Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms

With the advent of fog and edge computing paradigms, computation capabilities have been moved toward the edge of the network to support the requirements of highly demanding services. To ensure that the quality of such services is still met in the event of users’ mobility, migrating services across different computing nodes becomes essential. Several studies have emerged recently to address service migration in different edge-centric research areas, including fog computing, multi-access edge computing (MEC), cloudlets, and vehicular clouds. Since existing surveys in this area focus on either VM migration in general or migration in a single research field (e.g., MEC), the objective of this survey is to bring together studies from different, yet related, edge-centric research fields while capturing the different facets they addressed. More specifically, we examine the diversity characterizing the landscape of migration scenarios at the edge, present an objective-driven taxonomy of the literature, and highlight contributions that rather focused on architectural design and implementation. Finally, we identify a list of gaps and research opportunities based on the observation of the current state of the literature. One such opportunity lies in joining efforts from both networking and computing research communities to facilitate future research in this area.
Deep Learning Based Antenna Selection Aided Space-Time Shift Keying Systems

In this paper, we propose an antenna selection aided space-time shift keying(STSK) multiple-input multiple-output(MIMO) system based on deep learning method. More specifically, we first extract the channel characteristics from the channel state information, then labeling every sample by maximizing the key performance indicator. Finally, we consider antenna selection as multi-class classification learning and classify the samples through neural network to select the optimal antenna subset for supporting the actual communications. Simulation results demonstrate that neural network may achieve higher accuracy than other machine learning methods, and the corresponding performance stays close to the optimal ergodic search method.
Optimization of position and pose estimation by using Epipolar line segmentation optimization BA algorithm

Traditional Key Frame Bundle Adjustment Based Visual SLAM Algorithm, There are many shortcomings in minimizing the reprojection error that cause the pose estimation to be inaccurate. This paper optimizes the error function of BA, modifies the traditional reprojection error function and introduces the 2D Epipolar line constraint based on the limit division on the basis of the 3D constraint, The improved algorithm is applied to the ORB-SLAM2 system, and the comparison test is carried out on the EuRoC public data set. Experiments on positioning error and algorithm performance show that the improved ORB-SLAM2 system pose estimation is more accurate and robust than before.
Defect Detection of Solid Rocket Motor Based on Improved Ring Shadow Suppression Algorithm

An improved annular artifact suppression algorithm is proposed to solve the problem that the third generation CT images are often affected by annular artifacts. An image segmentation algorithm based on mathematical morphology and threshold segmentation algorithm is used to segment CT images after annuls suppression and calculate the feature values of the defects. Finally, a real solid rocket motor CT image is detected by defect detection algorithm, and the detection method proposed in this paper is verified. The results show that the annular artifact is effectively suppressed, and the solid rocket motor defect is extracted accurately.
Pre-detection and Type Discrimination of Human Trembling Diseases

The slight tremor of the human body is an early sign and symptom of many diseases. If it can be found at the beginning of the tremor, it will be beneficial for subsequent medical treatment. In this paper, we use deep learning-based motion magnification to analyze subtle changes in the image that cannot be observed by the human eye and to filter and amplify the frequencies of different trembling video for achieving prediction for different types of tremors. It can effectively reduce the medical evaluation time of tremble detection. The results of a video containing human respiration and tremor indicate that the proposed method can discriminate different tremors.
Research on Industrial Technology Innovation Strategic Alliance Based on Alliance Network: A Case Study Based on Three Industrial Alliances in Zhejiang Province

Based on the literature review and induction of resource-based theory, enterprise ability theory, social network theory and other related theories, the study analyzes the impact of enterprise resource integration capability on knowledge sharing. Through 3 typical case studies on the operation mechanism of technology innovation strategic alliance, we found: (1) Enterprise resource integration capability has positive impact on knowledge sharing. (2) Alliance Network has an influence on Knowledge Sharing, and the degree of influence of centrality and intensity on Knowledge Sharing is also different in alliance network. The above research has further expanded the research connotation of industrial alliance, and also has certain practical significance to the practice of industrial alliance in China.
Automatic Recognition of Machining Features Based on Point Cloud Data Using Convolution Neural Networks

Automatic recognition of machining features is the key technology to realize the integration of CAD/CAM. With the development of intelligent manufacturing technology, it has important significance to automatically recognize machining features from parts represented by real-time point clouds. At the same time, it puts new challenges for feature recognition technology. In this paper, based on the 3D point cloud data of part models and PointNet architecture, an approach and data structure for automatic recognition of machining features using convolution neural networks (CNN) is proposed. A sample library for learning 3D point cloud data is constructed by CAD model transformation and feature sampling. The presented CNN recognition system can recognize twenty-four kinds of machined features by sample training and recognition experiments. The recognition accuracy rate is more than 95%. This approach makes full use of the invariance of transformation of the point cloud model, and reduces the unnecessary data that is used by conventional 3D voxel network models. The presented method is simple and efficient, and has good robustness to the point cloud disturbance and noise.
Fully Dynamic Graph Algorithms Inspired by Distributed Computing

We study dynamic graphs in the fully dynamic centralized setting. In this setting, the vertex set of size n of a graph G is fixed, and the edge set changes step-by-step, such that each step either adds or removes an edge. Dynamic graphs have various applications in fields such as Communication Networks, Computer Graphics, and VLSI Design. The goal in this setting is maintaining a solution to a certain problem (e.g., maximal matching, edge coloring) after each step, such that each step is executed efficiently. The running time of a step is called update-time. One can think of this setting as a dynamic network that is monitored by a central processor that is responsible for maintaining the solution. Prior to the current work, for several central problems, the best-known deterministic algorithms for general graphs were the naive ones with update-time O(n). This is the case for maximal matching and proper O(Δ)-edge-coloring. The question of existence of sublinear in n update-time deterministic algorithms for dense graphs and general graphs remained wide open. In this article, we address this question by devising sublinear update-time deterministic algorithms for maximal matching in graphs with bounded neighborhood independence o(n/ log2 n), and for proper O(Δ)-edge-coloring in general graphs. The family of graphs with bounded neighborhood independence is a very wide family of dense graphs. In particular, graphs with constant neighborhood independence include line-graphs, claw-free graphs, unit disk graphs, and many other graphs. Thus, these graphs represent very well various types of networks. For graphs with constant neighborhood independence, our maximal-matching algorithm has Õ(√ n) update-time. Our O(Δ)-edge-coloring algorithms has Õ(√ Δ ) update-time for general graphs. To obtain our results, we employ a novel approach that adapts certain distributed algorithms of the LOCAL setting to the centralized fully dynamic setting. This is achieved by optimizing the work each processor performs and efficiently simulating a distributed algorithm in a centralized setting. The simulation is efficient, thanks to a careful selection of the network parts that the algorithm is invoked on, and by deducing the solution from the additional information that is present in the centralized setting, but not in the distributed one. Our experiments on various network topologies and scenarios demonstrate that our algorithms are highly efficient in practice. We believe that our approach is of independent interest and may be applicable to additional problems.
Virtual Grasping Feedback and Virtual Hand Ownership

In this work, we investigate the influence of different visualizations on a manipulation task in virtual reality (VR). Without the haptic feedback of the real world, grasping in VR might result in intersections with virtual objects. As people are highly sensitive when it comes to perceiving collisions, it might look more appealing to avoid intersections and visualize non-colliding hand motions. However, correcting the position of the hand or fingers results in a visual-proprioceptive discrepancy and must be used with caution. Furthermore, the lack of haptic feedback in the virtual world might result in slower actions as a user might not know exactly when a grasp has occurred. This reduced performance could be remediated with adequate visual feedback. In this study, we analyze the performance, level of ownership, and user preference of eight different visual feedback techniques for virtual grasping. Three techniques show the tracked hand (with or without grasping feedback), even if it intersects with the grasped object. Another three techniques display a hand without intersections with the object, called outer hand, simulating the look of a real world interaction. One visualization is a compromise between the two groups, showing both a primary outer hand and a secondary tracked hand. Finally, in the last visualization the hand disappears during the grasping activity. In an experiment, users perform a pick-and-place task for each feedback technique. We use high fidelity marker-based hand tracking to control the virtual hands in real time. We found that the tracked hand visualizations result in better performance, however, the outer hand visualizations were preferred. We also find indications that ownership is higher with the outer hand visualizations.
A Summary of Knowledge Reasoning Research Based on Distributed Representation

With the development of information technology and the rapid growth of Internet data, knowledge mapping technology gradually enters people's vision. It makes knowledge expression more intuitive and rich. Knowledge reasoning, as an important link in the construction technology of knowledge map, plays an important role in the updating, complement and denoising of information base. It is diverse to the knowledge reasoning method oriented to knowledge map, this paper mainly organizes and classifies the knowledge reasoning method based on distributed representation, starts from the basic concept, expounds the research progress of knowledge reasoning based on distributed representation, and looks forward to the prospect.
The Re-Granulation on Topological Structure of Granular Computing

Granular computing is an important research hotspot in artificial intelligence field. As one of the most important granular computing models, the quotient space theory mainly focuses on converting the fine granularity (original topological space) into the coarse granularity (quotient space). However, the granulation is an information decreasing and irreversible process which cannot be recovered losslessly from quotient space to original space. Therefore, this paper focuses on study the regranulation on topological structure of granular computing: Firstly, it defines concept of re-granulation which provides a mathematical method for reversibly transforming coarse granularity to fine one; Secondly, by theorems and examples it presents two reversible conditions of re-granulation --- defining a bijective mapping or keeping every open-set being saturated set on original space, which guarantee the information unchanging while restoring the problem from quotient space to original space. This paper has provided a formal computing framework for the reversibility of granularity on topological structure of granular computing.
Reported animal exposures and rabies testing in Allegheny County, Pennsylvania 2013-2014

Rabies prevention is a local, national, and global public health concern. As rabies is essentially fatal once symptoms develop, the prevention and proper treatment of high-risk animal exposures (e.g. bites, scratches) is of paramount importance. Therefore, healthcare practitioners in Allegheny County are required to report all animal bites within 24 hours to the Allegheny County Health Department (ACHD) Infectious Diseases (ID) Program. Animal exposure information collected on an Animal Bite Report (ABR) form and results from rabies testing at the ACHD Public Health Lab are used to assess the risk of rabies and advise victims on proper care. The objective of this study is to summarize information about animal exposures reported to ID from January 1, 2013 to December 31, 2014 and results from rabies testing conducted during the same time period. Information from ABR forms and rabies testing lab slips for incidents occurring from January 1, 2013 to December 31, 2014 was extracted from a database and analyzed using Microsoft Excel 2010. Records from the ACHD Public Health Lab were reviewed for rabies testing information. During the study period, 3,693 animal exposures were reported to ID. The highest number of exposures occurred during the summer months. Over half (58.4%) of all incidents involved female victims. All age groups were affected but most victims (89.5%) were younger than 65: 20.0% of victims were 0-14 years old, 32.4% were 15-34 years old, and 24.5% were 35-54 years old. The majority of incidents involved pets (76.4%) with 68.3% of incidents involving dogs and 24.4% of incidents involving cats. The ACHD Public Health Lab tested 1,553 animals during the study period; 34 from Allegheny County tested positive for rabies. The majority of rabies positive animals were bats (50.0%) and raccoons (29.4%). This information can be used in the development of public health messages specific to Allegheny County, which supports the public health significance of this study. Several potential messages and interventions are described as well as suggested improvements for the reporting system.
A Novel Infrared and Visible Image Fusion Using Low-rank Representation and Simplified Dual Channel Pulse Coupled Neural Network

This paper establishes a novel fusion scheme for infrared (IR) and visual (VI) images via low-rank representation (LRR), total variation (TV) model and simplified dual channel pulse coupled neural network (S-DPCNN) to effectively extract the major and salient information, which address some problems in existing fusion methods such as blurry edge, low-contrasting heterogeneous and information redundancy. The first step of the proposed method is to extract the valuable features of the IR images based on frequency tuned based- LRR (FT-LRR) algorithm aims to separate the corresponding salient regions and backgrounds. Furthermore, we adopt a choose-maximum rule to retain the significance information of the source images to the maximum extent for the salient region. For the background, IR and VI images are decomposed into a low-pass coefficient and a series of high-pass coefficients using non-subsampled shearlet transform (NSST). Then, the TV model is utilized to fuse the low-pass coefficient, in the meanwhile, the modified average gradient (MAG) is used to stimulate the S-DPCNN which aims to fuse high-pass coefficients. The fused background is obtained by taking inverse NSST. Finally, the robust fused image is generated by adding the fused salient region and background. Large amounts of experiment results and metrics demonstrate that the proposed framework exhibits good visual performance and has obvious superiorities over other state-of-the-art methods in both subjective and objective evaluation.
Dynamic Malware Analysis in the Modern Era—A State of the Art Survey

Although malicious software (malware) has been around since the early days of computers, the sophistication and innovation of malware has increased over the years. In particular, the latest crop of ransomware has drawn attention to the dangers of malicious software, which can cause harm to private users as well as corporations, public services (hospitals and transportation systems), governments, and security institutions. To protect these institutions and the public from malware attacks, malicious activity must be detected as early as possible, preferably before it conducts its harmful acts. However, it is not always easy to know what to look for—especially when dealing with new and unknown malware that has never been seen. Analyzing a suspicious file by static or dynamic analysis methods can provide relevant and valuable information regarding a file's impact on the hosting system and help determine whether the file is malicious or not, based on the method's predefined rules. While various techniques (e.g., code obfuscation, dynamic code loading, encryption, and packing) can be used by malware writers to evade static analysis (including signature-based anti-virus tools), dynamic analysis is robust to these techniques and can provide greater understanding regarding the analyzed file and consequently can lead to better detection capabilities. Although dynamic analysis is more robust than static analysis, existing dynamic analysis tools and techniques are imperfect, and there is no single tool that can cover all aspects of malware behavior. The most recent comprehensive survey performed in this area was published in 2012. Since that time, the computing environment has changed dramatically with new types of malware (ransomware, cryptominers), new analysis methods (volatile memory forensics, side-channel analysis), new computing environments (cloud computing, IoT devices), new machine-learning algorithms, and more. The goal of this survey is to provide a comprehensive and up-to-date overview of existing methods used to dynamically analyze malware, which includes a description of each method, its strengths and weaknesses, and its resilience against malware evasion techniques. In addition, we include an overview of prominent studies presenting the usage of machine-learning methods to enhance dynamic malware analysis capabilities aimed at detection, classification, and categorization.
Analysis of Urban Service Reliability and Its Effect on Traffic Congestion

Urban service reliability represents the level of service in the aspects of travel time, arrival time, and seat availability from the user's perspective. Service reliability focuses on the demand and supply sides of transportation systems. The supply side consists of services provided by the operator, hence it affects the travel time. Meanwhile, demand side focuses on passenger side which includes passenger behavior and experience. The main objective of this research is to analyze urban service reliability and its impact on traffic congestion. Several main factors impacting the service reliability were evaluated using system dynamics simulation model. It has been used to test and evaluate the alternatives of future strategies to increase service reliability and to mitigate the traffic congestion. Simulation results show that service reliability is influenced by two factors, those are passenger's perception and public transportation agency performance. Travel time and waiting time are two important factors of passenger's perception. Meanwhile, some factors such as the ratio of public transportation supply and demand as well as the service effectiveness are the important factors of public transportation agency performance. Based on the existing condition, service reliability in public transportation sector is around 44%. This service reliability is projected could be increased to 76% by conducting a strategy such as the addition of public transportation facilities, which aims to decrease the passenger's waiting time and to increase the ratio of public transportation supply and demand. All of these efforts will increase the passenger's perception and public transportation agency performance. With the increased in service reliability, traffic congestion is projected could be reduced to 40% - 75%.
Research on Signal Source Estimation Based on Clustering Algorithm

There is great need to estimate the number of sources before the blind source separation technique implemented. Based on the clustering algorithm in machine learning, this study constructs the inter-frequency distance according to the magnitude of each frequency in the signal spectrum curve, uses clustering algorithms such as Kmeans, DBSCAN and Gaussian mixture to cluster the number of frequency bands of the observed signals, then analysis the comparative performance.
Pet Robot Emotional Interaction for Urban Autism

With the fast development of network information technology, more and more people are immersed in the virtual community environment brought by the network, ignoring the social interaction in real life. The consequent urban autism problem has become more and more serious. This article focuses on "eliminating loneliness through emotional communication between pet robots and breeders" to solve this problem, and has developed a design called "Tom". It also proposes a pet robot named "Tom" with an emotional interaction algorithm to recognize users' emotions, simulate animal emotions and communicate emotionally with users. This paper designs experiments and analyzes the results. The results show that our pet robots have a good effect on solving urban autism problems.