Reviewerly/exHarmony huggingface dataset:
Collaborative Object Manipulation Through Indirect Control of a Deformable Sheet by a Mobile Robotic Team

Using deformable sheets to handle objects are convenient and found in many applications. This paper presents a novel design to collaboratively move an object that is held by a deformable sheet attached to a multi-robot team. One challenge in these applications is to design a collaborative multirobot motion control to keep the object under certain motion constraints. We first present the object motion models and restrictions as a set of geometric and kinematic properties of the robotic team. A formation control scheme is presented to incorporate these motion constraints and the stability under the control design is also guaranteed. Finally, we demonstrate the design and performance using a group of three omni-directional mobile robots to conduct manipulation experiments.
A Novel Task Provisioning Approach Fusing Reinforcement Learning for Big Data

The large-scale tasks processing for big data using cloud computing has become a hot research topic. Most of previous work on task processing is directly customized and achieved through existing methods. It may result in relatively more system response time, high algorithm complexity and resource waste, etc. Based on this argument, aiming at realizing overall load balancing, bandwidth cost minimization and energy conservation while satisfying resource requirements, a novel large-scale tasks processing approach called TOPE (Two-phase Optimization for Parallel Execution) is developed. The deep reinforcement learning model is designed for virtual link mapping decisions. We treat whole network as a multi-agent system and the whole process of selecting each node's next hop node is formalized via Markov decision process. We train the learning agent by deep neural network to store parameters of deep network model while approximating the value function, rather than tons of state-action values. The virtual node mapping is achieved by designed distributed multi-objective swarm intelligence to realize our two-phase optimization for task allocation in topology structure of Fat-tree. We provide experiments to show the ability of TOPE in analyzing task requests and infrastructure network. The superiority of TOPE for large-scale tasks processing is convincingly demonstrated by comparing with state-of-the-art approaches in cloud environment.
Pixelwise JPEG compression detection and quality factor estimation based on convolutional neural network

JPEG compression is one of image degradations that often occurs in image storing and retouching process. Estimating JPEG compression degradation property is important for JPEG deblocking algorithm and image forensic analysis. JPEG degradation exists not only in JPEG file format but also in other image formats because JPEG distortion remains after converting to another image format. Moreover, JPEG degradation property is not always uniform within an image in case that the image is collaged from different JPEG-compressed photos. In this paper, pixelwise detection of JPEG-compression degradation and estimation of JPEG quality factor using a convolutional neural network is proposed. The proposed network outputs an estimated JPEG quality factor map and a compression flag map from an input image. Experimental results show that the proposed network successfully infers the quality factors and discriminates between non-JPEG-compressed images and JPEG-compressed images. We also demonstrate that the proposed network can spot a collaged region in a fake image which is comprised of images that have different JPEG compression properties. Additionally, the network reveals that image datasets Set5 and Set14, often used to evaluate super-resolution algorithms, contain JPEG-compressed low quality images, which are inappropriate for such evaluation.
Interactive Ant Colony Optimization to Support Adaptation in Serious Games

The success of serious games usually depends on their capabilities to engage learners and to provide them with personalized gaming and learning experiences. Therefore, it is important to equip a game, as an autonomous computer system, with a certain level of understanding about individual learning trajectories and gaming processes. AI and machine learning technologies increasingly enter the field; these technologies often fail, however, since serious games either pose highly complex problems (combining gaming and learning process) or do not provide the extensive data bases that would be required. An interesting new direction is augmenting the strength of AI technologies with human intuition and human cognition. In the present paper, we investigated performance of the MAXMIN Ant System, a combinatorial optimization algorithm, with and without human interventions to the algorithmic procedure. As a testbed, we used a clone of the Travelling Salesman problem, the Travelling Snakesman game. We found some evidence that human interventions result in superior performance than the algorithm alone. The results are discussed regarding the applicability of this pathfinding algorithm in adaptive games, exemplified by Micro Learning Space adaptation systems.
Information oversaturation: key problems

He main goal of the research is the explication of the specific traits of the present information environment that form the situation of information oversaturation and the conduct of the analysis of their influence upon the individual cognitive abilities and social communications. The article analyzes the role of information and digital technologies as a special technical phenomenon that cause present social transformations. Author analyzes the concepts of «data», «information» and «knowledge» where information is seen as a message that can be transformed into knowledge only when certain conditions are met. The main character traits of the present information environment are outlined that lead to information oversaturation of the person:1) the easy access and high speed of the information transfer; 2) aggressive imposing of the unnecessary information; 3) one-sidedness of the surrounding information due to the specifics of the Internet services; 4) dependence of the individual and of the society on communication technologies. The possible effects of information oversaturation are formulated: 1) the decrease of the success rate of cognitive activity of the individuals; 2) transformations of the individual memory (its «externalization») that bring about the necessity of the education system revision; 3) transformation of the information sources into the sources of constant stress for the individuals; 3) social conflicts that are brought about by moral unreadiness of the individuals and society for the use of the present communicative possibilities provided by information technologies. The conclusion is drawn that the full analysis of the aforementioned problems is needed that should help to find the balance in the system «human-society-information» on the present level of societal development.
A Methodological Framework for Dictionary and Rule-based Text Classification

Recent research on dictionary- and rule-based text classification either concentrates on improving the classification quality for standard tasks like sentiment mining or describe applications to a specific domain. The focus is mainly on the underlying algorithmic approach. This work in contrast provides a general methodological approach to dictionary- and rule-based text classification based on a systematic literature analysis. The result is a process description that enables the application of these technologies on specific problems by guidance through major decision points from the definition of the classification goals to the actual classification of texts.
A Generalized Approach for Anomaly Detection From the Internet of Moving Things

Internet of Moving Things are connected to a variety of different types of sensors to form a world of moving things, including people, animals, vehicles, drones, and boats, etc. As the data of collectible moving things continue to increase, anomaly detection of moving things has become an increasingly popular data mining task. Traditional trajectory outlier detection algorithms can detect common anomalies effectively, but it is hard to detect generalized anomalies, such as viewable direction anomalies, gravity anomalies, and magnetic field anomalies which can be collected by the accelerometer, gyroscope, magnetometer, and RPM sensor, etc. For this, we proposed a generalized approach for anomaly detection from the Internet of Moving Things, called the moving things outlier detection algorithm (MTOD). We propose the distance of moving things, which is equal to the weighted sum of the location distance and the multi-sensor distance, and then use the multi-sensor data generalization and moving things partitioning and anomaly detection three-step framework to detect the generalized anomaly. The experimental results show that our MTOD algorithm can detect moving things anomaly efficiency and accurately.
Extract, Attend, Predict: Aspect-Based Sentiment Analysis with Deep Self-Attention Network

Aspect-based sentiment analysis aims to predict sentiment polarities for given aspect terms in a sentence. Previous work typically encodes the aspect and the sentence separately, with either RNNs or CNNs along with sophisticated attention mechanisms. However, CNNs and RNNs suffer from problems such as restricted local receptive field and long-term dependency, respectively. Besides, separately encoding aspects and sentences also results in problems such as the aspect has no context information and neighboring aspects are not considered. To address these problems, we propose a novel approach that conducts an extract-attend-predict process with deep self-attention for aspect-based sentiment analysis. Unlike previous methods that use either RNNs or CNNs as the basic encoder, we utilizes a pre-trained deep self-attention encoder to avoid the difficulty in capturing long-distance words. Moreover, instead of performing separately encoding, our model directly extracts the aspect representation from contextualized sentence representations based on the span boundary of target aspect. A multi-granularity attending mechanism is further applied to capture the interaction between aspects and sentences, which is later used to predict the sentiment polarity. We conduct experiments on two benchmark datasets and the results show that our approach outperforms previous state-of-the-art models.
Automated breast ultrasound (ABUS) as a screening tool: initial experience

Abstract Background Breast cancer is a major health problem, being the most common cancer in women. Early detection of breast cancer aims to the reduction of mortality and morbidity rates. Conventional screening methods include mammography and ultrasonography; however, both modalities have their limitations. Automated breast ultrasound (ABUS) is a recent technological advancement in the field of breast imaging having the benefit of standardization of the scans and lack of operator dependence as in conventional handheld ultrasound scans. The aim of this work was to report our initial experience of the added value of ABUS as a breast screening tool. The study included 200 patients who had screening mammograms, ultrasound, and ABUS. Results A significant difference was found between the number of lesions detected by ABUS and conventional ultrasound. A significant difference was found between lesions detected by ABUS and mammography which was most evident in patients with dense breasts. Conclusions ABUS is a valuable tool in the screening of the breast with improved lesion detection, especially in patients with dense breasts.
Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction

We study the problem of k-center clustering with outliers in arbitrary metrics and Euclidean space. Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem. Our idea is inspired by the greedy method, Gonzalez's algorithm, for solving the problem of ordinary k-center clustering. Based on some novel observations, we show that this greedy strategy actually can handle k-center clustering with outliers efficiently, in terms of clustering quality and time complexity. We further show that the greedy approach yields small coreset for the problem in doubling metrics, so as to reduce the time complexity significantly. Our algorithms are easy to implement in practice. We test our method on both synthetic and real datasets. The experimental results suggest that our algorithms can achieve near optimal solutions and yield lower running times comparing with existing methods.
Multi-aspect Ontology for Interoperability in Human-machine Collective Intelligence Systems for Decision Support

A collective intelligence system could significantly help to improve decision making. Its advantage is that often collective decisions can be more efficient than individual ones. The paper considers the human-machine collective intelligence as shared intelligence, which is a product of the collaboration between humans and software services, their joint efforts and conformed decisions. Usually, multiple collaborators do not share a common view on the domain or problem they are working on. The paper assumes usage of multi-aspect ontologies to overcome the problem of different views thus enabling humans and intelligent software services to self-organize into a collaborative community for decision support. A methodology for development of the above multi-aspect ontologies is proposed. The major ideas behind the approach are demonstrated by an example from the smart city domain.
Light-Weight and Scalable Hierarchical-MVC Architecture for Cloud Web Applications

Nowadays, with the prevalence of cloud computing, the demand for modular and scalable Web application development technologies is urgent. Dynamic contents and ubiquitous user interactions make Web applications increasingly complicated. A majority of current web applications leverage a Model-View-Controller (MVC) architectural style. Since the MVC triad does not provide feature-based modularization, Web applications in pure MVC style are experiencing scalability and maintainability issues. In this paper, we propose a light-weight and scalable hierarchical-MVC architecture for Web application development in Cloud environment, named Web Module Definition (WMD), which supports feature-based modularization and application structure. In WMD, the entire Web application is decomposed into interconnect WMD modules, which contains controllers and views for a single feature. WMD modules are able to include and extend others to handle complex business logic. In the meantime, we provide a Web application framework implementation supporting WMD-based architecture, and present a demonstration website using WMD.
Do Remittances Contribute to the Development of Financial Institutions? New Evidence from the Developing World

This study empirically examines the impact of remittances on the development of financial institutions in 50 countries selected from low (LI), lower-middle (LMI) and middle-income (MI) groups. The income group effect is inspected using the system Generalised Method of Moment Regression (SGMM), while for individual economies we employ the dynamic panel bootstrap Granger causality approach. The results reveal that remittances increase financial depth in three groups, stabilise the institutions in low-income and increase profitability in middle-income group. The remittances used for consumption play a negative role in financial expansion. Suitable government policies uplift the position of financial institutions whereas, corruption exerts an adverse effect on it. The causality evidence shows that remittances have a more robust effect on financial institutional development especially in lower-middle and middle-income countries. Moreover, remittances and institutions cause each other in three-fifths of lower-middle and three-fourths of middle-income countries. The developed financial institutions have the additive capability to attract more remittances and employ them in a productive way. We notice the fact that, the economic relationship between remittances and financial institutions is more country-specific. Sound economic policies, tax exemptions and a competitive environment in the financial sector can have dual effects on both the remitters and the intermediary financial institutions.
Nanoparticle shapes: Quantification by elongation, convexity and circularity measures

Abstract The goal of the nanoparticle synthesis is, first of all, the production of nanoparticles that will be more similar in size and shape. This is very important for the possibility of studying and applying nanomaterials because of their characteristics that are very sensitive to size and shape such as, for example, magnetic properties. In this paper, we propose the shape analysis of the nanoparticles using three shape descriptors – elongation, convexity and circularity. Experimental results were obtained by using TEM images of hematite nanoparticles that were, first of all, subjected to segmentation in order to obtain isolated nanoparticles, and then the values of elongation, convexity and circularity were measured. Convexity C x ( S ) is regarded as the ratio between shape’s area and area of the its convex hull. The convexity measure defines the degree to which a shape differs from a convex shape while the circularity measure defines the degree to which a shape differs from an ideal circle. The range of convexity and circularity values is (0, 1], while the range of elongation values is [1, ∞). The circle has lowest elongation (ε = 1), while it has biggest convexity and circularity values ( C x = 1; C = 1). The measures ε( S ), C x ( S ), C ( S ) proposed and used in the experiment have the few desirable properties and give intuitively expected results. None of the measures is good enough to describe all the shapes, and therefore it is suggested to use a variety of measures so that the shapes can be described better and then classify and control during the synthesis process.
Iris Image Super Resolution Based on GANs with Adversarial Triplets

Iris recognition is a safe and reliable biometric technology commonly used at present. However, due to the limitations of equipment and environment in a variety of application scenarios, the obtained iris image may be of low quality and not clear enough. In recent years, there are many attempts to apply neural networks to iris image enhancement. This paper is inspired by SRGAN, and introduces the adversarial idea into the triplet network, finally proposing a novel iris image super-resolution architecture. With triplet loss, the Network can keep reducing intra-class distance and expanding inter-class distance during iris image reconstruction. The experiments on CASIA’s several benchmark iris image datasets yield considerable results. This architecture makes a contribution to enhancing iris images for recognition.
Software code complexity assessment using EEG features

This paper provides a study using Electroencephalography (EEG) to investigate the brain activity during code comprehension tasks. Three different code complexity levels according to five complexity metrics were considered. The use of EEG for this purpose is relevant, since the existing studies were mostly focused on neuroimaging techniques. Using Leave-One-Subject-Out cross-validation procedure for 30 subjects, it was found that the features related with the Gamma activity were the most common in all the folds. Regarding the brain regions, right parietal was the most frequent region contributing with more features. A Linear Discriminant Analysis Classifier for task classification, obtained a F-Measure of 92.71% for Code complexity easy, 52.25% for Code complexity intermediate and 53.13% for Code complexity advanced, revealing an evidence of mental effort saturation with the code complexity degree. This suggests that current code complexity metrics do not capture cognitive load and might not be the best approach to assess bug risk.
Handwritten Mathematical Recognition Tool

The recognition of handwritten mathematical expressions has received an increasing amount of attention in pattern recognition research. It is the process of taking in raw data and making actions based on the category of the data. In this paper, we present a tool for recognizing handwritten mathematical expressions. The proposed architecture aims at handling the handwritten expressions by performing segmentation of the input based on each pen ups and pen downs followed by symbol classification. As a classifier, an Extreme Learning Machine and Support Vector machines are used, the classifier which produces a best accuracy is selected and then the symbols are trained among various handwritten mathematical expression and a promising result are achieved at symbol classification stage. Once the symbols are classified, the corresponding output is converted to LaTex format.
Service Level Agreements for Communication Networks: A Survey

Information and Communication Technology (ICT) is being provided to the variety of end-users demands, thereby providing a better and improved management of services is crucial. Therefore, Service Level Agreements (SLAs) are essential and play a key role to manage the provided services among the network entities. This survey identifies the state of the art covering concepts, approaches and open problems of the SLAs establishment, deployment and management. This paper is organised in a way that the reader can access a variety of proposed SLA methods and models addressed and provides an overview of the SLA actors and elements. It also describes SLAs' characteristics and objectives. SLAs' existing methodologies are explained and categorised followed by the Service Quality Categories (SQD) and Quality-Based Service Descriptions (QSD). SLA modelling and architectures are discussed, and open research problems and future research directions are introduced. The establishment of a reliable, safe and QoE-aware computer networking needs a group of services that goes beyond pure networking services. Therefore, within the paper this broader set of services are taken into consideration and for each Service Level Objective (SLO) the related services domains will be indicated. The purpose of this survey is to identify existing research gaps in utilising SLA elements to develop a generic methodology, considering all quality parameters beyond the Quality of Service (QoS) and what must or can be taken into account to define, establish and deploy an SLA. This study is still an active research on how to specify and develop an SLA to achieve the win-win agreements among all actors.
A retinex-based image enhancement scheme with noise aware shadow-up function

This paper proposes a novel image contrast enhancement method based on both a noise aware shadow-up function and Retinex (retina and cortex) decomposition. Under low light conditions, images taken by digital cameras have low contrast in dark or bright regions. This is due to a limited dynamic range that imaging sensors have. For this reason, various contrast enhancement methods have been proposed. Our proposed method can enhance the contrast of images without not only over-enhancement but also noise amplification. In the proposed method, an image is decomposed into illumination layer and reflectance layer based on the retinex theory, and lightness information of the illumination layer is adjusted. A shadow-up function is used for preventing over-enhancement. The proposed mapping function, designed by using a noise aware histogram, allows not only to enhance contrast of dark region, but also to avoid amplifying noise, even under strong noise environments.