Reviewerly/exHarmony huggingface dataset:
Tree-Like Branching Network for Single Image Super-Resolution with Divide-and-Conquer

In this paper, we propose a tree-like branching network for image super-resolution. Specifically, the network consists of information divide-and-conquer groups (IDCG) to preserve the low-frequency structure of images as well as restore high-frequency information. The kernel of IDCG contains several essential components: (a) a simple attention module and an effective residual attention module to maintain low-frequency structures and restore high-frequency information, (b) a novel local merge cell alleviates information redundancy that flexibly and adaptively fuses multiple informative features from different states. Lastly, a multi-scale aggregation unit is designed to improve the final reconstruction. Through a series of experiments, we prove that our method is more effective than previous state-of-the-art results in both quantitative and qualitative evaluation.
A multi- piecewise Sine chaotic map and its performance analysis

Sine chaotic map is another type of chaotic map, but the map has problems such as small parameter range and uneven sequence distribution. In view of this, this paper proposes a multi-piecewise Sine chaotic map (MPSC) based on the idea of multi-piecewise representation of chaotic map. MPSC uses modular arithmetic, and can make the generated chaotic sequence more uniform by increasing in the number of Piecewise interval and control parameters. The experimental results show that MPSC not only has a larger parameter range, better randomness and initial-value sensitivity, but also increases the range of chaos interval with the increase of the number of segments and control parameters, and the chaos performance is further improved.
Data preprocessing techniques for MRI brain scans using deep learning models

In recent years, medical diagnosis has improved with computer-aided diagnosis systems. In the medical imaging field, preprocessing of the image is a most exciting and challenging task to provide accurate details for the diagnosis process, such as tumor identification, segmentation, noise removal, etc. Brain tumor segmentation is the most demanding task with the presence of noise or unwanted signals. These factors make the manual diagnosis process a tedious job. Researchers suggest several automatic preprocessing methods that improve the structural quality and preserve the edges of MR (Magnetic Resonance) brain images to overcome this issue. This chapter contains the several preprocessing techniques for magnetic resonance image (MRI) such as image augmentation to enhance the contrast of the MR brain image and denoising technique to degrade the noise appeared in the MR brain images. This method utilizes the modified nonlocal means (NLM) filter with adaptive window size (MNLM-AW) on the grayscale intensity difference (GID) with graphics processing unit (GPU). The transformation between traditional NLM and proposed MNLM-AW algorithm has the different sizes of search window (SWS) for degrading the noise. The traditional NLM has 3 × 3 or 7 × 7 SWS and proposed MNLM-AW has adaptive SWS (ASWS) 3 × 3, 7 × 7, and 9 × 9. Hence, the proposed method has MNLM-AW with ASWS based on the GID to remove noise effectively. MNLM-AW preserves the better edge details in the MR brain image compared to existing methods. Finally, a matrix-based edge preservation technique helps to preserve and prove that the proposed work gives more accuracy than existing methods. The experiments are done using Brain web, internet brain segmentation repository and BraTS-2012 datasets with validation metrics such as structure similarity index measure and peak-signal-to-noise-ratio. As a result, MNLM-AW is compared with available preprocessing approaches such as denoising, edge preservation, contrast enhancement, and image augmentation methods. We prove that our proposed MNLM-AW produces better outcomes than the existing methods.
Deep motion‐compensation enhancement in video compression

This work introduces the multiframe motion-compensation enhancement network (MMCE-Net), a deep-learning tool aimed at improving the performance of current video coding standards based on motion-compensation, such as H.265/HEVC. The proposed method improves the inter-prediction coding efficiency by enhancing the accuracy of the motion-compensated frame and thereby improving the rate-distortion performance. MMCE-Net is a neural network that jointly exploits the predicted coding unit and two co-located coding units from previous reference frames to improve the estimation of the temporal evolution of the scene. This letter describes the architecture of MMCE-Net, how it is integrated into H.265/HEVC and the corresponding performance.
A comparative study on various pre-processing techniques and deep learning algorithms for text classification

Pre-processing is the primary technique employed in sentiment analysis and selecting the suitable techniques for the corresponding application can increase the classifier accuracy. It reduces the complexity innate in the raw data which makes the classifier to learn faster and precisely. Despite its importance, the pre-processing in polarity deduction has not attained much attention in the deep learning literature. So, in this paper, 13 popularly used pre-processing techniques are evaluated on three different domain online user review datasets. For evaluating the impact of each pre-processing technique, four deep neural networks are utilised, and they are auto-encoder, convolution neural network (CNN), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM). The purpose of this paper is to identify the appropriate pre-processing techniques and the best classifier which achieves higher accuracy. Experimental results of this study show that using appropriate pre-processing techniques can significantly improve the classifier accuracy. Also, it is noted that Bi-LSTM model achieves higher accuracy rate than the remaining neural networks.
Modeling Centralised Automotive E/E Software Architectures

The automotive domain is living an exciting period triggered by challenging business and technology drivers, like electrification, autonomous driving, over-the-air software updates and connected vehicles, just to mention a few. This profoundly impacted the electric and electronic automotive architecture and pushed more and more manufacturers to shift towards more centralised electric and electronic architectures for their future automotive software systems. In this work, we first analyse the readiness of four main automotive architectural languages to represent novel vehicle centralised architectures. Based on the analysis results, we propose an extension to one of these languages to fully support the modelling of technical reference architectures for centralised vehicles.We validate the proposed extension using workshops with experts in the automotive domain and using an automotive use case describing an autonomous quarry.
A Complementary Resource Relation of Concurrence and Roughness for a Two Qubits State

Quantum resources lie at the core of quantum computation as they are responsible for the computational advantage in many tasks. The complementary relations are a pathway to understand how the physical quantities are related. This work presents a complementary relationship for the quantumness of a two qubits state and the degree of entanglement, respectively measured by Roughness and Concurrence. There is a finite R 2 region which can be used as characterization of subspaces of two qubits. Heat maps for accumulation points over the surfaces of pure and non-pure randomly sampled states are presented and an investigation of a decoherence process using a bath under zero and non-zero temperature is performed using the Roughness vs Concurrence parameter space.
An End-to-End Approach for Seam Carving Detection Using Deep Neural Networks

Seam carving is a computational method capable of resizing images for both reduction and expansion based on its content, instead of the image geometry. Although the technique is mostly employed to deal with redundant information, i.e., regions composed of pixels with similar intensity, it can also be used for tampering images by inserting or removing relevant objects. Therefore, detecting such a process is of extreme importance regarding the image security domain. However, recognizing seam-carved images does not represent a straightforward task even for human eyes, and robust computation tools capable of identifying such alterations are very desirable. In this paper, we propose an end-to-end approach to cope with the problem of automatic seam carving detection that can obtain state-of-the-art results. Experiments conducted over public and private datasets with several tampering configurations evidence the suitability of the proposed model.
Interactive Technology Integrating with the Physically Active Human Body: Learnings from Rider and Ebike Integration

Supporting the exerting body through interactive technology has been the focus of human-computer integration research labelled as “integrated exertion”. Due to advances in technology where integration systems can sense and interpret data in real-time, systems can participate in the experience alongside the user. In this chapter, we discuss integrated exertion through the lens of an eBike rider, where movement data generated from the rider's leaning forward posture results in increased engine support to afford a “superpower”-like experience. We present our learnings from our case study from the perspective of how to practically assist designers and researchers in designing integrated exertion by offering five take-aways that aim to help users to: “self-attribute superpowers acquired from the machine's functionality”, “map movement to machine actuation to facilitate integration”, “move simultaneously with the machine as one continuous expression”, “exploit visceral qualities to ignite imaginaries of integration” and “amplify the sensation of being integrated with the system by engaging other senses that complement the experience”. Finally, we provide reflections on supporting the exerting body through a human-computer integration approach towards enabling engaging integrated exertion experiences.
Dynamic Mixture Vector Autoregressions with Score-Driven Weights

We propose a novel dynamic mixture vector autoregressive (VAR) model in which time-varying mixture weights are driven by the predictive likelihood score. Intuitively, the state weight of the k-th component VAR model in the subsequent period is increased if the current observation is more likely to be drawn from this particular state. The model is not limited to a specific distributional assumption and allows for straightforward likelihood-based estimation and inference. We conduct a Monte Carlo study and find that the score-driven mixture VAR model is able to adequately filter the mixture dynamics from a variety of different data generating processes which most other observation-driven dynamic mixture VAR models cannot appropriately cope with. Finally, we illustrate our approach by an application where we model the conditional joint distribution of economic and financial conditions and derive generalized impulse responses.
Neural Dynamic Programming with Application to Wheeled Mobile Robot

In this paper we propose a new application sensor-based navigation method for navigation of wheeled mobile robot, based on neural dynamic programming (NDP). We discuss a sensor-based approach to path design and control of wheeled mobile robot (WMR) in an unknown 2-D environment with static obstacles. A strategy of navigation is developed including two main behaviors: a reaching the middle of a collision-free space behavior, and a goal-seeking. The NDP navigator which is the main theme of this paper, can fuse behaviors so that the mobile robot can go for the goal position without colliding with obstacles for the concave and convex obstacles. This solution is based on the reinforcement learning (RL) method of actor-critic architecture for continuous-time, and does not require pre-learning and working on-line. Computer simulations have been conducted to illustrate the performance of the proposed solution by a series of experiments.
Steam Game Discount Prediction Using Machine Learning Methods

Data classification came into widespread in various fields in recent years. However, basically no one paid attention to the discount prediction on the biggest game website in the world-Steam. In this work, we aim to predict whether the game on Steam is on discount or not. First, we collect data from the official website and model them via machine learning based models, including Logistic Regression and Random Forest, test their performances via the true data, and we conclude that the Random Forest achieves the best performance, which reaches the accuracy of 79.5%. This model will help gamers to save money while making purchases. Moreover, we can predict the popularity of game in our future work.
Support Vector Machine Classifier For Prediction Of Breast Malignancy Using Wisconsin Breast Cancer Dataset

Cancer is the world's second largest cause of death. In 2018, 9.6 million people died from cancer. In any medical sickness, breast cancer is one of the most delicate and endemic diseases. This is one of the primary causes of female death in the world. Breast cancer kills one out of every eleven women around the world. "Early detection equals improved odds of survival," says a well-known cancer adage. As a result, early detection is essential for successfully preventing breast cancer and lowering morality. Breast Cancer is a type of cancer that affects one of the most significant issues that humanity has faced in recent decades has been diagnosis and prediction. Cancer detection that is accurate can save millions of lives. Effective technologies for diagnosing malignant breasts aid healthcare providers in diagnosing and treating patients in a fast and accurate manner. Experiments were carried out in this study to categorise breast cancer as benign or malignant using the Wisconsin Diagnosis Breast Cancer (WDBC) database. Support Vector Machine is a supervised learning technique (SVM). The SVM classifier's classification performance is evaluated. Experiments demonstrate that the SVM model has a fantastic performance, with a classification accuracy of 96.09 percent on the testing subset.
CURIOCITY Framework: Managing Heterogeneous Cultural Heritage Data

The huge amount of data managed by information tools and services of cultural heritage, demands the use of a more complex and formal knowledge representation, such as ontologies. However, existing platforms, aimed at supporting the management of semantic knowledge and the development of applications are limited, and mainly focused on serving a single user application. In this context, we propose CURIOCITY Framework, a platform to manage semantic knowledge and support applications in the context of cultural heritage. We implement a first version of our framework to demonstrate that it meets the objectives of storage, data management, mapping, population, and query handling. Moreover, a time response evaluation shows that it is possible to attend queries in an appropriate way according to application requirements.
Bi-directional Joint Embedding of Encyclopedic Knowledge and Original Text for Chinese Medical Named Entity Recognition

Named entity recognition is a basic task in natural language processing and can be used for building knowledge graph. Named entities generally refer to the entities in the text that have a specific meaning or a strong reference. In medical area, a great deal of medical information exists in the form of electronic text and we can acquire valuable part by this method. In this article, we take extra knowledge information of medical encyclopedia into account and we associate the original text in the named entity recognition task with its encyclopedic knowledge to enhance the ability of entity recognition through the establishment of the connection and interaction of the joint-network. Based on this, (1) the attention distribution based on medical encyclopedia knowledge was generated with entity embedding and context information in the sentence, which can be directly used to generate encyclopedia knowledge subnet; (2) the bi-directional joint embedding model integrates knowledge subnet and text subnet into one network so that the connection between them are considered. We conduct experiments on tow electronic medical record datasets proposed by CCKS. The experimental result shows that our proposed method has achieved a better result.
Heterogeneous Multidomain Recommender System Through Adversarial Learning

To solve the user data sparsity problem, which is the main issue in generating user preference prediction, cross-domain recommender systems transfer knowledge from one source domain with dense data to assist recommendation tasks in the target domain with sparse data. However, data are usually sparsely scattered in multiple possible source domains, and in each domain (source/target) the data may be heterogeneous, thus it is difficult for existing cross-domain recommender systems to find one source domain with dense data from multiple domains. In this way, they fail to deal with data sparsity problems in the target domain and cannot provide an accurate recommendation. In this article, we propose a novel multidomain recommender system (called HMRec) to deal with two challenging issues: 1) how to exploit valuable information from multiple source domains when no single source domain is sufficient and 2) how to ensure positive transfer from heterogeneous data in source domains with different feature spaces. In HMRec, domain-shared and domain-specific features are extracted to enable the knowledge transfer between multiple heterogeneous source and target domains. To ensure positive transfer, the domain-shared subspaces from multiple domains are maximally matched by a multiclass domain discriminator in an adversarial learning process. The recommendation in the target domain is completed by a matrix factorization module with aligned latent features from both the user and the item side. Extensive experiments on four cross-domain recommendation tasks with real-world datasets demonstrate that HMRec can effectively transfer knowledge from multiple heterogeneous domains collaboratively to increase the rating prediction accuracy in the target domain and significantly outperforms six state-of-the-art non-transfer or cross-domain baselines.
FORMING THE PROFESSIONAL DIRECTION OF PERSONALITY IN THE CONDITIONS OF THE CHANGING PROFESSIONAL WORLD

The problem of professional direction of modern students is discussed. Optimal level of professional direction development, level of mastering the activities of the future specialist significantly depend on the motivation to studies. Many researches show that personality professional direction is one of the main indicators of personal ability to work. The scientists developed theoretical preconditions to solve the problem under study. At the same time there are contradictions to be solved. Consequently, the concept of “profession direction” has been specified, and its structural components have been revealed. The factors influencing personal motivation to choosing a profession have been revealed. The authors emphasize the necessity to develop a model of personal professional direction and specify its components. The results of respondents’ inquiry are presented that made it possible to define students’ professional direction. Practical experiences of career guidance counseling are described.
Deep Multimodal Complementarity Learning

Complementarity plays a significant role in the synergistic effect created by different components of a complex data object. Complementarity learning on multimodal data has fundamental challenges of representation learning because the complementarity exists along with multiple modalities and one or multiple items of each modality. Also, an appropriate metric is needed for measuring the complementarity in the representation space. Existing methods that rely on similarity-based metrics cannot adequately capture the complementarity. In this work, we propose a novel deep architecture for systematically learning the complementarity of components from multimodal multi-item data. The proposed model consists of three major modules: 1) unimodal aggregation for extracting the intramodal complementarity; 2) cross-modal fusion for extracting the intermodal complementarity at the modality level; and 3) interactive aggregation for extracting the intermodal complementarity at the item level. To quantify complementarity, we utilize the TUBE distance metric to measure the difference between the composited data object and its label in the representation space. Experiments on three real datasets show that our model outperforms the state-of-the-art by +6.8% of mean reciprocal rank (MRR) on object classification and +3.0% of MRR on hold-out item prediction. Qualitative analyses reveal that complementarity is significantly different from similarity.
Hard Edges: Hardware-Based Control-Flow Integrity for Embedded Devices

Control-Flow Integrity (CFI) is a popular technique to defend against State-of-the-Art exploits, by ensuring that every (indirect) control-flow transfer points to a legitimate address and it is part of the Control-flow Graph (CFG) of a program. Enabling CFI in real systems is not straightforward, since in many cases the actual CFG of a program can only be approximated. Even in the case where there is perfect knowledge of the CFG, ensuring that all return instructions will return to their actual call sites, without employing a shadow stack, is questionable. In this work, we explore the implementation of a full-featured CFI-enabled Instruction Set Architecture (ISA) on actual hardware. Our new instructions provide the finest possible granularity for both intra-function and inter-function Control-Flow Integrity. We implement hardware-based CFI (HCFI) by modifying a SPARC SoC and evaluate the prototype on an FPGA board by running SPECInt benchmarks instrumented with a fine-grained CFI policy. HCFI can effectively protect applications from code-reuse attacks, while adding less than 1% average runtime and 2% power consumption overhead, making it particularly suitable for embedded systems.
TAGNet: Learning Configurable Context Pathways for Semantic Segmentation

State-of-the-art semantic segmentation methods capture the relationship between pixels to facilitate contextual information exchange. Advanced methods utilize fixed pathways for context exchange, lacking the flexibility to harness the most relevant context for each pixel. In this paper, we present Configurable Context Pathways (CCPs), a novel model for establishing pathways for augmenting contextual information. In contrast to previous pathway models, CCPs are learned, leveraging configurable regions to form information flows between pairs of pixels. We propose TAGNet to adaptively configure the regions, which span over the entire image space, driven by the relationships between the remote pixels. Subsequently, the information flows along the pathways are updated gradually by the information provided by sequences of configurable regions, forming more powerful contextual information. We extensively evaluate the traveling, adaption, and gathering (TAG) stages of our network on the public benchmarks, demonstrating that all of the stages successfully improve the segmentation accuracy and help to surpass the state-of-the-art results. The code package is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/dilincv/TAGNet</uri> .
Classical and quantum walks on paths associated with exceptional Krawtchouk polynomials

Classical and quantum walks on some finite paths are introduced. It is shown that these walks have explicit solutions given in terms of exceptional Krawtchouk polynomials, and their properties are explored. In particular, fractional revival is shown to take place in the corresponding quantum walks.