi-target Tracking Algorithm for the IoT

With the rapid development of the Internet of Things and advanced sensors, vision-based monitoring and forecasting applications have been widely used. In the context of the Internet of Things, visual devices can be regarded as network perception nodes that perform complex tasks, such as real-time monitoring of road traffic flow, target detection, and multi-target tracking. We propose the High-Performance detection and Multi-Correlation measurement algorithm (HPMC) to address the problem of target occlusion and perform trajectory correlation matching for multi-target tracking. The algorithm consists of three modules: 1) For the detection module, we proposed the You Only Look Once(YOLO)v3_plus model, which is an improvement of the YOLOv3 model. It has a multi-scale detection layer and a repulsion loss function. 2) The feature extraction module extracts appearance, movement, and shape features. A wide residual network model is established, and the coefficient k is added to extract the appearance features of the target. 3) In the multi-target tracking module, multi-correlation measures are used to fuse the three extracted features to increase the matching degree of the target track and improve the tracking performance. The experimental results show that the proposed method has better performance for small and occluded targets than comparable algorithms.
New Approach in Human-AI Interaction by Reinforcement-Imitation Learning

Reinforcement Learning (RL) provides effective results with an agent learning from a stand-alone reward function. However, it presents unique challenges with large amounts of environment states and action spaces, as well as in the determination of rewards. Imitation Learning (IL) offers a promising solution for those challenges using a teacher. In IL, the learning process can take advantage of human-sourced assistance and/or control over the agent and environment. A human teacher and an agent learner are considered in this study. The teacher takes part in the agent’s training towards dealing with the environment, tackling a specific objective, and achieving a predefined goal. This paper proposes a novel approach combining IL with different types of RL methods, namely, state-action-reward-state-action (SARSA) and Asynchronous Advantage Actor–Critic Agents (A3C), to overcome the problems of both stand-alone systems. How to effectively leverage the teacher’s feedback—be it direct binary or indirect detailed—for the agent learner to learn sequential decision-making policies is addressed. The results of this study on various OpenAI-Gym environments show that this algorithmic method can be incorporated with different combinations, and significantly decreases both human endeavors and tedious exploration process.
Human daily activities recognition using decision tree

Abstract Decision tree is a supervised classifier that is easy to understand. There are various decision tree methods. This study aimed to compare the performance of decision tree methods in human activity recognition using acceleration and jerk data. The subjects performed human activity daily living, namely walking on a flat surface, walking upstairs, walking downstairs, sitting, standing, and lying down. The features were grouped into three categories: acceleration features, jerk features, and combined features of acceleration and jerk. The evaluation was done using Random Forest, J48, Logistic Model Tree, Reduced Error Pruning Tree, Decision Stump, Random Tree, and Hoeffding Tree. The results showed that Random Forest outperformed the other classifiers with acceleration features performed better than the jerk features. However, the combined acceleration and jerk features yielded the highest accuracy. In conclusion, Random Forest is the best decision tree technique in recognizing the pattern in human activity.
Black holes as clouded mirrors: the Hayden-Preskill protocol with symmetry

The Hayden-Preskill protocol is a qubit-toy model of the black hole information paradox. Based on the assumption of scrambling, it was revealed that quantum information is instantly leaked out from the quantum many-body system that models a black hole. In this paper, we extend the protocol to the case where the system has symmetry and investigate how the symmetry affects the leakage of information. We especially focus on the conservation of the number of up-spins. Developing a partial decoupling approach, we first show that the symmetry induces a delay of leakage and an information remnant. We then clarify the physics behind them: the delay is characterized by thermodynamic properties of the system associated with the symmetry, and the information remnant is closely related to the symmetry-breaking of the initial state. These relations bridge the information leakage problem to macroscopic physics of quantum many-body systems and allow us to investigate the information leakage only in terms of physical properties of the system.
Subspace clustering via stacked independent subspace analysis networks with sparse prior information

Sparse subspace clustering (SSC) method has gained considerable attention in recent decades owing to its advantages in the fields of clustering. In essence, SSC is to learn a sparse affinity matrix followed by striving for a low-dimensional representation of data. However, the SSC and its variants mainly focus on building high-quality affinity matrix while ignoring the importance of low-dimensional feature derived from the affinity matrix. Moreover, due to their intrinsic linearity of models, they cannot efficiently handle data with the nonlinear distribution. In this paper, we propose a stacked independent subspace analysis (ISA) with sparse prior information called stacked-ISASP to deal with these two issues. Powered by handling data with nonlinear structure, our method aims at seeking a low-dimensional feature from the image data. Concretely, the model can stack the modified independent subspace analysis networks by incorporating the prior subspace information from the original data. To validate the efficiency of the proposed method, we compare our proposed stacked-ISASP method with the state-of-the-art methods on real datasets. Experimental results show that our approach can not only learn a better low-dimensional structure from the data but also achieve better performance for the classification task.
Secure Data Transfer Through Internet Using Cryptography and Image Steganography

Hacking became a big problem these days. Transmission of secure Data or communication through internet turns to be challenging due to security concerns. In order to forestall these security obstacles, we used Cryptography, Image Steganography. Over the centuries, there are numerous advancements in information security. Steganography and cryptography are the two emerging techniques for secure data transmission over years. Cryptography is the art of encrypting the text whereas Steganography is used to hide the text inside any multimedia element that appears to be nothing. Steganography makes the data secure as if any person views the file, he/she cannot sense that there is some secret message hide in that, human senses cannot detect or sense the data inside the multimedia element. In this paper, we proposed a system that uses both cryptography and steganography to ensure two levels of security to the data. The purpose of this paper is to develop new methodology using XOR operation for encrypting the data and embedding the encrypted data into the image pseudo randomly using user chosen key.
Prediction of Solar Radiation Using Artificial Neural Network

Most solar applications and systems can be reliably used to generate electricity and power in many homes and offices. Recently, there is an increase in many solar required systems that can be found not only in electricity generation but other applications such as solar distillation, water heating, heating of buildings, meteorology and producing solar conversion energy. Prediction of solar radiation is very significant in order to accomplish the previously mentioned objectives. In this paper, the main target is to present an algorithm that can be used to predict an hourly activity of solar radiation. Using a dataset that consists of temperature of air, time, humidity, wind speed, atmospheric pressure, direction of wind and solar radiation data, an Artificial Neural Network (ANN) model is constructed to effectively forecast solar radiation using the available weather forecast data. Two models are created to efficiently create a system capable of interpreting patterns through supervised learning data and predict the correct amount of radiation present in the atmosphere. The results of the two statistical indicators: Mean Absolute Error (MAE) and Mean Squared Error (MSE) are performed and compared with observed and predicted data. These two models were able to generate efficient predictions with sufficient performance accuracy.
A Blockchain-based framework for Agriculture subsidy disbursement

Abstract Agriculture is the primary livelihood factor in the growth of India’s economy. One of the significant challenges faced by the Government of India in the agriculture sector is to increase the delivery of subsidies by re-engineering the existing process. One of the solutions initiated by the government of India on 1 st January 2013 with the highest priority is called a Direct Cash transfer scheme under the Direct Benefit Transfer (DBT) scheme. It involves crediting subsidies (cash/payments) directly to the farmer’s account. However, the major problem associated with this scheme is that the government subsidies do not reach all the eligible farmers due to corruption, delay, intermediaries, and other reasons. The present direct cash transfers system’s major drawbacks such as (key loopholes are) lack of proper auditing, inability to track subsidy, and inadequate digital banking infrastructure especially in rural areas. In this regard, the disbursement of direct cash transfer scheme is addressed benefitting the farmers directly via Blockchain Smart Contracts technology thereby ensuring transparency, de-duplication, reduced delays, and reducing instances of fraud in the existing government subsidy delivery system. We aim to develop a blockchain-based smart contracts prototype model called a single-window approach for automating farmer’s subsidies and eliminating middlemen. The automated model will be fast, transparent, and immutable by reducing the complexities faced by Indian farmers.
An Intelligent Hybrid Control to Enhance Applicability of Mobile Robots in Cluttered Environments

Trajectory planning and tracking are the most important aspects of mobile robot research for industrial application.In this research, an intelligent hybrid control is presented to enhance the usability of mobile robots in environments cluttered with static obstacles.The control is hybrid in two ways.On one hand, the algorithm combines obstacles avoidance and trajectory generation.The generated trajectory acts as a reference path to be followed by the mobile robot.On the other hand, the algorithm not only generates trajectory but also tracks the generated trajectory.An optimization-based intelligent algorithm, simulated annealing, is designed to plan and generate a trajectory for collision-free robot motion in an environment with stationary obstacles.The PID (Proportional-Integral-Derivative) control algorithm is designed to track the generated trajectory with minimum error.Both algorithms are hybridized such that the generated trajectory becomes a reference trajectory for the tracking control algorithm.The simulated annealing generates a realistic trajectory that consists of a series of the best points with obstacles on the path.The best point is selected with the shortest distance from the robot's destination.These points are selected one from each of the grids, generated between the start and destination point of the robot motion.The dimensions of the robot are considered and included in the obstacles dimension to reduce the dimension complexity of the robot.The effectiveness of the proposed hybrid technique is tested in simulation and in real-time experiments for seven types of cluttered environments.The environments vary with size, shape, placement and arrangement of the obstacles.The results show that the simulated annealing generates a collision-free trajectory intelligently without trapping in local minima in these cluttered environments while the PID control tracks the reference trajectory with a maximum absolute error of 0.2 mm.Hence, the proposed method enhances the usability of mobile robots in environments cluttered with static objects.
An Efficient 3D Human Pose Retrieval and Reconstruction from 2D Image-Based Landmarks

We propose an efficient and novel architecture for 3D articulated human pose retrieval and reconstruction from 2D landmarks extracted from a 2D synthetic image, an annotated 2D image, an in-the-wild real RGB image or even a hand-drawn sketch. Given 2D joint positions in a single image, we devise a data-driven framework to infer the corresponding 3D human pose. To this end, we first normalize 3D human poses from Motion Capture (MoCap) dataset by eliminating translation, orientation, and the skeleton size discrepancies from the poses and then build a knowledge-base by projecting a subset of joints of the normalized 3D poses onto 2D image-planes by fully exploiting a variety of virtual cameras. With this approach, we not only transform 3D pose space to the normalized 2D pose space but also resolve the 2D-3D cross-domain retrieval task efficiently. The proposed architecture searches for poses from a MoCap dataset that are near to a given 2D query pose in a definite feature space made up of specific joint sets. These retrieved poses are then used to construct a weak perspective camera and a final 3D posture under the camera model that minimizes the reconstruction error. To estimate unknown camera parameters, we introduce a nonlinear, two-fold method. We exploit the retrieved similar poses and the viewing directions at which the MoCap dataset was sampled to minimize the projection error. Finally, we evaluate our approach thoroughly on a large number of heterogeneous 2D examples generated synthetically, 2D images with ground-truth, a variety of real in-the-wild internet images, and a proof of concept using 2D hand-drawn sketches of human poses. We conduct a pool of experiments to perform a quantitative study on PARSE dataset. We also show that the proposed system yields competitive, convincing results in comparison to other state-of-the-art methods.
Full-field mechanical characterization of polyurethane foams under large deformations by digital image correlation

In this work the response of soft open-cell polyurethane foams with three different densities, respectively of 85, 63 and 46 kg/m3, were obtained by quasi-static tensile, shear and cyclic compression tests. The presence of an intrinsic random pattern inside the material given by the small black dot of expanded graphite, that makes it fireproof, allowed to detect large displacement fields exploiting digital image correlation technique, without painting the surface to quantify the full-field displacement of the samples. The displacements' maps, the strain fields and the stress-strain curves obtained by digital image correlation allowed the analysis and the evaluation of the mechanical properties.
The (Un-)Sustainability of Bitcoin Investments

Climate-related criticism toward Bitcoin is primarily based on the network’s absolute carbon emissions without consideration of Bitcoin’s market value. Taking a relative emission perspective and utilizing the mean–variance portfolio optimization framework, we study the financial and carbon implications of Bitcoin investments. Our results show that adding Bitcoin to a diversified equity portfolio can enhance the risk–return relationship of the portfolio. Furthermore, we identify realistic scenarios for which the addition of Bitcoin to a diversified equity portfolio reduces the portfolio’s aggregate carbon emissions. This finding persists under various, conservative assumptions about Bitcoin prices, carbon emission estimates, and carbon prices.
Poverty/investment slow distribution effect analysis based on Hopfield neural network

With the rapid development of the Internet platform, the Internet economy has become a new situation for evaluating the poverty/investment slow effect. What has brought development to the Internet economy is a variety of e-commerce platforms. However, in the face of huge Internet users and different users’ shopping habits, reasonably personalized e-commerce platform recommendations for users can improve the user’s website shopping experience. However, due to the huge user data and the diversity of e-commerce platforms, it is a huge challenge to reasonably recommend e-commerce platforms to users. The quality of the e-commerce’s personalized recommendations also affects the purchase conversion rate. Clustering algorithm is an algorithm involved in grouping data in machine learning. The same set of data has the same attributes and characteristics, and the attributes or features between different sets of data will be relatively large. In this paper, by using the mean shift clustering algorithm to characterize the behavior data. According to the characteristics of the grouping, we can recommend the e-commerce platform commonly used between the groups. However, using mean shift clustering for personalized recommendation faces the problem of too high user data dimensions. Therefore, we first conduct behavioral analysis of user data by leveraging the well-known Hopfield Neural Network. We define user behavior sequences for user behavior data and classify user behavior. We transform the grouped user behavior into an embedded vector, and linearly transform the embedded vectors of different lengths into the same semantic space. We process the vectors in the semantic space through the self-attention layer and perform mean shift clustering. Experiments show that our method can reduce the complexity of user poverty distribution effect toward complex data and improve the quality of personalized recommendation. • Hopfield Neural Network is used to conduct behavioral analysis of user data. • User behavior sequences is defined and user behavior is classified. • The grouped user behavior is converted into an embedded vector. • The embedded vector of different lengths are converted into the same semantic space. • Vectors in the semantic space is processed via self-attention layer.