Reviewerly/exHarmony huggingface dataset:
Metrics of graph Laplacian eigenvectors

The application of graph Laplacian eigenvectors has been quite popular in the graph signal processing field: one can use them as ingredients to design smooth multiscale basis. Our long-term goal is to study and understand the dual geometry of graph Laplacian eigenvectors. In order to do that, it is necessary to define a certain metric to measure the behavioral differences between each pair of the eigenvectors. Saito (2018) considered the ramified optimal transportation (ROT) cost between the square of the eigenvectors as such a metric. Clonginger and Steinerberger (2018) proposed a way to measure the affinity (or ‘similarity’) between the eigenvectors based on their Hadamard (HAD) product. In this article, we propose a simplified ROT metric that is more computational efficient and introduce two more ways to define the distance between the eigenvectors, i.e., the time-stepping diffusion (TSD) metric and the difference of absolute gradient (DAG) pseudometric. The TSD metric measures the cost of “flattening” the initial graph signal via diffusion process up to certain time, hence it can be viewed as a time-dependent version of the ROT metric. The DAG pseudometric is the l 2 -distance between the feature vectors derived from the eigenvectors, in particular, the absolute gradients of the eigenvectors. We then compare the performance of ROT, HAD and the two new “metrics” on different kinds of graphs. Finally, we investigate their relationship as well as their pros and cons.
Spatial Attention Network for Few-Shot Learning

Metric learning is one of the feasible approaches to few-shot learning. However, most metric learning methods encode images through CNN directly, without considering image contents. The general CNN features may lead to hard discrimination among distinct classes. Based on observation that feature maps correspond to image regions, we assume that image regions relevant to target objects should be salient in image features. To this end, we propose an effective framework, called Spatial Attention Network (SAN), to exploit spatial context of images. SAN produces attention weights on clustered regional features indicating the contributions of different regions to classification, and takes weighted sum of regional features as discriminative features. Thus, SAN highlights important contents by giving them large weights. Once trained, SAN compares unlabeled data with class prototypes of few labeled data in nearest-neighbor manner and identifies classes of unlabeled data. We evaluate our approach on three disparate datasets: miniImageNet, Caltech-UCSD Birds and miniDogsNet. Experimental results show that when compared with state-of-the-art models, SAN achieves competitive accuracy in miniImageNet and Caltech-UCSD Birds, and it improves 5-shot accuracy in miniDogsNet by a large margin.
Image Encryption Method Using Dependable Multiple Chaotic Logistic Functions

This article explores an efficient way of image encryption using chaotic logistic function. A set of two chaotic logistic functions and a 256 bit long external secret key are employed to enhance the security in the encrypted images. The initial condition of first logistic function has been obtained by providing the suitable weights to all bits of the secret key. The initial condition of second logistic function has been derived from first chaotic logistic function. In this proposed algorithm, ten different operations are used to encrypt the pixel of an image. The outcome of the second logistic map decides the operation to be used in the encryption of the particular image pixel. Various statistical parameter comparisons show that the proposed algorithm provides an image encryption method with better security and efficiency for all real-time applications.
Wheel Slip Avoidance through a Nonlinear Model Predictive Control for Object Pushing with a Mobile Robot

Wheel slip may cause a significative worsening of control performance during the movement of a mobile robot. A method to avoid wheel slip is proposed in this paper through a nonlinear model predictive control. The constraints included within the optimization problem limit the force exchanged between each wheel and the ground. The approach is validated in a dynamic simulation environment through a Pioneer 3-DX wheeled mobile robot performing a pushing manipulation of a box.
Technologies in supply chain management and logistics

Until recently, technology has been considered an enabler for improvements in underlying supply chain and logistics operations. However, recent trends in society and business, such as mobile computing, social media, and online retailing, have significantly changed almost every aspect of the supply chain and logistics landscape. In this chapter the following technologies were found to have a pervasive role in altering this landscape: Maturing technologies Optimization software Sensors/Telematics Cloud computing Data warehouse and integration Automated storage and retrieval Growth technologies Mobility Wearability Data analytics Social media Emerging technologies 3D printing Drones Autonomous vehicles Exponential Blockchain Internet of Things Virtual reality Machine learning Each of these technologies will be discussed along with videos illustrating their use.
MFL Data Feature Extraction based on KPCA-BOMW Model

Magnetic Flux Leakage (MFL) inspection for submarine pipelines is the most common non-destructive testing method currently. MFL data feature extraction is a vital part in MFL data processing. In order to deal with this problem, this paper proposes a MFL data feature extraction algorithm based on improved KPCA-BOMW model. This algorithm processes the data matrixes directly, chooses SURF to instead the SIFT feature in traditional BOW model to appropriate the MFL data, and uses KPCA to reduce the dimension of SURF to increase the running speed. Finally, verify the effectiveness of the algorithm by three experiments.
Modelling competing theories

We introduce a complete many-valued semantics for two normal lattice-based modal logics.This semantics is based on reflexive many-valued graphs.We discuss an interpretation and possible applications of this logical framework in the context of the formal analysis of the interaction between (competing) scientific theories.
Towards 3D smooth pursuit interaction

In this position paper, we encourage the use of novel 3D gaze tracking possibilities in the field of gaze-based interaction. Smooth pursuit offers great benefits over other gaze interaction approaches, like the ability to work with uncalibrated eye trackers, but also has disadvantages like the produced visual clutter in more complex user interfaces. We examine the basic concept of smooth pursuits, its hardware and algorithmic requirements and how this can be applied to real world problems. Then we evaluate how the recent change in availability of 3D eye tracking hardware can be used to approach the challenges of 2D smooth pursuit interaction. We take a look at different research opportunities, show concrete ideas and discuss why they are relevant for future research.
Backpropagation Implementation To Classify Dysgraphia In Children

Writing is one of the main needs that must be mastered by everyone. Writing has been taught since childhood, but there are several factors that interfere with the learning process of writing. There are some who experience impairments in writing, so special treatment is needed. Not a few parents or teachers label these children with lazy children. From this problem, several studies have involved these children so that they can be detected and get special treatment immediately. Kurniawan's research is to create an expert system that is used to detect dysgraphia in children. This study produced Accuracy of 94.71%. While Rosenblum conducted research on the characteristics that can be used for writing skills in dysgraphic children. This study concludes that the contents of writing and time when writing are the most suitable characteristics. Therefore, this research was conducted by making an Android application based on Kurniawan's research to collect data, then comparing the attributes or characteristics that actually influence the dysgraphia classification process. From this study, it can be concluded that with 4 classes, all attributes are needed for classification considerations. Whereas by using 2 classes, the attributes “consistency” and “distance” does not need to be used because it increases accuracy errors. Backpropagation implementation produces 82.6% of accuracy in cases with 4 classes, while cases with 2 classes produce 84.7% both with 6 hidden neurons.
Artificial Bee Colony Algorithm and Its Application in Traveling Salesman Problems

The purpose of applying the artificial bee colony algorithm to the traveling salesman problems pursuant to its principle is to find out solutions to the traveling salesman problems of different cities and use local search strategy to effectively make such issues less complicated, so as to optimize the path. It is necessary to analyze the correlation between the artificial bee colony algorithm and relevant algorithm parameters and the number of cities. Simulate result denotes the artificial bee colony algorithm can be applied to settle the problems of traveling salesman and is much reliable in effectiveness, usability and researchability.
Improving Readability of Text in Realistic Virtual Reality Scenarios

The resolution of current head-mounted displays leads to poor readability of text inrealistic virtual reality (VR) simulations. This results in unnatural user behavior. Here, three approaches for improving the readability of text in VR have been investigated:1) constantly magnifying a part of the screen (version "lens"), 2) magnifying sceneelements such as signs depending on the gaze direction (version "magnify") and3) augmenting the scene by adding floating texts (version "AR"). These approaches havebeen implemented as prototypes and tested by twelve users, using a virtual supermarketas an exemplary VR scene. The participants filled out a questionnaire, rating theirexperience with the different approaches. Additionally, the time required for readinggiven texts (price tags) has been measured and compared for each approach. With theAR version, the reading time was significantly shorter than the reading time without anyaids. The other versions offered no advantages. The AR version was also rated best in thequalitative evaluation. However, the other versions offer potential for improvement, which can be addressed in future research.
M3B corpus

This paper is the first trial to create a corpus on human-to-human multi-modal communication among multiple persons in group discussions. Our corpus includes not only video conversations but also the head movement and eye gaze. In addition, it includes detailed labels about the behaviors appeared in the discussion. Since we focused on the micro-behavior, we classified the general behavior into more detailed behaviors based on those meaning. For example, we have four types of smile: response, agree, interesting, sympathy. Because it takes much effort to create such corpus having multiple sensor data and detailed labels, it seems that no one has created it. In this work, we first attempted to create a corpus called "M3B Corpus (Multi-Modal Meeting Behavior Corpus)," which includes 320 minutes discussion among 21 Japanese students in total by developing the recording system that can handle multiple sensors and 360-degree camera simultaneously and synchronously. In this paper, we introduce our developed recording system and report the detail of M3B Corpus.
Coverage Flight Path Planning for Multi-rotor UAV in Convex Polygon Area

Coverage flight path planning for multi-rotor UAV in convex polygon area i s studied. When given the two known conditions that the ground scan width of multi-rotor UAV sensor and the anticlockwise vertexes sequence of the convex polygon area, a "Geometric-vector algorithm" is proposed to calculate the flight points of the UAV accurately, and then the UAV can complete the coverage of convex polygon area by flying in turn according to the calculated flight points. The detailed solution process of flight points is given, the programming, simulation and real flight experiment are carried out for the proposed method, and the results proves that the method in solving the problem of coverage flight path planning for multi-rotor UAV in convex polygon area is effective and versatile. And through comparative analysis, for the problem of Multi-UAV common coverage convex polygon area, the Multi-UAV task allocation based on the total flight path length has better effect.
Seasonal Variability of the Solar Energy Potential of the City of Abidjan: Experimental Comparative Study of Two Sunshine Measurement Techniques

Abstract Most countries have a national weather station. In Côte d’Ivoire, it is the Aeronautical and Meteorological Exploitation Company (SODEXAM) which is in charge of it through its databank. The inaccessibility of these data leads many solar energy entrepreneurs to move toward free sites that are often out of phase with the meteorological realities of the area considered. We propose in this paper, a comparative study of two sunshine measurement techniques, namely, the technique of the heliograph and that of the thermoelectric pyranometer, to discuss the reliability of the data provided by these techniques, but also to analyze the seasonal variations of the insolation of Abidjan from 1978 to 2017. The comparison of the results produced by these two approaches showed a discrepancy of up to 43%, with much higher values for the national station. The analysis of the solar field of this city over a period of 19 years has shown a great variability of the solar energy potential which starts from a decrease in energy, from 4447 Wh/m2 (1978–1988) to 3303 Wh/m2 (2003–2009), to achieve an energy increase up to 4782 Wh/m2 (2016–2017). The solar year has been characterized in three solar seasons depending on the mensuel solar irradiation.
Peacock Algorithm

The paper proposes a novel nature-inspired swarm algorithm: peacock algorithm (PA). It is based on the lekking and mating behavior of peacocks. The best solutions are identified as peacocks, while the remaining solutions are called peahens. The peahens are assigned to the peacocks based on their relative positions. Peacocks perform exploitation while peahens perform both, exploration as well as exploitation. A peahen explores the search space and moves towards the lek peacock. Once it is near, it may to choose to mate (exploitation) or pursue another peacock (exploration). Every peacock moves towards the dominant peacock (best solution) to enhance its mating chances. The dominant peacock maintains his lek by pushing away other peacocks if they encroach upon its lekking area. The algorithm maintains diversity by adding random solutions whenever two solutions are found to be similar. To test the efficiency of the proposed work, it is compared over a set of 23 benchmark functions to nine other algorithms, which are a mixture of well-established and new algorithms. Its performance is also compared over the CEC'14 test suite to six algorithms. Experimental results demonstrate the superiority of PA, establishing it as a promising novel algorithm.
Investigating Correlations of Inter-coder Agreement and Machine Annotation Performance for Historical Video Data

Video indexing approaches such as visual concept classification and person recognition are essential to enable fine-grained semantic search in large-scale video archives such as the historical video collection of the former German Democratic Republic (GDR) maintained by the German Broadcasting Archive (DRA). Typically, a lexicon of visual concepts has to be defined for semantic search. But the definition of visual concepts can be more or less subjective due to individually differing judgments of annotators, which may have an impact on training data quality for supervised machine learning methods. In this paper, we analyze the inter-coder agreement on historical TV data of the former GDR for visual concept classification and person recognition. The inter-coder agreement is evaluated for a group of expert as well as non-expert annotators. Furthermore, correlations between visual recognition performance and inter-annotator agreement are measured. In this context, information about training dataset size and agreement are used to predict average precision for concept classification. Finally, the impact of expert vs. non-expert annotations on person recognition is analyzed.
Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System

Video transmission over wireless computer networks is increasingly popular as new applications emerge and wireless networks become more widespread and reliable. An ability to quantify the quality of a video transmitted using a wireless computer network is important for determining network performance and its improvement. The process requires analysing the images making up the video from the point of view of noise and associated distortion as well as traffic parameters represented by packet delay, jitter and loss. In this study a modular fuzzy logic based system was developed to quantify the quality of video transmission over a wireless computer network. Peak signal to noise ratio, structural similarity index and image difference were used to represent the user's quality of experience (QoE) while packet delay, jitter and percentage packet loss ratio were used to represent traffic related quality of service (QoS). An overall measure of the video quality was obtained by combining QoE and QoS values. Systematic sampling was used to reduce the number of images processed and a novel scheme was devised whereby the images were partitioned to more sensitively localize distortions. To further validate the developed system, a subjective test involving 25 participants graded the quality of the received video. The image partitioning significantly improved the video quality evaluation. The subjective test results correlated with the developed fuzzy logic approach. The video quality assessment developed in this study was compared against a method that uses spatial efficient entropic differencing and consistent results were observed. The study indicated that the developed fuzzy logic approaches could accurately determine the quality of a wirelessly transmitted video.
Understanding Algorithms through Exploration

We investigate exploration as an alternative to explanation to improve user understanding of algorithms and algorithmic decision-making. Drawing on complex problem-solving as defined in cognitive science, we conducted a think-aloud study in the lab (N=10) as well as an MTurk online study (N=123) using a flight booking scenario to see if and how exploration supports knowledge acquisition in two different tasks. One group was told to focus on booking the cheapest flight (knowledge acquisition as a secondary task), the other on understanding the system logic (knowledge acquisition as a primary task).
Resource needs prediction in virtualized systems: Generic proactive and self-adaptive solution

Resource management of virtualized systems in cloud data centers is a critical and challenging task due to the fluctuating workloads and complex applications in such environments. Over-provisioning is a common practice to meet service level agreement requirements, but this leads to under-utilization of resources and energy waste. Thus, provisioning virtualized systems with resources according to their workload demands is essential. Existing solutions fail to provide a complete solution in this regard, as some of them lack proactivity and dynamism in estimating resources, while others are environment- or application-specific, which limits their accuracy in the case of bursty workloads. Effective resource management requires dynamic and accurate prediction. This work presents a novel prediction algorithm, which (1) is generic, and can thus be applied to any virtualized system, (2) is able to provide proactive estimation of resource requirements through machine learning techniques, and (3) is capable of real-time adaptation with padding and prediction adjustments based on prediction error probabilities in order to reduce under- and over-provisioning of resources. In several virtualized systems, and under different workload profiles, the experimental results show that our proposition is able to reduce under-estimation by an average of 86% over non-adjusted prediction, and to decrease over-estimation by an average of 67% versus threshold-based provisioning.
EPARS

The sedentary lifestyle of elderly people may cause chronic diseases, reduce muscle mass and mobility. Moreover, elderly physical inactivity increases the susceptibility to mood disorders, such as anxiety and depression, and can permanently lead to disability. Physical activity reminders might be a useful tool to motivate the elderly to do exercises. Even though the importance of them, the related works only use interactive calendars to set up activity reminders to the user, without personal and autonomous feedback about the user's activities. Some studies have also established physical activities recognition in a smart-home context associated with interactive calendars, it's a personalized approach, but the activity recognition is limited to a small environment, excluding the possibility of the elderly exercising outdoors. Therefore, in this work, we presented the Elderly Physical Activity Reminder System (EPARS), which is a digital assistant to reminder elderly people to practice physical exercises in an unobtrusive way. EPARS addresses the activity recognition system using wearable and mobile devices with 98.63% accuracy and the context-appropriate activity reminder module, providing a barrier-free and non-intrusive approach to guide elderly people to a healthier lifestyle. The EPARS also includes a database consisting of data collected from 32 elderly volunteers, that helped us feed the activities recognition system.
Implementation of New Secure Encryption Technique for Cloud Computing

To maintain sensitive information and confidentiality in cloud computing is of great importance which means the need for more robust ways to keep it from the attackers. In this paper, several levels of multi-coding levels were developed using more than one method to obtain more confidentiality through DNA encryption and adding a higher level of confidentiality by adding Advanced Encryption Standard (AES) and then loading it into the cloud storage. A Matlab program (R2014a) has been used for implementing proposed scheme with several analyses. The analyses show that proposed scheme has a convincing level of security, efficiency, complexity and speed.
Blockchain in Agriculture 4.0 - An Empirical Study on Farmers Expectations towards Distributed Services based on Distributed Ledger Technology

Digitalization, which proceeds in all branches, as well in agriculture, by using new technology, sensors and networking, requires responsible usage of data. One possibility to manage data and use them to create value is the blockchain-technology. It is primary enforced by the food industries and consumers to ensure traceability and transparency. To put blockchain-technology into beneficial use in agriculture, this domain has to be analyzed regarding social and business aspects. This paper presents the results of a qualitative study where 41 actors from the agricultural domain participated in focus groups and delivered a written statement. It was found that farmers are interested in adapting new markets and technologies early to get an economic advantage. On the other hand, the fear of losing traditional local business partners and the social surroundings of the farmers must be considered.