Reviewerly/exHarmony huggingface dataset:
Image Restoration and Enhancement in Monitoring Systems

Backlight will result in unclear surveillance images, and the important information of criminal suspects will disappear, hindering the investigation of cases, which is particularly unfavorable for the construction of smart city. In this paper, the backlight images are improved in two aspects. According to the previous researches on backlight images, a new brightness adjustment model is designed, which abandons the single HSV or RGB model and combines the advantages of the models to make the image information not be lost as much as possible. In this paper, a new adjusting algorithm is designed, which can enhance the image information more effectively. The experimental results show that the new method improves the quality of the backlight images and prevents the non-backlight areas from being excessively enhanced. More importantly, the new method in this paper can do what the previous methods can't do: the algorithm in this paper is applicable to both backlight images and normal images, and the previous methods will make the normal images distorted and can't be widely used in the monitoring systems.
Application of Differential Privacy in Location Trajectory Big Data

With the development of mobile internet technology, GPS technology and social software have been widely used in people's lives. The problem of big data privacy protection related to location trajectory is becoming more and more serious. The traditional location trajectory privacy protection method requires certain background knowledge and it is difficult to adapt to massive mass. Privacy protection of data. differential privacy protection technology protects privacy by attacking data by randomly perturbing raw data. The method used in this paper is to first sample the position trajectory, form the irregular polygons of the high-frequency access points in the sampling points and position data, calculate the center of gravity of the polygon, and then use the differential privacy protection algorithm to add noise to the center of gravity of the polygon to form a new one. The center of gravity, and the new center of gravity are connected to form a new trajectory. The purpose of protecting the position trajectory is well achieved. It is proved that the differential privacy protection algorithm can effectively protect the position trajectory by adding noise.
Step-Enhancement of Memory Retention for User Interest Prediction

User behavior modeling and interest prediction are always the key elements in preference analysis, product recommendation and personalized service. The psychological memory theory has been proved capable of reflecting changes in user interest. However, merely focusing on the memory forgetting mechanism (the retention-only model) or considering the superimposition of interest retention in short term (the gradual-retention model), existing methods have a poor prediction capability because of ignoring the long-term impact of repeated behaviors. In this paper, we propose a step-enhancement of memory retention (SEMR) model which integrates the cross-enhancement-effects of multiple historical behaviors under different time windows to characterize user interest. In addition, we use some extended correction methods to eliminate the effect of discontinuous records. Numerical experiments using real TV viewing data validate the efficiency of our proposed model and methods, which reduce the average prediction error to 0.3, outperforming the traditional models by around 50%.
Integrated Replay Spoofing-Aware Text-Independent Speaker Verification

A number of studies have successfully developed speaker verification or presentation attack detection systems. However, studies integrating the two tasks remain in the preliminary stages. In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection: an end-to-end monolithic approach and a back-end modular approach. The first approach simultaneously trains speaker identification, presentation attack detection, and the integrated system using multi-task learning using a common feature. However, through experiments, we hypothesize that the information required for performing speaker verification and presentation attack detection might differ because speaker verification systems try to remove device-specific information from speaker embeddings, while presentation attack detection systems exploit such information. Therefore, we propose a back-end modular approach using a separate deep neural network (DNN) for speaker verification and presentation attack detection. This approach has thee input components: two speaker embeddings (for enrollment and test each) and prediction of presentation attacks. Experiments are conducted using the ASVspoof 2017-v2 dataset, which includes official trials on the integration of speaker verification and presentation attack detection. The proposed back-end approach demonstrates a relative improvement of 21.77% in terms of the equal error rate for integrated trials compared to a conventional speaker verification system.
Leveraging the Information Contained in Theory Presentations

A theorem prover without an extensive library is much less useful to its potential users. Algebra, the study of algebraic structures, is a core component of such libraries. Algebraic theories also are themselves structured, the study of which was started as Universal Algebra. Various constructions (homomorphism, term algebras, products, etc.) and their properties are both universal and constructive. Thus they are ripe for being automated. Unfortunately, current practice still requires library builders to write these by hand. We first highlight specific redundancies in libraries of existing systems. Then we describe a framework for generating these derived concepts from theory definitions. We demonstrate the usefulness of this framework on a test library of 227 theories.
Toward an ICT-Based Service Oriented Health Care Paradigm

Health care platforms rapidly shift toward the ICT-based solutions. In this context, a wide range of consumer electronics technologies come into play ranging from robotics, embedded systems, sensors, and communication infrastructures. Driven by such observations, the H2020 RADIO project set forward a service-oriented, easily expandable design paradigm that emphasizes on how heterogenous commercial off-the-shelf (COTS) ICT technologies can be used as enablers of existing and new health-care services. Such services focus on activities of daily life (ADL) monitoring algorithms, on facilitating the indoor everyday life activities of the end user emphasizing on energy multifaceted conservation and security provision. All software components produced during the RADIO project with relative guidelines are freely available through dedicated GitHub repository (https://github.com/RADIO-PROJECT-EU).
Can photovoltaics be used to estimate cloud cover?

The current study evaluates a new method for the estimation of cloud cover using only photovoltaics (PV) as ground-based irradiance sensors. Seven different scenarios were examined using 30, 50, 75, 100, 500 and 1000 randomly and 1000 non-randomly distributed residential PV stations in a study area of 10 km × 10 km For each scenario, 3500 different fractal-based cloud shadows were produced and the accuracy of the model in estimating the cloud cover, in percentage cover and in oktas, based on the number of stations being shaded, was calculated. With 30 PV stations, the model produced an average accuracy of only 57% for the predicted okta cover, while its accuracy increased to 90% and 94% with 500 and 1000 PV stations.
Humanidades Digitales en Filipinas: proyectos, dificultades y oportunidades de la colaboración Norte-Sur

Abstract Situated within the debate that has taken place in the recent years on how Digital Humanities can break down barriers between countries of the Global North and South (Intersectionality in Digital Humanities Conference, 2016), and how materials in minority langauges can have presence in the network for the generation of new knowledge (Thieberger, 2017; Rodríguez-Ortega and Cruces Rodríguez, 2018), the objective of this article is to explain how (and if) digitalization and Digital Humanities can facilitate research in the Philippines, as well as make it visible, and how this can be facilitated by cooperation projects, citing the example of the project Philperiodicals, carried out by the University of Antwerp and the University of the Philippies. What opportunities and difficulties were encountered upon proposing a project with such characteristics? What problems (ethical, at times) do we encounter when subsidizing projects in the South from the North? We shall address these questions based on the current status of digitalization and Digital Humanities in the country. Lastly, we offer a series of good practices concluded from debates and experiences from the project Philperiodicals, in the hopes that our previous difficulties and discussions may be of use for the development of similar projects in what has been called the Global South.
Parameter Optimization of Deep Learning Models by Evolutionary Algorithms

Deep learning is a very popular gradient based search technique nowadays. In this field of machine learning we usually apply neural networks with various structure. The algorithms of the deep learning techniques and the structure of the applied networks have several parameters that have a huge impact on the performance of the search technique. These parameters are called hyperparameters. The aim of our current research is to optimize these hyperparameters using evolutionary and swarm based optimization algorithms.
A Viewport-Driven Multi-Metric Fusion Approach for 360-Degree Video Quality Assessment

We propose a new viewport-based multi-metric fusion (MMF) approach for visual quality assessment of 360-degree (omnidirectional) videos. Our method is based on computing multiple spatio-temporal objective quality metrics (features) on viewports extracted from 360-degree videos, and learning a model that combines these features into a metric, which closely matches subjective quality scores. The main motivations for the proposed method are that: 1) quality metrics computed on viewports better captures the user experience than metrics computed on the projection domain; 2) no individual objective image quality metric always performs best for all types of visual distortions, while a learned combination of them is able to adapt to different conditions and produce better results overall. Experimental results, based on the largest available 360-degree videos quality dataset, demonstrate that the proposed metric outperforms state-of-the-art 360-degree and 2D video quality metrics.
Dual Vote Confirmation based Consensus Design for Blockchain integrated IoT

Blockchain technology is adopted by many research to provide underlying security protection for the booming Inter-net of Things (IoT) industry. Nonetheless, due to some aspects of IoT, such as resource-constrained devices, heterogeneous mass devices, and real-time transactions, the traditional blockchain consensus algorithm is not suitable for the scene of blockchain-IoT integration. This paper proposes a Dual Vote Confirmation based Consensus (DVCC) mechanism for the Blockchain-IoT integration (BlockIoT). First, we adopt a role division approach to solve the problem of limited resources of devices in IoT. Second, multi-dimensional credit score, dual confirmation mechanism, classified random selection mechanism, and credit score based incentivization are designed to improve the security and fairness of the consensus. Third, a voting mechanism is proposed to improve the delay performance and avoid energy waste. Finally, the experimental results show that DVCC gains better delay and throughput performance on a large scale while guaranteeing the security and fairness of the consensus.
Semantic Similarity Estimation Using Vector Symbolic Architectures

For many natural language processing applications, estimating similarity and relatedness between words are key tasks that serve as the basis for classification and generalization. Currently, vector semantic models (VSM) have become a fundamental language modeling tool. VSMs represent words as points in a high-dimensional space and follow the distributional hypothesis of meaning, which assumes that semantic similarity is related to the context. In this paper, we propose a model whose representations are based on the semantic features associated with a concept within the ConceptNet knowledge graph. The proposed model is based on a vector symbolic architecture framework, which defines a set of arithmetic operations to encode the semantic features within a single high-dimensional vector. In addition to word distribution, these vector representations consider several types of information. Moreover, owing to the properties of high-dimensional spaces, they have the additional advantage of being interpretable. We analyze the model's performance on the SimLex-999 dataset, a dataset where commonly used distributional models (e.g., word2vec or GloVe) perform poorly. Our results are similar to those of other hybrid models, and they surpass several state-of-the-art distributional and knowledge-based models.
Evaluation of Cross Domain Text Summarization

Extractive-abstractive hybrid summarization can generate readable, concise summaries for long documents. Extraction-then-abstraction and extraction-with-abstraction are two representative approaches to hybrid summarization. But their general performance is yet to be evaluated by large scale experiments.We examined two state-of-the-art hybrid summarization algorithms from three novel perspectives: we applied them to a form of headline generation not previously tried, we evaluated the generalization of the algorithms by testing them both within and across news domains; and we compared the automatic assessment of the algorithms to human comparative judgments. It is found that an extraction-then-abstraction hybrid approach outperforms an extraction-with-abstraction approach, particularly for cross-domain headline generation.
2-D Bi-Level Block Coding for Color Image Compression and Transmission With Bit-Error Awareness

In this paper, we develop a new color image lossless compression algorithm with bit-error awareness based on a general bi-level block coding method. The proposed method contains three stages. First, a color image in the RGB color space is converted to the YCrCb color space by lossless reversible transformations. Next, predictors in the YCrCb color space are applied to generate residue sequences. In order to achieve a better image compression ratio, a particle swarm optimization (PSO) algorithm is adopted to search the best combination from the candidates of color transformations and predictors which generate the minimum residue entropy. Third, a new 2-D bi-level block coding algorithm is developed to further encode the residue sequences. Comparing with the existing residue coding methods, including 1-D bi-level block coding, interval Huffman coding, and standard Huffman coding, the 2-D bi-level block coding algorithm can improve image compression ratio as well as preserving the bit-error resilience. Finally, the key parameters such as color transformation information, predictor parameters and residue coding parameters are protected using (7, 4) Hamming codes in the bit stream before transmission. The performances are validated in terms of compression ratio (CR) and peak signal to noise ratio (PSNR). The compression algorithm with bi-level coding achieves the highest PSNR when the bit-error rate (BER) is larger than 0.001 and maintains an acceptable PSNR for BER less than 0.001. In particular, the compression algorithm using the new 2-D bi-level block coding scheme achieves the highest CR.
Full-Time Infrared Feature Pedestrian Detection Based on CSP Network

Due to the big variance between infrared images in day and night, the detection of infrared pedestrians in full-time has been a difficult point and has great significance both in theory and practice. Based on the deep learning method, we proposed three full-time infrared pedestrian detection models based on CSP (Center and Scale Prediction) network, namely, night model, daytime model, full-time model. Then, the performance of the three models are analyzed and compared quantitatively, and the results show that the performance of the full time model is the optimal one, the detection accuracy rate in day and night reach 83.86% and 80.22% respectively. Compared with the state-of-the-art method, the miss rate in day and night detection of the full time model in this paper is 12.23% and 4.56% lower respectively, thus the performance of the full-time model in our work is more effective.
Spectrally-Enforced Global Receptive Field For Contextual Medical Image Segmentation And Classification

Deep convolutional neural networks (CNNs) have recalibrated the state-of-the-art for a plethora of applications in medical image analyzing such as segmentation and classification. Large receptive field is crucial for modeling long-range spatial dependency in medical images. In this paper, we propose a novel architectural network design for accomplishing a full-image global receptive field, which we call spectral residual block (SRB). Specifically, we propose to utilize a unitary transform that essentially conducts a local-to-global transform. All elements are mapped to spectral domain and thus globally depend on each other. A variety of global operators are carefully devised and efficiently enforce a full-image receptive field, including spectral ReLU for frequency-sensitive filtering and spectral convolutions. The output in spectral domain is eventually converted back global-to-local via a reverse unitary transform. The proposed framework is generic and flexible, and could be applied to various network structures and tasks. Comprehensive evaluations on skin lesion segmentation and Chest X-Ray classification show that our method achieves the state-of-the-art performance, demonstrating both effectiveness and efficiency.
Close Reading for Visualization Evaluation

Visualizations produced by collaborations between artists, scientists, and visualization experts lay claim to being not only more effective in delivering information but also more effective in their abilities to elicit qualities like human connection. However, as prior work in the visualization community has demonstrated, it is difficult to evaluate these claims because characteristics associated with human connection are not easily measured quantitatively. In this Visualization Viewpoints piece, we address this problem in the context of our work to develop methods of evaluating visualizations created by Sculpting Visualization, a multidisciplinary project that incorporates art and design theory and practice into the process of scientific visualization. We present the design and results of a study in which we used close reading, a formal methodology used by humanities scholars, as a way to test reactions and analyses from evaluation participants related to an image created using Sculpting Visualization. In addition to specific suggestions about how to improve future iterations of the visualization, we discuss key findings of the evaluation related to contextual information, visual perspective, and associations that individual viewers brought to bear on their experience with the visualization.
Explaining Local, Global, And Higher-Order Interactions In Deep Learning

We present a simple yet highly generalizable method for explaining interacting parts within a neural network’s reasoning process. First, we design an algorithm based on cross derivatives for computing statistical interaction effects between individual features, which is generalized to both 2-way and higher-order (3-way or more) interactions. We present results side by side with a weight-based attribution technique, corroborating that cross derivatives are a superior metric for both 2-way and higher-order interaction detection. Moreover, we extend the use of cross derivatives as an explanatory device in neural networks to the computer vision setting by expanding Grad-CAM, a popular gradient-based explanatory tool for CNNs, to the higher order. While Grad-CAM can only explain the importance of individual objects in images, our method, which we call Taylor-CAM, can explain a neural network’s relational reasoning across multiple objects. We show the success of our explanations both qualitatively and quantitatively, including with a user study. We will release all code as a tool package to facilitate explainable deep learning.
Influence of block size on JPEG compression performance

This paper focuses on investigating the block size influence on the performance of the JPEG compression process. Apart from 8x8 blocks, 4x4,16x16 and 32x32 block sizes are also tested. A variable size block division is also investigated. As a quality measure, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are calculated. The compression ratios (CR) for each case are compared also.
Variable Skipping for Autoregressive Range Density Estimation

Deep autoregressive models compute point likelihood estimates of individual data points. However, many applications (i.e., database cardinality estimation) require estimating range densities, a capability that is under-explored by current neural density estimation literature. In these applications, fast and accurate range density estimates over high-dimensional data directly impact user-perceived performance. In this paper, we explore a technique, variable skipping, for accelerating range density estimation over deep autoregressive models. This technique exploits the sparse structure of range density queries to avoid sampling unnecessary variables during approximate inference. We show that variable skipping provides 10-100$\times$ efficiency improvements when targeting challenging high-quantile error metrics, enables complex applications such as text pattern matching, and can be realized via a simple data augmentation procedure without changing the usual maximum likelihood objective.
On Computer-Aided Diagnosis of Breast Cancers Using Nuclear Atypia Scoring

Computer-aided systems are gaining interest in every field over the world. The medical field has also enhanced to a great extent with the use of computer-aided diagnosis. Among the different diseases in the era, breast cancer has shown a rapid hike in the number of deaths. Scoring of nuclear atypia is an efficient method for the prognosis of breast cancer. The biopsy samples taken from the suspicious tissues are analysed under microscope by the pathologists and are graded. But manual grading highly depends on the pathologists and can cause variation in the results. Hence, the requirement of computer-aided systems for grading has increased. Many studies related to nuclear atypia scoring have taken place in the literature based on different algorithms and classifiers. This paper gives an overview of the different studies in the literature, related to nuclear atypia scoring. Various techniques are used for nuclear atypia scoring. Multifarious image processing techniques are used for this. The aim of this study is to analyse these techniques and their results and know the most efficient one from them. Our analysis shows that promising results are achieved by machine learning techniques. Scores obtained using these techniques are comparable to manual grading.