Miracle-dz/UniFAR-FTUs huggingface dataset:
Verifying global minima for rotation problems

The least-square method for rotation problems with known coordinates of feature points in both world frame and image plane is considered in this paper. Although optimization algorithms have been given for the rotation problems under L-infinity cost function, finding optimal least-square solutions to this problem is difficult, since the cost function is not convex, and in the worst case it may have multiple-minima. Iterative methods can be used to find a good solution, which may be a local minimum. This paper provides a method for verifying whether a local optimal solution is globally optimal by providing a simple and rapid test involving the Hessian Matrix of the specific total cost function. The basic idea is that if the total cost function is convex in a definite region (convex set) which is large enough for all the optimal solutions, a sufficient condition for globally optimal solution can be obtained.
Foundations of Context-aware Preference Propagation

Preferences are a fundamental ingredient in a variety of fields, ranging from economics to computer science, for deciding the best choices among possible alternatives. Contexts provide another important aspect to be considered in the selection of the best choices, since, very often, preferences are affected by context. In particular, the problem of preference propagation from more generic to more specific contexts naturally arises. Such a problem has only been addressed in a very limited way and always resorts to practical, ad hoc approaches. To fill this gap, in this article, we analyze preference propagation in a principled way and adopt an abstract context model without making any specific assumptions on how preferences are stated. Our framework only requires that the contexts form a partially ordered set and that preferences define a strict partial order on the objects of interest. We first formalize the basic properties that any propagation process should satisfy. We then introduce an algebraic model for preference propagation that relies on two abstract operators for combining preferences, and, under mild assumptions, we prove that the only possible interpretations for such operators are the well-known Pareto and Prioritized composition. We then study several propagation methods based on such operators and precisely characterize them in terms of the stated properties. We finally identify a method meeting all the requirements, on the basis of which we provide an efficient algorithm for preference propagation.
Japanese Patterns as NFC Antennas for Interactive Urushi-ware

Urushi (Japanese lacquer) is a natural resin paint with electrical insulating capability. We focused on Japanese patterns on an urushi-ware that is a product coated urushi. To make urushi-wares interactive without losing elegance and beauty, we transformed Japanese patterns into near-field communication (NFC) antenna patterns. We developed three types of prototype antennas and confirmed their functionality. In addition, we developed an IC key tag and an interactive lunch box as example applications of interactive urushi-wares.
Application of Feed-Forward and Feedback Control in Tensile Controller

According to the disturbance of speed changing in the tension, in order to overcome the speed change for the influence of the system tension control, a tension controller based on the Proportion Integration Differentiation feedback control additionally the feed-forward compensation is designed. The control principle of system, hardware control system and the program design are introduced, the hardware control circuit is mainly composed of digital signal processing. The controller has the advantages of fast speed response, smooth adjustment, Strong disturbance ability, etc. The actual application proved the design is correct and effective.
Visualization Analysis of Markov Chain Based on CiteSpace

Since markov chain is widely used in various fields, the relevant literatures of markov chain theories are analyzed base on CiteSpace in this paper, adopting 10,000 literatures in CNKI and 10,000 literatures in WOS as datas. The relationship among markov chain, monte carlo integral and bayesian theory is analyzed primarily. What's more, the hidden markov model, markov random field, markov decision process and grey-markov model derived from markov chain are investigated emphatically. Ultimately, we reviews the development history of markov chain, makes a visual analysis of the authors, sorts out the research status of markov chain, and makes a comprehensive review of the application of markov chain.
Analysis of Factors Influencing Negative e-Word of Mouth in Online Social Media Marketing

The advent of online social media (OSM) marketing necessitates further examination of existent studies on the negative word of mouth (NWOM). Earlier studies have focused mostly on the effect of consumption of NWOM. The present research paper, on the other hand, strives to find out the influence of various factors that may motivate users of the OSM to propagate NWOM through these platforms. The results of the study confirm that external motivation factor -- Deal Proneness - significantly induces electronic NWOM (e-NWOM). Also, the moderation effect of Deal Proneness on the relationship between N-eWOM and the factors influencing e-NWOM is discussed.
Measuring the Effectiveness of Twitter’s URL Shortener (t.co) at Protecting Users from Phishing and Malware Attacks

In this paper we investigate how effective Twitter’s URL shortening service (t.co) is at protecting users from phishing and malware attacks. We show that over 10,000 unique blacklisted phishing and malware URLs were posted to Twitter during a 2-month timeframe in 2017. This lead to over 1.6 million clicks which came directly from Twitter users – therefore exposing people to potentially harmful cyber attacks. However, existing research does not explore if blacklisted URLs are blocked by Twitter at time of click. Our study investigates Twitter’s URL shortening service to examine the impact of filtering blacklisted URLs that are posted to the social network. We show an overall reduction in the number of blacklisted phishing and malware URLs posted to Twitter in 2018-19 compared to 2017, suggesting an improvement in Twitter’s effectiveness at blocking blacklisted URLs at time of tweet. However, only about 12% of these tweeted blacklisted URLs – which were not blocked at time of tweet and therefore posted to the platform – were blocked by Twitter in 2018-19. Our results indicate that, despite a reduction in the number of blacklisted URLs at time of tweet, Twitter’s URL shortener is not particularly effective at filtering phishing and malware URLs - therefore people are still exposed to these cyber attacks on Twitter.
A Blockchain-based trust model for crowd environments.

Nowadays, social media has become an important platform for people to share information and opinions. More and more businesses owners and individual users rely on information shared on social media and contributed by the crowd for product promotion, decision making, etc. However, it is difficult to evaluate the trustworthiness of such information effectively as the open environment allows all people with different backgrounds and expertise to contribute. Furthermore, it gives chances for malicious users or astroturfers who pursue profits by giving fake reviews or plausible answers, and those who want to build up their business’s reputation without improving quality of good or service. For solving this challenging problem, the paper considers trust as a key element and define it in the expert-based context. Then we propose a Blockchain-based trust model for information sharing in crowd environments. The proposed model uses a weighted consensus mechanism to infer the trustworthiness of shared information basing on reviews from the crowd and defines the accuracy of an agent as the ratio of his winning reviews to his total reviews. The model can reward agents effectively equivalent to his winning reviews. Experimental results show that the proposed model has higher accuracy in defining the trustworthiness of information and obtains better Collective Intelligence than existing models.
Petroleum Production Forecasting Based on Machine Learning

Reservoir numeric simulation is the most commonly used method for oilfield petroleum production forecasting, but its accuracy is based on accurate geological models and high-quality history matching. In order to overcome the shortcomings of numeric simulation requires, like time consuming, high cost, and lot of data required, an machine learning method was adopted and trained for predicting oilfield production using static and dynamic developing parameters. Since the traditional BP neural networks cannot accurately capture the time correlation between data, a long short-term memory model was used to establish production prediction model that can consider the trends and context correlations of production data. Mean Decrease Impurity method was first conducted to analyze the relative importance of predictor variables. Relative unimportant features then can be excluded according to their relative importance. The dimension reduction of predictor variables was combined with production data to train and optimize LSTM network. Thereby predictive model for production prediction was established after the training. The actual oilfield data was used to verify the proposed approach and conducting application effect analysis. The results show that the predicted production computed by LSTM network is highly consistent with the actual production, which can accurately reflect the dynamic variation of production.
Understanding usability evaluation setup for VR products in industry: a review study

VR development practices have a diverse set of practices compared to traditional software development. Tasks like scene design, acoustic design, vergence manipulation, image depth, etc. are specific to VR apps and hence require evaluation processes that may be different from the traditional means. Usability Evaluation is one such process which is being executed in an unconventional way by Industrial Practitioners today. In this paper, the researchers detail a Systematic Literature Review of the Usability Evaluation Methods practised by Industrial researchers while building VR Products. The researchers found that VR Product teams follow unique methods to improve usability in their products. Further, the researchers consolidate these methods and provide insights into choosing the best to build a real-world VR Product based on the defined product constraints
ExpandFab: Fabricating Objects Expanding and Changing Shape with Heat

ExpandFab is a fabrication method for creating expanding objects using foam materials. The printed objects change their shape and volume, which is advantageous for reducing the printing time and transportation costs. For the fabrication of expanding objects, we investigated a basic principle of the expansion rate and developed materials by mixing a foam powder and elastic adhesive. Furthermore, we developed a fabrication method using the foam materials. A user can design expanded objects using our design software and sets the expansion areas on the surface. The software simulates and exports the 3d model into a three-dimensional (3D) printer. The 3D printer prints the expandable object by curing with ultraviolet light. Finally, the user heats the printed objects, and the objects expand to maximum approximately 2.7 times of their original size. ExpandFab allows users to prototype products that expand and morph into various shapes, such as objects changing from one shape to various shapes, and functional prototype with electronic components. In this paper, we describe the basic principle of this technique, implementation of the software and hardware, application examples, limitations and discussions, and future works.
Building Scene Recognition Based on Deep Multiple Instance Learning Convolutional Neural Network Using High Resolution Remote Sensing Image

As the typical man-made objects, buildings play a special role in remote sensing images. This paper proposes a building scene recognition method based on deep multiple instance convolutional neural network. Scene classification is formulated as a multiple instance learning (MIL) problem in which local scene regions are regarded as instances and assigned with different labels. Through a trainable MIL pooling based on a spatial attention mechanism to select the most relevant instances adaptively and produce the scene-level predictions. Experimental results on UCM data set and small targets data set collected based on the GF-2 show that the method can improve the accuracy of building scene recognition using high resolution remote sensing image. Particularly, it solves the problem that it is difficult to detect the small buildings correctly and provide a new idea of scene recognition of buildings.
A Survey of Cache Simulators

Computer architecture simulation tools are essential for implementing and evaluating new ideas in the domain and can be useful for understanding the behavior of programs and finding microarchitectural bottlenecks. One particularly important part of almost any processor is the cache hierarchy. While some simulators support simulating a whole processor, including the cache hierarchy, cores, and on-chip interconnect, others may only support simulating the cache hierarchy. This survey provides a detailed discussion on 28 CPU cache simulators, including popular or recent simulators. We compare between all of these simulators in four different ways: major design characteristics, support for specific cache design features, support for specific cache-related metrics, and validation methods and efforts. The strengths and shortcomings of each simulator and major issues that are common to all simulators are highlighted. The information presented in this survey was collected from many different sources, including research papers, documentations, source code bases, and others. This survey is potentially useful for both users and developers of cache simulators. To the best of our knowledge, this is the first comprehensive survey on cache simulation tools.
Artificial intelligence and machine learning: what is the role of social workers, occupational therapists, audiologists, nurses and speech language pathologists according to academic literature and canadian newspaper coverage?

Artificial Intelligence/Machine Learning (AI/ML) increasingly influences products and processes used by social workers, occupational therapists, audiologists, nurses and speech language pathologists (health professionals for short) in general and in their rehabilitation practice. Health professionals are expected to fulfill many roles and within the narrative of AI/ML health professionals can hold multiple roles. We performed a scoping review using the academic database Scopus, the 70 databases accessible through EBSCO-Host and the database Canadian Newsstream through which we accessed 300 Canadian English language papers as sources. We found minimal engagement with the roles of the covered health professionals related to AI/ML whereby nurses were covered much more than the other health professionals. The main role mentioned for all occupations covered in our study was the one of clinical user. Many other roles expected from health professionals such as being advocates for their field and clients or being policy developers, educators and researchers were rarely or not at all mentioned depending on the health professional. Our role narrative analysis of AI/ML related to the covered health professionals reveals significant gaps in need to be filled.
Tracking Control of a Single Link Manipulator with Actuator Dynamics under Output Constraints

In this paper, controller design for handling constraints on joint position and angular velocity of a single link manipulator with actuator dynamics is presented. Instead of using optimization approaches for constraint satisfaction, an analytical Barrier Lyapunov Function (BLF) based design has been presented to derive a control law. In this approach, barrier functions are used to construct a candidate Lyapunov function which inherently includes the constraints in its definition. Multiple controllers are designed using the same backstepping procedure with only modifications to the candidate Lyapunov function. The asymptotic stability of the closed loop system has been shown. Finally, a comparison on the performance of the designed controllers has been made through numerical simulations.
Amazighe POS tagging using Long Short Term Memory Networks

Morphosyntactic processing of natural languages is mainly restricted by the lack of labelled data sets. Deep Learning methods proved their efficiency in domains such as imaging or acoustic process. Part-of-speech tagging is an important preprocessing step in many natural language processing applications. Long Short-Term Memory Recurrent Neural Network (LSTMRNN) has been shown to be very effective for tagging sequential data, e.g. speech utterances or handwritten documents. While word embedding has been demoed as a powerful representation for characterizing the statistical properties of natural language. Despite much work already carried out in this field, there is still room for improvement, especially in Amazighe language. We propose here to use LSTM-RNN with word embedding for Amazighe part-of-speech (POS) tagging task. When tested on our test set, a state-of-the-art performance of 92.70 tagging accuracy is achieved. Without using morphological features, this approach can also achieve a good performance comparable with the already existing POS tagger.
Poisonous Spider Recognition through Deep Learning

Deep learning and neural networks have recently gained considerable attention and are now one of the most popular topics in modern computer science. One of the most promising applications of deep learning is in the field of computer vision and especially in the application of convolutional neural networks (CNNs) for object detection and classification of images. In this paper, we explore various CNN models to identify and classify common species of spiders found in Australia with specific focus on poisonous spiders. We compare the accuracy and performance of the deep learning models on a range of diverse spider species. We also develop an iOS application as the front-end user application.
Isolating P2P Botnet Using EigenTrust Algorithm in P2P File Sharing Networks

In computing networks, a peer to peer botnet is a cluster of malicious peers connected to each other via a peer to peer application, each of which runs one or more robots. Generally, botnets can be used to perform a distributed Denial of Service attack (DDoS attack), steal data, send unwanted e-mails and allow the attacker to access the device and its connection. Recently, the most dangerous attacks on the Internet caused by the peer to peer botnet. In the reason to detect and isolate peer to peer botnets in p2p file-sharing networks. It is more importantly, to study not only the existing models that described this issue in the past, but also the other proposed models that giving us a view in the future. In this paper, we introduce a new deletion links system based on the EigenTrust algorithm and the P2P network properties, to analyze and isolate P2P botnet (malicious cluster). We investigate its performances by presenting a P2P-Worm propagation model based on the SEIS epidemic model. Where a generated cluster of malicious peers sharing infected files try to turn the other peers in P2P file-sharing network to botnetclients. We show that increasing the neighbors rating is of great importance in isolating peer to peer botnet. By varying a threshold Tr, the link deletion system produces very different topologies that could isolate peer to peer botnet. We conclude that our new system can create a P2P file-sharing network satisfying very diverse goals.
Deep URL: design of adult URL classifier using deep neural network

Nowadays, many people rely on internet for various information needs, due to the development of advanced technologies. The internet has unlimited web resources, but some contents are not appropriate for all the age groups, especially children under 18. The number of adult websites increases every day thereby posing challenge for existing content-based / black listing approaches, which require entire web page contents for classification purpose / frequent database updates. To overcome the above issues, we propose an URL based deep learning model that not only avoids the unnecessary content downloads, but also handles the dynamic nature of web. As the URL is a sequence of characters, a novel embedding method is proposed for effective URL representation. A Recurrent Convolutional Neural Network based approach is also proposed that can classify the Adult websites by learning the significant features derived only from URLs. By conducting various experiments on the benchmark ODP dataset, we have analyzed the performance of the proposed approach. From the experimental results, it is shown that an accuracy of 87.6% has been achieved which is a significant improvement over the existing approaches.
Wearable interaction and home automation to improve the patient wellness: study of perceived benefits

The concept of Health Smart House aims at giving an autonomous life, in their own home, to people who suffering from a chronic disease, older, handicapped people, etc. However, developing a smart health system for home is a challenge. A long-term, it must meet the specific needs of any user. In addition, it must provide the most important daily services. That is, to design and develop smart health system is important to know patients' needs and the point of view of caregivers, doctors, nurses, etc. In this paper, we have based on a smart-home environment that use home automation and tangible and wearable interaction to improve the patients' quality of life. In order to know the point of view the patient, caregivers, doctors, etc. We conducted an expert survey to identify challenges and advantages in home automation and system interaction. The survey is divided into three sections: the patients' load cognitive using the system; the most useful variables related to data gathering through sensors, and the system social acceptance. Thanks to the results obtained, we contribute to a better understanding about important factors in healthcare, home automation and user interaction.
Mitigating Flow Table Overloading Attack with Controller-based Flow Filtering Strategy in SDN

Controller is a key component in the three layers of Software - Defined Networking (SDN), which is to process a huge number of flow requests from network devices. As a result, it puts a flow rule into flow table in switch according to every incoming packet. However, the capacity of flow table is limited and can be the target of malicious attacks by taking advantage of installing rules from controller. Specifically, malicious rules can be pushed from controller to occupy the available space for new benign traffic due to controlling and directing the packets in SDN relied on flow rules installation. These tables can be full of a massive number of flow entries populated from controller, leading to be out of space for new benign flows. This paper gives a method to mitigate flow table overloading attack after a DDoS attack notification. It can help the data plane to be more secured by improving the availability of flow table with a strategy of real-time packet monitoring and flow management in controller.