Reviewerly/exHarmony huggingface dataset:
No-reference Image Quality Assessment Based on Multi-scale Convolutional Neural Network Assisted with Visual Saliency

Nowadays, methods based on deep learning have achieved state-of-the-art performance in image quality assessment (IQA). Many efforts have been made to design convolutional neural network (CNN) for IQA. However, current CNN-based methods mainly have the following shortcomings. (1) CNN-based methods usually segment the image into image patches of the same size for data enhancement, which cannot retain both local spatial information and global spatial information of images with large differences in resolution. (2) Most CNN-based methods input all image patches of the entire image into CNN and assign the image patches the same quality score as the entire image, ignoring the difference in the attention of the human visual system (HVS) to different regions of the image. (3) CNN-based methods design neural network structures with the same channel weight without considering the difference in correlation between different image channels and the key information of image patches. Thus, we propose a multi-scale CNN-based model assisted with visual saliency, which we call MS-SECNN in this paper. We fuse two single-scale CNNs into a multi-scale CNN through a fully connected layer. The Squeeze and Excitation (SE) module is embedded to each single-scale CNN to assign corresponding weight to each image channel. Moreover, we select the image patches input to the multi-scale CNN according to the visual saliency map. Experiments results validate the effectiveness of our proposed method compared to typical full-reference (FR) IQA methods and state-of-the-art no-reference (NR) IQA methods. Our code and two self-built datasets are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A formula on the weight distribution of linear codes with applications to AMDS codes

The determination of the weight distribution of linear codes has been a fascinating problem since the very beginning of coding theory. There has been a lot of research on weight enumerators of special cases, such as self-dual codes and codes with small Singleton's defect. We propose a new set of linear relations that must be satisfied by the coefficients of the weight distribution. From these relations we are able to derive known identities (in an easier way) for interesting cases, such as extremal codes, Hermitian codes, MDS and NMDS codes. Moreover, we are able to present for the first time the weight distribution of AMDS codes. We also discuss the link between our results and the Pless equations.
Factorizing Historical User Actions for Next-Day Purchase Prediction

It is common practice for many large e-commerce operators to analyze daily logged transaction data to predict customer purchase behavior, which may potentially lead to more effective recommendations and increased sales. Traditional recommendation techniques based on collaborative filtering, although having gained success in video and music recommendation, are not sufficient to fully leverage the diverse information contained in the implicit user behavior on e-commerce platforms. In this article, we analyze user action records in the Alibaba Mobile Recommendation dataset from the Alibaba Tianchi Data Lab, as well as the Retailrocket recommender system dataset from the Retail Rocket website. To estimate the probability that a user will purchase a certain item tomorrow, we propose a new model called Time-decayed Multifaceted Factorizing Personalized Markov Chains (Time-decayed Multifaceted-FPMC), taking into account multiple types of user historical actions not only limited to past purchases but also including various behaviors such as clicks, collects and add-to-carts. Our model also considers the time-decay effect of the influence of past actions. To learn the parameters in the proposed model, we further propose a unified framework named Bayesian Sparse Factorization Machines. It generalizes the theory of traditional Factorization Machines to a more flexible learning structure and trains the Time-decayed Multifaceted-FPMC with the Markov Chain Monte Carlo method. Extensive evaluations based on multiple real-world datasets demonstrate that our proposed approaches significantly outperform various existing purchase recommendation algorithms.
A novel network fabric for efficient spatio-temporal reduction in flexible DNN accelerators

Increasing deployment of Deep Neural Networks (DNNs) in a myriad of applications, has recently fueled interest in the development of specific accelerator architectures capable of meeting their stringent performance and energy consumption requirements.
Embedded Hardware Appropriate Fast 3D Trajectory Optimization for Fixed Wing Aerial Vehicles by Leveraging Hidden Convex Structures

Most commercially available fixed-wing aerial vehicles (FWV) can carry only small, lightweight computing hardware such as Jetson TX2 onboard. Solving non-linear trajectory optimization on these computing resources is computationally challenging even while considering only the kinematic motion model. Most importantly, the computation time increases sharply as the environment becomes more cluttered. In this paper, we take a step towards overcoming this bottleneck and propose a trajectory optimizer that achieves online performance on both conventional laptops/desktops and Jetson TX2 in a typical urban environment setting. Our optimizer builds on the novel insight that the seemingly non-linear trajectory optimization problem for FWV has an implicit multi-convex structure. Our optimizer exploits these computational structures by bringing together diverse concepts from Alternating Minimization, Bregman iteration, and Alternating Direction Method of Multipliers. We show that our optimizer outperforms the state-of-the-art implementation of sequential quadratic programming approach in optimal control solver ACADO in computation time and solution quality measured in terms of control and goal reaching cost.
Legendre pairs of lengths ℓ≡0(mod3)

We prove a proposition that connects constant-PAF sequences and the corresponding Legendre pairs with integer PSD values. We show how to determine explicitly the complete spectrum of the $(\ell/3)$-rd value of the discrete Fourier transform for Legendre pairs of lengths $\ell \equiv 0 \, (\mod 3)$. This is accomplished by two new algorithms based on number-theoretic arguments. As an application, we prove that Legendre pairs of the open lengths 117, 129, 133, and 147 exist by finding Legendre pairs of these lengths with a multiplier group of order at least 3. As a consequence, 85, 87, 115, 145, 159, 161, 169, 175, 177, 185, 187, 195 are the twelve integers in the range < 200 for which the question of existence of Legendre pairs remains unsolved.
Pre-biopsy Multi-class Classification of Breast Lesion Pathology in Mammograms

Characterization of lesions by artificial intelligence (AI) has been the subject of extensive research. In recent years, many studies demonstrated the ability of convolution neural networks (CNNs) to successfully distinguish between malignant and benign breast lesions in mammography (MG) images. However, to date, no study has assessed the specific sub-type of lesions in MG images, as detailed in histolopathology reports. We present a method for finer classification of breast lesions in MG images into multiple pathology sub-types. Our approach works well with radiologists’ diagnostic workflow, and uses data available in radiology reports. The proposed Dual-Radiology Dual-Resolution Network (Du-Rad Du-Res Net) receives dual input from the radiologist and dual image resolutions. The radiologist input includes annotation of the lesion area and semantic radiology features; the dual image resolutions comprise a low resolution of the entire mammogram and a high resolution of the lesion area. The network estimates the likelihood of malignancy, as well as the associated pathological sub-type. We show that the combined input of the lesion region of interest (ROI) and the entire mammogram is important for optimizing the model’s performance. We tested the AI in a reader study on a dataset of 100 heldout cases. The AI outperformed three breast radiologists in the task of lesion histopathology sub-typing.
Play for Real(ism) - Using Games to Predict Human-AI interactions in the Real World

AI-enabled decision support systems have repeatedly failed in real world applications despite the underlying model operating as designed. Often this was because the system was used in an unexpected manner. Our goal is to enable better prediction of how systems will be used prior to their implementation as well as to improve existing designs, by taking human behavior into account. There are several challenges to collecting such data. Not having access to an existing prediction engine requires the simulation of such a system's behavior. This simulation must include not just the behavior of the underlying model but also the context in which the decision will be made in the real world. Additionally, collecting statistically valid samples requires that test subjects make repeated choices under slightly varied conditions. Unfortunately, in such repetitious conditions fatigue can quickly set in. Games provide us the ability to address both of these challenges by providing both systems context and narrative context. Systems context can be used to convey some or all of the information the player needs to make a decision in the game environment itself, which can help avoid the onset of fatigue. Narrative context can provide a broader environment within which the simulated system operates, adding a sense of progress, showing the effect of decisions, adding perceived social norms, and setting incentives and stakes. This broader environment can further prevent player fatigue while replicating many of the external factors that might affect choices in the real world. In this paper we describe the design of the Human-AI Decision Evaluation System (HADES), a test harness capable of interfacing with a game environment, simulating the behavior of an AI-enabled decision support system, and collecting the results of human decision making based upon such a system's predictions. Additionally, we present an analysis of data collected by HADES while interfaced with a visual novel game focused on software cyber-risk assessment.
Chapter 10. Emoji as graphic discourse markers

We investigate emoji as graphic discourse markers in German WhatsApp® messages. As comparably novel devices in the rapidly evolving domain of digital messenging, emoji provide an interesting example to observe change in progress. We present a corpus study and an experimental study. Main results are (i) an overall salience of subjective and intersubjective discourse meanings for emoji, with (ii) a general advantage for the former, especially for emoji that iconically include more active elements, while (iii) dominance relations can be modulated by left- vs. right-peripheral positions in favor of subjective vs. intersubjective meanings, respectively. By approaching emoji as discourse markers, the studies contribute to our understanding of their pragmatic contribution and provide novel evidence on positional-functional associations for pragmatic markers.
Study on Anomaly Detection Technique in an Industrial Control System Based on Machine Learning

This study proposed an anomaly detection technique in an industrial control system using supervised and unsupervised machine learning algorithms. For the dataset for learning, the HIL-based Augmented ICS (HAI) dataset provided for the study on security in industrial control systems was used. For the learning model, Light Gradient Boosted Machine -- a supervised learning algorithm and One-Class Support Vector Machine and Isolation Forest as unsupervised learning algorithms were employed. The proposed technique is presented in this paper, which is organized as follows: Feature selection, Data preprocessing, Hyperparameter optimization and verification, and Experiment and analysis of results. The performance difference according to the algorithm and model configuration was exhibited through the experimental results. In addition, issues to be considered in model configuration and future study directions for anomaly detection techniques in industrial control systems were presented based on the experimental results.
Improving the Relation Classification Using Convolutional Neural Network

Abstract Relation extraction has been the emerging research topic in the field of Natural Language Processing. The proposed work classifies the relations among the data considering the semantic relevance of words using word2vec embeddings towards training the convolutional neural network. We intended to use the semantic relevance of the words in the document to enrich the learning of the embeddings for improved classification. We designed a framework to automatically extract the relations between the entities using deep learning techniques. The framework includes pre-processing, extracting the feature vectors using word2vec embedding, and classification using convolutional neural networks. We perform extensive experimentation using benchmark datasets and show improved classification accuracy in comparison with the state-of-the-art methodologies using appropriate methods and also including the additional relations.
A Wearable Foot Pressure Sensor

A foot pressure sensor with an impedance adaptive RFID chip is proposed in this paper. It is used as a shoe pad and is easy to wear. The newly designed sensor consists of three components: RFID chip and antenna, supporting layer, and reflective surface. The distance between the RFID antenna and the reflective surface corresponds to the impedance sensor code of the RFID chip, and the supporting layer transforms the foot pressure into the distance. The detective device is portable as the designed antenna is only in a baby foot size (90x24x 0.03mm). The simulations and the test validate the effectiveness of such a sensor.
Effectiveness evaluation of smart equipment support information system based on Entropy-Revised G1 method

Focusing on the complexity of the structure and the diversification of indicators in the equipment support information system, a combination weighting method was proposed to evaluate the system effectiveness. Firstly, based on the working principle of the equipment support information system, this method constructed its evaluation indicator system; Secondly, based on the subjective and objective characteristics of the indicators, it determined the weight of each indicator by combining the entropy method and the G1 sequence method; Subsequently, through the comparative analysis of the model, the efficiency ranking of the system was carried out. The results of theoretical analysis and simulation verified that this method is an effectively integration of qualitative and quantitative analysis, and is a feasible plan in the effectiveness evaluation of equipment support information system.
Bigram Based Deep Neural Network for Extremism Detection in Online User Generated Contents in the Kazakh Language

Countering the spread of aggressive information and extremism in the global network is an urgent problem of society and government agencies, which is solved in particular by filtering unwanted Internet resources. A necessary condition for such filtering is the classification of the content of websites, texts and documents of the information flow. Therefore, an urgent problem of information technologies is the classification of texts in natural languages in order to detect extremist texts, such as calls for extremism and other messages that threaten the security of citizens.
ASLDP: An Active Semi-supervised Learning method for Disk Failure Prediction

Disk failure has always been a major problem for data centers, leading to data loss. Current research works used supervised learning to offline training through a large number of labeled samples. However, these offline methods are no longer suitable for disk failure prediction tasks in the current big data environment. Behind this explosive amount of data, most methods do not take into account the label values used in the model training phase may not be easy to obtain, or the obtained label values are not completely accurate. These problems further restrict the development of supervised learning and offline modeling in disk failure prediction. In this paper, ASLDP, a novel disk failure prediction method is proposed, which uses active learning and semi-supervised learning. According to the characteristics of data in the disk life cycle, ASLDP carries out active learning for those clear labeled samples, which selects valuable samples and eliminates redundancy. For those samples that are unclearly labeled or unlabeled, ASLDP combines with semi-supervised learning for pre-labeled, and enhances the generalization ability by active learning. The results on three realistic datasets demonstrate that ASLDP achieves stable failure detection rates of 80-85% with low false alarm rates, compared to current online learning methods. Furthermore, ASLDP can overcome the problems of the sample label missing and data redundancy in the massive data environment, compared to current offline learning methods.
CoinJoin in the Wild

CoinJoin is the predominant means to enhance privacy in non-private cryptocurrencies, such as Bitcoin. The basic idea of CoinJoin is to create transactions that combine equal-valued coins of multiple users. This mixing of coins aims to prevent linkage of the users’ transactional in- and outputs. The cryptocurrency Dash employs a built-in CoinJoin service and, therefore, is ideal for empirically studying CoinJoin. This paper presents the first empirical analysis of Dash, which reveals that over 40% of all private transactions can be de-anonymized depending on underlying assumptions. The main issue of these attacks is the coin-aggregation problem, i.e. the need to combine outputs of several CoinJoin transactions. The coin aggregation problem is not specific to Dash and affects other cryptocurrencies as empirical evidence in Bitcoin suggests. We show that the logical solution to the problem, namely CoinJoin transactions with non-fixed arbitrary values, suffers from other privacy weaknesses. We propose a novel mixing algorithm to mitigate the need for coin aggregation without introducing additional privacy vulnerabilities. In contrast to prior mixing algorithms, our approach removes the need for fixed values by dynamically creating equal-valued CoinJoin transactions. The mixing algorithm is not specific to Dash, and integration into other cryptocurrencies, especially into Bitcoin, is possible.
Class-Specific Affinity based Weakly Supervised Semantic Segmentation with Neutral Region Exploration

Image-level weakly supervised semantic segmentation (WSSS) reduces the cost of semantic segmentation significantly as only category labels are required. In the WSSS pipeline, the initially obtained seed areas are imprecise and should be refined. AffinityNet is a widely used seed area refinement method which learns pixel-level affinity between coordinates to adjust the object regions. However, the capabilities of AffinityNet are limited due to the complex simultaneous exploration of the affinities of all object classes, as the classes have different characteristics. This paper proposes a class-specific AffinityNet (CSANet) in which the affinities of each class are learnt separately by the class-specific affinity layers. Moreover, as the learning task is simple because the affinity of only one class is focused, the information contained by the uncertain neutral regions is extracted to provide additional information in the training of the class-specific affinity layers. The empirical results demonstrate the effectiveness of our method, which captures more precise boundaries and significantly improves segmentation results on the PASCAL VOC 2012 segmentation benchmark.
CA-SSD-Based Real-time Smoking Target Detection Algorithm

Smoking in public places is not only a key legislative project proposed by many countries, but also a hot topic in society. The traditional smoking ban supervision model has obvious disadvantages. For people's health and public safety. This paper uses an improved CA-SSD target detection model to detect smoking behavior, uses existing camera equipment in public places, and uses convolutional neural networks to extract, integrate and predict features. The images collected by the camera can accurately locate the smoker's position in real time. Aiming at the problem of too small cigarette butt targets and unobvious features, this paper improves on the original SSD network, uses resnet-50 as the feature extraction network, and integrates the feature fusion module based on CARAFE operator and the attention mechanism. Since there is no public data set, this article uses the smoking data set produced by ourselves, optimizes the parameters during training, and improves the accuracy of cigarette detection on the basis of ensuring real-time performance. Through the ablation experiment, the performance of the attention module and the feature fusion module is verified, which proves that the method in this paper has a better performance in the research of smoking detection.
Algorithms and methods for computerized analysis of mammography images in breast cancer risk assessment

The computerized analysis of mammograms for the development of quantitative biomarkers is a growing field with applications in breast cancer risk assessment. Computerized image analysis offers the possibility of using different methods and algorithms to extract additional information from screening and diagnosis images to aid in the assessment of breast cancer risk. In this work, we review the algorithms and methods for the automated, computerized analysis of mammography images for the task mentioned, and discuss the main challenges that the development and improvement of these methods face today.We review the recent progress in two main branches of mammography-based risk assessment: parenchymal analysis and breast density estimation, including performance indicators of most of the studies considered. Parenchymal analysis methods are divided into feature-based methods and deep learning-based methods; breast density methods are grouped into area-based, volume-based, and breast categorization methods. Additionally, we identify the challenges that these study fields currently face.Parenchymal analysis using deep learning algorithms are on the rise, with some studies showing high-performance indicators, such as an area under the receiver operating characteristic curve of up to 90. Methods for risk assessment using breast density report a wider variety of performance indicators; however, we can also identify that the approaches using deep learning methods yield high performance in each of the subdivisions considered.Both breast density estimation and parenchymal analysis are promising tools for the task of breast cancer risk assessment; deep learning methods have shown performance comparable or superior to the other considered methods. All methods considered face challenges such as the lack of objective comparison between them and the lack of access to datasets from different populations.
Residual Enhancement Network for Realistic Face Sketch-Photo Synthesis

Face sketch-photo synthesis is a significant challenge task in computer vision area, due to the blurred facial details and color distortion produced by the existing approaches. In this paper, we propose a realistic face sketch-photo synthesis method based on residual enhancement network. In the network, a residual enhancement module is constructed and embedded in U-Net to improve the feature representation capability of the deep network. In addition, a detail loss and a perception loss are adopted to constrain the synthesized image has abundant detail and realistic photo style. Experimental results on multiple face sketch datasets indicate that the proposed method obtains superior performance than the state-of-the-art methods, both in terms of visual perception and objective evaluations.