Reviewerly/exHarmony huggingface dataset:
Experimental Study on Albedo of Solar Radiation in Different Underlying Surfaces in Western Sichuan Plateau

In this paper, the albedo of several common underlying surfaces in the western Sichuan Plateau is tested experimentally and the solar radiation albedo of different underlying surfaces is obtained by analytical calculation. The results show that the surface albedo of grassland, cement brick land, and sandy land is directly related to the instant total solar radiation received on the underlying surface. The greater the total solar radiation received on the surface, the greater the solar albedo is, and vice versa. While the albedo of water surface is negatively correlated with the total solar radiation intensity, that is, the smaller the total solar radiation intensity is, the larger the albedo is, and vice versa. The average albedo of grassland, cement brick surface, sandy surface, and water surface was 0.32, 0.47, 0.49, and 0.33, respectively. The research results fill the gap of basic data of active and passive solar energy applications in plateau area.
Resources and Constraints in Abuse Identity Performance

The theoretical underpinning to the concept of identity is the central topic of Chapter 5, where we examine particular facets of identity such as the performance of age, relationships and communities of practice. Keeping in mind our view of identity as being continuously negotiated through discursive practices, we focus here particularly on how age is treated as a relevant identity category by participants, and how relationships between adult offenders and child victims are performed. We probe the question of how these performances are situated in the wider social context, and in relation to other, more recognisable types of relationship that might be relied upon as resources.
Data Analytic Framework with Hybrid Security

With the increased usage of internet, the concerns on data security and accuracy have become major challenges. Usage of internet and impact of various social networks amplify the security concerns for data storage. Report generation of attribute based analysis and better security model for clients were implemented with the attribute based association rule which helps to generate keys using improved apriori algorithm. This mainly focus on enabling and ensuring the data security through data analytic integrated framework which uses asymmetric key based RSA algorithm up to 8192 bit to secure from unauthorized users. This model focuses on the security of data when incorporated with embedded technique. Result analysis of key generation for strong password are carried out by increasing the bit size and analysed the execution time of both public and private key generation.
Reading Dyslexia and Other LDs with Piperita Patty

This contribute presents the idea, purpose and impact of the free editorial project Leggere i DSA con Piperita Patty (“Reading LDs with Peppermint Patty”). Charles Schulz’s strips, and in particular those with Piperita Patty, were here used in order to raise awareness amongst schoolteachers of all levels, students and their families on the subject of specific learning difficulties and the related new legislation. Selected strips have been integrated into some significant passages of the text of the law in order to draw up a free publication (thanks to the grant of rights by Peanuts Worldwide) made available on the website of the Regional School Office of Emilia-Romagna. The purpose was trying to outline the complexity of the situations of LDs and the educational intervention keeping it light.
Analysis of the Statistical Fluctuations of Text Documents Classification Accuracy

Often, when solving the task of classifying text documents, we can use only a limited amount of input data. Increasing input leads to a nonlinear increase in resources such as time and memory. There may also be cases where we have to use private / paid datasets. This paper proposes an approach based on the analysis of statistical fluctuations depending on the size of the data set, which will assess the expected accuracy and save resources.
Exploiting Silicon photomultipliers for measuring nonclassical optical states

The search for optimal receivers endowed with photon-number-resolving capability has led us to consider Silicon photomultipliers for Quantum Optics experiments. By adopting an optimization procedure in order to reduce the spurious effects characterizing these detectors, we succeeded in detecting nonclassical correlations between the two parties of mesoscopic twin-beam states and in obtaining subPoissonian conditional states. These results put SiPMs forward for the implementation of Quantum Information and Communication protocols.
Bayesian Extended Object Smoothing for the Random Matrix Model

The random matrix model is popular in extended object tracking, due to its relative simplicity and versatility. In this model, the extended object state consists of a kinematic vector for the position and motion parameters (velocity, etc), and an extent matrix. Two versions of the model can be found in literature, one where the state density is modelled by a conditional density, and one where the state density is modelled by a factorized density. In this paper, we present closed form Bayesian smoothing expression for both the conditional and the factorised model. In a simulation study, we compare the performance of different versions of the smoother.
Obscure

We develop a secret-sharing-based prototype, entitled Obscure that provides communication-efficient and information-theoretically secure algorithms for aggregation queries using multi-party computation (MPC). The query execution algorithms over secret-shared data are developed to deal with an honest but curious, as well as, a malicious server by providing result verification algorithms. Obscure prevents an adversary to know the data, the query, and the tuple-identity satisfying the query.
A Hue Correction Scheme Based on Constant-Hue Plane for Color Image Enhancement

In this paper, we propose a novel hue correction scheme based on constant-hue plane in the RGB color space for color image enhancement. A number of hue-preserving image enhancement methods have already been proposed. Although these methods can preserve hue, these methods cannot be applied to the state-of-the-art enhancement methods such as deep-learning based ones. We therefore generalize a hue-preserving method based on the constant-hue plane in this paper. This generalization derives our novel hue correction scheme. In the proposed scheme, any existing image enhancement method including deep-learning based ones can be used to enhance images. The hue distortion due to the enhancement is then removed by replacing the maximally saturated colors of an enhanced image with those of the corresponding input one. Experimental results show that the proposed scheme is effective to suppress the hue distortion due to two color enhancement methods including a deep-learning based one. Furthermore, objective quality evaluations demonstrate that the proposed scheme can maintain the performance of image enhancement methods.
Web Service Classification and Prediction Using Rule-Based Approach with Recommendations for Quality Improvements

Web-based applications are more popular and are increasing the demand to use of applications. Service quality and user satisfaction are the most significant for the designer. IT provides the best solutions for various applications such as B2B, B2C, e-commerce and other applications. The client demands high-quality service, regarding minimum response time, high availability and more security. The existing approaches and models may not improve the overall performance of web-based application due to not covering all functional, nonfunctional parameters. The proposed model-approach gives the best solution using rule-based classification of web services using QWS dataset, and predictions of quality parameters based on user specifications. The model is implemented in Java, the results of quality parameters will classify and predict the class labels Class-1(high quality), Class-2, Class-3 and Class-4 (low quality), and the system will give recommendations to improve the quality parameters. By using suggested guidelines and instructions to the software developer, that he will meet the client specifications and provides best quality values which will improve the overall performance (in specification parameters including functional and nonfunctional values). The result clearly suggests the improvement of quality parameters by classification and prediction. This paper can be extended to mixed attributes of quality parameters.
Advanced Gameplay Strategy Based on Grey Wolf Optimization

Artificial intelligence is one of the most important part of the gaming industry. It has been part of the gaming industry since 1950. In games, Artificial Intelligence is used to generate responsive, adaptive and intelligent behavior just like a human player. Modern games use existing techniques from the field of AI, like - Path Finding and Decision Making to guide the actions of the NPC's (Non-Playable Characters). AI is also used in mechanisms which are not immediately visible to the user, like - Data Mining and Procedural Content generation. In this paper, we have derived a new algorithm. Our proposed algorithm is based on Gray Wolf Optimizer Algorithm. We have proposed and discussed a new technique which can be used to provide new features in video games.
High-Quality 3D Medical Imaging by Wavelet Filters with Reduced Coefficients Bit-Width

The problem of the quantization noise effect in coefficients of discrete wavelet transform (DWT) filters for 3D medical imaging is analyzed in this paper. We proposed the coefficients quantizing method which allows reducing hardware implementation costs. We derived formulas for determining the minimum wavelet coefficients bit-width that provide high quality in terms of the peak signal-to-noise ratio of 3D medical image processing using DWT. The experiments with 3D tomographic images confirmed the accuracy of theoretical analysis results. All data are presented in the fixed-point format which allows the possibility for the efficient implementation of image denoising and compression on modern devices such as field-programmable gate arrays and application-specific integrated circuits.
IoT Based Longitudinal Monitoring of Activity and Posture Transitions in Smart Homes

Substantial growth in the number of older adults around the globe, typically with multiple chronic conditions and insufficient number of geriatricians, created significant need for technological solutions that can support independence of older adults in their own homes, also known as aging in place. Continuous monitoring of activities of daily living provides opportunity for monitoring of behavior and the overall state of the users. Although many projects use commercially available wearable sensors based on low power/low range sensors, rapid development of IoT infrastructure provides new integration modalities. We present an implementation of a WiFi based IoT sensor for monitoring of user's activity and posture, using ESP8266 controller and BNO055 Inertial Measurement Unit (IMU). Sensor intelligence facilitates low duty cycle that extends battery life, which is necessary for prolonged and longitudinal monitoring. We developed a simple model to recognize likely posture transitions that can be run on limited resources of ESP8266, our primary focus is recognition of sitting-to-standing (Si2St) and standing-to-sitting (St2Si) transitions. All probable events are transmitted to a home server/gateway, in our case Raspberry Pi 3+, for final processing and storage in subject's record on a remote medical server. In this paper we present system implementation, signal processing, and results from the pilot test of the system.
Fuzzy Rule Interpolation Toolbox for the GNU Open-Source OCTAVE

In most fuzzy control applications (applying classical fuzzy reasoning), the reasoning method requires a complete fuzzy rule-base, i.e all the possible observations must be covered by the antecedents of the fuzzy rules, which is not always available. Fuzzy control systems based on the Fuzzy Rule Interpolation (FRI) concept play a major role in different platforms, in case if only a sparse fuzzy rule-base is available. This cases the fuzzy model contains only the most relevant rules, without covering all the antecedent universes. The first FRI toolbox being able to handle different FRI methods was developed by Johanyak et. al. in 2006 for the MATLAB environment. The goal of this paper is to introduce some details of the adaptation of the FRI toolbox to support the GNU/OCTAVE programming language. The OCTAVE Fuzzy Rule Interpolation (OCTFRI) Toolbox is an open-source toolbox for OCTAVE programming language, providing a large functionally compatible subset of the MATLAB FRI toolbox as well as many extensions. The OCTFRI Toolbox includes functions that enable the user to evaluate Fuzzy Inference Systems (FISs) from the command line and from OCTAVE scripts, read/write FISs and OBS to/from files, and produce a graphical visualisation of both the membership functions and the FIS outputs. Future work will focus on implementing advanced fuzzy inference techniques and GUI tools.
Shallow vs. Deep Image Representations: A comparative Study Applied for the Problem of Generic Object Recognition

The traditional approach for solving the object recognition problem requires image representations to be first extracted and then fed to a learning model such as an SVM to learn the classification decision boundary. These representations are handcrafted and heavily engineered by running the object image through a sequence of pipeline processes that require a good prior knowledge of the problem domain. However, in end-to-end deep learning models, image representations along with classification decision boundary are all learnt directly from the raw image pixels requiring no prior knowledge of the problem domain. Moreover, the deep model features are more discriminative than handcrafted ones since the model is trained to discriminate between features belonging to different classes. The purpose of this study is six fold: (1) review the literature of the pipeline processes used in the previous state-of-the-art codebook model approach for tackling the problem of generic object recognition, (2) Introduce several enhancements in the local feature extraction and normalization processes of the recognition pipeline, (3) compare the enhancements proposed to different encoding methods and contrast them to previous results, (4) experiment with current state-of-the-art deep model architectures used for object recognition, (5) compare between deep representations extracted from the deep learning model and shallow representations handcrafted by an expert and produced through the recognition pipeline, and finally, (6) improve the results further by combining multiple different deep learning models into an ensemble and taking the maximum posterior probability.
High-quality waveform generator from fundamental frequency, spectral envelope, and band aperiodicity

This paper introduces a waveform generation algorithm from three speech parameters (fundamental frequency fo, spectral envelope, and band aperiodicity). The conventional speech analysis/synthesis system based on a vocoder mainly has a waveform generator based on pitch synchronous overlap and add (PSOLA). Since it uses the fast Fourier transform (FFT) to generate the vocal cord vibration, the processing speed is proportional to the f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sub> . The algorithm also uses the spectral representation of the aperiodicity, whereas the band aperiodicity is mainly used in speech synthesis applications such as statistical parametric speech synthesis. We propose a waveform generation algorithm that reduces the computational cost and memory usage without degrading the synthesized speech. The algorithm utilizes excitation signal generation by directly using the band aperiodicity. The computational cost in a certain period is fixed because the excitation signal is filtered and processed by the overlap-add (OLA) algorithm. We used the re-synthesized speech to perform two evaluations for the processing speed and sound quality. The results showed that the sound quality of speech synthesized was almost the same by our proposed algorithm as by the conventional algorithm. The proposed algorithm can also reduce computational cost and memory usage.
The Similarity of Images Generated by Bag Context Shape Grammars

Shape grammars are a class of grammar in formal language that uses spatial rules to generate images. Shape grammars generate images in a way that is not always regulated. That is, they generate images without considering the similarity between them. This is largely because they are context free. As such, the application of a rule is not controlled during derivation. Bag Context Shape Grammars (BCSGs), a class of shape grammars that uses context to control the application of context free rules during derivation, was used to generate images in a regulated manner. In this paper, we examine the similarity of the images generated by BCSGs. The Spatial Colour Distribution Descriptor (SpCD), a content based image retrieval was used to mathematically check the similarity of the bag context shape grammar generated images. The outcome of this study shows that BCSGs are good for the generation of images that are similar. This type of approach can be useful in the generation of distractor (similar) images for visual password systems and others.
Untangling Security and Privacy

Presents the table of contents for this issue of the publication.
Structure-Constrained Feature Extraction by Autoencoders for Subspace Clustering

Clustering the data points drawn from a union of subspaces, i.e., subspace clustering, is a hot topic in recent years. The assumption of the nonlinear subspace is more general for the real-world data, but also more difficult for the traditional methods. The deep neural network is a powerful technique extracting nonlinear features, so the autoencoders are usually adopted to handle this unsupervised problem. However, how to add constraints on the features to make them more effective is not heavily addressed by previous work. In this paper, we consider both the global and local structure of the features in the autoencoders by the low-rank property and Laplace operator, respectively. The low-rank property can make the learned features favoring similarity extraction by self-representation and the Laplace operator can help our method explore the useful information in the data set. Note that our way of placing constraints can also be employed in other deep neural networks. In fact, our method is closely associated with previous work. It can be viewed as the more general case of the structured autoencoders. Extensive experiments demonstrate the effectiveness of our method.
A New Network Structure for Semantic Segmentation of Ship Targets in Remote Sensing

Accurate detection of ship targets is a research hotspot in computer vision. Most of the researches have achieved instance-level detection in the way of bounding box. But we intend to achieve more accurate detection of ship targets in pixel-level through semantic segmentation. However, there are still two main challenges: the first one is the difficulty to segment small targets caused by the difference among ship targets&#x0027; scales, and the other one is the lack of localization information caused by insufficient recovery ability of the decoder part. In this paper, we propose an effective solution. First, a multi-scale pooling fusion module is proposed to fuse multi-scale feature maps and acquire more multi-scale context information, then we improve the capability of precise decoding by taking the place of convolution operation with deconvolution in the decoder part to gather more localization information. At last, we integrate above two schemes into an encoder-decoder symmetry training network with less training parameters and less training time. Furthermore, we construct a dataset for ship semantic segmentation called HRSC2016-SS by labeling HRSC2016 dataset to evaluate our solution. Experiments show that comparing with the existing methods, our proposed solution has a better performance.
Face Recognition using Hybrid Methods of Transformation and Statistical Measures

Abstract One of the most interesting problems and challenging issues within the pattern recognition computer vision is face recognition. Face recognition has gained special attention in the past few years due to its importance in relation to current applications such as security, forensic analysis, and surveillance systems. The whole system can be explained as the follows, after pre-processing, the first step is edge detection for the input image using the proposed filter, the second step is extraction the matrix of the Local Ternary Pattern (LTP) of the input image, the third step is capturing the features based on The Singular Value Decomposition (SVD). Fourth, extracted features are merged into a single vector. Finally, the matching has been performed between the features stored at the database and the features of the reference (test) image. The Proposed System has been applied to the variety databases. In the proposed system, the recognition ratio is 98% using city block distance and 98.5% using Structural Similarity (SSIM) as a classification method, the ORL ( AT&amp;T) database is used.
An Optimized Sucddes to Control Access in Cloud

Cloud computing is employed as integration means in several application domains, covering from the manufacturing to the healthcare, point towards attaining flexible storage of data and easy to access and interchange amongst geographically and heterogeneous sparse organizations. The existing technique explained encrypted data through Secure Uncrackable Cipher Dynamic Double Encryption Standard (SUCDDES) reduces the issue of unauthorized users/ hackers accessing data. In this proposed method, used a secure uncrackable cipher dynamic double encryption standard (SUDDES) algorithm to secure the data access control and privacy-preserving. To randomly select the key length reduced the data security issues. After dynamically selecting the key length the data governor sent the key request to the authority. Then based on the obtained key length the data governor generated the partial secret key. It is further used to decrypt the data and stored in the cloud storage. The outcome of the study enhances the data access control and increases the effectiveness of the cloud security and reduces the decryption and encryption time and also it prevents from dictionary attacks, brute force attacks, collision attacks, SQL injection attacks and so on.
BlockCom: Blockchain-based Efficient Communication and Storage Protocol

Smart application has been focused more to solve real-world problems like smart home, smart street lighting system etc., but the concept of data transmission between devices require precise attention to preserve security with better performance output in device communication. The blockchain is a decentralized computation and information sharing platform to enable different players to verify on the fly. This paper presents a hyper-ledger fabric-based blockchain architecture using a one-way hash function to employ distributed data storage, to enhance the security of the smart applications, and to improve performance in the computation. The proposed protocol resists to different attacks, i.e., modification, impersonation, replay, man-in-the-middle, stolen verifies, device injection, false reputation, and appending. Moreover, performance results are comparatively better rather than relevant vehicular communication protocols.