Reviewerly/exHarmony huggingface dataset:
FIPHN: Feature-Integrated Patch-Hierarchical Network for Single Image Reflection Removal

Single image reflection removal is a challenging task because important features will be completely obstructed by strong reflections. During removing strong reflections, existing methods focus on mining global contextual features, which will lose some useful local features and fail to remove strong reflections completely. To solve this problem, we propose a feature-integrated patch-hierarchical network (FIPHN), which progressively removes reflections by focusing on integrating global and local features. Specifically, we design a layer-wise feature integration module (LFIM) to integrate global and local features layer by layer across multi-scale stages, effectively enhancing the representation ability of features. Moreover, we design a patch-wise feature integration module (PFIM) to extract contextual features between adjacent patches, avoiding the loss of important features. Meanwhile, with the guidance of ground-truth images, PFIM provides intermediate supervision signals to promote the subnetwork training at each stage. Experimental results show the superiority of our method compared with existing methods.
Providing Location Information at Edge Networks: A Federated Learning-Based Approach

Recently, the development of mobile edge computing has enabled exhilarating edge artificial intelligence (AI) with fast response and low communication costs. The location information of edge devices is essential to support the edge AI in many scenarios, like smart home, intelligent transportation systems, and integrated health care. Taking advantage of deep learning intelligence, the centralized machine learning (ML)-based positioning technique has received heated attention from both academia and industry. However, some potential issues, such as location information leakage and huge data traffic, limit its application. Fortunately, a newly emerging privacy-preserving distributed ML mechanism, named federated learning (FL), is expected to alleviate these concerns. In this article, we illustrate a framework of FL-based localization systems as well as the involved entities at edge networks. Moreover, the advantages of such a system are elaborated. On the practical implementation of it, we investigate the field-specific issues associated with system-level solutions, which are further demonstrated over a real-word database. Moreover, future challenging open problems in this field are outlined.
RIANet: Road Graph and Image Attention Network for Urban Autonomous Driving

In this paper, we present a novel autonomous driving framework, called a road graph and image attention network (RIANet), which computes the attention scores of objects in the image using the road graph feature. The process of the proposed method is as follows: First, the feature encoder module encodes the road graph, image, and additional features of the scene. The attention network module then incorporates the encoded features and computes the scene context feature via the attention mechanism. Finally, the low-level controller mod-ule drives the ego-vehicle based on the scene context feature. In the experiments, we use an urban scene driving simulator named CARLA to train and test the proposed method. The results show that the proposed method outperforms existing autonomous driving methods.
Evaluating the Security of Merkle-Damgård Hash Functions and Combiners in Quantum Settings

In this work, we evaluate the security of Merkle-Damgård (MD) hash functions and their combiners (XOR and concatenation combiners) in quantum settings. Two main quantum scenarios are considered, including the scenario where a substantial amount of cheap quantum random access memory (qRAM) is available and where qRAM is limited and expensive to access. We first convert a rich set of known tools invented for generic attacks in the classical setting to quantum versions. That includes Joux’s multi-collision, expandable message, diamond structure, and interchange structure. With these basic tools in hand, we then present generic quantum attacks on the MD hash functions and hash combiners, and carefully analyze the complexities under both quantum scenarios. The considered securities are fundamental requirements for hash functions, including the resistance against collision, (second-)preimage, and herding attacks. The results are consistent with the conclusions in the classical setting, that is, the considered resistances of the MD hash functions and their combiners are far less than ideal, despite the significant differences in the expected security bounds between the classical and quantum settings. Particularly, the generic attacks can be improved significantly using quantum computers under both scenarios. These results serve as an indication that classical hash constructions require careful security re-evaluation before being deployed to the post-quantum cryptography schemes.
Prediction Of Cardiovascular Diseases Using Neural Networks And Machine Learning

The field of data analysis, which is experiencing rapid growth, plays an important role in the field of health care. The health industry has become an enormous enterprise. The healthcare industry generates massive volumes of data on a day-to-day basis. This data makes it possible to extract hidden information, which is helpful for anticipating the infection as early as possible. In the medical field, the anticipation of cardiac disease is treated as one of the multifaceted tasks. With a surge in stroke rates in the younger age groups, we must set up a system to detect the symptoms of a stroke at an early stage and therefore prevent it. It is inconvenient for an ordinary man to be frequently subjected to expensive tests such as ECG. As a result, we must put in place a practical and reliable system to predict the risks of heart disease. Hence, we propose developing an app that can predict the vulnerability of a heart disease based on basic symptoms such as age, sex, pulse, cholesterol, and so on. The machine learning algorithm Random Forest turned out to be the most precise and reliable algorithm and, as a consequence, is used in the proposed system. The impact of AI is vital in anticipating the infection. Machine learning plays a crucial part in predicting cardiac arrest. In this project, several machine learning methods were used and their individual performances were compared to predict heart attack. The different techniques are, namely, decision trees, random forest, XGBoost, and neural networks.
LODChain: Strengthen the Connectivity of Your RDF Dataset to the Rest LOD Cloud

It is not an easy task for a data owner to publish a dataset as Linked Data with connections to existing datasets since there are too many datasets, thus it is hard to find the related ones, to download them and to check their content (let alone to apply entity matching over them). However, the connections with other datasets are important for discoverability, browsing, and querying in general. To alleviate this problem in this paper we introduce LODChain, a service that can help a provider to strengthen the connections between his/her dataset and the rest of datasets. LODChain finds the common entities, schema elements and triples among the dataset at hand and hundreds of LOD Datasets and through equivalence reasoning it suggests to the user various inferred connections, as well as related datasets. In addition, it detects erroneous mappings, and offers various content-based dataset discovery services, for enabling the enrichment of datasets’ content. The key difference with the existing approaches is that they are metadata-based, while what we propose is data-based. We present an implementation of LODChain, and we report various experimental results over real and synthetic datasets.
A Reusable Methodology for Player Clustering Using Wasserstein Autoencoders

Identifying groups of player behavior is a crucial step in understanding the player base of a game. In this work, we use a recurrent autoencoder to create representations of players from sequential game data. We then apply two clustering algorithms–k-means and archetypal analysis–to identify groups, or clusters, of player behavior. The main contribution to this work is to determine the efficacy of the Wasserstein loss in the autoencoder, evaluate the loss’s effect on clustering, and provide a methodology that game analysts can apply to their games. We perform a quantitative and qualitative analysis of combinations of models and clustering algorithms and determine that using the Wasserstein loss results in better clustering.
Gated Dual Hypergraph Convolutional Networks for Recommendation with Self-supervised Learning

Recommender systems have become a crucial intelligent tool, which provides users with personalized services. Graph learning-based recommendation methods treat user-item interactions and the item transitions as pairwise relations but ignore the complex, higher-order interaction information between nodes. Moreover, since most users often interact with few or even no items, graph learning-based recommendation methods suffer from the data sparsity problem, as well as the unbalanced distribution of edges and nodes. To tackle these issues, we propose a Dual Hypergraph-based Self-supervised Learning recommendation model, named DHSL-GM. Specifically, we derive two dual hypergraphs from the user-item bipartite graph, which models the complex high-order user-item interactions by using hypergraph convolution with spectral hypergraph convolution operator. Meanwhile, we design a gated network-based message passing mechanism to dynamically guide message propagation, addressing the problem of the unbalanced distribution of edges and nodes. In addition, to alleviate the data sparsity problem, we design another dual hypergraph convolutional network based on a node discard strategy, which innovatively integrates self-supervised learning into the training of the hypergraph convolutional network. Experimental results on several real datasets demonstrate the superiority and effectiveness of the proposed model.
Optical character recognition with transformers and CTC

Text recognition tasks are commonly solved by using a deep learning pipeline called CRNN. The classical CRNN is a sequence of a convolutional network, followed by a bidirectional LSTM and a CTC layer. In this paper, we perform an extensive analysis of the components of a CRNN to find what is crucial to the entire pipeline and what characteristics can be exchanged for a more effective choice. Given the results of our experiments, we propose two different architectures for the task of text recognition. The first model, CNN + CTC, is a combination of a convolutional model followed by a CTC layer. The second model, CNN + Tr + CTC, adds an encoder-only Transformers between the convolutional network and the CTC layer. To the best of our knowledge, this is the first time that a Transformers have been successfully trained using just CTC loss. To assess the capabilities of our proposed architectures, we train and evaluate them on the SROIE 2019 data set. Our CNN + CTC achieves an F1 score of 89.66% possessing only 4.7 million parameters. CNN + Tr + CTC attained an F1 score of 93.76% with 11 million parameters, which is almost 97% of the performance achieved by the TrOCR using 334 million parameters and more than 600 million synthetic images for pretraining.
The similarity between movie scripts using Multilayer Network Laplacian Spectra Descriptor

In this paper, we investigate the performance of the graph distance measure "Network Laplacian Spectra Descriptor" in comparing the similarity between movie stories. We rely on a multilayer network model to extract three entities of networks (Characters in Scenes, Dialogue Keywords, Scene Location). Then, we compute the distance between the layers regarding the three aspects. We investigate the effectiveness of the measure using the 3-cycle movies of the Scream Saga.
A System Design to Create Mixed 360 Video and 3D Content for Virtual Field Trip

As VR becomes more widely accessible, an increasing number of VR educational solutions have been proposed for remote teaching. However, most of the current virtual reality teaching platforms are not friendly for non-VR educators to create immersive custom scenes. Virtual reality content development still needs expertise and is time-consuming, even with the growing wave of 3D digitization efforts. We present a prototype system to address the challenge of using VR as a tool to enable educators to create immersive collaborative field experiences in a manner that they can build their own instructional content as a combination of real-time 3D assets, immersive video, and interaction. This system combines the advantage of 360 videos and 3D models. 360 video serves as a backdrop to capture environmental detail and dynamic changes. 3D models could be added at runtime via our web-based application for detailed observation and interaction. We present the system performance analysis, lessons learned from system development, and suggestions for further research. We live-streamed a field trip event from the seashore to a local science center with the help of marine biologists and conducted a system performance analysis. We also present system limitations at the current stage and suggestions for future research.
A Survey of Semantic Segmentation Methods in Traffic Scenarios

Semantic segmentation has always been a very challenging research topic in computer vision and deep learning and has extensive applications in real-life scenarios. With the development of computing hardware and deep learning technology, researchers have a higher research enthusiasm for semantic segmentation. This work briefly introduces several semantic segmentation models, datasets, and the main issues of traditional semantic segmentation. Afterward, the current mainstream semantic segmentation algorithm and the experimental results were compared. Finally, future research directions of semantic segmentation are presented and discussed.
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks

In today's era of smart cyber-physical systems, Deep Neural Networks (DNNs) have become ubiquitous due to their state-of-the-art performance in complex real-world applications. The high computational complexity of these networks, which translates to increased energy consumption, is the foremost obstacle towards deploying large DNNs in resource-constrained systems. Fixed-Point (FP) implementations achieved through post-training quantization are commonly used to curtail the energy consumption of these networks. However, the uniform quantization intervals in FP restrict the bit-width of data structures to large values due to the need to represent most of the numbers with sufficient resolution and avoid high quantization errors. In this paper, we leverage the key insight that (in most of the scenarios) DNN weights and activations are mostly concentrated near zero and only a few of them have large magnitudes. We propose CoNLoCNN, a framework to enable energy-efficient low-precision deep convolutional neural network inference by exploiting: (1) non-uniform quantization of weights enabling simplification of complex multiplication operations; and (2) correlation between activation values enabling partial compensation of quantization errors at low cost without any run-time overheads. To significantly benefit from non-uniform quantization, we also propose a novel data representation format, Encoded Low-Precision Binary Signed Digit, to compress the bit-width of weights while ensuring direct use of the encoded weight for processing using a novel multiply-and-accumulate (MAC) unit design.
Exploring Word Embedding for Arabic Sentiment Analysis

In Natural Language Processing (NLP), the manual features (part-of-speech tagging, stemming…) might not be helpful sometimes to deciding the feeling expressed in a sentence. That More properties need to be considered. Word embeddings which are the key component for learning the text features, has just started to appear in Arabic sentiment analysis. On the other hand, Deep Neural Networks were widely used recently for this task, especially for the English language. In this paper, we focus on the Tunisian dialect sentiment analysis used on social media using a Convolutional Neural Networks and Bidirectional Long Short-Term Memory. The results show that our models on the publicly available TUNIZI dataset achieved superior performances than the other models applied for the same dataset.
SEMICON: A Learning-to-Hash Solution for Large-Scale Fine-Grained Image Retrieval

In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks. In SEMICON, we first develop a suppression-enhancing mask (SEM) based attention to dynamically localize discriminative image regions. More importantly, different from existing attention mechanism simply erasing previous discriminative regions, our SEM is developed to restrain such regions and then discover other complementary regions by considering the relation between activated regions in a stage-by-stage fashion. In each stage, the interactive channel transformation (ICON) module is afterwards designed to exploit correlations across channels of attended activation tensors. Since channels could generally correspond to the parts of fine-grained objects, the part correlation can be also modeled accordingly, which further improves fine-grained retrieval accuracy. Moreover, to be computational economy, ICON is realized by an efficient two-step process. Finally, the hash learning of our SEMICON consists of both global- and local-level branches for better representing fine-grained objects and then generating binary hash codes explicitly corresponding to multiple levels. Experiments on five benchmark fine-grained datasets show our superiority over competing methods. (Codes are available at https://github.com/NJUST-VIPGroup/SEMICON ).
Towards Quantum Logarithm Number Systems

Quantum computers, which process qubits, offer the promise of spectacular performance improvement over ordinary computers that deal only with classical bits, but there are obstacles to this vision. First, current quantum technology only allows a small number of qubits, and these are susceptible to noise. Second, quantum algorithms must be reversible, which often requires ancillary data that consume precious qubits. Third, interesting algorithms amenable to quantum implementation, such as chemistry simulation, require representing real numbers. Although quantum integer arithmetic has been studied extensively, the few works on quantum floating point demand more ancillary qubits than input data making floating point impractical for current quantum hardware. This paper suggests an alternative to floating point, known as the Logarithmic Number System (LNS), which has proven effective for approximate arithmetic with classical hardware. Reversible LNS multiplication and division are easy and exact with one ancillary qubit. Here we explore the quantum cost of difficult LNS operations (addition and subtraction). LNS offers implementation tradeoffs between accuracy and qubit cost that suggest highly-approximate LNS will be practical on quantum hardware sooner than when quantum technology has improved enough for floating-point to be practical.
How Resilient Are Imitation Learning Methods to Sub-optimal Experts?

Imitation Learning (IL) algorithms try to mimic expert behavior in order to be capable of performing specific tasks, but it remains unclear what those strategies could achieve when learning from sub-optimal data (faulty experts). Studying how Imitation Learning approaches learn when dealing with different degrees of quality from the observations can benefit tasks such as optimizing data collection, producing interpretable models, reducing bias when using sub-optimal experts, and more. Therefore, in this work we provide extensive experiments to verify how different Imitation Learning agents perform under various degrees of expert optimality. We experiment with four IL algorithms, three of them that learn self-supervisedly and one that uses the ground-truth labels (BC) in four different environments (tasks), and we compare them using optimal and sub-optimal experts. For assessing the performance of each agent, we compute two metrics: Performance and Average Episodic Reward. Our experiments show that IL approaches that learn self-supervisedly are relatively resilient to sub-optimal experts, which is not the case of the supervised approach. We also observe that sub-optimal experts are sometimes beneficial since they seem to act as a kind of regularization method, preventing models from data overfitting. You can replicate our experiments by using the code in our GitHub ( https://github.com/NathanGavenski/How-resilient-IL-methods-are ).
Similarity-Based Unsupervised Evaluation of Outlier Detection

The evaluation of unsupervised algorithm results is one of the most challenging tasks in data mining research. Where labeled data are not available, one has to use in practice the so-called internal evaluation, which is based solely on the data and the assessed solutions themselves. In unsupervised cluster analysis, indices for internal evaluation of clustering solutions have been studied for decades, with a multitude of indices available, based on different criteria. In unsupervised outlier detection, however, this problem has only recently received some attention, and still very few indices are available. In this paper, we provide a new internal index based on criteria different from the ones available in the literature. The index is based on a (generic) similarity measure to efficiently evaluate candidate outlier detection solutions in a completely unsupervised way. We evaluate and compare this index against existing indices in terms of quality and run time performance using collections of both real and synthetic datasets.
Role of Artificial Intelligence in Online Shopping and its Impact on Consumer purchasing behaviour and Decision

The E-Commerce Industry has experienced rapid growth over the last decade. Due to the rapid growth of online customers, it is critical to understand their needs and behaviours. Thus, E-Commerce businesses integrated AI (Artificial Intelligence) enabled technology to ascertain customer needs and preferences regarding online products and services. The artificial intelligence monitors the customer’s choice, preferences, taste, and purchasing pattern, the frequency of purchases and the average amount spent over a specified period. It provides detailed customer information to ECommerce Companies. Thus, this information enables businesses to tailor their products and services to their customers’ specific needs and preferences. By providing various strategies for product recommendations, discounts, and multiple offers, the AI assists the customer in selecting and purchasing the best suggested/recommended products and services. The current study collected data from 200 respondents from various socio-economic backgrounds in Haryana. The findings represent the significant association between Artificial Intelligence (Voice and Visual Search, Assortment Intelligence Tools, personal touch with chatbots, Virtual Personal Shoppers, Fake Review Detection, AI-based sales process, localise the customer experience) and Consumer buying behaviour (Trust, Attitude and Perceived Risk) for online shopping. AI has a significant impact on customer purchasing decisions with a positive correlation. The purchasing pattern is measured with the variables: preferred Payment Method, the value of money spent on online shopping, frequency of online shopping and electronic Gadgets used in online shopping.