Reviewerly/exHarmony huggingface dataset:
Motion Recognition Algorithm Based on Deep Edge-Aware Pyramid Pooling Network in Human–Computer Interaction

Action recognition is an important research direction in computer vision, which has worldwide applications, such as video surveillance, human-robot interaction and so on. Due to the influence of complex background and multi-angle changes, accurate recognition and analysis of human motion in real-life scenarios is still a challenging problem. In order to improve the accuracy of pedestrian detection and motion recognition, this paper proposes a novel edge-aware end-to-end deep network method, which uses the edge-aware pooling module to improve pedestrian contour accuracy and captures video sequences using multi-scale pyramid pooling layer spatial-time context feature. The complementary features of the edge-related features can effectively preserve the clear boundary, and the combination of the auxiliary side output and the pyramid pooling layer output can extract rich global context information. A large number of qualitative and quantitative experimental results show that the proposed model can effectively improve the performance of existing pedestrian detection and motion recognition networks on the UCF-101, HMDB-51, and KTH dataset.
Attention Guided Anomaly Detection and Localization in Images.

Anomaly localization is an important problem in computer vision which involves localizing anomalous regions within images with applications in industrial inspection, surveillance, and medical imaging. This task is challenging due to the small sample size and pixel coverage of the anomaly in real-world scenarios. Most prior works need to use anomalous training images to compute a class-specific threshold to localize anomalies. Without the need of anomalous training images, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information. In the unsupervised setting, we propose an attention expansion loss where we encourage CAVGA to focus on all normal regions in the image. Furthermore, in the weakly-supervised setting we propose a complementary guided attention loss, where we encourage the attention map to focus on all normal regions while minimizing the attention map corresponding to anomalous regions in the image. CAVGA outperforms the state-of-the-art (SOTA) anomaly localization methods on MVTec Anomaly Detection (MVTAD), modified ShanghaiTech Campus (mSTC) and Large-scale Attention based Glaucoma (LAG) datasets in the unsupervised setting and when using only 2% anomalous images in the weakly-supervised setting. CAVGA also outperforms SOTA anomaly detection methods on the MNIST, CIFAR-10, Fashion-MNIST, MVTAD, mSTC and LAG datasets.
Strongly Secure Authenticated Key Exchange from Supersingular Isogenies

This paper aims to address the open problem, namely, to find new techniques to design and prove security of supersingular isogeny-based authenticated key exchange (AKE) protocols against the widest possible adversarial attacks, raised by Galbraith in 2018. Concretely, we present two AKEs based on a double-key PKE in the supersingular isogeny setting secure in the sense of CK$$^+$$, one of the strongest security models for AKE. Our contributions are summarised as follows. Firstly, we propose a strong OW-CPA secure PKE, $$\mathsf {2PKE_{sidh}}$$, based on SI-DDH assumption. By applying modified Fujisaki-Okamoto transformation, we obtain a [OW-CCA, OW-CPA] secure KEM, $$\mathsf {2KEM_{sidh}}$$. Secondly, we propose a two-pass AKE, $$\mathsf {SIAKE}_2$$, based on SI-DDH assumption, using $$\mathsf {2KEM_{sidh}}$$ as a building block. Thirdly, we present a modified version of $$\mathsf {2KEM_{sidh}}$$ that is secure against leakage under the 1-Oracle SI-DH assumption. Using the modified $$\mathsf {2KEM_{sidh}}$$ as a building block, we then propose a three-pass AKE, $$\mathsf {SIAKE}_3$$, based on 1-Oracle SI-DH assumption. Finally, we prove that both $$\mathsf {SIAKE}_2$$ and $$\mathsf {SIAKE}_3$$ are CK$$^+$$ secure in the random oracle model and supports arbitrary registration. We also provide an implementation to illustrate the efficiency of our schemes. Our schemes compare favourably against existing isogeny-based AKEs. To the best of our knowledge, they are the first of its kind to offer security against arbitrary registration, wPFS, KCI, and MEX simultaneously. Regarding efficiency, our schemes outperform existing schemes in terms of bandwidth as well as CPU cycle count.
UVTON: UV Mapping to Consider the 3D Structure of a Human in Image-Based Virtual Try-On Network

Image-based virtual try-on is an area of research that is attracting attention as the demand for online apparel shopping continues to increase. The methods proposed thus far have focused on how to generate a dress-up image while preserving the clothing details. However, the posture of the model in the image is limited to an upright position, and other positions frequently do not work well. In this study, based on a kind of generative adversarial network (GAN) that utilizes UV mapping to consider the 3D structure of the human body, we propose a novel virtual try-on method called a UV Try-On Network (UVTON). We use a DensePose to estimate a point corresponding to the 3D surface of a human model for each pixel point of a 2D image and incorporate the estimated information into our model. It is thus possible to change the clothes of users holding various postures. Our proposed method uses UV mapping and two other modules. One module generates parts to be used in the mapping, and the other refines the image and produces a more realistic image. Based on both qualitative and quantitative comparison with existing methods, we experimentally demonstrated that our method achieved better results with various postures.
Code smells for multi-language systems

Software quality becomes a necessity and no longer an advantage. In fact, with the advancement of technologies, companies must provide software with good quality. Many studies introduce the use of design patterns as improving software quality and discuss the presence of occurrences of design defects as decreasing software quality. Code smells include low-level problems in source code, poor coding decisions that are symptoms of the presence of anti-patterns in the code. Most of the studies present in the literature discuss the occurrences of design defects for mono-language systems. However, nowadays most of the systems are developed using a combination of several programming languages, in order to use particular features of each of them. As the number of languages increases, so does the number of design defects. They generally do not prevent the program from functioning correctly, but they indicate a higher risk of future bugs and makes the code less readable and harder to maintain. We analysed open-source systems, developers' documentation, bug reports, and programming language specifications and extracted bad practices related to multi-language systems. We encoded these practices in the form of code smells. We report in this paper 12 code smells.
Analysis of Indonesia Politics Polarization before 2019 President Election Using Sentiment Analysis and Social Network Analysis

The development of the Internet in Indonesia is quite rapid, it is marked by the increasing use of social networks, especially Twitter.Not only to share status or stories, Twitter has become become a means of promotion and campaign for elections.The Twitter data can be used to find out the political polarization in Indonesia that is needed in the 2019 presidential election.The method used in this research is sentiment analysis using naï ve bayes classifier and social network analysis using the calculation of network attribute values and centrality values.8.814 Twitter data was collected using data crawling method.The data are divided into three subsets consisting of jokowi's sentiments, prabowo's sentiments, and pilpres's sentiments.Final result of the sentiment analysis is classified sentiments into positives, negatives, and neutral.The average value of the classification results was 91.27% positive sentiment, 7.56% negatives sentiment, and 1.17% neutral sentiment.This classification yielded the average accuracy of 69.2% for jokowi's sentiments and 100% for prabowo sentiments.The classification accuracy calculation uses ROCs method.Final results of the social network analysis based on the calculation of network attributes yielded 277 nodes, 7.950 edges, 57,401 average degree, 56.44 average weighted degree, network diameter is 4, 1.853 average path length, 0.201 density, and 5 of number communities.Centrality values generates the 5 most influential actors in social network interactions are jokowi's of first rank, 2 nd SBYudhoyono's, 3 rd detikcom, 4 th yjuniardi, 5 th mohmahfudmd.
Tree-Structured Attention with Hierarchical Accumulation

Incorporating hierarchical structures like constituency trees has been shown to be effective for various natural language processing (NLP) tasks. However, it is evident that state-of-the-art (SOTA) sequence-based models like the Transformer struggle to encode such structures inherently. On the other hand, dedicated models like the Tree-LSTM, while explicitly modeling hierarchical structures, do not perform as efficiently as the Transformer. In this paper, we attempt to bridge this gap with Hierarchical Accumulation to encode parse tree structures into self-attention at constant time complexity. Our approach outperforms SOTA methods in four IWSLT translation tasks and the WMT'14 English-German task. It also yields improvements over Transformer and Tree-LSTM on three text classification tasks. We further demonstrate that using hierarchical priors can compensate for data shortage, and that our model prefers phrase-level attentions over token-level attentions.
Curvature Graph Network

Graph-structured data is prevalent in many domains. Despite the widely celebrated success of deep neural networks, their power in graph-structured data is yet to be fully explored. We propose a novel network architecture that incorporates advanced graph structural features. In particular, we leverage discrete graph curvature, which measures how the neighborhoods of a pair of nodes are structurally related. The curvature of an edge (x, y) defines the distance taken to travel from neighbors of x to neighbors of y, compared with the length of edge (x, y). It is a much more descriptive feature compared to previously used features that only focus on node specific attributes or limited topological information such as degree. Our curvature graph convolution network outperforms state-of-the-art on various synthetic and real-world graphs, especially the larger and denser ones.
DeFINE: Deep Factorized Input Word Embeddings for Neural Sequence Modeling

For sequence models with large word-level vocabularies, a majority of network parameters lie in the input and output layers. In this work, we describe a new method, DeFINE, for learning deep word-level representations efficiently. Our architecture uses a hierarchical structure with novel skip-connections which allows for the use of low dimensional input and output layers, reducing total parameters and training time while delivering similar or better performance versus existing methods. DeFINE can be incorporated easily in new or existing sequence models. Compared to state-of-the-art methods including adaptive input representations, this technique results in a 6% to 20% drop in perplexity. On WikiText-103, DeFINE reduces total parameters of Transformer-XL by half with minimal impact on performance. On the Penn Treebank, DeFINE improves AWD-LSTM by 4 points with a 17% reduction in parameters, achieving comparable performance to state-of-the-art methods with fewer parameters. For machine translation, DeFINE improves a Transformer model by 2% while simultaneously reducing total parameters by 26%
Detection and Classification of Breast Cancer in Mammography Images Using Pattern Recognition Methods

Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other normal parts of the breast image. In this study, 19 final different features of each image were extracted to generate the feature vector for classifier input. The proposed method not only determined the boundary of masses but also classified the type of masses such as benign and malignant ones. The neural network classification methods such as the radial basis function (RBF), probabilistic neural network (PNN), and multi-layer perceptron (MLP) as well as the Takagi-Sugeno-Kang (TSK) fuzzy classification, the binary statistic classifier, and the k-nearest neighbors (KNN) clustering algorithm were used for the final decision of mass class. Results: The best results of the proposed method for accuracy, sensitivity, and specificity metrics were obtained 97%±4.36, 100%±0 and 96%±5.81, respectively for support vector machine (SVM) classifier. Conclusions: By comparing the results of the proposed method with the results of the other previous methods, the efficiency of the proposed algorithm was reported.
Knowledge management processes and innovation performance: The moderating effect of employees’ knowledge hoarding

The positive impact of knowledge management on innovation performance is no longer a mystery.However, the moderating role of knowledge hoarding remains undisclosed.Therefore, the present study aims to explore the moderating role of knowledge hoarding on the effect of knowledge management on innovation performance.On the ground of its design as a descriptive research, a questionnaire-based survey was developed, validated, and administered to a sample of 314 managers chosen from commerce and service organizations.Using multiple regression analysis and structural equation modeling (SEM), the results indicated that knowledge management processes (knowledge acquisition, dissemination, storage, sharing and application) exerted significant effects on innovation performance.Moreover, Knowledge hoarding exerted a significant negative effect on innovation performance.However, the results indicate that knowledge hoarding did not moderate the effect of knowledge management processes on innovation performance.This finding can be explained by the fact that, despite the negative impact of knowledge hoarding on innovation performance, this effect did not affect the relationship between knowledge management processes and innovation performance and this may be due to the nature of knowledge hoarded by employees in terms of relevance and scarcity.In the light of these findings, the study recommends that it is necessary to determine whether employees hoard knowledge, and distinguish how important this knowledge is for innovation performance.In fact, this is the contribution of the current study to knowledge management literature.
Symmetrical Synthesis for Deep Metric Learning

Deep metric learning aims to learn embeddings that contain semantic similarity information among data points. To learn better embeddings, methods to generate synthetic hard samples have been proposed. Existing methods of synthetic hard sample generation are adopting autoencoders or generative adversarial networks, but this leads to more hyper-parameters, harder optimization, and slower training speed. In this paper, we address these problems by proposing a novel method of synthetic hard sample generation called symmetrical synthesis. Given two original feature points from the same class, the proposed method firstly generates synthetic points with each other as an axis of symmetry. Secondly, it performs hard negative pair mining within the original and synthetic points to select a more informative negative pair for computing the metric learning loss. Our proposed method is hyper-parameter free and plug-and-play for existing metric learning losses without network modification. We demonstrate the superiority of our proposed method over existing methods for a variety of loss functions on clustering and image retrieval tasks.
Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series

Integrating deep learning with latent state space models has the potential to yield temporal models that are powerful, yet tractable and interpretable. Unfortunately, current models are not designed to handle missing data or multiple data modalities, which are both prevalent in real-world data. In this work, we introduce a factorized inference method for Multimodal Deep Markov Models (MDMMs), allowing us to filter and smooth in the presence of missing data, while also performing uncertainty-aware multimodal fusion. We derive this method by factorizing the posterior p(z|x) for non-linear state space models, and develop a variational backward-forward algorithm for inference. Because our method handles incompleteness over both time and modalities, it is capable of interpolation, extrapolation, conditional generation, label prediction, and weakly supervised learning of multimodal time series. We demonstrate these capabilities on both synthetic and real-world multimodal data under high levels of data deletion. Our method performs well even with more than 50% missing data, and outperforms existing deep approaches to inference in latent time series.
Identifying Model Weakness with Adversarial Examiner

Machine learning models are usually evaluated according to the average case performance on the test set. However, this is not always ideal, because in some sensitive domains (e.g. autonomous driving), it is the worst case performance that matters more. In this paper, we are interested in systematic exploration of the input data space to identify the weakness of the model to be evaluated. We propose to use an adversarial examiner in the testing stage. Different from the existing strategy to always give the same (distribution of) test data, the adversarial examiner will dynamically select the next test data to hand out based on the testing history so far, with the goal being to undermine the model's performance. This sequence of test data not only helps us understand the current model, but also serves as constructive feedback to help improve the model in the next iteration. We conduct experiments on ShapeNet object classification. We show that our adversarial examiner can successfully put more emphasis on the weakness of the model, preventing performance estimates from being overly optimistic.
OE-CAM: A Hybrid Opto-Electronic Content Addressable Memory

A content addressable memory (CAM) is a type of memory that implements a parallel search engine at its core. A CAM takes as an input a value and outputs the address where this value is stored in case of a match. CAMs are used in a wide range of applications including networking, cashing, neuromorphic associative memories, multimedia, and data analytics. Here, we introduce a novel opto-electronic CAM (OE-CAM) utilizing the integrated silicon photonic platform. In our approach, we explore the performance of an experimental OE-CAM and verify the efficiency of the device at 25 Gbit/s while maintaining the bit integrity under noise conditions. We show that OE-CAM enables a) two orders of magnitude faster search functionality resulting in b) a five orders of magnitude lower power-delay-product compared to CAMs implementations based on other emerging technologies. This remarkable performance potential is achieved by utilizing i) a high parallelism of wavelength-division-multiplexing in the optical domain, combined with ii) 10's of GHz-fast opto-electronic components, packaged in iii) integrated photonics for 10-100's ps-short communication delays. We further verify the upper optical input power limit of this OE-CAM to be given by parasitic nonlinearities inside the silicon waveguides, and the minimal detectable optical power at the back-end photoreceiver's responsivity given channel noise. Such energy-efficient and short-delay OE-CAMs could become a key component of functional photonic-augmented ASICS, co-processors, or smart sensors.
Improved Cube Attacks on Some Authenticated Encryption Ciphers and Stream Ciphers in the Internet of Things

With technical development and internet popularization, Internet of Things (IoT) technology is gaining a wider application in various fields. Key challenges in the growth of IoT are related to privacy and security. To avoid any possible malicious attacks, employing cryptosystems is widely recognized as one of the most effective approaches to implement confidentiality, integrity, and message authentication for the security of IoT. In this work, we investigate the security of Authenticated Encryption ciphers and stream cipher by using the improved cube attack. Firstly, we introduce a method to identify good cubes, which leads to the largest-round distinguisher. Our idea is based on the greedy algorithm of finding cubes and the numeric mapping method for estimating the algebraic degree of the NFSR-based cryptosystem. By using this method, we can efficiently explore useful cubes from a large search space. Further, we evaluate the security of several cryptographic primitives against the cube attack by using the SAT model of division property and flag technique, which can make the propagation of division property more accurately. Experiments show that we can obtain some new or improved cryptanalysis on MORUS-640-128, TRIAD, Quartet, TriviA-ck-v2, and Enhanced-bivium. The attacks can improve the number of attacking rounds and efficiency, and provide a vital reference for security analysis of other Authenticated Encryption ciphers and lightweight stream ciphers.
Collaborating Online with Four Different Google Apps : Benefits to Learning and Usefulness for Future Work

This project is in response to the need to integrate new forms of technology in language-learning classrooms, to not only enhance learning, but equip students with the technological and collaborative tools to prepare them for future academic and professional contexts.The purpose of this study was to 1) investigate student attitudes towards doing various language-learning tasks on Google apps, 2) explore some of the advantages and limitations of using Google apps as a cloud-based collaborative tool, and 3) investigate student behaviors while collaborating on Google apps.Surveys and interviews were used with 31 participants in a pre-university EAP course in the UAE to explore student perceptions of using four Google apps (Sheets, Slides, Docs, and Forms) to do various languagelearning tasks.Field notes based on observations of one in-class task were also analyzed to investigate student behaviors while collaborating.Participants reported advantages of collaborating on Google apps such as ease of use, working together from different places, and being able to give feedback online.In regards to behaviors, participants showed a tendency to divide their work while collaborating.Some possible limitations to doing language-learning activities on Google apps will be discussed, as well as implications of the study.