Reviewerly/exHarmony huggingface dataset:
Research on Two-Stage Path Planning Algorithms for Storage Multi-AGV

In order to solve the problem of path conflict of multiple AGV (Automated Guided Vehicle) in warehousing environment during handling shelves, in this paper, a two-stage path planning algorithm is proposed. In the first stage, on the premise of ignoring the conflicts between robots, the optimal path of each AGV is obtained by using A* algorithm. In this paper, an improved A* algorithm with directional search is proposed, which can effectively reduce the search of unnecessary nodes in the path finding process. In the second stage, conflict is checked by time window, when collision conflicts occur in multi-robot system. When a post-conflict situation occurs, excessive energy consumption is caused by a AGV waiting for another AGV to pass, in order to solve the energy consumption caused by waiting, a path planning method of coupling conflict car is proposed to realize dynamic path planning of multi-AGV. The simulation results show that the proposed algorithm can effectively reduce the number of searching nodes and waiting times in the process of path finding and it can also improve the overall efficiency of the system under the condition of ensuring the optimal or sub-optimal path.
Probabilistic Approach to Human Activity Recognition from Accelerometer Data

Due to the wide availability of personal mobile and wearable devices, it is now easier than ever before to obtain information from a wide range of sources, including the actions performed by the users or the environment in which a user is located. This is advantageous in a number of applications including, in particular, remote health monitoring and diagnostics, where the manner in which an action is performed or the conformance to some treatment or living regime may be relevant to treatment or health outcomes. The goal of human activity recognition is to determine the identity of the action the user is currently performing based on the data streamed from some sensor modality. In this work we consider the accelerometer signals streamed through a wearable IMU unit and use this data to recognize the user's activity. We consider typical activities of daily living (ADLs). We adopt a simple approach based on the probabilistic modeling of the streamed signals. Some of the training samples are taken as templates and are modeled by empirical distributions. Testing for new sample consists essentially of measuring the probabilistic distribution of the test sample against those of the templates using a chosen set of distance/dissimilarity measures. This approach has proven to be both very computationally efficient and effective regarding its predictive performance.
The phylogenetic tree of boosting has a bushy carriage but a single trunk

The phylogenetic tree of boosting has a bushy carriage, with early influencers: Ref. 1 was undoubtedly one for the making and use of popular application packages, some used nowadays in almost every Kaggle competition (2), and in that respect it is an understatement to say that the recent work of Luna et al. (3), embedded in the framework of interpretability, is important.There is, however, a single trunk to this tree, and it grows from roots 1) in the probably approximately correct (PAC) model of Valiant (4), which led to 2) opening the boosting problem (5)—namely, how to use an oracle returning “weak” classifiers slightly better … [↵][1]1To whom correspondence may be addressed. Email: Richard.Nock{at}data61.csiro.au. [1]: #xref-corresp-1-1
Some lessons learned using health data literature for smart information retrieval

Big Data paradigm is leading both research and industry effort calling for new approaches in many computer science areas. In this paper, we show how semantic similarity search for natural language texts can be leveraged in biomedical domain by Word Embedding models obtained by word2vec algorithm, exploiting a specifically developed Big Data architecture. We tested our approach using a dataset extracted from the whole PubMed library. Moreover, we describe a user friendly web front-end able to show the usability of this methodology on a real context that allowed us to learn some useful lessons about this peculiar kind of data.
The Effect of Cafe Mobile App’s Convenience Service on Perceived Value and Reuse Intention

In this study, based on previous studies, we examine the causal relation model that influences reuse intention, which is a dependent variable through perceived value as a parameter by measuring the service convenience for cafe mobile application. The questionnaires were surveyed by an online company(EMBRAIN). Online research was conducted for one week from October 26 to November 8, 2018. There are 13 items of the collected data were excluded and 324 items suitable for irradiation were used. After data collection, frequency analysis was performed on the general characteristics of questionnaire survey using SPSS, and data conclusions were derived by verifying reliability and validity of data and hypotheses. Study results show that service convenience of cafe mobile application has a positive effect on perceived value and reuse intention. In addition, in the relationship that cafe mobile app’s service convenience has a significant (+) influence on reuse intention, perceived value proved to have meaningful results as intermediary roles. Implications of this study are as follows. First of all, this study will be helpful for cafe companies and consumers if utilize the service convenience of cafe mobile application in perceived value and reuse intention in marketing applications. Therefore, theoretically, we propose the development direction of cafe mobile application and present academic data for marketing strategy innovation and competitive advantage in the food service industry that conforms to the fourth industrial revolution era.
Image Segmentation Algorithm Based on the Background Skeleton Feature

In order to improve the segmentation accuracy of adhered rice images, an automatic segmentation algorithm for adhered rice images based on background skeleton features is proposed. The experimental results show that the proposed algorithm can adapt well to the adhesion segmentation of different grains under complex condition. Compared with the classic distance transformation watershed algorithm and the improved watershed algorithm, Algorithm accuracy has improved a lot, and the formed grain segmentation boundary is smoother, with less influence on the shape.
A Trust-Based Ranking Model for Cloud Service Providers in Cloud Computing

With the rapid growth of Cloud services, many Cloud Service Providers (CSPs) offer similar service functionalities. Hence, guaranteeing the available CSPs having a trust degree would increase the performance of the cloud environment. Therefore, selecting the trusted CSP whose services satisfy the Cloud Service Consumers’ (CSC) requirements becomes a challenge. According to the work in this paper, a ranking model for CSPs has been introduced based on a combination of the trust degree for each CSP and the similarity degree between the CSPs’ parameters and the CSCs’ requested parameters. The proposed model consists of four phases; Filtrating, Trusting, Similarity, and Ranking. In the Filtrating phase, the existing CSPs in the system will be filtered based on their parameters. The CSPs trust values are calculated in the Trusting phase. Then, the similarity between CSCs’ requirements and CSPs’ services is calculated. Finally, the ranking of CSPs will be performed. To evaluate the performance of the proposed CSPs Ranking model, a comparative study has been done among the proposed CSP Ranking model and the most up to date four models using two QoS case studies and Armor data set. According to the comparative results, it is found that the proposed CSPs Ranking model outperforms the existing models with respect to execution time, time complexity, and precision of the system.
Global-scale GDPR Compliant Data Sharing System

Personal data protection associated with regulatory compliance is often siloed as legal documentation that is not suitable for GDPR compliant system implementation. In a real global-scale industrial environment, applying novel personal data protection technologies directly to industrial sites has the potential risk to cause unintended disclosure of personal information, which results in a huge monetary penalty. In this paper, the Global-scale GDPR Compliant Data Sharing (GDS) system is introduced which enables efficient data retrieval and data sharing process while protecting personal information.
PrivateEx

Bitcoin introduces a new type of cryptocurrency that does not rely on a central system to maintain transactions. Inspired by the success of Bitcoin, all types of alt cryptocurrencies were invented in recent years. Some of the new cryptocurrencies focus on privacy enhancement, where transaction information such as value and sender/receiver identity can be hidden, such as Zcash and Monero. However, there are few schemes to support multiple types of cryptocurrencies/assets and offer privacy enhancement at the same time. The major challenge for a multiple asset system is that it needs to support two-way assets exchange between participants besides one-way asset transfer. Thus, we propose a privacy-preserving exchange scheme, PrivateEx, which preserves the privacy of the exchange of different assets. PrivateEx utilizes zero-knowledge proof and a novel way to "lock" assets involved in the exchange to guarantee the correctness, fairness, and privacy of exchange of assets in the system. We also implement a prototype of PrivateEx and evaluate its performance to show that it is practical with modern computers.
Rank Based Pixel-Value-Differencing: A Secure Steganographic Approach

Das, Debashis Basak, Ratan KumarImage steganography is a frequently used method in digital data security in modern communication system. The two prime objectives of steganography lie in hiding capacity and security of hidden data. In this paper, we propose a data hiding scheme, focusing on message security, employs pixel-value differencing to embed the secret bits where the bitstream is shuffled before embedding in each pixel block. The bit-shuffling process ensures the data security in a great extent. The original image is partitioned into non-overlapping blocks which are subsequently marked with specific ranks, estimated from pixel-difference value of each candidate block. A set of unique bins, same in number as the total number of ranks obtained, are constructed to store the secret bitstreams. In the embedding process, secret bits are considered from various bins that are selected based on the rank matrix. The proposed method is implemented and tested on standard gray-scale images. The experimental results establish the efficiency of the algorithm. A comparative analysis with the state-of-the-art method shows the strength of the proposed algorithm in the ground of security issues.
Gene-Similarity Normalization in a Genetic Algorithm for the Maximum k-Coverage Problem

The maximum k-coverage problem (MKCP) is a generalized covering problem which can be solved by genetic algorithms, but their operation is impeded by redundancy in the representation of solutions to MKCP. We introduce a normalization step for candidate solutions based on distance between genes which ensures that a standard crossover such as uniform and n-point crossovers produces a feasible solution and improves the solution quality. We present results from experiments in which this normalization was applied to a single crossover operation, and also results for example MKCPs.
IMU (inertial measurement unit) device for internet of things based disaster early warning system: applications and innovation

The various natural disasters which happen in this world push us to develop an integrated and thorough natural disaster prevention system. In an era full of technologies, accessing data become much easier that it has become probable to decrease the loss caused by natural disaster. The internet of things has powerful role in increasing the prevention of natural disaster. Also, the application of inertial measurement unit has been promoted as universal component that can serve as a tool to monitor the parameters of a natural disaster. This is some examples of several applications of IMU device in general uses and in disaster related uses. This paper serve as a way to develop new ideas about IoT based IMU device, which can be a pillar in developing a disaster responsive society.
Unconventional quantum correlations of light emitted by a single atom in free space

We present an approach to engineer the photon correlations emerging from the interference between an input field and the field scattered by a single atom in free space. Nominally, the inefficient atom-light coupling causes the quantum correlations to be dominated by the input field alone. To overcome this issue, we propose the use of separate pump and probe beams, where the former increases the atomic emission to be comparable to the probe. Examining the second-order correlation function ${g}^{(2)}(\ensuremath{\tau})$ of the total field in the probe direction, we find that the addition of the pump formally plays the same role as increasing the coupling efficiency, even though the physical atom-light coupling efficiency remains unchanged. We show that one can tune the correlation function ${g}^{(2)}(0)$ from zero (perfect antibunching) to infinite (extreme bunching) by a proper choice of pump amplitude. We further elucidate the origin of these correlations in terms of the transient atomic state following the detection of a photon, and show that these correlations can be observed under realistic conditions.
Android App Success Prediction based on Reviews

The amount of apps that are being generated in this computer-driven visual world is colossal. The problem arises when customers have a huge collection of apps to select from to quench their day-to-day needs. Google's Play Store and Apple's App Store alone have about 2.6 million apps and 1.8 million apps each for customers to choose from. It is important to use the data generated by these apps on the stores to analyze past trends, and other such information to predict the success of an app. This research work proposes to use sentiment analysis on the selected app reviews combined with certain mentioned attributes to predict the star ratings which determines the app's success.
Web Crawler Scheduler Based on Coroutine

The application of coroutine has greatly accelerated the programs switching. Compared with threads and processes, it not only satisfies most applications, but also consumes very little system resources. This paper proposes a web crawler scheduler based on coroutine.
Security analytics for real‐time forecasting of cyberattacks

Summary Protection of networked computing infrastructures (such as Internet of Things, Industrial Control Systems, and Edge computing) is dependent on the continuous monitoring of interaction between such devices and network/Cloud‐based hosts (especially in Industry 4.0 environments). This real‐time monitoring enables an analyst to quantify evolving and emerging threats to such network infrastructures. A framework for identifying patterns in observed cyberthreats and the use of these patterns for forecasting the growth of an emerging threat to network infrastructure is proposed. This framework enables predicting the maximum threat intensity and the time period over which this maximum intensity is likely to occur. The proposed framework integrates: (a) continuous monitoring of device/network activity, (b) forecasting behavior using exponentially weighted moving averages, (c) utilizing Fibonacci retracement for estimating the potential intensity of a cyberattack, and (d) linear regression for predicting response time for high risk thresholds and a machine learning strategy to predict potential risk over a pre‐defined time window. Using this approach, we can produce time intervals between the forecast and the actual attacks using real‐world network activity data. Our results show an average lead time of around 1.75 hours, providing a window of opportunity to limit the impact of an attack and counter it.
Child Action Recognition in RGB and RGB-D Data

The paper presents an ongoing work that aims for real-time action recognition specifically tailored for child-centered research. To this end, we collected and annotated a dataset of 200 primary school children aged 6 to 11 years old. Each child was asked to perform seven actions: boxing, waving, clapping, running, jogging, walking towards the camera, and walking from side to side. Two camera perspectives are provided, with a top view in RGB format and a frontal view in both RGB and RGB-D formats. Body keypoints (skeleton data) are extracted using OpenPose and OpenNI tools. The results of this work are expected to bridge the performance gap between activity recognition systems for adults and children.
Question Answering System-Based Chatbot for Health care

Chatbot helps to provide automated as well as instant output at the absence of human intervention. It is more essential in an emerging domain like health care to manage the emergency condition without the presence of medical experts. In this research, we are motivated to develop a health-care chatbot system to recognize diseases from user-provided health conditions or symptoms. This research helps to overcome the above-mentioned challenges in partially. Primarily, these challenges are introduced due to the rapid development of information and communication technology. On the other hand, the chatbot industry is rapidly growing while promising to cut the costs. Also, less involvement of domain experts and lack of automated information extraction system introduced more difficulties in this task. Hence, we have employed an unsupervised machine learning technique to build this chatbot. Additionally, we have prepared an experimental dataset that assists in validating the output of the proposed system. Primarily, this system recognized a set of diseases from the user given a set of symptoms and vice versa.
Towards an approach using grammars for automatic classification of masses in mammograms

Abstract Approximately 15% of all cancer deaths among women worldwide is due to breast cancer. Mammography is one of the most useful methods for the early detection of this disease. Over the last decade, several papers were published reporting the usage of different computer‐aided diagnosis systems using pattern recognition techniques as a second opinion to obtain a more accurate diagnosis. However, the theory of formal languages has not been explored in this field. In this context, the main contribution of this study is to present the usage of a new syntactic approach that is able to classify breast masses found in mammograms as benign or malignant. The experimental tests were performed using a dataset that contains 111 images from different sources. The grammar‐based classifiers achieved accuracy values ranging from 89% to 100% depending on the features and the model employed. Furthermore, to achieve a feature dimension reduction, a feature selection technique based on the Gini importance of each feature was employed. Additionally, we compared the obtained results with the grammar‐based classifiers to the more traditional classifiers used in this research area, such as artificial neural networks, support vector machines, k‐nearest neighbors, and random forest. The best result achieved by the grammar‐based classifiers was approximately 10% higher, in terms of accuracy, than the best results produced by the traditional classifiers, showing the strength of this grammatical approach.
LoOP: Iterative learning for optimistic planning on robots

Efficient robotic behaviors require robustness and adaptation to dynamic changes of the environment, whose characteristics rapidly vary during robot operation. To generate effective robot action policies, planning and learning techniques have shown the most promising results. However, if considered individually, they present different limitations. Planning techniques lack generalization among similar states and require experts to define behavioral routines at different levels of abstraction. Conversely, learning methods usually require a considerable number of training samples and iterations of the algorithm. To overcome these issues, and to efficiently generate robot behaviors, we introduce LoOP, an iterative learning algorithm for optimistic planning that combines state-of-the-art planning and learning techniques to generate action policies. The main contribution of LoOP is the combination of Monte-Carlo Search Planning and Q-learning, which enables focused exploration during policy refinement in different robotic applications. We demonstrate the robustness and flexibility of LoOP in various domains and multiple robotic platforms, by validating the proposed approach with an extensive experimental evaluation.
Mixed data generation packages and related computational tools in R

This paper is concerned with providing some computation-related details of the 16R packages that have been developed by Demirtas and his colleagues in the context of random number generation. The dominant theme is multivariate mixed data generation. However, univariate and multivariate data generation from different distributions as well as some other tools such as modeling the correlation transitions in latency and discretization domains are also included. This is intended for interested people who would benefit from access to a comprehensive set of data simulation tools at one single place. While the focus is on conceptual and implementation issues, the ideas are supported by appropriate references for methodological development.
Acceleration of DNN Training Regularization: Dropout Accelerator

The training time of a deep neural network has increased such that training process may take many days or even weeks using a single device. Further, conventional devices such as CPU and GPU pursuit generality on their use, it is inevitable that they have drawbacks on energy efficiency for a specific use case such as DNN training. So accelerating DNN training is becoming an important part of DNN accelerator to achieve a high energy efficiency. This paper proposes an idea to save both execution time and DRAM energy consumption during DNN training by implementing dropout hardware efficiently. Simulation results show that our idea can save execution time and DRAM energy consumption on backward propagation as much as the dropped activations.
Clustering K Means for Criteria Weighting With Improvement Result of Alternative Decisions Using SAW and TOPSIS

In today's world, decision making can be helped by using decision support systems. This system is an approach to support decision making. Decision makers use relative weights for each attribute. The obtained total score is the score of each alternative decision. The process of making alternative decision uses a combination of SAW and TOPSIS methods. In addition, the determination of criteria weight is also influenced by K Means method. This clustering method serves to provide an alternative weighting value so that decision makers no longer need to give the initialization value. The combination of these methods generate a more absolute alternative value than only using SAW. This research is a development of previous research by adding TOPSIS method. The resulting alternative decision has a more absolute and significant alternative value. This is indicated by comparison of TOPSIS usage results and using only SAW. This research uses data student of information system in Universitas Nusantara PGRI Kediri. It aims to proof the significant result for comparing with further method.