Reviewerly/exHarmony huggingface dataset:
RecSys Challenge 2022 Dataset: Dressipi 1M Fashion Sessions

As part of the RecSys Challenge 2022, the Dressipi 1M Fashion Sessions dataset is publicly released. This paper gives an overview of the content and structure of the dataset, as well as explaining the process by which it was constructed. The dataset contains anonymous browsing sessions, a purchase for each session, as well as content data of the items. The content data consists of IDs that represent descriptive fashion characteristics of the items and have been assigned using Dressipi's human-in-the-loop labelling system. We hope that this dataset will be valuable in recommender systems research beyond the RecSys Challenge and encourage more publications in the fashion domain.
REMAP: A Spatiotemporal CNN Accelerator Optimization Methodology and Toolkit Thereof

Designing convolutional neural network (CNN) accelerators is getting more difficult owing to the fast-increasing types of CNN models. Some approaches use constant dataflow and microarchitecture that have lower design complexity. However, these accelerators are difficult to adapt with the highly-diverse CNN models and often suffer from low process element utilization. Some other accelerators resort to reconfigurable devices, such as field-programmable gate array (FPGA) and coarse-grained reconfigurable array to support flexible dataflows in order to fit diverse CNN layers. However, layer-by-layer processing may require more energy for frequent reconfiguration and off-chip DDR access. In this work, we introduce a reconfigurable pipeline accelerator (RPA) that can reduce the latency and DDR access by pipelining the compuptation of CNN layers. Although there have been several researches that try to speedup the design process by automatically exploring subset of the accelerator design space, identifying an available automated design tool that can effectively find the complete and optimal design scheme remains a problem, especially for the novel RPA architecture type. Unfortunately, comprehensive exploration of the whole design space faces an excessive large searching space. To tackle this problem, we propose REMAP, a toolkit for designing CNN accelerators based on the Monte Carlo tree search (MCTS) method. To efficiently search the huge design space, we propose several methods to improve searching efficiency. Evaluations show that REMAP significantly outperforms some state-of-the-art approaches; compared with GAMMA, it achieves an average speed increase of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$14.75\times $ </tex-math></inline-formula> , and an energy reduction of 45.45%; it also achieves a speed increase of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$32.6\times $ </tex-math></inline-formula> against ConfuciuX on MobileNetV2 and ResNet50. We also show an FPGA accelerator implementation which is based on REMAP’s search result, and it achieves high performance in real-time CNN tasks. This indicates that REMAP can provide high-quality design exploration with valuable insights and useful architecture design guidances.
Stereo Matching With Multiscale Hybrid Cost Volume

Stereo matching estimates the disparity between a pair of rectified left and right images, which plays an important role in robot navigation, autonomous driving, and other related tasks.Despite the remarkable progress of deep learning in stereo matching, there are still many challenges.One of the challenges is that current stereo models mostly generate a single scale cost volume based on costly 3D convolutions method or quick 2D convolutions method, but neither of these two methods can achieve a fair trade-off between quality and time.In this paper, we propose to construct multi-scale hybrid cost volume which aims at achieving fast speed while maintaining comparable accuracy.Concretely, we generate the correlation cost volume and the concatenation cost volume respectively, and then integrate together to form a hybrid cost volume which can significantly improve the accuracy and reduce the computational complexity.At the multi-scale level, we generate three hybrid cost volumes at different scales and then aggregate them by 2D convolutions which are faster than 3D convolutions.In addition, we adopt 2D CNN stacked hourglass with fused cost volume for cost aggregation.Specifically, the proposed method provides competitive performance with state-of-the-art methods, while being faster than most top-performing methods (e.g., 3.7× than PSMNet, 8.1× than GCNet, 16.3× than GANet).Extensive experiments of our model on the SceneFlow, KITTI 2012, and KITTI 2015 datasets demonstrate the competitiveness of our results.
1+1$&gt;$2: Programming Know-What and Know-How Knowledge Fusion, Semantic Enrichment and Coherent Application

Software programming requires both API reference (know-what) knowledge and programming task (know-how) knowledge. Lots of programming know-what and know-how knowledge is documented in text, for example, API reference documentation and programming tutorials. To improve knowledge accessibility and usage, several recent studies use Natural Language Processing (NLP) methods to construct API know-what knowledge graph (API-KG) and programming task know-how knowledge graph (Task-KG) from software documentation. Although being promising, current API-KG and Task-KG are independent of each other, and thus are void of inherent connections between the two types of knowledge. Our empirical study on Stack Overflow questions confirms that only 36% of the API usage problems can be answered by the know-how or the know-what knowledge alone, while the rest questions requires a fusion of both. Inspired by this observation, we make the first attempt to fuse API-KG and Task-KG by API entity linking. This fusion creates nine categories of API semantic relations and two types of task semantic relations which are not present in the stand-alone API-KG or Task-KG. According to the definitions of these new API and task semantic relations, our approach dives deeper than surface-level API linking of API-KG and Task-KG, and infer nine categories of API semantic relations from task descriptions and two types of task semantic relations with the assistance of API-KG, which enrich the declaration or syntactic relations in the current API-KG and Task-KG. Our fused and semantically-enriched API-Task KG supports coherent API/Task-centric knowledge search by text or code queries. We have implemented our approach on Java programming documentation and built a web tool to search and explore API and programming task knowledge. Our evaluation confirms the high-accuracy of our knowledge extraction, fusion and enrichment methods, and the effectiveness and usefulness of our API-Task KG for answering Stack Overflow questions.
Heterogeneous Knowledge Network for Visual Dialog

Visual dialog requires an agent to answer successive questions considering an image and dialog history, which is a classic vision-language task. Despite progress, there are still two key challenges: 1) parsing long or complex questions and answers and 2) dealing with the visual scene containing complicated interactions among entities. These challenges bring about the unsatisfactory consequence of current visual dialog methods. In this paper, we propose a novel Heterogeneous Knowledge Network (HKNet), which leverages textual sequence knowledge and graph knowledge to address the above issues. Specifically, the textual sequence knowledge is derived from the sentences that are retrieved from the image captions of the visual dialog dataset. The textual sequence knowledge can supplement essential common sense for parsing long or complex questions and answers. The graph knowledge is constructed via scene graph, which provides complete visual relationships for understanding the complicated interactions. These two kinds of heterogeneous knowledge complement each other and jointly improve the logical reasoning ability of the visual dialog. Extensive experimental results on two benchmark datasets: VisDial v0.9 and v1.0 demonstrate the superiority of the proposed HKNet. Ablation studies and visualization results further verify the effectiveness of our method.
A Fractional order method for Meta-Learning about aerospace target classification

Meta-learning can sum up the experience from the learned tasks, and solve problems where datasets are scarce or expensive and model generalization, which traditional machine learning is inadequate. Meta-learning can obtain a well initial model, which can quickly generalize after a few adjustments to solve new tasks with good performance. However, MAML faces the problem of convergence difficulties, and easily prone to concussion late in training. We employ fractional order to MAML, which can retain past task gradient for a better stabilized and generalize model. We proposed a method based on fractional-order and model-agnostic meta-learning (MAML), and named as FracMAML, which can apply to a method based on meta-learning such as MAML, Reptile. Our proposed method can obtain a better initial model via fractional order. We take advantage of meta-learning on few-shot problems to solve the aerospace targets classification problem, and proposed an Aerospace dataset. We employed FracMAML on MiniImagenet and Omniglot, which obtain state of art accuracy than some classical meta-learning method such as MAML and Reptile and so on. Finally, we verify FracMAML method on aerospace targets classification task based on our Aerospace dataset, which performed well, further verify the versatility of our algorithm.
Key-Based Data Deduplication via Homomorphic NTRU for Internet of Vehicles

In recent years, the gradual improvement of the Internet of Vehicles (IoV) ecosystem has highlighted the commercial potential of IoV. The IoV not only shows excellent performance in terms of road safety and driving comfort but also has great significance in terms of building new models of automobiles and transportation services. In addition, as the foundation of intelligent transportation, IoV has emerged as a research hotspot. However, the IoV has some shortcomings in terms of security, privacy, and storage of massive amounts of data. First, from the viewpoint of security and privacy, the transmission of encrypted data is bound to cause traffic wastage. Second, the massive amount of data stored in the cloud inevitably leads to data redundancy. To address the conflict between semantic security and data redundancy, this study formalizes a secure scheme for duplicated data that can prevent unnecessary storage and traffic on the cloud. This method ensures that an authorized intelligent connected vehicle (ICV) can logically outsource and retrieve data regardless of duplication. Under the premise of semantic security, a two-party deduplication protocol is constructed in terms of the homomorphic number theory research unit (NTRU). Our experimental results show that the proposed scheme is not only secure, but also able to provide complete semantic privacy without any third party. In the context of large-scale real-time IoV, this study not only provides a safe and reliable solution for data deduplication but also optimizes the efficiency of the solution.
Cross Modal Facial Image Synthesis Using a Collaborative Bidirectional Style Transfer Network

In this paper, we present a novel collaborative bidirectional style transfer network based on generative adversarial network (GAN) for cross modal facial image synthesis, possibly with large modality gap. We think that representation decomposed into content and style can be effectively exploited for cross modal facial image synthesis. However, we have observed that unidirectional application of decomposed representation based style transfer in case of large modality gap does not work well for this purpose. Unlike existing image synthesis methods that typically formulate image synthesis as an unidirectional feed forward mapping, our network utilizes mutual interaction between two opposite mappings in a collaborative way to address complex image synthesis problem with large modality gap. The proposed bidirectional network aligns shape content from two modalities and exchanges their appearance styles using feature maps of the layers in the encoder space. This allows us to effectively retain the shape content and transfer style details for synthesizing each modality. Focusing on facial images, we consider facial photo, sketch, and color-coded semantic segmentation as different modalities. The bidirectional synthesis results for the pairs of these modalities show the effectiveness of the proposed approach. We further apply our network to style-content manipulation to generate multiple photo images with various appearance styles for a same content shape. The proposed method can be adopted for solving other cross modal image synthesis tasks. The dataset and source code are available at https://github.com/kamranjaved/Bidirectional-style-transfer-network.
Artificial Intelligence in Finance: Possibilities and Threats

Artificial intelligence (AI) alongside one of its main subsets, machine learning (ML), is no longer a sheer propaganda, it has nearly become a household name, though the use of the term AI by the public and at times technologists is often a misnomer. This paper explores AI and ML, outlining the main categories of extensive ML algorithmic techniques. Importantly, it provides handy timeline and distinction between the duo, whilst also introducing multiple lens views as to their potentials in the finance industry, covering the triad of financial, regulatory and insurance technologies (FinTech, RegTech, InsurTech). Certainly, AI/ML has found practical applications in finance; whether it is generating insights on customer spending, obtaining informed underwriting risk outcomes, detecting anomalous fiscal transactions or interacting with customers using natural language, AI/ML potentials in finance is gaining significant momentum in today’s world of near ubiquity Internet of Things (IoT), advanced computing and telecommunication technologies. Without downplaying the potential capabilities, what is less certain however is whether there are any frontiers to its applications in finance, and whether it will provide panaceas to the pressing challenges, especially in relation to transparency from a collective viewpoint of AI/ML solution design, development and implementation.
The Dilemma and Countermeasures of Music Education under the Background of Big Data

The fundamental subject matter in the area of quality research in education is music education, which is significant. Based on a big data theory, this study proposes a data management platform of music education that can use data analysis techniques to perform analysis and research on the situation facing music education. According to the layered concept through a demand analysis of the music education big data analysis platform, the model divides the platform into a data support layer, data storages and calculation layer, platform function layers, and platform application layer. Based on a platform function layer, the technical route is formulated, and functional modules are designed to solve the issue of quantitative data analysis. The plan uses the data aggregating component to combine music data into a real-time data stream for 3D visualization simulation and then distributes a data stream using messaging middleware. Through a real-time computing framework, the downstream completes a real-time calculation of musical information. Weak and strong real-time needs for music apps can be processed using features. The greatest average relative inaccuracy of the model, as determined by testing a simulation verification module, is 5.1374 percent, and getting the specifics of the visualization work using getViewTaskInfo is a trustworthy technique. It successfully encourages the capacity to overcome the challenges of music education in order to achieve the accuracy standards of high-quality music education.
LPAdaIN: Light Progressive Attention Adaptive Instance Normalization Model for Style Transfer

To improve the generation quality of image style transfer, this paper proposes a light progressive attention adaptive instance normalization (LPAdaIN) model that combines the adaptive instance normalization (AdaIN) layer and the convolutional block attention module (CBAM). In the construction of the model structure, first, a lightweight autoencoder is built to reduce the information loss in the encoding process by reducing the number of network layers and to alleviate the distortion of the stylized image structure. Second, each AdaIN layer is progressively applied after the three relu layers in the encoder to obtain the fine-grained stylized feature maps. Third, the CBAM is added between the last AdaIN layer and the decoder, ensuring that the main objects in the stylized image are clearly visible. In the model optimization, a reconstruction loss is designed to improve the decoder’s ability to decode stylized images with more precise constraints and refine the structure of the stylized images. Compared with five classical style transfer models, the LPAdaIN is visually shown to more finely apply the texture of the style image to the content image, in order to obtain a stylized image, in which the main objects are clearly visible and the structure can be maintained. In terms of quantitative metrics, the LPAdaIN achieved good results in running speed and structural similarity.
An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images

Computer-aided diagnosis (CAD) systems can be used to process breast ultrasound (BUS) images with the goal of enhancing the capability of diagnosing breast cancer. Many CAD systems operate by analyzing the region-of-interest (ROI) that contains the tumor in the BUS image using conventional texture-based classification models and deep learning-based classification models. Hence, the development of these systems requires automatic methods to localize the ROI that contains the tumor in the BUS image. Deep learning object-detection models can be used to localize the ROI that contains the tumor, but the ROI generated by one model might be better than the ROIs generated by other models. In this study, a new method, called the edge-based selection method, is proposed to analyze the ROIs generated by different deep learning object-detection models with the goal of selecting the ROI that improves the localization of the tumor region. The proposed method employs edge maps computed for BUS images using the recently introduced Dense Extreme Inception Network (DexiNed) deep learning edge-detection model. To the best of our knowledge, our study is the first study that has employed a deep learning edge-detection model to detect the tumor edges in BUS images. The proposed edge-based selection method is applied to analyze the ROIs generated by four deep learning object-detection models. The performance of the proposed edge-based selection method and the four deep learning object-detection models is evaluated using two BUS image datasets. The first dataset, which is used to perform cross-validation evaluation analysis, is a private dataset that includes 380 BUS images. The second dataset, which is used to perform generalization evaluation analysis, is a public dataset that includes 630 BUS images. For both the cross-validation evaluation analysis and the generalization evaluation analysis, the proposed method obtained the overall ROI detection rate, mean precision, mean recall, and mean F1-score values of 98%, 0.91, 0.90, and 0.90, respectively. Moreover, the results show that the proposed edge-based selection method outperformed the four deep learning object-detection models as well as three baseline-combining methods that can be used to combine the ROIs generated by the four deep learning object-detection models. These findings suggest the potential of employing our proposed method to analyze the ROIs generated using different deep learning object-detection models to select the ROI that improves the localization of the tumor region.
Occupancy Reward-Driven Exploration with Deep Reinforcement Learning for Mobile Robot System

This paper investigates the solution to a mobile-robot exploration problem following autonomous driving principles. The exploration task is formulated in this study as a process of building a map while a robot moves in an indoor environment beginning from full uncertainties. The sequence of robot decisions of how to move defines the strategy of the exploration that this paper aims to investigate, applying one of the Deep Reinforcement Learning methods, known as the Deep Deterministic Policy Gradient (DDPG) algorithm. A custom environment is created representing the mapping process with a map visualization, a robot model, and a reward function. The actor-critic network receives and sends input and output data, respectively, to the custom environment. The input is the data from the laser sensor, which is equipped on the robot. The output is the continuous actions of the robot in terms of linear and angular velocities. The training results of this study show the strengths and weaknesses of the DDPG algorithm for the robotic mapping problem. The implementation was developed in MATLAB platform using its corresponding toolboxes. A comparison with another exploration algorithm is also provided.
Application of knowledge graph in power system fault diagnosis and disposal: A critical review and perspectives

Knowledge graph (KG) has good knowledge expression ability and interpretation, and its application to power system fault diagnosis and disposal can effectively integrate data of the whole life cycle of equipment and form a novel knowledge-driven operation and maintenance management mode. This is crucial to assist dispatchers in fault disposal and effectively improve the power system emergency handling capability and dispatch intelligence level. This paper conducts a systematic review and summary of the application of KG in power system fault diagnosis and disposal, so as to provide an adequate and comprehensive guide for further research in this field. Firstly, the definition, status, and classification of KGs are systematically described, and the general process of KG construction is sorted out. Secondly, the basic framework, construction process, key technologies, and typical practices of KG for power system fault diagnosis and disposal are summarized and reviewed in detail. Ultimately, several challenges, opportunities, and perspectives of KG in this field are carefully presented.