Reviewerly/exHarmony huggingface dataset:
<scp>PolyRec Gesture Design Tool</scp>: A tool for fast prototyping of gesture‐based mobile applications

Abstract In this article, we present PolyRec Gesture Design Tool ( PolyRec GDT ), a tool that allows the definition of gesture sets for mobile applications. It supports the entire process of designing gesture sets, from collecting gestures to analyzing and exporting the set created for use in a mobile application. It is equipped with the PolyRec gesture recognizer, a unistroke recognizer based on template matching with good accuracy and performances while still allowing support for other recognizers. The main features of PolyRec GDT include the ability to collect gestures directly on mobile devices, to detect similarities between gestures to help the designer to detect ambiguities in the design phase, and the ability to automatically select the most representative gestures for each gesture class by using a clustering technique to increase recognition speed without affecting accuracy. Experimental results show that this latter feature allows an increase in accuracy compared with when the same operation is performed manually by developers or randomly by software. Finally, a user study was carried out to evaluate PolyRec GDT , in which participants were asked to use it to add gesture functionality to a mobile application. The results showed that with the support of PolyRec GDT this operation took only a few minutes for participants with mobile development experience, whereas it would have taken much longer without the support of PolyRec GDT .
Automatic Collection Creation and Recommendation

We present a collection recommender system that can automatically create and recommend collections of items at a user level. Unlike regular recommender systems, which output top-N relevant items, a collection recommender system outputs collections of items such that the items in the collections are relevant to a user, and the items within a collection follow a specific theme. Our system builds on top of the user-item representations learnt by item recommender systems. We employ dimensionality reduction and clustering techniques along with intuitive heuristics to create collections with their ratings and titles. We test these ideas in a real-world setting of music recommendation, within a popular music streaming service. We find that there is a 2.3x increase in recommendation-driven consumption when recommending collections over items. Further, it results in effective utilization of real estate and leads to recommending a more and diverse set of items. To our knowledge, these are first of its kind experiments at such a large scale.
On the Security of the Stream Ciphers RCR-64 and RCR-32

Abstract The stream ciphers RCR-64 and RCR-32 designed by Sekar et al. are the most recent additions to the Py-family of stream ciphers, originally designed by Biham et al. The ciphers are among the fastest stream ciphers on software. To the best of our knowledge, the only reported attacks on the ciphers are due to Ding et al., published in the Journal of Universal Computer Science. In this paper, we review these alleged attacks on the RCR ciphers and show that they are based on non-existent keystream biases stemming from flawed probability calculations.
Multi-lingual Hybrid Handwritten Signature Recognition Based on Deep Residual Attention Network

The writing styles of Uyghur, Kazak, Kirgiz and other ethnic minorities in Xinjiang are very similar, so it is extremely difficult to extract the effective features of handwritten signatures of different languages by hand. To solve this problem, a multi-lingual hybrid handwritten signature recognition method based on deep residuals attention network was proposed. Firstly, an offline handwritten signature database in Chinese, Uyghur, Kazak and Kirgiz was established, with a total of 8,000 signed images. Then, the signature image is pre-processed by grayscale, median filtering, binarization, blank edge removal, thinning and size normalization. Finally, transfer learning method is used to input the signature image into the deep residual network, and the high-dimensional features are extracted automatically by the fusion channel attention for classification. The experimental results show that the highest recognition accuracy of this method is 99.44% for multi-lingual hybrid handwritten signature database, which has a high application value.
Attribute saliency network for person re-identification

This paper proposes the Attribute Saliency Network (ASNet), a deep learning model that utilizes attribute and saliency map learning for person re-identification (re-ID) task. Many re-ID methods used human pose or local body parts, either fixed position or auto-learn, to guide the learning. Person attributes, though can describe a person in greater details, are seldom used in retrieving the person's images. We therefore propose to integrate the person attributes learning into the re-ID model, and let it learns together with the person identity networks. With this arrangement, there is a synergistic effect and thus better representations are encoded. In addition, both visual and text retrievals, such as query by clothing colors, hair length, etc., are possible. We also propose to improve the granularity of the heatmap, by generating two global person attributes and body part saliency maps to capture fine-grained details of the person and thus enhance the discriminative power of the encoded vectors. As a result, we are able to achieve state-of-the-art performances. On the Market1501 dataset, we achieve 90.5% mAP and 96.3% Rank 1 accuracy. On DukeMTMC-reID, we obtained 82.7% mAP and 90.6% Rank 1 accuracy.
DCT-in-DCT: A Novel Steganography Scheme for Enhanced Payload Extraction Quality

A common practice of many steganography schemes is to replace the high frequency insignificant Discrete Cosine Transform (DCT) coefficients with secret data. The replaced secret data, however, needs to be scaled appropriately before replacing the insignificant DCT coefficients. The secret is typically scaled down to closely match the replaced insignificant coefficients of the cover. State of the art techniques show that using multiple scales to adaptively scale the secret improves the stego quality without compromising the embedding capacity. Scaling can also potentially lead to degradation of the extracted image quality due to high scaling factors. Therefore, this paper proposes a novel DCT-in-DCT-Adaptive Scaling technique that achieves higher payload imperceptibility without compromise in the stego quality for a fixed embedding capacity. The proposed technique utilizes DCT transform to find the DCT magnitude of the secret before adaptively scaling the secret and replacing the scaled secret with high frequency insignificant DCT coefficients of the cover image. Results compared with Spatial-in-DCT-Adaptive Scaling show improvement of up to 20.25 dB in the extracted quality of the payload.
Domain Randomization on Deep Learning Models for Image Dehazing

Haze is a naturally occurring phenomenon that obstructs vision and affects the quality of images and videos. Recent literature has shown that deep learning-based image dehazing gives promising results both in terms of image quality and execution time. However, the difficulty of acquiring realworld hazy - clear paired images for training still remains a challenge. Widely available datasets use synthetically generated hazy images that suffer from flaws due to difficulty in acquiring accurate depth information to synthesize realistic-looking haze, causing a gap in the real and synthetic domain. In this paper, we propose the usage of domain randomization for image dehazing by generating a completely simulated training dataset for deep learning models. A standard UNET based dehazing model is trained on the simulated dataset without using any real-world data to obtain high quality dehazed images. The performance of the proposed approach is evaluated on the Sun-Dehaze dataset and RESIDE Standard (SOTS outdoor) dataset. We obtain favorable PSNR and SSIM scores on both sets and we also show how our approach yields better visual results compared to other learning-based approaches.
One-Shot Meta-learning for Radar-Based Gesture Sequences Recognition

Radar-based gesture recognition constitutes an intuitive way for enhancing human-computer interaction (HCI). However, training algorithms for HCI capable of adapting to gesture recognition often require a large dataset with many task examples. In this work, we propose for the first time on radar sensed hand-poses, the use of optimization-based meta-techniques applied on a convolutional neural network (CNN) to distinguish 16 gesture sequences with only one sample per class (shot) in 2-ways, 4-ways and 5-ways experiments. We make use of a frequency-modulated continuous-wave (FMCW) 60 GHz radar to capture the sequences of four basic hand gestures, which are processed and stacked in the form of temporal projections of the radar range information (Range-Time Map - RTM). The experimental results demonstrate how the use of optimization-based meta-techniques leads to an accuracy greater than 94% in a 5-ways 1-shot classification problem, even on sequences containing a type of basic gesture never observed in the training phase. Additionally, thanks to the generalization capabilities of the proposed approach, the required training time on new sequences is reduced by a factor of 8,000 in comparison to a typical deep CNN.
A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation

Boundary-based instance segmentation has drawn much attention since of its attractive efficiency. However, existing methods suffer from the difficulty in long-distance regression. In this paper, we propose a coarse-to-fine module to address the problem. Approximate boundary points are generated at the coarse stage and then features of these points are sampled and fed to a refined regressor for fine prediction. It is end-to-end trainable since differential sampling operation is well supported in the module. Furthermore, we design a holistic boundary-aware branch and introduce instance-agnostic supervision to assist regression. Equipped with ResNet-101, our approach achieves 31.7% mask AP on COCO dataset with single-scale training and testing, outperforming the baseline 1.3% mask AP with less than 1% additional parameters and GFLOPs. Experiments also show that our proposed method achieves competitive performance compared to existing boundary-based methods with a lightweight design and a simple pipeline.
Performance Analysis of Ensemble Based Approaches to Mitigate Class Imbalance Problem after Applying Normalization

In class imbalanced data set, one class contains more instances than the other class and it is a critical problem in data mining. Many approaches such as oversampling, undersampling, and cost sensitive methods are developed to mitigate the effects of class imbalance but these methods suffer from various shortcomings. In the existing methods, the researchers have hardly used normalization on the imbalanced data set to mitigate the effects. In this work, we implemented two state-of-the-art data balancing methods, Random Undersampling (RUS) and Random Oversampling (ROS), ensembled by AdaBoost algorithm. Then we investigated and compared the two methods with a recently developed approach called Random Splitting data balancing (SplitBal) method with and without applying normalization on the imbalanced data set. For normalization, three well known normalization techniques are used called min-max, z-score and robust-scaling normalization. Our concerned approach, SplitBal is an ensemble method which firstly converts the imbalanced data set into several balanced data set. From the balanced data set, multiple classification models are built and ensembled by max ensemble rule. The empirical analysis using fifteen imbalanced data set elucidates that SplitBal with min-max normalization is dominant over the concerned data balancing methods in this work for Random Forest classifier.
Handwriting Recognition with Novelty

This paper introduces an agent-centric approach to handle novelty in the visual recognition domain of handwriting recognition (HWR). An ideal transcription agent would rival or surpass human perception, being able to recognize known and new characters in an image, and detect any stylistic changes that may occur within or across documents. A key confound is the presence of novelty, which has continued to stymie even the best machine learning-based algorithms for these tasks. In handwritten documents, novelty can be a change in writer, character attributes, writing attributes, or overall document appearance, among other things. Instead of looking at each aspect independently, we suggest that an integrated agent that can process known characters and novelties simultaneously is a better strategy. This paper formalizes the domain of handwriting recognition with novelty, describes a baseline agent, introduces an evaluation protocol with benchmark data, and provides experimentation to set the state-of-the-art. Results show feasibility for the agent-centric approach, but more work is needed to approach human-levels of reading ability, giving the HWR community a formal basis to build upon as they solve this challenging problem.
A Deep Reinforcement Learning Model using Long Contexts for Chatbots

Recently, deep neural network (DNN) based chat-bots offer great promise for generating responses. With architecture using an encoder-decoder integrated with attention mechanism, they showed a good one for generating natural responses, however they still are be short-sighted in predicting response one at a time while ignoring their influence on future outcomes. This is one of the reason DNN based models usually tend to generate responses not relevant to previous responses. In this paper, we show how to apply reinforcement learning to those models that can help them achieve a specific goal. By simulating dialogues between two pretrained chatbots with DNN, followed by using policy gradient methods to reward sequences. We also present our forward-looking function for desirable goal which will be used to improve the models. The experimental results show that the proposed model generates appropriate responses to content with more information relevant to the conversation context. In addition, our model improves up to 43% BLEU score compared to the baseline.
Adversarial Affine Registration for Real-Time Intraoperative Registration of 3-D US-US for Brain Shift Correction

One of the most frequent tumors in the central nervous system is glioma. The high-grade gliomas grow relatively fast and eventually lead to death. The tumor resection improves the survival rate. However, an accurate image-guidance is necessary during the surgery. The problem may be addressed by image registration. There are three main challenges: (i) the registration must be performed in real-time, (ii) the tumor resection results in missing data that strongly influence the similarity measure, and (iii) the quality of ultrasonography images. In this work, we propose a solution based on generative adversarial networks. The generator network calculates the affine transformation while the discriminator network learns the similarity measure. The ground-truth for the discriminator is defined by calculating the best possible affine transformation between the anatomical landmarks. This approach allows real-time registration during the inference and does not require defining the similarity measure that takes into account the missing data. The work is evaluated using the RESECT database. The dataset consists of 17 US-US pairs acquired before, during, and after the surgery. The target registration error is the main evaluation criteria. We show that the proposed method achieves results comparable to the state-of-the-art while registering the images in real-time. The proposed method may be useful for the real-time intraoperative registration addressing the brain shift correction.
Topic Enhanced Multi-head Co-Attention: Generating Distractors for Reading Comprehension

In the construction of multiple-choice Machine Reading Comprehension(MRC), in addition to questions and answers, it is necessary to generate distractors corresponding to them. Recent models based on Seq2Seq have shown good results in text generation, while previous work has only succeeded in producing a few interfering words or phrases per question. Our goal is to generate more meaningful distractors based on reading comprehension of articles that are closer to the semantics of the question to help better diagnose gaps in text understanding. A major drawback of recent studies is that they do not take the relationship between the distractors and background text into account when generating distractors. This often results in the distractors being either too general or too close to the correct answer. We propose a Topic Enhanced Multi-head Co-Attention model (TMCA) based on hierarchical networks to better capture the interactions between sentences. By adding query-relevance loss, our model enables the distractors to be as semantically relevant to the question as possible based on the reading comprehension of the article, while trying to ensure that they are false answers. The results show that the proposed approach achieves superior performance over the baselines in terms of automatic metrics on two multiple-choice datasets (RACE and DREAM). For further evaluation, we use another advanced reading comprehension model and human evaluation to demonstrate that our model outperforms several strong baselines in generating high-quality and educationally meaningful distracters.
Research on the application and development prospect of network echnology in physical education teaching management of Higher Vocational Colleges

This paper analyzes the current network technology in higher vocational college sports teaching management and its development prospects. The development of computer network technology makes colleges and universities pay more and more attention to students' physical quality. At this time, integrating network technology into the process of physical education teaching can improve the teaching quality and efficiency. The application of network technology in educational administration can also make the physical education teaching management system be effectively developed to meet the needs of system operation and the completion of the project. This paper analyzes the reasons for the application of network technology in Physical Education in higher vocational colleges, the current situation of the application of physical education, the application research of physical education, the application and development prospect of educational administration management, in order to play a reference role for the management and development of physical education in Higher Vocational colleges.
Causal Explanation of Convolutional Neural Networks

In this paper we introduce an explanation technique for Convolutional Neural Networks (CNNs) based on the theory of causality by Halpern and Pearl []. The causal explanation technique (CexCNN) is based on measuring the filter importance to a CNN decision, which is measured through counterfactual reasoning. In addition, we employ extended definitions of causality, which are responsibility and blame to weight the importance of such filters and project their contribution on input images. Since CNNs form a hierarchical structure, and since causal models can be hierarchically abstracted, we employ this similarity to perform the most important contribution of this paper, which is localizing the important features in the input image that contributed the most to a CNN’s decision. In addition to its ability in localization, we will show that CexCNN can be useful as well for model compression through pruning the less important filters. We tested CexCNN on several CNNs architectures and datasets. (The code is available on https://github.com/HichemDebbi/CexCNN)
An Improved Genetic Transmission and Dynamic-Opposite Learning Strategy for Multitasking Optimization

Multi-task evolutionary optimization is a new method proposed in recent years to solve optimization problems. Compared with traditional single-task optimization, the multi-task optimization mechanism based on evolutionary algorithms promotes the evolution of population by sharing the potential similarity and complementarity between different problems, so as to improve the performance and efficiency of solving problems. However, with the evolution of the population, the ability of one task to learn from other tasks may decrease, and the efficiency of knowledge transfer will also decrease. To address this issue and improve the quality of knowledge transfer among the tasks, this article proposes a new genetic transmission strategy and mutation strategy of multi task optimization algorithm, namely DOMLMFEA. In particular, in the genetic process, if two parent individuals are specific to different tasks, one parent individual is mapped to the vicinity of the other parent individual through task space mapping, and high-quality offspring individuals are generated through crossover. In addition, a dynamic opposite mutual learning mutation strategy based on wavelet basis function is introduced to generate promising solutions. It can effectively explore and develop in the unified search space and the subspace of each task, and improve the local search ability of the algorithm and the diversity of solutions. In order to evaluate the performance of DOMLMFEA, this paper tests on classical multi-task optimization problems, and compared with several other state-of-the-art MFEA variants. Numerical and simulation results show the effectiveness of the proposed DOMLMFEA.
Incorporating Discrete Wavelet Transformation Decomposition Convolution into Deep Network to Achieve Light Training

The deep neural network achieves superior performance in various tasks. However, it is notoriously known that its training needs a considerable time cost to refine a large number of parameters. We proposed a deep wavelet network to tackle the issue. The proposed network is built by processing blocks with various decomposition levels which named Discrete Wavelet Transformation Decomposition Convolution (DWTDC). The DWTDC aims to fulfill the task of feature map discrete wavelet transformation decomposition and subbands differential fusion. We employ the DWTDC block to act as the convolution layer so that the parameters are estimated within the frequency domain space. Because of the merits of economic representation in the wavelet domain, the training parameters are greatly reduced, only requiring \(33\%\) of the parameters of the popular networks. Extensive experiments by comparing with benchmark models show that the proposed DWTDC dramatically reduced the number of parameters and achieved light training without sacrificing the classification performance.
Measuring Web Session Security at Scale

Session management is a particularly delicate component of web applications, which might suffer from a range of severe security issues, including impersonation attacks. Unfortunately, the scope and significance of prior work on web session security in the wild are limited by the complexity of the attack surface and the challenges of automating the login process on existing websites. In the present article, we fill this gap by proposing the first comprehensive, large-scale web session security measurement based on post-login data. Our analysis is comprehensive in that it deals with all key aspects of web sessions, i.e., the login process, the logout process and the authentication cookie handling. Our automated approach analysed an extensive set of session management practices of over 6,000 sites where login was successful and authentication cookies could be automatically detected, uncovering a widespread adoption of insecure practices in the wild.