Reviewerly/exHarmony huggingface dataset:
Single image rain removal via densely connected contextual and semantic correlation net

Rainy images severely degrade visibility. Thus, deraining is an important task for applications ranging from image processing to computer vision. We propose a deep learning-based method to remove rain streaks from a single image. Specifically, we first design a deraining unit that employs dilation convolution and squeeze-and-excitation operations, respectively, to obtain more spatial contextual information and semantic correlation. In the deraining unit, multifeatures at different levels can be obtained by using convolutions with different dilation factors, and they are fused to maintain the primary features of rain streaks. Then, we interconnect the deraining units by dense connections that can maximize the information flow along features from different levels and make them be associated. Both deraining units and dense connections make our network have stronger representative ability of the rain streaks layer. Experimental results show that our proposed deraining method outperforms state-of-the-art methods by a good margin in Rain100H, Rain100L, and Rain1200 datasets, while using fewer parameters.
An Efficient Fully Homomorphic Encryption Scheme for Private Information Retrieval in the Cloud

Information retrieval in the cloud is common and convenient. Nevertheless, privacy concerns should not be ignored as the cloud is not fully trustable. Fully Homomorphic Encryption (FHE) allows arbitrary operations to be performed on encrypted data, where the decryption of the result of ciphertext operation equals that of the corresponding plaintext operation. Thus, FHE schemes can be utilized for private information retrieval (PIR) on encrypted data. In the FHE scheme proposed by Ducas and Micciancio (DM), only a single homomorphic NOT AND (NAND) operation is allowed between consecutive ciphertext refreshings. Aiming at this problem, an improved FHE scheme is proposed for efficient PIR where homomorphic additions and multiplications are based on linear operations on ciphertext vectors. Theoretical analysis shows that when compared with the DM scheme, the proposed scheme allows multiple homomorphic additions and a single homomorphic multiplication to be performed. The number of allowed homomorphic additions is determined by the ratio of the ciphertext modulus to the upper bound of initial ciphertext noise. Moreover, simulation results show that the proposed scheme is significantly faster than the DM scheme in the homomorphic evaluation for a series of algorithms.
Convolutional sparse coding for demosaicking with panchromatic pixels

Image demosaicking is the problem of reconstructing color images from raw images captured by a digital camera covered by a Color Filter Array (CFA). CFAs allow only part of the incident light to transmit, so they will lose some incident light. Recently, CFAs with panchromatic pixels are used to avoid excessive loss of light. These CFAs’ color is sampled at a sparse set of locations, making demosaicking more challenging. In this paper, we propose a Convolutional Sparse Coding based method for Demosaicking (CSCD) with panchromatic pixels. The CSCD learns a set of filters to decompose training images into color sparse feature maps, then it estimates a set of feature maps of the to-be-reconstructed raw images and gets color images. Convolutional Sparse Coding (CSC) commonly operates on gray images. Naively extending CSC to color images will make the learned filters to be gray, which means that the values of the red, green, and blue channels are almost equal. To address this issue, we add a regularization term to penalize gray filters. We use the Alternating Direction Method of Multipliers (ADMM) to solve the CSCD. We compare the proposed CSCD with the demosaicking methods that are applicable to CFAs with panchromatic pixels. Experimental results validate its advantages in terms of CPSNR and visual quality.
Multitask learning on monocular water images: Surface reconstruction and image synthesis

Abstract In this paper, we present a new strategy, a joint deep learning architecture, for two classic tasks in computer graphics: water surface reconstruction and water image synthesis. Modeling water surfaces from single images can be regarded as the inverse of image rendering, which converts surface geometries into photorealistic images. On the basis of this fact, we therefore consider these two problems as a cycle image‐to‐image translation and propose to tackle them together using a pair of neural networks, with the three‐dimensional surface geometries being represented as two‐dimensional surface normal maps. Furthermore, we also estimate the imaging parameters from the existing water images with a subnetwork to reuse the lighting conditions when synthesizing new images. Experiments demonstrate that our method achieves an accurate reconstruction of surfaces from monocular images efficiently and produces visually plausible new images under variable lighting conditions.
Visual behavior during engagement with tangible and virtual representations of archaeological artifacts

In this paper, we present results from a study of users' visual behavior while engaging with tangible and virtual representations of archaeological artifacts. We replicated and extended a recent study that introduced an augmented reality system implemented using HoloLens, for engaging with the artifacts. Our study goes beyond the original study to estimate the distribution of users' visual attention for both tangible and virtual representations of the artifacts. Our study confirmed the results of the original study in various aspects. Specifically, participants in both studies confirmed the immersive nature of the HoloLens condition and showed similar learning outcomes in terms of post-task open questions. Additionally, our findings indicate that users allocate their visual attention in similar ways when interacting with virtual and tangible learning material, in terms of total gaze duration, gaze on object duration, and object fixation duration.
(2, 2) Threshold Robust Visual Secret Sharing Scheme for QR Code Based on Pad Codewords

Quick response (QR) code has been used widely because of its advantages, such as, fast reading, error correction, and encoding multiple types of data. More and more researches on the combination of QR and visual cryptography have emerged, but there are few robust visual secret sharing scheme (VSS) for QR code. In this paper, we propose a robust (2, 2) threshold VSS for QR code based on pad codewords. We utilize the bug of no checking on the pad codewords when encoding the QR code, we encode initial shadows shared with secret image as pad codewords of cover QR code images to generate QR code shadows. In this way, the decoding results of shadow QR code images are identical with those of cover QR code images, no doubt arises when shadow images are exposed to the public channel. While a certain degree of damage and noise appearing in the shadow images, the secret can still be revealed, so the scheme is robust, which is important for practice. The experimental results demonstrate the effectiveness and robustness of our scheme.
A Multi-sprite Based Anaglyph 3D Video Watermarking Approach Robust Against Collusion

3D video watermarking is far from the maturity of watermarking algorithms dedicated to audio, image or 2D video. In fact, little work has been proposed for anaglyph 3D videos. Such methods have not been robust against malicious attacks such as compression and collusion. The latter one is a very dangerous attack that can be applied on video content and can easily remove an embedded signature to obtain original content. This paper proposes an efficient approach for anaglyph 3D videos which can resist this malicious attack thanks to the use of a static multi-sprite generated from the different sets of frames which compose the original video. First, a sprite is generated for every set of 25 frames. Then every sprite is marked using a hybrid scheme: the discrete wavelet transform based algorithm and the middle significant bit technique, which improve signature invisibility and enhance robustness. Finally, a marked anaglyph 3D video is generated from every marked sprite. The use of multi-sprites as an embedding target provides robustness against collusion attacks and a high quality of marked video reconstruction compared with static mosaics. The experimental results show good robustness against collusion, compression and other attacks such as geometric manipulation and temporal attacks. Besides, the proposed technique presents a high level of invisibility.
A Constructivist Desktop Virtual Reality-Based Approach to Learning in a Higher Education Institution

Virtual reality (VR) technology has recently started shaping learning, especially language learning, with the aim of immersing learners into a VR learning environment. However, because of the high system cost of fully immersive VR, desktop VR has been implemented and preferred in educational settings. Based on a constructivist approach, desktop VR has drawn attention to the need for learner autonomy and an authentic VR learning environment. Therefore, this chapter describes empirical research on desktop VR-based learning using a constructivist approach. The research examined university students' interaction and perceptions of learning in this kind of learning environment. Based on the empirical findings gathered from observations and interviews, this chapter has aimed to discuss not only the issues observed both in previous studies and in this chapter, but also additional issues such as scaffolding, self-paced learning, collaboration, and learner differences in order for learning to occur in a well-designed desktop VR learning environment.
Measuring and Facilitating Data Repeatability in Web Science

Accessible and reusable datasets are a necessity to accomplish repeatable research. This requirement poses a problem particularly for web science, since scraped data comes in various formats and can change due to the dynamic character of the web. Further, usage of web data is typically restricted by copyright-protection or privacy regulations, which hinder publication of datasets. To alleviate these problems and reach what we define as "partial data repeatability", we present a process that consists of multiple components. Researchers need to distribute only a scraper and not the data itself to comply with legal limitations. If a dataset is re-scraped for repeatability after some time, the integrity of different versions can be checked based on fingerprints. Moreover, fingerprints are sufficient to identify what parts of the data have changed and how much. We evaluate an implementation of this process with a dataset of 250 million online comments collected from five different news discussion platforms. We re-scraped the dataset after pausing for one year and show that less than ten percent of the data has actually changed. These experiments demonstrate that providing a scraper and fingerprints enables recreating a dataset and supports the repeatability of web science experiments.
Distance, Virtual, Electronic, Mobile and Ubiquitous Learning Environments

Distance, virtual, electronic, mobile and ubiquitous learning are widely used as interchangeable terms. It is uncommon that researchers face difficulties to distinguish between different learning environments. A clear understanding of the concepts of these learning models and their fundamental differences are necessary for both learning and educational communities. This article reveals the differences and reviews the concepts of learning models. A mixed-method analysis of research articles has been implemented to find out an appropriate set of comparison criteria between learning models including context, accessibility, availability, time and place restriction, dependence, capacity, communication and interaction. Based on the selected criteria's the results reveal that there are a number of differences between learning models and distinguishing between learning models is important. This is to assure clear understanding of the learning concepts and thus build reliable communication between different members of technical teams, clients, vendors, and the research community.
A paraconsistent approach to actions in informationally complex environments

Contemporary systems situated in real-world open environments frequently have to cope with incomplete and inconsistent information that typically increases complexity of reasoning and decision processes. Realistic modeling of such informationally complex environments calls for nuanced tools. In particular, incomplete and inconsistent information should neither trivialize nor stop both reasoning or planning. The paper introduces ACTLOG, a rule-based four-valued language designed to specify actions in a paraconsistent and paracomplete manner. ACTLOG is an extension of 4QLBel, a language for reasoning with paraconsistent belief bases. Each belief base stores multiple world representations. In this context, ACTLOG’s action may be seen as a belief bases’ transformer. In contrast to other approaches, ACTLOG actions can be executed even when the underlying belief base contents is inconsistent and/or partial. ACTLOG provides a nuanced action specification tools, allowing for subtle interplay among various forms of nonmonotonic, paraconsistent, paracomplete and doxastic reasoning methods applicable in informationally complex environments. Despite its rich modeling possibilities, it remains tractable. ACTLOG permits for composite actions by using sequential and parallel compositions as well as conditional specifications. The framework is illustrated on a decontamination case study known from the literature.
Bilingual Ontology-Based Automatic Question Generation

Virtual chemical laboratory teaching helps develop and acquire skills. To help to refine the construction of knowledge and the development of such skills, learners need self-evaluation which is an important strategy that helps learners to measure the value or quality of their work. Creating fair and objective assessment in order to help learners self-evaluate their knowledge is a difficult task. Difficulties grow up if the test is in a bilingual format. This paper proposes a bilingual (English, French) ontology that will help learners to generate questions to self-evaluate on laboratory material, product, safety notices and the stages through which chemistry practical work is carried out.
Integrate Receptive Field Block into Large-margin Softmax Loss for Face Recognition

Large-margin softmax (L-Softmax) loss for deep neural networks in face recognition tasks. Different from the softmax cross-entropy loss, L-Softmax explicit encourages intra-class compactness and inter-class separability between learned features. RFB Net proposed RFB module to simulate Receptive Fields (RFs) in human visual systems and gain higher accuracy. In this paper, we proposed Integrate Receptive Field Block into L-Softmax Loss for Face Recognition, an enhanced L-Softmax loss with a RFB module, which not only enhance the feature discriminability, but also enhance the feature robustness. Extensive experiments on recognition benchmarks like MNIST, LFW, Experiments with two benchmark datasets show that our proposed approach makes deep learning features more discriminating and thus significantly improves the performance of various visual classification and verification tasks .
Dog recognition in public places based on convolutional neural network

With the increase in the number of dogs in the city, the dogs can be seen everywhere in public places. At the same time, more and more stray dogs appear in public places where dogs are prohibited, which has a certain impact on the city environment and personal safety. In view of this, we propose a novel algorithm that combines dense–scale invariant feature transform and convolutional neural network to solve dog recognition problems in public places. First, the image is divided into several grids; then, the dense–scale invariant feature transform algorithm is used to split and combine the descriptors, and the channel information of the eight directions of the image is extracted as the input of the convolutional neural network; and finally, we design a convolutional neural network based on Adam optimization algorithm and cross-entropy to identify the dog species. The experimental results show that the algorithm can fully combine the advantages of dense–scale invariant feature transform and convolutional neural network to achieve dog recognition in public places, and the correct rate is 94.2%.
Evolving and combining technical indicators to generate trading strategies

Technical analysis is a widely used approach for trading securities. Various indicators are used, such as moving average, stochastic oscillator and relative strength index. Applications of these indicators are typically based on experiences and rules of thumb which hardly are effective in general. This paper presents a technique for evolving indicator parameters using Non-Dominated Sorting Genetic Algorithm II and combining the indicators to generate a trading strategy. Experiments are conducted using actual stocks from the Stock Exchange of Thailand show that the proposed technique generates trading strategies that outperform other well-known techniques and is applicable to real world security trading.
Review on the Status and Development Trend of AI Industry

The status and development of artificial intelligence attract scholars from different countries. This paper focuses on status of AI and majors to conduct in-depth research in AI industry. The paper draws the outline of general status based on AI from quantitative to qualitative scientific practice, the development level of AI is inferred. We investigated the differential AI status of all of the verified cases distributed from 1990 to 2017. Choosing the artificial intelligence industry as data source, AI research output was inspected and some key elements of AI were analyzed. We found that AI papers increased to more than 400%, AI venture capital surged 6 times, AI start-ups surged 14 times, New AI application skills become the core skills of job searching, image labeling accuracy rate was up more than 26%, robot imports increased to 2.5 times. And then its future development trend is predicted. A large study reveals that `AI+ Industry' is the future development direction, computer vision research highlights image and video applications, sales of Robot increase year by year, increased types of work and high efficiency of human-machine complement, and interactive interface makes people go head with empty hands.
Fast and Energy-Efficient OLAP Data Management on Hybrid Main Memory Systems

This paper studies the problem of efficiently utilizing hybrid memory systems, consisting of both Dynamic Random Access Memory (DRAM) and novel Non-Volatile Memory (NVM) in database management systems (DBMS) for online analytical processing (OLAP) workloads. We present a methodology to determine the database operators that are responsible for most main memory accesses. Our analysis uses both cost models and empirical measurements. We develop heuristic decision procedures to allocate data in hybrid memory at the time that the data buffers are allocated, depending on the expected memory access frequency. We implement these heuristics in the MonetDB column-oriented database and demonstrate performance improvement and energy-efficiency as compared to state-of-the-art application-agnostic hybrid memory management techniques.
New distance weighted k Nearest Neighbor model for hourly global solar irradiation estimation

In this work, climatological data from Meteonorm software are used to estimate global solar irradiation in the city of Fez, Morocco. Our main contribution is to propose two regression rules weighting the influence of a neighbor closeness to an unknown observation, leading to two new kernels. The idea is to adapt the kernels to our regression problem by accentuating differently the attributed weights of the neighbors. Several evaluations of the proposed models were tested: the best choice of climatological parameters, the number of neighbors and the size of the database. All tested models start giving excellent performances by using beyond 50% of the total database. The proposed Weighted k Nearest Neighbors-kernel2 fits better reduces databases than the Weighted k Nearest Neighbors-kernel1 model which is more effective for larger datasets. The overall best performance is obtained with WkNN-kernel1 using three neighbors with RRMSE = 7.23%, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 98.05%, and MAPE = 8.77%.
Visual-Linguistic Methods for Receipt Field Recognition

Receipts are crucial for many businesses' operation, where expenses are tracked meticulously. Receipt documents are often scanned into images, digitized and analyzed before the information is streamed into institutional financial applications. The precise extraction of expense data from receipt images is a difficult task owed to the high variance in fonts and layouts, the frailty of the print paper, unstructured scanning environments and an immeasurable amount of domains. We propose a method that combines visual and linguistic features for automatic information retrieval from receipt images using deep network architectures, which outperforms existing approaches. Our Skip-Rect Embedding (SRE) descriptor is demonstrated in two canonical applications for receipt information retrieval: field extraction and Optical Character Recognition (OCR) error enhancement.
COSONet: Compact Second-Order Network for Video Face Recognition

In this paper, we study the task of video face recognition. The face images in the video typically cover large variations in expression, lighting, or pose, and also suffer from video-type noises such as motion blur, out-of-focus blur and low resolution. To tackle these two types of challenges, we propose an extensive framework which contains three aspects: neural network design, training data augmentation, and loss function. First, we devise an expressive COmpact Second-Order network (COSONet) to extract features from faces with large variations. The network manages to encode the correlation (e.g. sample covariance matrix) of local features in a spatial invariant way, which is useful to model the global texture and appearance of face images. To further handle the curse of high-dimensional problem in the sample covariance matrix, we apply a layer named 2D fully connected (2D-FC) layer with few parameters to reduce the dimension. Second, due to no video-type noises in still face datasets and small inter-frame variation in video face datasets, we augment a large dataset with both large face variations and video-type noises from existing still face dataset. Finally, to get a discriminative face descriptor while balancing the effect of images with various quality, a mixture loss function which encourages the discriminability and simultaneously regularizes the feature is elaborately designed. Detailed experiments show that the proposed framework can achieve very competitive accuracy over state-of-the-art approaches on IJB-A and PaSC datasets.
Utility-based scheduling for public displays with live content

The pervasiveness of public displays is prompting an increased need for "fresh" content to be shown, that is highly engaging and useful to passerbys. As such, live or time-sensitive content is often shown in conjunction with "traditional" static content, which creates scheduling challenges. In this work, we propose a utility-based framework and a novel scheduling algorithm for handling live and non-live content on public displays. We also experimentally evaluate our proposed algorithm against a number of alternatives under a variety of workloads.