Reviewerly/exHarmony huggingface dataset:
An Occlusion Handling Evaluation Criterion for Deep Learning Object Segmentation

Abstract This paper introduces a novel evaluation criterion to occlusion handling for deep learning object segmentation. The occlusion is defined as objects blocking each other on an image. It affects deep learning object segmentation. More and more researches focus on occlusion handling for object segmentation. However, these researches do not clearly show the evaluation of their occlusion handling method, because there is no suitable evaluation criterion. Traditionally, people just use images results or use the entire object boundary accuracy to show the occlusion handling of their methods. Conversely, these ideas cannot give a numerical evaluation focusing on occlusion handling. This research reports an evaluation criterion to measure occlusion handling performance. This evaluation criterion uses the shortest distances between the pixels from the ground truth occlusion edges and the segmentation (result) shape. The shortest distances are segmentation errors. Then, the average value of these errors is the final parameter for occlusion handling evaluation criterion. The experiment uses a deep learning based segmentation model as an example. It shows that this criterion (or method) successfully measures the occlusion handling for deep learning based object segmentation.
Research on the relationship between e-commerce ratings and reviews based on Naive Bayes

Abstract With the popularization of the Internet, online shopping has gradually replaced traditional shopping methods. Product ratings and comments from buyers have become important indicators for users when purchasing products. Because of this, the behavior of employing someone to rate high, known as click farming, has gradually risen. To deal with this phenomenon, users pay more attention to the content of the review itself rather than the score to make judgments. Then why are the stores still keen on employing click farm? We think that the high scores seem to play a guiding role in the user’s comments. To explore whether this phenomenon exists, we crawled sales information from Amazon and built a mathematical model. A Naive Bayes Classifier is employed to screen out the high-frequency vocabulary from the comments. And we defined the important concept of Irrational Discrimination base on Multiple Linear Regression . After analyzing the data for the whole year, we found that discrimination does exist. The specific vocabulary appearing in the review has a large star rating relevance shows that not only the time guiding role exists between star ratings and reviews, but also a strong interaction.
KDAS-ReID: Architecture Search for Person Re-Identification via Distilled Knowledge with Dynamic Temperature

Person re-Identification(Re-ID) based on deep convolutional neural networks (CNNs) achieves remarkable success with its fast speed. However, prevailing Re-ID models are usually built upon backbones that manually design for classification. In order to automatically design an effective Re-ID architecture, we propose a pedestrian re-identification algorithm based on knowledge distillation, called KDAS-ReID. When the knowledge of the teacher model is transferred to the student model, the importance of knowledge in the teacher model will gradually decrease with the improvement of the performance of the student model. Therefore, instead of applying the distillation loss function directly, we consider using dynamic temperatures during the search stage and training stage. Specifically, we start searching and training at a high temperature and gradually reduce the temperature to 1 so that the student model can better learn from the teacher model through soft targets. Extensive experiments demonstrate that KDAS-ReID performs not only better than other state-of-the-art Re-ID models on three benchmarks, but also better than the teacher model based on the ResNet-50 backbone.
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning

Multiple Instance Learning (MIL) involves predicting a single label for a bag of instances, given positive or negative labels at bag-level, without accessing to label for each instance in the training phase. Since a positive bag contains both positive and negative instances, it is often required to detect positive instances (key instances) when a set of instances is categorized as a positive bag. The attention-based deep MIL model is a recent advance in both bag-level classification and key instance detection (KID). However, if the positive and negative instances in a positive bag are not clearly distinguishable, the attention-based deep MIL model has limited KID performance as the attention scores are skewed to few positive instances. In this paper, we present a method to improve the attention-based deep MIL model in the task of KID. The main idea is to use the neural network inversion to find which instances made contribution to the bag-level prediction produced by the trained MIL model. Moreover, we incorporate a sparseness constraint into the neural network inversion, leading to the sparse network inversion which is solved by the proximal gradient method. Numerical experiments on an MNIST-based image MIL dataset and two real-world histopathology datasets verify the validity of our method, demonstrating the KID performance is significantly improved while the performance of bag-level prediction is maintained.
Creating research topic map for NIMS SAMURAI database using natural language processing approach

In this study, we present an approach to create a visual research topic map for materials science researchers from a large collection of archived research papers using natural language processing (NLP). We apply this approach on SAMURAI, a directory service for the researchers of the National Institute for Materials Science (NIMS), Japan. Visualization of research content will support exploratory searches by maximizing the information absorbance and intuitively capturing the research characteristics of each materials science researcher. In addition, a research topic map can connect researchers with similar topics aiming to find potential collaborators. Collaboration can support the advance of scientific research. We analyze all available publications of a researcher using frequent term analysis. In addition, materials science knowledge resources were utilized including dictionaries and automatic extraction for the material names. Noise reduction was implemented using stop word filtering. The topics were then visualized using a word cloud technique for each researcher. An analysis of the topic similarity was conducted to find researchers that share similar topics leading to the creation of a topic map for each researcher. The approach aims at maximizing information absorbance for public knowledge by applying NLP approaches to information mining from materials science research papers. NLP analysis and visualization code are available https://github.com/ThaerDieb/Topic_map.
Behavioural Biometrics in Feature Profiles-Engineering Healthcare and Rehabilitation Systems

With biometrics replacing the age-old password verification process and identification of an individual, it is time to explore the applications of biometrics in health monitoring of patients, specifically senior citizens, and for rehabilitation. The aim of this study is to use behavioural biometrics to save data of patients and make their feature profiles. A check-up can then include the comparison of newly taken data with the feature profiles to facilitate an early diagnosis of brain deteriorating diseases. These biometrics can also be applied to patients in rehabilitation to provide therapy which can be personalised by comparing their data to that of a healthy person. The data would include patients' gait, handwriting (signature), speech and keystroke dynamics. This novel study is also proposing a framework named BBFP (Behavioural Biometrics in Feature Profiles) for implementing the techniques in hospitals.
Toward Performance Portability of Highly Parametrizable TRSM Algorithm Using SYCL

Presented in 1979, BLAS is, to this day, the de-facto standard for low-level linear algebra routines. BLAS provides essential linear algebra routines used in various domains such as numerical and scientific computing, weather simulation, computational fluid dynamics, machine learning and adopted for a broad range of hardware from HPC to embedded systems and AI specialized accelerators.
Identification of Significant Eye Blink for Tangible Human Computer Interaction

This paper introduces a mode of interaction with computers through Visual Tracking Technique (VTT) along with robust Eye Blink Detection (EBD). The visible facial rigid movements of human eye blinks, lip movement etc. and nonrigid movements including head rotation and orientations are combined together to infer significant and precise interaction order with consumer grade devices. The conjugate machine learning and heuristic approach involving cascades of regressors with accurate calibrations have been established successfully to transform facial movements into a meaningful interaction cues to navigate or trigger events as of any conventional input devices. The optimized implementation of the close loop interaction cycle is accomplished within 400 ms, making the system as natural as the traditional keyboard or mouse input. The proposed interaction has a spectrum of application areas including assistive technology, augmented interaction, gaming and entertainment.
Weak Measurement Effects on Dynamics of Quantum Correlations in a Two-Atom System in Thermal Reservoirs

The dynamical behaviour of quantum correlations captured by different forms of Measurement-Induced Nonlocality (MIN) between two atoms coupled with thermal reservoirs is investigated and compared with the entanglement. It is shown that the MIN quantities are more robust, while noise causes sudden death in entanglement. Further, we quantified the quantum correlation with weak measurement and the effect of measurement strength is observed. The role of mean photon number and weak measurements on quantum correlation is also highlighted.
Application of Neural Network and Computer in Intelligent Robot

Abstract Autonomous mobile robot is widely used in security patrol, freight, autopilot, and family life and other fields, improve the application effect is one of the most important factor is to be able to accurate navigation path, so how to design a highly efficient, intelligent autonomous mobile robot navigation path algorithm has been a popular research topic in the field. This paper mainly studies the application of the combination of neural network and computer in the field of intelligent robot. The Pninnet model is used to simulate the 3D simulation environment built in this paper, and the path navigation method of the autonomous mobile robot based on the end-to-end model is realized, and an algorithm based on ray detection is proposed to achieve obstacle avoidance. Through several groups of comparative experiments (such as comparison with Pilotnet model, etc.) and analysis, it is proved that the algorithm in this paper improves the efficiency of robot navigation research and the robot developed has a better performance in navigation in unknown or familiar environments.
Requirements engineering in the pre-contract stage

Performing requirements engineering (RE) during the pre-contract stage; i.e., at pre-selling time, is mandatory for software providers to conceive a project proposal and deliver a bid. In bespoke projects, this process requires that providers deal with several constraints that are usually not present in a regular RE process. For instance, the process must be done quickly, under a competition situation, and hopefully involving few resources since its cost is assumed by the provider. These constraints make this process unique, and different to the RE conducted at post-selling; i.e., during a project contract. Few is reported about the RE activities performed in practice by providers, the cost-effectiveness of the process, and the software practices used to support it. This article presents an exploratory study that uses a mixed method approach to gain knowledge on these aspects. The results indicate that providers assume this process as unstructured, context-aware, expert-based and with various possible outcomes. In part, these features are a consequence of the constraints imposed by the project pre-contract scenario. Moreover, the providers use regular RE practices to support this process, which usually ends up having a medium-low cost-effectiveness. These results show the need to perform more empirical research in this domain, and open several opportunities to propose new RE techniques and practices (or adjust the existing ones) to improve the cost-effectiveness of this process.
SWARMGRAPH: Analyzing Large-Scale In-Memory Graphs on GPUs

Graph computation has attracted a significant amount of attention since many real-world data come in the format of graph. Conventional graph platforms are designed to accommodate a single large graph at a time, while simultaneously processing a large number of in-memory graphs whose sizes are small enough to fit into the device memory are ignored. In fact, such a computation framework is needed as the in-memory graphs are ubiquitous in many applications, e.g., code graphs, paper citation networks, chemical compound graphs, and biology graphs. In this paper, we design the first large-scale in-memory graphs computation framework on Graphics Processing Units (GPUs), SWARMGRAPH. To accelerate single graph computation, SWARMGRAPH comes with two new techniques, i.e., memory-aware data placement and kernel invocation reduction. These techniques leverage the fast memory components in GPU, remove the expensive global memory synchronization, and reduce the costly kernel invocations. To rapidly compute many graphs, SWARMGRAPH pipelines the graph loading and computation. The evaluations on large-scale real-world and synthetic graphs show that SWARMGRAPH significantly outperforms the state-of-theart CPU- and GPU-based graph frameworks by orders of magnitude.
Prototype Network for Text Entity Relationship Recognition in Metallurgical Field Based on Integrated Multi-class Loss Functions

It is of great significance to recognize the metallurgical entity relations in order to construct the Knowledge graph of Metallurgical Literature and to further understand the metallurgical literature. However, there are few researches on the textual entity relations in metallurgical fields either few marked Corpora. The syntactic structure of the same entity relationship category is relatively simple and has strong domain characteristics. The traditional entity relationship model can not identify the domain entity relationship well. Meanwhile the syntactic structure of the same entity relations class is relatively simple, and the syntactic structure is relatively simple in the recognition of entity relations in metallurgy field. Furthermore, the entities with similar syntactic structure often have the same entity relations and the different words in the sentence have different contribution to the entity relations. In order to solve the mentioned problems, this paper will combine the algorithm that can highlight the syntactic structure in sentences and improve the accuracy of the model with the Algorithm that can highlight the contribution of words in sentences and the loss function level integration is carried out in the framework of small sample prototype network, so as to maximize the advantages of each algorithm and improve the accuracy –firstly, in the coding layer of the prototype network, we use the CNN algorithm which can highlight the important words in the sentences and the TreeLSTM algorithm which can parse the sentences in the text so that the syntactic relations between the words in the sentences can be acted on in the relation recognition, the sentences are coded together by two algorithms, then, the EUCLIDEAN distance loss is calculated by using this high quality coding and the prototype coding, finally, the traditional entity relation recognition model with Attention Mechanism is integrated into the loss function, further highlighting the decisive role of important words in text sentences in relation recognition and improving the generalization of the model. The results showed that compared with the traditional methods such as CNN, RNN, PCNN and Bi-LSTM, the proposed method in this paper has better performance in the case of small sample data set.
A Blockchain-Based Access Control Framework for Secured Data Sharing in Industrial Internet

The Industrial Internet is the integration and linking of big data, analytical tools, and wireless networks with physical and industrial equipment to distributed systems. Since the industrial data are shared, it is necessary to use access control techniques to regulate who or what can view or use data. Currently, most access control systems in the Industrial Internet are designed for cloud computing platforms, assuming that all the industrial data are shared through the cloud. In this paper, we consider another scenario where the industrial data are locally stored on the company side rather than the cloud side. For this scenario, we propose a distributed access control system based on blockchain and smart contract technologies. We also evaluate its performance under different system configurations.
Hybrid Model of Vehicle Recognition Based on Convolutional Neural Network

With the improvement of people's living standard, the number of cars on the road has increased dramatically. Vehicle recognition is greatly significant for intelligent traffic management. In this paper, a hybrid model of vehicle recognition algorithm based on VGG16-softmax hybrid model is proposed. The convolutional neural network called VGG16 is used, Imagenet is used for pre-training, migration learning is used to migrate parameters to the new training model, variational auto-encoder is used for data reconstruction, and finally softmax multi-classifier is used for classification. Experiments show that this method can save time, get better vehicle feature of details and higher accuracy.
Comparative Analysis of Market Basket Analysis through Data Mining Techniques

Market basket analysis is a technique for evaluating buyer's preferences in order to find the connection between various items in the cart. The exploration of these relationships help the vendor to propound the sales strategy by considering the frequent purchased of items and with this kind of approach data-mining techniques best fits in analyzing and implementing the logic. The points of comparisons, which include the concept of buying patterns from the consumer end and the production pattern from the company, end which alternatively helps in procuring or buying the product. Evaluating the activities of business consumers is very important and this can be achieved by various data mining techniques available. This paper provides a comparative study of two widely used data mining techniques in understanding the frequent activities of buyer i.e. Association Rule Mining (ARM) and Collaborative filtering (CF) technique used in product recommendation.
A Parallel Heuristic Scheduler for Cloud Computing Environment

Cloud computing is a different paradigm from traditional computing. It consists of providing IT services such as servers, storage, databases, network management, software, data analytics, artificial intelligence, etc., via the Internet (the Cloud) to provide faster innovation, flexible resources, productivity, and competitiveness. Several challenges in handling end-user applications need to be addressed more efficiently. Task Scheduling is a significant problem in Cloud computing since the cloud provider has to deal with many user applications. Consequently, task scheduling can no longer be handled by traditional schedulers. This paper's primary purpose is to propose a parallel multi-core hybrid heuristic scheduler based on exceeding the computing capacity of any processor while guaranteeing the results' accuracy. The main objective is to determine the feasible schedule that minimizes applications execution time while maximizing cloud resource utilization. Tests on benchmark instances showed that the proposed approach finds optimum/near-optimum solutions in many cases, while the computational times are minimal compared to other sequential techniques found in the literature.
Intelligent access method of prefabricated building Dragonfly algorithm based on artificial intelligence technology

The traditional algorithm access method uses the way of hardware port communication, which requires manual debugging, resulting in poor efficiency of algorithm access and accuracy of data connection. Aiming at the above problems, the intelligent access method of prefabricated building dragonfly algorithm based on artificial intelligence technology is studied. Based on the prefabricated building demand load model, the dragonfly algorithm is used to solve the model optimization. The neural network is selected to plan the access path of dragonfly algorithm in the building to realize the intelligent access of dragonfly algorithm. Through the form of example verification, it is proved that this access method shortens about 45% access cycle, and improves the accuracy and stability of algorithm access.
Distributing Sparse Matrix/Graph Applications in Heterogeneous Clusters - an Experimental Study

Many problems in scientific and engineering applications contain sparse matrices or graphs as main input objects, e.g., numerical simulations on meshes. Large inputs are abundant these days and require parallel processing for memory size and speed. To optimize the execution of such simulations on cluster systems, the input problem needs to be distributed suitably onto the processing units (PUs). More and more frequently, such clusters contain different CPUs or a combination of CPUs and GPUs. This heterogeneity makes the load distribution problem quite challenging. Our study is motivated by the observation that established partitioning tools do not handle such heterogeneous distribution problems as well as homogeneous ones. In this paper, we first formulate the problem of balanced load distribution for heterogeneous architectures as a multiobjective, single-constraint optimization problem. We then split the problem into two phases and propose a greedy approach to determine optimal block sizes for each PU. These block sizes are then fed into numerous existing graph partitioners, for us to examine how well they handle the above problem. One of the tools we consider is an extension of our own previous work (von Looz et al., ICPP'18) called Geographer. Our experiments on well-known benchmark meshes indicate that only two tools under consideration are able to yield good quality. These two are ParMetis (both the geometric and the combinatorial variant) and Geographer. While ParMetis is faster, Geographer yields better quality on average.
SEM Medical Image Processing using VLSI

The aim of this paper is to reduce the size of the Scanning Electron Microscope (SEM) medical images for Tele-radiology applications using VLSI technology. From the highly magnified images, diseases can be deeply analyzed; an efficient way to measure blood cell count and it is also used to diagnose the presence of many life-threatening diseases such as leukemia, allergies, etc. SEM medical images such as lung, respiratory system, red blood cells, white blood cells, scaffold, kidney stone, etc., are taken for MIC (Medical Image Compression). Lossy image compression is used for compressing SEM medical images and discrete wavelet transform is used for this purpose. Modified Parallel and pipelined architecture (MPPA) is used for implementing SMIC. The area occupied by this architecture is very less and the speed of operation is high compared to other architectures.