Reviewerly/exHarmony huggingface dataset:
Statistical Aspects of High-Dimensional Sparse Artificial Neural Network Models

An artificial neural network (ANN) is an automatic way of capturing linear and nonlinear correlations, spatial and other structural dependence among features. This machine performs well in many application areas such as classification and prediction from magnetic resonance imaging, spatial data and computer vision tasks. Most commonly used ANNs assume the availability of large training data compared to the dimension of feature vector. However, in modern applications, as mentioned above, the training sample sizes are often low, and may be even lower than the dimension of feature vector. In this paper, we consider a single layer ANN classification model that is suitable for analyzing high-dimensional low sample-size (HDLSS) data. We investigate the theoretical properties of the sparse group lasso regularized neural network and show that under mild conditions, the classification risk converges to the optimal Bayes classifier’s risk (universal consistency). Moreover, we proposed a variation on the regularization term. A few examples in popular research fields are also provided to illustrate the theory and methods.
A Grassmannian Graph Approach to Affine Invariant Feature Matching

In this work, we present a novel, theoretical approach to address one of the longstanding problems in computer vision: 2D and 3D affine invariant feature matching. Our proposed Grassmannian Graph (GrassGraph) framework employs a two stage procedure that is capable of robustly recovering correspondences between two unorganized, affinely related feature (point) sets. In the ideal case, the first stage maps the feature sets to an affine invariant Grassmannian representation, where the features are mapped into the same subspace. It turns out that coordinate representations extracted from the Grassmannian differ by an arbitrary orthonormal matrix. In the second stage, by approximating the Laplace-Beltrami operator (LBO) on these coordinates, this extra orthonormal factor is nullified, providing true affine invariant coordinates which we then utilize to recover correspondences via simple mutual nearest neighbor relations. Our validation benchmarks use large number of experimental trials performed on 2D and 3D datasets. Experimental results show that the proposed Grass-Graph method successfully recovers large affine transformations.
GPS/INS Integrated Navigation Based on Grasshopper Optimization Algorithm

In GPS/INS integrated navigation, Kalman filter is usually used for data fusion between GPS and INS. The filtering algorithm of integrated navigation is related to whether the advantages of each sensor in the integrated navigation can be utilized, the navigation accuracy is improved, the reliable working time of the navigation system is improved and the navigation requirement are met. In this paper, we proposed an approach based on the Grasshopper optimization algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarm of grasshoppers. The goal of the proposed approach is to optimize the parameters of the Kalman filter. The Kalman filter method is improved by grasshopper optimization algorithm, which improves the accuracy of integrated navigation and reduces the errors caused by system noise and measurement noise. For verification, the proposed approach is compared with particle swarm optimization. The simulation experiment results show that the proposed approach has a better effect.
Using profiling to assemble an agile collaborative software development team made up of freelancers

Assembling software development freelancer comprised teams is a challenging task for a project manager. In an Agile environment, this is even more challenging because of the cross-functional character and multi-disciplinary requirements such methodology enforces. The project manager needs to identify individuals that are highly compatible from a collaborative point of view and have the skills to match project complexity. This is a lengthy endeavor which is prone to human bias as the collaborative nature of individuals that have never worked together is difficult to evaluate. The selection process also has considerable restrictions imposed by the sheer volume of information a human decision maker can process and analyze. Computerized data analysis, on the other hand, is free of bias and it can process large data sets very fast. For the current research process data was collected automatically from a freelance platform that allows free access to developers’ profiles. The targeted platforms make information available about freelancer’s skills, experience, background, and education. Data was validated, ensuring it’s correct and relevant. Data was normalized to ensure it is easily quantifiable and can be measured using a common scale. Statistical methods were used to predict the data that was missing or incomplete. Based on the identified criteria a template was built to match the profile of the ideal team member. Machine learning techniques were used to group team members that would work together effectively from a collaborative point of view. An indicator was built to benchmark the team member’s profile against the template and determine the highest ranking candidates. Conclusions were formulated regarding the topic of using profiling to assemble collaborative agile freelance software development teams. Research process limitations were enunciated and future research topics were submitted for debate.
Laplacian least learning machine with dynamic updating for imbalanced classification

When handling imbalanced datasets, the existing least learning machine ignores both the interrelationship between instances and the prior knowledge about the classes and hence tends to provide unfavorable accuracies across the classes. With the Laplacian matrix based loss function, Laplacian least learning machine (L2MM) is developed to address the infeasibility of the existing least learning machine for imbalanced data classification in this study. In order to find out the optimal number of hidden nodes in L2MM, by means of the derived update equations without any re-calculation of inverse matrix, its two incremental versions (i.e., with and without regularization) are invented for dynamically updating the hidden nodes in one-by-one way, thereby saving much training time. L2MM and its incremental versions inherit fast learning and good generalization capability of least learning machine. Experimental results on 19 benchmarking imbalanced datasets indicate the effectiveness of the proposed incremental L2MM for imbalanced classification tasks.
Blockchain for Child Labour Decrease in Cocoa Production in West and Central Africa

The blockchain technology impacts the supply chain and allows the creation of smart contract. This contract is executed digitally and securely without the presence of a middleman when all requirements are met. West and Central Africa countries produce 70% of world's cocoa, while child labour is an issue as millions of children work in that sector. This paper contribution is mainly to enlighten smart contract adoption with respect to child abuse and to contribute to strategies for child labour reduction.
Convolutional Subspace Clustering Network With Block Diagonal Prior

Standard methods of subspace clustering are based on self-expressiveness in the original data space, which states that a data point in a subspace can be expressed as a linear combination of other points.However, the real data in raw form are usually not well aligned with the linear subspace model.Therefore, it is crucial to obtain a proper feature space for performing high quality subspace clustering.Inspired by the success of Convolutional Neural Networks (CNN) for extraction powerful features from visual data and the block diagonal prior for learning a good affinity matrix from self-expression coefficients, in this paper, we propose a jointly trainable feature extraction and affinity learning framework with the block diagonal prior, termed as Convolutional Subspace Clustering Network with Block Diagonal prior (ConvSCN-BD), in which we solve the joint optimization problem in ConvSCN-BD via an alternating minimization algorithm, which updates the parameters in the convolutional modules and the self-expression coefficients with stochastic gradients descent and updates other variables with close-form solutions alternatingly.In addition, we derive the connection between the block diagonal prior and the subspace structured norm, and reveal that using the block diagonal prior on the affinity matrix is essentially incorporating the feedback information from spectral clustering.Experiments on three benchmark datasets demonstrated the effectiveness of our proposal.
Social Network Analytics: Natural Disaster Analysis Through Twitter

Today, we are in the era of social networks producing a huge quantity of data accentuated by mobile technologies. This evolution of the actors and the behavior of social networks users leads to massive data on social web. People share their experiences and opinions on miscellaneous topics in social networks, such as politics, health issues, and natural disasters. This makes publicly available data an inestimable resource for different addressed topics. In this paper, we investigate natural disasters including avalanche, drought, earthquake, flood, hurricane, landslide, tornado, tsunami, volcano, and wildfire. So, we extract data from Twitter Network, we classified relevant tweets to positive, very positive, negative, very negative, or neutral. Then, we define the most dominant disasters information through Word Cloud and we analyze this information using the pie chart. Furthermore, we plot the data on the map based on the coordinates (longitude and latitude). Finally, we deduce the most frequent disaster and which region is the most influenced.
Software developers need help too! Developing a methodology to analyse cognitive dimension-based feedback on usability

Software developers use various methods to evaluate usability and identify usability issues that exist in systems they develop. Cognitive dimensions framework (CDF) based usability evaluation is one of the popular usability evaluation methods. It uses an open-ended questionnaire to collect qualitative feedback from users after using a system. To identify usability issues, evaluators should analyse this qualitative feedback. However, the approach to follow when performing this analysis is not explored in detail. We conducted a systematic literature review and reviewed 70 studies that used various CDF questionnaires for usability evaluations and investigated how those studies have analysed CDF questionnaire responses to identify usability issues. This revealed five methods that previous research has used for data analysis and four methods for identifying usability issues from CDF questionnaire responses. We applied the results of the literature review to develop a methodology and a set of guidelines to analyse qualitative feedback collected via a CDF questionnaire that targets evaluating security application programming interfaces. We tested the developed guidelines by conducting an empirical investigation. The results of the experiment revealed that using the proposed guidelines helps to identify significantly more usability issues with a higher validity.
Safety Functions and Software Verification of NPP Safety Important Systems

One of the most critical tasks in the software complexes quality assurance is the procedure of forming requirements to a developed or modified system and subsequent their verification. The essential errors are making in the first life cycle stages - these are errors in determining requirements, selecting the architecture, high-level design. Faults of safety critically important software may considerably damage the equipment or properties, as well to lead to an essential detriment of the environment and human victims. Increasing requirements to the software quality of NPP (nuclear power plant) safety important systems at all stages of the life cycle is concerned with increasing the software complexity and functionality and has led the necessity of developing approaches to justify both the system itself safety and software involved in the systems make-up. In the paper, an approach is considered, based on the “safety functions”, meeting which in the sequel is verifying. This approach is used under the software verification of upper level systems of automated process control systems and may be applied for the fault tolerance analysis, information- and cyber- security of soft- and hardware complexes.
Observation of tower vibration based on subtle motion magnification

The vibration of the tower will cause fatigue damage of the tower, reduce the bearing performance of the tower, and result partially damage of the tower. It is difficult to implement and maintenance for the traditional contact measurement method. Motivated by this fact, we propose a non-contact method to measure the vibration of the tower. We apply the video subtle motion magnification algorithm to the tower vibration observation. By processing the video sequence of the tower, we can directly observe the tiny movement of the tower using the naked eye without complicated sensor equipment. The proposed method is used Eulerian Video Magnification technology which processes the image as a whole to reduce computational cost and make the processed image real.
A Novel Multi-Modal One-Shot Learning Method for Texture Recognition

Most machine learning algorithms require a large set of training samples in order to achieve satisfactory performance. However, this requirement may be difficult to satisfy in practice. Take the one-shot learning (OSL) problem on texture recognition for example; the machine learning algorithm is difficult to achieve satisfactory results. In order to solve this problem, a novel multi-modal one-shot learning method for texture recognition is presented. First, in order to improve the robustness of identification and the anti-interference to noise, we addressed the nontravel texture recognition challenges of learn information about object categories from only one training sample by fusing varied modalities data, including image, sound and acceleration, which provides rich information regarding textures. Second, a novel dictionary learning model is designed, which contains the various modalities information, and can simultaneously learn the latent common sparse code for the different modalities. Third, an original regularization term is developed to enhance the degree of distinction of different classes. Furthermore, the common features of the three modalities are evaluated in the case of one-shot learning and used as the basis for feature selection. In the end, experiments were performed based on a data set which was published openly to validate the effectiveness of the presented method.
Proposal and Preliminary Fall-related Activities Recognition in Indoor Environment

Falls are a noteworthy reason for grievances and deaths in elderlies. Notwithstanding when no damage happens, about majority of elderlies are identity unfit to get up without help. The expanded time of lying on the floor frequently prompts restorative complications, including muscle impairment, lack of hydration, unease, and trepidation of falling. Here, a fall sensing unit is accounted that is affixed to a subjects' midsection and incorporates a 3-axis accelerometer, 3-axis gyroscope, a multiplexer, a filter, and a microcontroller. Moreover, the fall detection system also used IMU data on the mobile phone. Change in angular velocity, noise cancelation, and the ADC transformation was achieved by the hardware. The handled flag is conveyed to a PC through ZigBee and processed through the dedicated programming. Fall sensing approach comprised feature selection, mining and a machine learning calculation for characterizing the parameters. In this paper, we propose a fall discovery calculation which is shaped by feature selection, discovery, mining and handling. An aggregate of six highlights was ascertained in feature selection. Four of them are identified with the gravity vector which is extricated from accelerometer information by utilizing the low-pass filter. As falling generally happens in a vertical course, the gravity-related characteristics are helpful. The system also uses one of the ambient sensing units, which is a movement sensing unit. The PIR sensor-based movement sensing unit is used to enhance the accuracy of fall detection activity. The feature from the movement sensing unit substantially reduced the false alarms.
A Linear Regression-Based Resource Utilization Prediction Policy for Live Migration in Cloud Computing

Mandal, Gopa Dam, Santanu Dasgupta, Kousik Dutta, ParamarthaA new emerging state-of-the-art challenging research area has been found in cloud computing. Cloud Computing is an idea, rely on service and delivery, it is distributed over the Internet and governed by appropriate set of protocol. In last few decades, Internet is growing rapidly as a result cloud computing and also expanded exponentially. Cloud computing is said to provide resoruces such as Software, Platform, and Infrastructure as services, namely, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Cloud profaned the infrastructure resources like CPU, bandwidth, and memory to its end users as a part of its IaaS service. To meet the end users' heterogeneous needs for resources it profaned and unprofane the resources dynamically, with minimal management effort of the service providers over the Internet. Thus, eliminating the need to manage the expensive hardware resources by companies and institutes. However, to satisfy the need for resources of the users on time, Cloud Service Provider (CSP) must have to maintain the Quality of Service (QoS). Service Level Agreement (SLA) is done between the Datacenters and its end users. Minimization of the violation of the SLA ensures better QoS. Research fraternity has proposed that one of the main reasons for violation of SLA is inefficient load balancing approaches in hosts that fail to ensure QoS, without missing the deadline by the distribution of dynamic workload evenly. In this paper, we propose to extend our previous work of simulated annealing-based optimized load balancing [1] by adding VM migration policy from one host to another on the basis of linear regression-based prediction policy for futuristic resource utilization. In our approach, we are going to predict short-time future resource utilization using linear regression based on the history of the previous utilization of resources by each host. We further use it in migration process to predict the overloaded hosts to underloaded ones. Experiments were simulated in CloudAnalyst and the results are quite encouraging and outperform some previous existing strategies of load balancing for ensuring QoS.
A Re-Entry Path Planning Method for Service Robots Based on Dynamic Inver-Over Evolutionary Algorithm

A re-entry path planning method in omitting areas for service robots is suggested based on dynamic Inver-Over evolutionary algorithms after the robot automatically avoids obstacles. The complete coverage path planning is researched for cleaning service robots. Combined with features of dynamic travelling salesmen problem (DTSP), a local operator is employed for the path planning to enhance real-time dynamic properties of the Inver-Over algorithm. The method addresses the path planning problem that a number of cells undergo dynamic changes over time under work environment of cleaning robots. With simulations and experiments performed, it is discovered that the average relative error is 2.2% between the re-entry path planning and the best path, which validates the effectiveness and feasibility of the method.
Jointly Sparse Locality Regression for Image Feature Extraction

This paper proposes a novel method called Jointly Sparse Locality Regression (JSLR) for feature extraction and selection. JSLR utilizes joint L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm minimization on regularization term, and also introduces the locality to characterize the local geometric structure of the data. There are three main contributions in JSLR for face recognition. Firstly, it eliminates the drawback in ridge regression and Linear Discriminant Analysis (LDA) that when the number of the classes is too small, not enough projections can be obtained for feature extraction. Secondly, by using the local geometric structure as the regularization term, JSLR is able to preserve local information and find an embedding subspace which can detect the most essential data manifold structure. Moreover, since the L2,1-norm based loss function is robust to outliers in data points, JSLR provides the joint sparsity for robust feature selection. The theoretical connections of the proposed method and the previous regression methods are explored and the convergence of the proposed algorithm is also proved. Experimental evaluation on several well-known data sets shows the merits of the proposed method on feature selection and classification.
Towards Semantic Action Analysis via Emergent Language

Recent work on unsupervised learning has explored the feasibility of semantic analysis and interpretation via Emergent Language (EL) models. As EL requires some form of numerical embedding, it remains unclear which type is required in order for the EL to properly capture certain semantic concepts associated with a given task. In this paper, we compare different approaches that can be used to generate such embeddings: unsupervised and supervised. We start by producing a large dataset using a single-agent simulation environment. In these experiments, a purpose-driven agent attempts to accomplish a number of tasks. These tasks are performed in a synthetic cityscape environment, which includes houses, banks, theaters, and restaurants. Given such experiences, specification of the associated goal structure constitutes a narrative. We investigate the feasibility of producing an EL from raw pixel data with the hope that resulting descriptions can be used to infer the underlying narrative structure. Our initial experiments show that a supervised learning approach yields embeddings and EL descriptions that capture narrative structure. Alternatively, an unsupervised learning approach results in greater ambiguity with respect to the narrative.