Reviewerly/exHarmony huggingface dataset:
Improving Human Activity Recognition Algorithms using Wireless Body Sensors and SVM

The accurate individuals' activities description is an important assignments in human computer interface. Many applications can be developed based on correct and fast activity recognition, such as healthcare monitoring and fall detection applications. Human Activity Recognition (HAR) is an active research field, where some key factors still challenging and need to be enhanced for faster and accurate recognition. This paper presents a novel approach aims to improve the activity recognition time by aggregating the raw data from inertial sensors into one time series used as input, then various features are extracted from the resulted signal by dimensionality reduction procedure, Those features are used as input data for the classification algorithm without affecting the information associated with activity in the raw data. The results of the novel method showed an improvement in recognition time while retaining the same level of accuracy.
Mutual Learning: Part II --Reinforcement Learning

The concept of "Mutual Learning" was introduced by the authors in Part I of this paper which was presented at the 2019 ACC. This is the second of the series of papers the authors propose to write on this subject. The principal question addressed in all of them concerns the process by which two agents "learn" from each other. More specifically, the question is how two (or more) agents should share their information to improve their performance.In Part I, the concept of mutual learning was introduced and discussed briefly in the context of two deterministic learning automata learning from each other in a static random environment. In this paper, we first provide some reasons why the concept can become complex even in such simple situations, and propose some (weak) necessary conditions for the problem to be well defined. Following this, we consider similar questions which arise when two agents use reinforcement learning in both static and dynamic environments. In particular, in the latter category, mutual learning in Markov Decision Processes (MDP) with finite states is discussed. Simulation results are presented wherever appropriate to complement the theoretical discussions.
Static Rectangle Expansion A<sup>*</sup>Algorithm for Pathfinding

Incremental pathfinding algorithms can reuse previous search efforts to benefit the current search; therefore, they can work like online algorithms in dynamical environments while being sped up automatically in static environments without offline preprocessing. However, current incremental technologies must reuse the task-based information from the previous searches, so a series of tasks must be searches that are similar to each other. The rectangle expansion A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (REA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ) is a recently proposed online algorithm that explores maps in units of unblocked rectangles and can speed up A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> by an order of magnitude or more. In this article, a new map-based incremental search technique is proposed and applied to the basic REA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> and a new algorithm is introduced, called the static rectangle expansion A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (SREA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ). SREA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> learns the map online and records the visited terrains with nonoverlapping static rectangles during each task. The static rectangles are valid over the whole life of the map, so the current search will benefit from previous searches by querying the accumulated static rectangles and avoiding unnecessary reexploration. The experimental results on typical benchmark sets show that SREA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> can significantly speed up REA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> without any extra cost or risk while retaining the completeness and optimality. SREA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> performs very well on the grid-based path planning competition server; compared with current incremental technologies, SREA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> is not only much more efficient but is also not limited by “similar search” problems.
On the Decipherment of Sidel’nikov-Type Cryptosystems

Asymmetric Sidel’nikov-type code-based cryptosystem is one of the generalizations of McEliece-type cryptosystems. The secret key in this generalization is the set of u randomly selected generator matrices $$G_{C_1}$$ ,..., $$G_{C_u}$$ of different $$ [n_i, k] $$ -codes $$ C_i $$ , $$ i \in \{1, ..., u \} $$ . In addition, a part of the secret key is a random permutation $$( \sum _ {i = 1}^un_i \times \sum _ {i = 1}^un_i )$$ -matrix P. The public key matrix $$\tilde{G}$$ is the result of multiplying the concatenation of secret generator matrices and the matrix P: $$\tilde{G}=[G_{C_1}|...|G_{C_u}]P$$ . The security of these cryptosystems is based on the assumption that for $$ u \geqslant 2 $$ it is computationally difficult to find in $$ \tilde{G} $$ such $$ (k \times {n_i}) $$ -submatrices composed of $$ n_i $$ columns of $$ \tilde{G}$$ , which would be generator matrices of codes permutably equivalent to the codes $$ C_i $$ , $$ i = 1, ..., u $$ . In the present paper, we construct an algorithm for the efficient search for such submatrices in the case when several conditions are satisfied. One of the conditions is the decomposability of the square of the connected codes $$C_1$$ ,..., $$C_u$$ into the direct sum of the squares of the codes $$ C_i $$ , $$ i \in \{1, ..., u \} $$ . An experimental assessment of the probability of fulfilling this condition for some Reed–Solomon codes, binary Reed–Muller codes, and Goppa code are also provided.
Eigen vector based analysis of Landsat OLI principal components and constrained energy minimization maps for discriminating iron enriched zones in banded iron formation (BIF) in Sidhi, Madhya Pradesh

(2020). Eigen vector based analysis of Landsat OLI principal components and constrained energy minimization maps for discriminating iron enriched zones in banded iron formation (BIF) in Sidhi, Madhya Pradesh. Geocarto International. Ahead of Print.
An Efficient Trust Inference Algorithm with Local Weighted Centrality for Social Recommendation

The integration of trust system and recommendation system is a new hot spot in current research. Trust relationship has be exploited in social recommendation, which can effectively solve the problems of low recommendation quality, sparse data and cold start in the traditional recommendation system. Meanwhile, trust inference in social relations is necessary in completing trust information and expanding social recommendation knowledge base. In this work, we propose a new trust inference algorithm LWCTrust to improve the efficiency and accuracy of social recommendation. Firstly, we construct a local weighted centrality (LWC) metric based on the user's degree centrality and trust information, and propose a new adaptive breadth-first search algorithm. Then, based on the property of path decay, we compare two different trust decay strategies. In addition, considering inconsistencies and conflicts in trust opinion, we apply LWC metric to multi-path aggregation step and present a OWA dynamic aggregation strategy. A number of experiments are conducted on the real social network dataset Advogato, and the results validate the great performance of LWCTrust. Our work is the first to construct an efficient LWC metric using social graph trust information, and we explore the effect of attenuation functions on accuracy in path propagation.
Decoding Machine Learning Benchmarks

Despite the availability of benchmark machine learning (ML) repositories (e.g., UCI, OpenML), there is no standard evaluation strategy yet capable of pointing out which is the best set of datasets to serve as gold standard to test different ML algorithms. In recent studies, Item Response Theory (IRT) has emerged as a new approach to elucidate what should be a good ML benchmark. This work applied IRT to explore the well-known OpenML-CC18 benchmark to identify how suitable it is on the evaluation of classifiers. Several classifiers ranging from classical to ensembles ones were evaluated using IRT models, which could simultaneously estimate dataset difficulty and classifiers’ ability. The Glicko-2 rating system was applied on the top of IRT to summarize the innate ability and aptitude of classifiers. It was observed that not all datasets from OpenML-CC18 are really useful to evaluate classifiers. Most datasets evaluated in this work (84%) contain easy instances in general (e.g., around 10% of difficult instances only). Also, 80% of the instances in half of this benchmark are very discriminating ones, which can be of great use for pairwise algorithm comparison, but not useful to push classifiers abilities. This paper presents this new evaluation methodology based on IRT as well as the tool decodIRT, developed to guide IRT estimation over ML benchmarks.
Blockchain-driven mobile data access towards fully decentralized mobile video trading in 5G networks

The today's heterogeneous wireless network infrastructure is managed by different stakeholders that allow mobile data access to a prescribed set of end users. This network access model not only enforces end users to accept network-driven service provisioning but also raises critical burdens towards flexible utilization of the numerous 5G network assets (content, storage, radio resources, etc.). In this paper, we investigate on-the-fly user-driven network-assisted mobile video content delivery in 5G and Beyond networks using blockchain technologies enabling multi-million transactions per second. To this end, we present the RE-CENT crypto-currency platform enabling infrastructure providers, end users and content providers to be actively engaged in the blockchain consensus. Detailed discussions on the implementation of the proposed platform through smart contracts are provided and analytical results are used to quantify performance gains of the proposed mobile data access model.
Quality-Based Ranking of Translation Outputs

Translation ranking is inherently of great significance for machine translation (MT), as it allows the comparison of performances of multiple MT systems as well as for its efficient training. This article demonstrates a mechanism that is used for ranking the translation outputs generated by the MT systems from best to worst. To implement this approach, the system exploits a supervised learning algorithm trained over existing manual ranking by using various features obtained after the linguistic analysis of both source and target side sentences without relying on the reference translation.
A novel adaptive k-NN classifier for handling imbalance: Application to brain MRI

The problem of efficiently classifying imbalanced data has become one of the most challenging tasks in machine learning. Some real world examples include medical image analysis, fraud detection, fault diagnosis, and anomaly detection. Although several data-level algorithms have been developed to ad dress imbalance, they are typically subject to some restrictions. We propose a novel variant of the k-NN family of classifiers, and name this as Density-based Adaptive-distance kNN (DAkNN). It can effectively handle data with skewed distributions and varying class-densities using the concept of adaptive distance. Comparative superiority is experimentally established over related data-level algorithms (SMOTE, ADASYN), using ten sets of two-class data, in terms of geometric mean (of the true positive and negative rates) and accuracy. Additionally, five sets of multi-class data are considered and compared with different variants of k-NN, which are currently very popular. Finally, DAkNN is successfully applied on the highly imbalanced Lower Grade Glioma (LGG) MR images, with an Average-Dice score of 0.9082 for delineating the tumor regions. The results demonstrate clear superiority over state-of-the-art algorithms.
A Novel Image Classification Model Jointing Attention and ResNet for Scratch

In recent years, Scratch has been widely used in helping teenagers learn to program. Before programming with Scratch, users usually need to select a background image with a proper style to set the environment of role activities and emotional tone of works. In order to make the image styles rich and diverse, users can use the fast neural style transfer method to generate the image. However, when users use this method, they are usually puzzled with the optional styles, which makes it a common phenomenon that the contents of transformed images do not match the chosen styles. To resolve this problem, we design a novel image classification model to help recognize the scene of the image, which can guide users to select optional styles for the image. First, we improve the residual network with two attention modules and reconstruct the residual module structure, which improves the accuracy of model classification. Then, we propose a self-adjusting learning rate module, which can accelerate the convergence of the model and reduce fluctuations of the loss function. The experiment results show that our model outperforms other classic image classification methods in classifying the background image of Scratch. The classification accuracy on the testset reaches 98%.
Target Tracking in the Presence of Intermittent Measurements by a Sparsely Distributed Network of Stationary Cameras

An observer and predictor framework is developed for tracking a moving object using a sparsely distributed network of stationary cameras. The sparsity of the camera network is representative of complex environments where continuous image feedback is not available, which occurs when the target is transitioning between fields-of-view in the camera network or the target is occluded by objects in the scene. Using a Lyapunov-based stability analysis, estimates of the object's pose and motion model are proven to converge, provided the current network configuration satisfies certain dwell-time conditions (i.e., minimum time the object needs to be observed and maximum time the object may remain outside a feedback region). This approach allows for areas of the network that may cause instabilities in the pose estimates, based on the dwell-time conditions, to be identified ahead of time and offers insight into how the network configuration could be augmented to ensure stability of the target's pose estimates.
Hybrid Image Encryption Technique Using Genetic Algorithm and Lorenz Chaotic System

One of the important application of image encryption is storing confidential and important images on a local device or a database in such a way that only the authorized party can view or perceive it. The current image encryption technique employs the genetic algorithm to increase confusion in the image, but compromises in time and space complexity. The other method employs chaos or pseudo random number generating systems which have fast and highly sensitive keys but fails to make the image sufficiently noisy and is risky due to its deterministic nature. We propose a technique which employs the non-deterministic, optimizing power of genetic algorithm and the space efficiency and key sensitivity of chaotic systems into a unified, efficient algorithm which will retain the merits of both the methods whereas tries to minimize their demerits in a software system. The encryption process proceeds in two steps, generating two keys. First, an encryption sequence is generated using Lorenz Chaotic system of differential equation. The seed values used are the user’s actual key having key sensitivity of 10 -14 . Second, the encrypted image’s genetic encryption sequence is generated which will result in an encrypted image with entropy value greater than 7.999 thus ensuring the image is very noisy. Proposed technique uses variations of Lorenz system seed sets to generate all random mutations and candidate solutions in Genetic encryption. Since only the seed sets leading to desired solution is stored, space efficiency is higher compared to storing the entire sequences. Using this image encryption technique we will ensure that the images are hidden securely under two layers of security, one chaotic and other non-deterministic.
Human-computer system design of entrepreneurship education based on artificial intelligence and image feature retrieval

The key of deep learning is how to extract abstract, deep and nonlinear target features, in which algorithm plays a crucial role. In this paper, the authors analyze the intelligent system design of entrepreneurship education classroom based on artificial intelligence and image feature retrieval. Pyramid pooling is used to transform any size feature map into fixed size feature vector, which is finally sent to the full connection layer for classification and regression. Experimental results show that the algorithm accelerates the convergence of the whole network and improves the detection speed. The education taught by entrepreneurial class is not only to help college students to seek a stable career, but also to help college students develop their own potential, cultivate entrepreneurial awareness, improve entrepreneurial quality and ability. Entrepreneurship education should not only stay in the design of subject courses, but should integrate entrepreneurship education with internet entrepreneurship practice. On this basis, we provide new countermeasures and suggestions for improving the quality and ability of college students in the process of entrepreneurial activities.
Evaluating preprocessing and interpolation strategies to create moving regions from real-world observations

Spatio-temporal data may be used to represent the evolution of real-world objects and phenomena. Such data can be represented in discrete time, which associates spatial information (like position and shape) to time instants, or in continuous time, in which the representation of the evolution of the phenomena is decomposed into slices and interpolation functions are used to estimate the intermediate position and shape at any time. The use of a discrete model may seem more straightforward, but a continuous representation provides potential gains in terms of data management, including in compression and spatio-temporal operations. In this work, we study the use of the continuous model to represent deformable moving regions captured at discrete snapshots. We use a dissimilarity distance-based strategy to select the observations that should be used to define the time slices of the continuous representation, thus transforming data acquired at discrete steps into a continuous model. We also study how the use of geometry simplification algorithms and simplification levels may impact on moving regions interpolation quality. We evaluate our proposals using a dataset composed by thousands of aerial bush-fires images. After applying object simplification and slice decomposition, we use two region interpolation algorithms to generate in-between observations and compare them with geometries representing real images. The results prove the effectiveness of our proposals and their importance in terms of interpolation accuracy.
An Enhanced Algorithm for Dynamic Data Release Based on Differential Privacy

Privacy preserving of dynamic data release is a research hotspot all the while. From the previous study, the release of dynamic data set has the problems of high computational complexity, poor data availability and more noise. To solve these problems, we present a method named Dynamic Data Release on Numeric (DP-DDRN) and a modified strategy based on dynamic similarity for optimal noise sets. Firstly, when the privacy budget remains the same, the DP-DDRN is to classify data set, compute the weight and dynamic sensitivity of each group, and then add Laplace noise according to the weight and the dynamic sensitivity. Secondly, the similarity between the processing results and the original data is calculated using the dynamic similarity algorithm; if the similarity is under a threshold, DP-DDRN algorithm will adjust strategy to reprocess the original data in loops until the threshold is reached, and then the processing results are kept and released. Experiments show that, our algorithm can protect the privacy of data with a low computational complexity and a higher data accuracy result.
An Energy-Aware Dynamic Resource Management Technique Using Deep Q-Learning Algorithm and Joint VM and Container Consolidation Approach for Green Computing in Cloud Data Centers

These days by a high increase in the amount of computation and big data gathering and analysis, everybody needs more resources. Buying more computational and storage resources are so expensive. However, cloud computing solved this problem by providing a “pay as you go” plans therefor, users will only pay for resources that they used. However, using this technology has its challenges. One of them is resource management, which is focusing on the methodologies of dedicating resources to the users with the minimum of waste. In this paper, we propose a novel energy-aware resource management technique, using the concepts of both joint VM and container consolidation approach and deep Q-Learning algorithm for green computing in cloud data centers in order to minimize the waste of resources, migration rate, and energy.
Design and Application of 3D Path Planning Model of Agricultural Automatic Robot

The diversification of China's farming environment has led to a low degree of agricultural mechanization. Three-dimensional path planning can enable agricultural machinery to play a role in mountainous and hilly areas. In this paper, a three-dimensional scanner is used to collect position information, and the data is normalized to establish a mathematical space model. In this space model, the ant colony algorithm is used to search for the optimal path. This model provides a technical reference for automatic agricultural picking and positioning in hilly farming.
Considerations for a Distributed GraphBLAS API

The GraphBLAS emerged from an international effort to standardize linear-algebraic building blocks for computing on graphs and graph-structured data. The GraphBLAS is expressed as a C API and has paved the way for multiple implementations. The GraphBLAS C API, however, does not define how distributed-memory parallelism should be handled. This paper reviews various approaches for a GraphBLAS API for distributed computing. This work is guided by our experience with existing distributed memory libraries. Our goal for this paper is to highlight the pros and cons of different approaches rather than to advocate for one particular choice.