Reviewerly/exHarmony huggingface dataset:
Exploring the design space of electrochromic displays

In his vision of "calm technology", Weiser thought of people being surrounded by devices and displays that only come to the user's attention when needed. Non-light emitting electrochromic (EC) displays are one approach to conveying information in a calm and unobtrusive way. The dynamic graphical segment based EC displays are extremely low power, require no special driving electronics, and are not limited to rectangular form factors. In our demo, we present four prototypes exploring the design space for EC displays in different application domains for information presentation and decorative aesthetics.
Depth map upsampling with a confidence-based joint guided filter

Depth maps are often obtained simultaneously alongside color images, but their resolution is much lower than that of the respective color images. This paper proposes a confidence-based joint guided filter for depth map upsampling using corresponding color information. Many previous studies have been conducted to incorporate color images into the estimation of upsampling filter coefficients. The scheme proposed in this paper instead focuses on the order of the filter for unreliable pixel refinement, and the order is calculated using the confidence map derived from the shape of unreliable regions, depth map, and color pixel values. In terms of quantitative and qualitative measurements, the proposed method demonstrates superior performance over current state-of-the-art algorithms.
Limit properties of the monotone rearrangement for density and regression function estimation

The monotone rearrrangement algorithm was introduced by Hardy, Littlewood and Pólya as a sorting device for functions.Assuming that x is a monotone function and that an estimate x n of x is given, consider the monotone rearrangement xn of x n .This new estimator is shown to be uniformly consistent as soon as x n is.Under suitable assumptions, pointwise limit distribution results for xn are obtained.The framework is general and allows for weakly dependent and long range dependent stationary data.Applications in monotone density and regression function estimation are detailed.Asymptotics for rearrangement estimators with vanishing derivatives are also obtained in these two contexts.
Improved version of teaching learning-based optimization algorithm using random local search

Purpose This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm. Design/methodology/approach Random local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS). Findings At first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM workshop problem 25, where the results were compared with those resulting from classical TLBO, bat algorithm, hybrid TLBO, Nelder–Mead simplex method and other referenced work. Originality value New TLBO-RLS proposed algorithm contains a part of random local search, which allows good exploitation of the solution space. Therefore, TLBO-RLS provides better solution quality than classic TLBO.
An Approach to Rain Detection Using Sobel Image Pre-processing and Convolutional Neuronal Networks

Rain fall detection has been an important factor under study in a multitude of applications: estimation of floods in order to minimize damage before an environmental risk situation, rain removal from images, agriculture field, etc. Actually, there are numerous methods implemented in order to try to solve this issue. For example, some of them are based on the traditional weather station or in the use of radar technology. In this work, we propose an approach to rain detection using image processing techniques and Convolutional Neuronal Networks (CNN). In order to improve the results of classification, images in rain and no rain conditions are pre-processed using the Sobel algorithm to detect edges. The architecture that defines the CNN is LeNet and it is carried out with three convolutional layers, three pooling layers and a soft max layer. With the proposed method, it is possible to detect the presence of rain in certain region of the image with an accuracy of 89%. The purpose of the proposed system is just to complete with a different added value, other traditional methods for detection of rain.
A cost-sensitive meta-learning classifier: SPFCNN-Miner

Classification is a data mining technique that is used to predict the future by using available data and aims to discover hidden relations between variables and classes. Since the target data are high-dimensional, limited and class-unbalanced distribution in most real-world classification, most conventional classification methods can hardly achieve good classification results on these data. To explore an effective solution, this paper proposes the Siamese Parallel Fully-connected Neural Network (SPFCNN) as a binary classifier and uses the meta learning method to deal with the problem of class-unbalanced data distribution. Given that classified cases naturally come with costs, cost-sensitive learning is used to transform our cost-insensitive SPFCNN into cost-sensitive SPFCNN which is suitable for the classification of cost-sensitive issues. An extensive computational study is also performed on cost-insensitive and cost-sensitive versions of the proposed SPFCNN and effective results on different versions of SPFCNN which are obtained show that the performance of the proposed approach is better than that of the comparison methods.
Searching for spellcheckers

Misspellings in queries used to initiate online searches is an everyday occurrence. When this happens, users either rely on the search engine's ability to understand their query or they turn to spellcheckers. Spellcheckers are usually based on popular dictionaries or past query logs, leading to spelling suggestions that often better resonate with adult users because that data is more readily available. Based on an educational perspective, previous research reports, and initial analyses of sample search logs, we hypothesize that existing spellcheckers are not suitable for young users who frequently encounter spelling challenges when searching for information online. We present early results of our ongoing research focused on identifying the needs and expectations children have regarding spellcheckers.
Practical Password Hardening Based on TLS

Text-based passwords are still the dominant form of user authentication in remote services. Beyond the many usability issues associated with handling several text-based passwords, security is also an important dimension. Through the years, a significant amount of on-line services has been compromised and their stored passwords have been leaked. Once the database is compromised, it takes little time for a program to crack the cryptographically hashed (weak) passwords, no matter the algorithm used. In response to this problem, researchers have proposed cryptographic services for hardening all stored passwords. These services perform several sessions of cryptographic hashing combined with message authentication codes. The goal of these services is to coerce adversaries to use them while cracking the passwords. This essentially transforms off-line password cracking to on-line. Although these services incorporate elaborate cryptographic schemes for password hardening, it is unclear how easily typical web sites can utilize them without outsourcing the functionality to large providers. In this paper, we take a systems approach for making any web site that is serviced through TLS capable of strongly hardening their passwords. We observe that any TLS-enabled web server is already equipped with strong cryptographic functions. We modify mod_ssl, the module that offers TLS to any Apache web server, to act as a password-hardening service. Our evaluation shows that with an overhead similar to adapting hash functions (such as scrypt and bcrypt), our proposal can protect even the weakest passwords, once they are leaked.
Considering Manual Annotations in Dynamic Segmentation of Multimodal Lifelog Data

Multimodal lifelog data consists of continual streams of multimodal sensor data about the life experience of an individual. In order to be effective, any lifelog retrieval system needs to segment continual lifelog data into manageable units. In this paper, we explore the effect of incorporating manual annotations into the lifelog event segmentation process, and we present a study into the effect of high-quality manual annotations on a query-time document segmentation process for lifelog data and evaluate the approach using an open and available test collection. We show that activity based manual annotations enhance the understanding of information retrieval and we highlight a number of potential topics of interest for the community.
Blockchain Technology for the 5G-Enabled Internet of Things Systems: Principle, Applications and Challenges

The evolving fifth generation (5G) technologies will enable the pervasive deployment of Internet-of-Things (IoT) systems because of their advantages of massive connectivity, wide coverage, low latency and high throughput. However, the 5G-enabled IoT systems still face several critical challenges such as IoT device management, Big Data analytics and storage, and privacy and security protection. Blockchain technology is among the most promising solutions to address the related challenges. Specifically, blockchain is a decentralized database that can record and validate all the peer-to-peer (P2P) transactions executed in a distributed information and communication technology (ICT) system without relying on any central authority or third party for verification. The decentralized, secure and autonomous characteristics of blockchain technology make it possible to improve the performance of IoT systems through the introduction of many new mechanisms such as smart property trading, asset tracking, distributed data storage, and privacy and security management. This chapter provides a comprehensive overview of how blockchain technology could facilitate IoT systems. The fundamental principle of blockchain technology is first presented. Existing applications of blockchain technology on strengthening the IoT systems are then surveyed. Finally, the main challenges of integrating blockchain technology into IoT applications are analyzed and future research directions of related areas are also identified.
Survey: Image forgery and its detection techniques

Image forgery is the most widely used technique in various applications and used in the various investigations to find the duplicate images in social media, social networking etc. Many forgery techniques are done to get the better results on finding the duplicate images. This paper, mainly focused on discussing the different strategies connected with making the fraud picture.
Provisioning and automated scaling of network slices for virtual Content Delivery Networks in 5G infrastructures

The concept of network slicing in 5G infrastructures allows to deliver multiple virtual services over shared environments and fully customized based on vertical-driven requirements and target performance indicators. A key feature of 5G networks is the capability to dynamically re-optimize the allocation of computing and network resources, from the core up to the edge, to instantiate and manage different types of concurrent services over a shared infrastructure. In this demonstration, we present a network slicing and orchestration solution for vertical services in the media sector, where enhanced Mobile Broadband (eMBB) network slices are instantiated interconnecting physical and virtual functions, provisioned and configured on-demand using the concept of NFV Network Services. The eMBB slices are automatically dimensioned to match the requirements of the virtual Content Delivery Network (vCDN) service, e.g. in terms of number of target users, video quality and geographical coverage area. Arbitration and resource allocation schemas optimize the sharing of mobile communication services and virtual resources among concurrent media service instances, in compliance with the Service Level Agreement between network operators and vertical service providers.
A comparative performance analysis of different machine learning techniques

Abstract Over the past many decades, Machine Learning (ML) has advanced from the undertaking of few PC fans misusing the likelihood of PCs figuring out how to play diversions, and a piece of Mathematics (Statistics) that only here and there measured computational methodologies, to an autonomous research obedience that has not just given the essential base to measurable computational standards of learning systems, yet in addition has created different calculations that are routinely utilized for content translation, design acknowledgment, and a numerous other business purposes and has prompted a different research enthusiasm for information mining to recognize shrouded regularities or abnormalities in social information that developing by second. This paper centers around clarifying the idea and development of Machine Learning, a portion of the well known Machine Learning calculations and endeavor to think about three most prevalent calculations dependent on some essential thoughts. Sentiment140dataset was utilized and execution of every calculation as far as preparing time, forecast time and precision of expectation have been reported and analyzed.
Review and comparative study of three local based active contours optimizers for image segmentation

In this paper, we present a review of three local based formulations of the active contours model (ACM) as a mean of image segmentation. In real world images, especially in the field of medical images such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) etc. intensity inhomogeneity and noise are two major concerns that make segmentation a very challenging task. In order to assess the robustness of each method to noise and intensity inhomogeneity, we start by intensity inhomogeneity and noise free synthetic image samples, and apply different levels of added inhomogeneity varying from small to severe intensity, or randomly distributed additive gaussian noise with gradually varying variance. The parameters of the energy formulation of each method are initially tuned for the clean sample images and then kept constant for all of the experiments. Our goal is to assess how robust each method is, to still overcome the added noise and/or intensity inhomogeneity and produce the desired segmentation.
Combining Data from Fitness Trackers with Meteorological Sensor Measurements for Enhanced Monitoring of Sports Performance

Systematic analysis of training data has become an inherent element of building a sports condition and preparation for sports competitions. Today’s progress in the development of various fitness trackers, smart wearables and IoT devices allows monitoring the level of development of athletic abilities not only for professional athletes but also for sports enthusiasts and sports amateurs. Meteorological conditions prevailing on a given day can significantly affect the effectiveness of training and abilities of a person during sports competitions. However, in order to properly analyze particular sports achievements and the effectiveness of sports efforts, the training data from body sensors should be appropriately combined with weather sensor data. In this paper, we show that, due to approximate nature, this process can be implemented by using the fuzzy join technique.
Boosting Wavelet Neural Networks Using Evolutionary Algorithms for Short-Term Wind Speed Time Series Forecasting

This paper addresses nonlinear time series modelling and prediction problem using a type of wavelet neural networks. The basic building block of the neural network models is a ridge type function. The training of such a network is a nonlinear optimization problem. Evolutionary algorithms (EAs), including genetic algorithm (GA) and particle swarm optimization (PSO), together with a new gradient-free algorithm (called coordinate dictionary search optimization – CDSO), are used to train network models. An example for real speed wind data modelling and prediction is provided to show the performance of the proposed networks trained by these three optimization algorithms.
ERSCC

Each camera on digital devices is made of hundreds of thousands of sensors, with which it can separate and capture the light from spatial points in a fine-grain manner, enabling high-rate data transfer from the digital display to the camera, i.e. screen-camera communication. Compared with RF (Radio Frequency) technologies, visible light approaches are more convenient and secure.
Handwritten Arabic Word Spotting Using Speeded Up Robust Features Algorithm

Optical Character Recognition (OCR) of Arabic text from manuscripts images is virtually impossible, this is due to the semi-cursive nature of Arabic script which is very difficult to explore by algorithms and image processing methods.In this paper, we present a method of exploration and research in content of Arabic manuscript images. It's about characterizing segmented handwritten words with a set points of interest by providing a means of identification and research in these manuscripts. Each word segmented and described by key features will be compared to query words.The results of this method are encouraging in comparison with other methods, especially when it comes to the same of handwritten style used in the text.
Evaluating Long Term User Experience with Children

This paper reports on a study to understand the effects the MemoLine visualization tool has on the interview process for evaluating long term user experience with children. Modifications were made to the MemoLine to try to improve consistency in reporting periods of no play. A within-subject design study was conducted using the MemoLine with interviews, and just interviews, of a suite of educational games over a 12-week period. The participants were 22 children, aged between 8 and 9 years, from a UK primary school. Three constructs were analyzed: Game Play, Learning and Ease of Use. The results showed that using the MemoLine's visualization tool did not aid the children to recall past experience in comparison to just conducting an interview. In addition, the changes to the tool did not appear to aid consistency when reporting periods of no play and the appropriateness of the MemoLine is critically reflected upon.
IDC Methods into Industry and Innovation

The Interaction Design and Children (IDC) Community has a long history of innovating methods and techniques for the design and evaluation of technologies for children. Many innovations have been reported in the academic literature but the uptake of methods by industry has been slow and the community has hitherto failed to seriously consider how best to develop, present and promote their methods beyond academia. The aim of the workshop is to weave together IDC researchers and IDC key personnel coming from the industry, with genuine interest in industry-academia collaboration, into a community interested in building a coherent, high-impact collaboration channel. The goal of the workshop is to encourage a critical discussion and debate about how IDC methods can be further adopted, modified or even extended by the IDC related industry. This workshop is expected to reinforce IDC industry-academia collaboration with an ultimate goal to increase understanding and develop a community of interest that is going to co-develop ideas and novel design approaches that can bring IDC methods closer to the industrial practice
From Iterative Threshold Decoding to a Low-Power High-Speed Analog VLSI Decoder Implementation

Key capabilities of the fifth generation (5G) of cellular mobile communication systems are increased peak and network data rates and an energy efficient operation. Signal processing plays an important role to meet these goals. Recently, it has been shown that, for the problem of vector equalization, signal processing with analog electronic circuits has a large potential for a high-speed and low-power operation. In this paper we consider the problem of decoding for convolutional self-orthogonal codes. We report on a student project where we used standard off-the-shelf electronic components to realize an analog decoder circuit. The starting point is an iterative threshold decoder. Its structure corresponds to the one of a high-order recurrent neural network (HORNN). Structure as well as weights of the HORNN are given directly by the problem. The dynamics of the HORNN can be implemented in discrete-time, this corresponds to the iterative threshold decoder, or in continuous-time. Both implementations lead to the same asymptotic state, which represents the desired decoder output. The dynamical evolution of the continuous-time HORNN is governed by a system of coupled first-order nonlinear differential equations. Based on that, we design an analog electronic circuit, which solves this set of differential equations. Thus the analog circuit shows a similar dynamical behavior as the continuous-time HORNN, and especially also the same asymptotic state.
Facial Eigen-Feature based gender recognition with an improved genetic algorithm

This paper proposes a novel image processing method to extract the gender feature from frontal face combining Principal Component Analysis (PCA) and an improved Genetic Algorithm (GA) to reduce the interference of facial expression, lighting or wear. The collected facial images are first cropped an d aligned automatically, then the gray-level information can be converted to feature vectors via PCA. After eigen-features are extracted with high classification performance by the aid of an improved GA, the neural network classifier can be trained accordingly. Compared to the classification methods based on global gray-level information, the obtained classifier has better identification rate but less used feature dimension, so the calculation load can substantially be reduced during training and classification procedures, which benefits to the development of a real-time identification system. Furthermore, FERET dataset and FEI dataset are used to validate the generality of the proposed method, where 94% and 96% accuracy rates of the gender recognition can be achieved respectively.
NAVIGATE

We demonstrate NAVIGATE, an explai\underlineNA ble query engine for \underlineVI sual \underlineG r\underlineA ph explora\underlineT ion by \underlineE xamples. NAVIGATE interleavesquery rewriting and query answering to help users (1) search graphs \textslwithout writing complex queries, and (2) understand answers by providing intuitive explanations. Users can visually construct queries and specify missing or unwanted example entities to guide the exploration towards desired answers. NAVIGATE can rewrite queries with answers close to examples, by minimally altering their topological and semantic constraints. Another unique feature is its ability to explain query results by tracing the query manipulation operators that are responsible for transforming the original answers to desirable ones. In addition, NAVIGATE optimizes system response time by referring to dynamically cachedstar views to reduce both query evaluation and rewriting cost at run time. We also demonstrate its ease-of-use, efficiency, and explainable exploration in applications such as recommendation and knowledge base search.