Reviewerly/exHarmony huggingface dataset:
AR Basketball: Consolidating Fingerstroke-Level Model for a Visuo-Haptic Augmented Reality based Smartphone Application

In the era of extensive progression of smartphones, gaming applications have become an undisputed reality of our routine micro-moments. And when it comes to a realistic gaming experience, Augmented Reality has shown significant advancement to uplift itself from just a fantastical concept to one of the front-runners of todays gaming industry. In this work, we propose a Mobile Augmented Reality (MAR) based smartphone application, namely “AR Basketball” to justify the flexible adjustments required to design an intrinsic reality-based gaming UI. This research is one of the earliest investigations to revisit operators of Finger-stroke Level Model (FLM) for MAR and attempts to map different finger-stroke activities with touch-sensitive AR-based smartphone applications. The complete game was uploaded in Google play store for user feedback after being fused with several other common AR sporting applications developed by one of the authors conducting this research work. The empirical study shows that a redesigned FLM for MAR ensures better task completion time compared to the same action-based gaming applications designed for other platforms.
KELSA: A Knowledge-Enriched Local Sequence Alignment Algorithm for Comparing Patient Medical Records

Sequence alignment methods have the promise to reserve important temporal information in electronic health records (EHRs) for comparing patient medical records. Compared to global sequence alignment, local sequence alignment is more useful when comparing patient medical records. One commonly used local sequence alignment algorithm is Smith-Waterman algorithm (SWA), which is widely used for aligning biological sequence. However directly applying this algorithm to align patient medical records will obtain suboptimal performance since it fails to consider complex situations in EHRs such as the temporality of medical events. In this work, we propose a new algorithm called Knowledge-Enriched Local Sequence Alignment algorithm (KELSA), which incorporates meaningful medical knowledge during sequence alignments. We evaluate our algorithm by comparing it to SWA on synthetic EHR data where the reference alignments are known. Our results show that KELSA aligns better than SWA by inserting new daily events and identifying more similarities between patient medical records. Compared to SWA, KELSA is more suitable for locally comparing patient medical records.
Guided Reinforcement Learning via Sequence Learning

Applications of Reinforcement Learning (RL) suffer from high sample complexity due to sparse reward signals and inadequate exploration. Novelty Search (NS) guides as an auxiliary task, in this regard to encourage exploration towards unseen behaviors. However, NS suffers from critical drawbacks concerning scalability and generalizability since they are based off instance learning. Addressing these challenges, we previously proposed a generic approach using unsupervised learning to learn representations of agent behaviors and use reconstruction losses as novelty scores. However, it considered only fixed-length sequences and did not utilize sequential information of behaviors. Therefore, we here extend this approach by using sequential auto-encoders to incorporate sequential dependencies. Experimental results on benchmark tasks show that this sequence learning aids exploration outperforming previous novelty search methods.
Relational Visual-Textual Information Retrieval

With the advent of deep learning, multimedia information processing gained a huge boost, and astonishing results have been observed on a multitude of interesting visual-textual tasks. Relation networks paved the way towards an attentive processing methodology that considers images and texts as sets of basic interconnected elements (regions and words). These winning ideas recently helped to reach the state-of-the-art on the image-text matching task. Cross-media information retrieval has been proposed as a benchmark to test the capabilities of the proposed networks to match complex multi-modal concepts in the same common space. Modern deep-learning powered networks are complex and almost all of them cannot provide concise multi-modal descriptions that can be used in fast multi-modal search engines. In fact, the latest image-sentence matching networks use cross-attention and early-fusion approaches, which force all the elements of the database to be considered at query time. In this work, I will try to lay down some ideas to bridge the gap between the effectiveness of modern deep-learning multi-modal matching architectures and their efficiency, as far as fast and scalable visual-textual information retrieval is concerned.
A Mathematical Morphology Algorithm for Detection and Counting of Nuclear Tracks

Abstract Solid state nuclear track detection plays an extremely important role in the field of nuclear technology, but artificial cognition is extremely time-consuming and labor-consuming. In this paper, an automatic measurement system of solid state nuclear track is proposed, in which the microscopic image of the nuclear track is obtained by the camera and processed digitally, and then use software to identify the image content. Based on the image restoration theory, this paper preprocesses such as noise elimination and luminance correction, and the mathematical morphology algorithm is used for track counting and parameter estimation. The comparison results between automatic recognition and manual recognition show that the algorithm can effectively identify track points from images under different conditions and improve work efficiency.
An Accurate Detection System Based on the Convolutional Neural Network

Abstract The purpose of this project is trying to detect tumors by computer based on deep learning techniques when a picture of a tumor is shown. In this research, a fast and accurate colon cancer detection is proposed, which means this research can dramatically increase the speed of diagnosis and can also improve the accuracy of confirming a diagnosis. During the experiment, a Convolutional Neural Network (CNN) structure akin to that of VGG Net and ResNet was built. A GPU computer with two 2080 Ti GPUs was used for training. The result of training produced 94% accuracy with a loss lower than 10%. Respectively, this result improved over 10% of accuracy compared to the detection by human eyes. Lastly, this program can be used by any computer to predict the tumor, which allows it transits to a practical tool in the future.
Scalable In-Memory Graph Pattern Matching on Symmetric Multiprocessor Systems

Graph-structured data can be found in nearly every aspect of today’s world which contributes to an increasing importance of this data structure for storing and processing data. From a processing perspective, finding comprehensive patterns in graph-structured data is a processing primitive in a variety of applications, such as fraud detection, biological engineering or social graph analytics. On the hardware side, multiprocessor systems—consisting of multiple processors in a single scale-up server—are the next important wave on top of multi-core systems. In particular, symmetric multiprocessor systems (SMP) are characterized by the fact, that each processor has the same architecture, e.g., every processor is a multi-core and all multiprocessors share a common and huge main memory space. Moreover, large SMPs will feature a non-uniform memory access (NUMA), whose impact on the design of efficient data processing concepts is considerable. In this paper, we give an overview of NeMeSys, our system for scalable near-memory graph pattern matching (GPM) on SMPs. NeMeSys is built on a synthesis of well-known concepts of database systems including a set of graph-tailored and hardware-oriented optimization techniques for scalable GPM on SMPs.
VR Based Underwater Museum of Andaman and Nicobar Islands

Nowadays, world is full of technology and inventions. Many computer applications are developed in the favour of mankind and to ease human work. As a top trending, technology acts a human organ. Every day wakes up using technology and doze off using a technology. In short, technology is moving along with us in our day-to-day life. So it is all about how we are going to use the technology. Speaking of technology, we can see many technologies ruling the world. Like Big Data, Internet of Things, etc., virtual reality is also one of the promising fields. The term virtual reality is used to describe a 3D virtual environment created by sensors which allows interaction. The environment develops an immersive feel that the person is actually in a real environment and he can manipulate objects or perform some actions []. Using this virtual reality technology, our aim is to develop an underwater museum to experience the feel of being under the sea.
Time-aware Context and Feature Enhancement Sequence Recommendation

Recently, Recurrent Neural Network (RNN) leads to a paradigm shift in recommendation tasks, which achieves state-of-the-art performance compared with traditional methods. In context-aware recommendations, context-aware models are capable of getting context information based on time. However, they fail to capture the static long-term interests or the higher-order features of users' information. Motivated by the high efficiency of modeling both context and sequence characteristics simultaneously, we proposed Time-aware Context and Feature Enhancement Neural Network (CAPNN) to integrate the time context features and high-order features within users' behavior sequences, which captures the long and short-term static interests of users. Extensive experiments on real-world datasets demonstrate that our proposed method outperforms several state-of-the-art methods in recommendation tasks.
A method for position and posture calculation of stamping parts by polarization vision

Polarization is steadily attracting attention in machine vision due to its ability to capture the information not readily available in standard color or greyscale camera. In this paper, the degree of polarization image and intensity image are used to calculate the position and posture of stamping parts. First, the degree of polarization image and intensity image were acquired from polarization camera. Canny edge operator is used to filter the polarization degree image to get the edge image. Morphological analysis and connected domain statistics are performed on the edge image, and location holes are extracted from the connected domain according to the geometric characteristics. Combined with the extracted location holes region, the obtained polarization intensity image is segmented by local threshold, and the edge contour of the location hole is extracted and the coordinates of the center point are calculated.
Research on Multiple Abnormal Behaviour Detection Methods for Surveillance Video

Abstract This paper uses the centroid acceleration of the moving target and the aspect ratio operator of the outer frame of the moving target to identify the human moving target. It uses inter-frame difference method to detect stagnation, uses 3D convolutional neural networks to detect fall, and analyzes the experimental results. Abnormal behaviour detection can assist in case investigation, large-scale activities, and security in key areas, etc., and help ensure social harmony and stability.
Construction of Intelligent Maintenance Ontology Model for Shield Machine

Abstract In order to transform the shield machine maintenance engineers’ experience and related non-structural data into digital, structured and standardized maintenance knowledge, it is necessary to construct a shield machine intelligent maintenance ontology model. This paper has proposed a systematic method for the construction of this ontology model, which mainly includes seven steps. A professional ontology modeling tool protégé is used to complete the construction of the ontology model, and its own reasoning mechanism is applied to maintain the consistency of the ontology logical relationship. Moreover, a graph database by Neo4j is created to store the constructed shield machine intelligent maintenance ontology and related data for the shield machine its intelligent maintenance, ontology model construction technology can also migrate to the field of material synthesis.
Towards Computational Identification of Visual Attention on Interactive Tabletops

There is a growing interest in the ability to detect where people are looking in real-time to support learning, collaboration, and efficiency. Here we present an overview of computational methods for accurately classifying the area of visual attention on a horizontal surface that we use to represent an interactive display (i.e. tabletop). We propose a new model that utilizes a neural network to estimate the area of visual attention, and provide a close examination of the factors that contribute to the accuracy of the model. Additionally, we discuss the use of this technique to model joint visual attention in collaboration. We achieved a mean classification accuracy of 75.75% with a standard deviation of 0.14 when data from four participants was used in training the model and then tested on the fifth participant. We also achieved a mean classification accuracy of 98.8% with 0.02 standard deviation when different amounts of overall data was used to test the model.
Autonomous public transportation system

Disaster management has become a research area in its own right, with few reported cases of successful installation of robots in real disaster areas. Most of these disasters are using air, ground, or underwater platforms. However, studies involving private boats or Unmanned Surface Vehicles (USV’s) for Disaster Management (DM) are presently being distributed in many publications, of varying depths, and focuses mainly on single-seat vehicles - usually under the banned Ocean umbrella. Therefore, the current value of USVs in the DM process at its different stages is unclear. This paper presents the first comprehensive study on the use of USV DMs, as far as we know. This work shows that with few current issues in disaster situations, most research in the area focuses on USV Hardware and software platforms, such as Guidance Navigation and Control, do not focus on their actual DM value. Finally, to guide future researches, this paper also summarizes our contributions towards it, the lessons learned by everyone, the guidelines made by us, and the research gaps.
RETRACTED: Research on Jewelry Product Conceptual Design Modeling Based on Big Data Technology

Abstract With the development of the times, the progress of society and the continuous improvement of human science and technology, big data technology has been widely used in our daily production and life. Jewelry products are also in this era of rapid development of innovation, its design concept and design style are constantly changing, the level of processing technology continues to improve, the use of materials and precision is also constantly supplemented. Therefore, in order to cope with the development of jewelry products in this new era, this paper proposes a method of integrating big data technology with jewelry product conceptual design modeling. This paper analyzes and forecasts the new development direction of jewelry products through big data technology, so that the staff of jewelry industry can intuitively and accurately see the new development method and direction of jewelry products in the new era. Through the analysis, a series of methods proposed in this paper can basically meet the personalized needs of consumers for jewelry product aesthetics in the new era, as well as the rational cognition of emotion, which provides a new idea for the conceptual design of jewelry products.
Deep Domain Adaptation Based on Adversarial Network With Graph Regularization

Although most transfer learning methods can reduce the difference of the feature distributions between the source and target domains effectively, some classes in the two domains may still be misaligned after domain adaptation, especially for the classes with similar features such as “bicycle” and “motorcycle”. Therefore, a graph regularization based adversarial network model is proposed, whose innovations mainly include the following two aspects: First, a constraint function which is used to measure the difference between the features belonged to different classes is proposed, whose purpose is that not only the training accuracy is taken into account during supervised training, but also the difference between classes should be enlarged as much as possible; Then, a graph regularization constraint function is proposed, which makes all the classes have good local preserving properties after domain adaptation, and further reduces the possibility of all classes being misaligned. Experimental results on several cross-domain benchmark datasets show that our newly proposed approach outperforms state of the art methods.
TransMVG: Knowledge Graph Embedding Based on Multiple-Valued Gates

The essence of knowledge representation learning is to embed the knowledge graph into a low-dimensional vector space to make knowledge computable and inferable. Semantic discriminate models greatly improve the performance of knowledge embedding through increasingly complex feature engineering. For example, the projection calculation based on matrixes can achieve more detailed semantic interactions and higher accuracies. However, complex feature engineering results in high time complexity and discriminate parameters pressure, which make them difficult to effectively applied to large-scale knowledge graphs. TransGate is proposed to relieve the pressure of the huge number of parameters in semantic discriminate models and obtains better performance with much fewer parameters. We find that the gate filtering vector obtained by the traditional gate used by TransGate would rapidly fall in the state of a nearly boundary binary-valued distribution (most values are near 0 or near 1) after only a few hundred rounds of training. This means that most filtering gate values either allow the information element to pass completely or not at all, which can be called extreme filtering. We argue that this filtering pattern ignore the interaction between information elements. In this paper, TransMVG model is proposed to improve the traditional boundary binary-valued gate to a multiple-valued gate on the premise of ensuring the randomness. The experiments results show that TransMVG outperforms the state-of art baselines. This means it is feasible and necessary to multivalue the filter gate vectors in the process of knowledge representation learning based-on the gate structure.
Entanglement dynamics of photonic bell states under different noisy channels

Abstract Entanglement and decoherence are two intimately related phenomena that lie at the heart of quantum information. The EPR-Bell states are the most basic quantum systems, which form the complete entangled basis of the two-particle Hilbert space, and which are important in many quantum computation schemes. The bit flip, phase flip, and bit-phase flip noise channels represent all the possible errors in quantum computation. Thus, the quantum decoherence characteristic of maximally and partially Bell entangled states under different noisy channels is studied using the concurrence. Our work is useful for linear-optical quantum computation and quantum communication schemes whose building blocks are entangled state.
A development framework for blockchain technologies in digital government

Blockchain Technologies, known as BCT have been praised by most people as a new and revolutionary means of ensuring secure and transparent data sharing and record keeping with potential uses in a number of sectors. The government sector is one of them. The potentials of DLTs are among the motivating reasons why most government agencies are looking to this technology to help make them become more efficient, accountable, cost-effective, and transparent. Some, on the other hand, are looking for ways in which the DLTs can help increase the trust in government agencies as well as various governmental institutions. Despite the endless potentials of the DLTs, they are still in infant stage and at the same time having a number of risks that may deter its future integration and use. This paper proposes a framework towards the integration of DLTs into government organizations of various levels like municipalities and public services that will help in introduction of them in governmental tasks such as digital ID management, secure document handling and voting while keeping them simple for administrators to use and apply in every organization and domain.
Web Service-based Automata Testing: A Case Study on Online Airline Reservation

Information exchange between organizations, associates, and customers is essential for the success of any type of business. Thus, a web service (can be software, application, or cloud technology) is being used for backing communication between application-to-application and solving the interoperability problem. It offers homogeneous web protocols to interoperate, interconnect, and exchange data messaging with numerous applications with HTML, XML, WSDL, SOAP, and other open standards. In this study, we develop a framework to demonstrate how the idea of web services can be utilized for online airline reservations by utilizing the finite automata computational model. The model is a fundamental computing model that presents sufficient behaviour of the service based on its user consideration or inquiries. To express the transition flow of an online airline reservation with the given desired input, the nondeterministic finite automata (NFA) is being used. For the validation, the NFA is being converted to the Deterministic Finite Automata (DFA) to make out the anticipated patterns. The entire framework process was carried out using the Java Formal Languages and Automata Package (JFLAP) simulation tool and was tested with precise and valid simulating inputs.
Multi-Patch Blending improves lung cancer growth pattern segmentation in whole-slide images

In this study, we introduce a technique to generate synthetic histologic image data by blending parts of different images into a new image patch. The proposed approach, which we call multi-patch blending (MPB), crops parts of two histologic images of tumor growth patterns annotated with single but different labels and pastes them into a newly created image patch comprising areas with two different annotations. A Cycle-GAN model is employed in MPB to smooth out transitions between the pasted image crops and make the output image patch look realistic. The goal of implementing the MPB is to support the task of semantic segmentation of lung adenocarcinoma grown patterns in whole-slide images (WSI). We used MPB to increase the number of training patches extracted from a set of 18 WSIs with sparse annotations. De facto, MPB was implemented as a novel data augmentation strategy. U-Net trained with MPB-generated patches achieved 13% higher F1-score than the U-Net trained with original (sparsely annotated) patches in a 4-class semantic segmentation task.