Miracle-dz/UniFAR-FTUs huggingface dataset:
CoWLight: Hardware Assisted Copy-on-Write Fault Handling for Secure Deduplication

Memory deduplication in virtualized systems is shown to be a very useful memory optimization as it is simple to use and provides memory efficient cloud hosting. However, memory deduplication based side channel attacks---information disclosure attacks and covert channel construction across virtual machines---can be mounted using the timing information available because of Copy-on-Write (CoW) fault handling semantics. The CoW semantic has been a necessary-evil with regard to deduplication as it plays a vital role in supporting guest OS transparent deduplication but enables a timing channel for exploitation. Thus to decimate the huge access time difference between a normal write and a write to a shared page, we propose CoWLight, a combination of hardware and software techniques for handling the CoW page faults in an efficient manner. In this work, we propose to address the security issues at its genesis as opposed to mitigate the side-effects by offloading the CoW fault handling to the hardware itself. Further, we show that CoWLight can reduce the access latency differences significantly (by up to 30x) which is within the noise thresholds in a moderately busy system.
MFBN: An Efficient Base Model For Person Re-Identification

Person Re-IDentification (Re-ID) has developed rapidly with deep learning methods, as for these methods, the base mod- els used in most of them are not customized for Re-ID task. Although some studies have carefully designed the models special for Re-ID task, these models are always not easy to be the base model and expand with new methods due to their great complexity. In this paper, we propose a novel efficient base model named as Multi-granularity Feature Boosting Network (MFBN). MFBN consists of branches with information in different granularities. MFBN combines these branches into one whole, so MFBN is easy to be ex- tended as a base model. Moreover, MFBN applies feature boosting technique to boost fine granularitiy branch features with coarse granularity branch features, and applies channel- wise attention to increase diversities between features in multiple granularities. With these improves, MFBN has surpassed popular base models and got state-of-the-art results on mainstream Re-ID datasets including Market-1501, DukeMTMC-reID and CUHK-03. MFBN achieves results of rank-1/mAP=95.2%/93.2% on Market-1501 dataset and rank-1/mAP=90.3%/88.3% on DukeMTMC-reID dataset. Code and pretrained models are available at https://github.com/hsyi/Multi-granularity-Feature-Boosting-Network
A Structural Result for Personalized PageRank and its Algorithmic Consequences

Many systems, such as the Internet, social networks, and the power grid, can be represented as graphs. When analyzing graphs, it is often useful to compute scores describing the relative importance or distance between nodes. One example is Personalized PageRank (PPR), which assigns to each node v a vector whose i-th entry describes the importance of the i-th node from the perspective of v. PPR has proven useful in many applications, such as recommending who users should follow on social networks (if this i-th entry is large, v may be interested in following the i-th user). Unfortunately, computing n PPR vectors exactly for a graph of n nodes has complexity O(n^3), which is infeasible for many graphs of interest. In this work, we devise a scheme to estimate all n PPR vectors with bounded l_1 error and complexity O(nc), where c < 2 depends on the degrees of the graph at hand, the desired error tolerance, and a parameter that defines PPR. This improves upon existing methods, the best of which have complexity O(n2 łog n) in our setting. Our complexity guarantee holds with high probability, for certain choices of the PPR parameter, and for a certain class of random graphs (roughly speaking, the sparse directed configuration model with heavy-tailed in-degrees); our accuracy guarantee holds with probability 1 and for arbitrary graphs and PPR parameters. The complexity result arises as a consequence of our main (structural) result, which shows that the dimensionality of the set of PPR vectors scales sublinearly in n with high probability, for the same class of random graphs and for a notion of dimensionality similar to matrix rank. It is this coupling of the PPR vectors for the nodes on a common underlying graph that allows for estimating them faster. Hence, at a high level, our scheme is analogous to (but distinct from) low-rank matrix approximation. We also note that our scheme is similar to one that was proposed in [Jeh and Widom 2003] but lacked accuracy and complexity guarantees, so another contribution of our paper is to address this gap in the literature.
IDSS-BM: Intelligent Decision Support System for Business Models

For decision makers, the Business Model is crucial for the success of organizations. It reflects manager's assumptions about what customers want, and how an enterprise can best meet those needs and get paid for. Decision support systems can assist in making Business Model design decisions. However, previous work on decision support systems for business models provides no solution for explaining their reasoning for managers. The purpose of this paper is to discuss the development of a new generation of intelligent decision support system that have ability of providing explanations to increase confidence in proposed solutions. The intelligent system by giving some support for the suggested conclusions could be used as learning tool in business schools. The proposed system uses Case-Based Reasoning (CBR) as the resolution methodology, which is an important aspect of artificial intelligence. It is a computational methodology designed to generate new solutions to given problems by analyzing known solutions to previously solved problems. The Business Model decision-making tool would provide both the solution and explanation on how it is obtained.
Requirements customized recommendation in a decision-making system

Decision-making requirements differ from one decisionmaker to another, hence the need to study decision-making requirements personalization. Customization is an effective response to the information overload problem by injecting into queries more restrictive filtering criteria, and by possibly reformulating queries to better take into account the interest centers and the decision maker preferences. The decision maker's preferences are obtained at the end of the exhaustive study of the decision-maker profile and taking into account the decision-maker's status with regard to the requested products. We have decided to improve the existent models by adding to the profile a part mentioning the decision-maker belief, which allows us to elaborate the needs personalization or to pass to the customized recommendation. To develop our proposed solution we chose the sports articles trade as a study domain.
Congestion-Aware Ride-Sharing

In its current form, ride-sharing is responsible for a small fraction of traffic load compared to other transportation modes, especially private vehicles. As its benefits became more evident, and obstacles, e.g., lack of liability legislation, that may hinder its larger scale adoption are being overcome, ride-sharing will be a more common mode of transportation. In particular, autonomous vehicles (AVs) are showing their proficiency on the roads, which may also catalyze ride-sharing ubiquity. For example, while an AV owner is at work, he may find it appealing to offer his AV as a service or rent it to Uber so that the vehicle serves others’ transportation requests. Furthermore, this disruptive technology is backed up by companies like Google (Waymo), Tesla, and Uber. Therefore, ride-sharing will soon become a source of traffic congestion itself. In this article, we present an efficient congestion-aware ride-sharing algorithm which, instead of finding optimal travel plans based on traffic load generated by other means of transportation, it computes optimal travel plans for thousands of ride-sharing requests within a time interval. Note that in this problem, an optimal travel plan for a group of requests may affect an already computed travel plan for another concurrent group of requests, therefore plans cannot be isolated from each other.
Research on Financial Network Loan Risk Control Model based on Prior Rule and Machine Learning Algorithm

With the development of big data and artificial intelligence, it is more and more common to apply machine learning algorithms to financial risk control. However, the current research work still lack the integration of prior rule knowledge and big data algorithms. This paper proposes a novel risk control model, which firstly uses the financial industry's prior rules to conduct risk control identification, and rapid screens data of rise that is easy to judge. Then, we use machine learning algorithms such as SVM to train and learn financial big data. The combination of the two makes full use of prior risk control support for fast and effective data judgment and machine learning for efficient and real-time monitoring of financial dynamic changes.
Scaling Intel® Software Guard Extensions Applications with Intel® SGX Card

Cloud computing revolutionized the way internet-scale services are deployed and scaled. However, general security concerns and protecting business critical data are still major factors holding companies back from moving their IT infrastructure to the cloud. Intel® Software Guard Extensions (Intel® SGX) technology provides a hardware enforced trusted execution environment specifically developed to compute on confidential data in untrusted public clouds. To date, Intel SGX is available only on single-socket platforms and its secure memory limited to 128 MB. This paper describes how the Intel SGX Card makes the Intel SGX technology available on dual-socket server platforms today and easily integrated into existing data center infrastructure. Also, with software enabling, there is potential for applications to scale-out across the cards's three Intel® Xeon® E3 processors for additional secure memory. We propose four software architectures to efficiently utilize the card's resources and present use cases that benefit from Intel SGX card based deployments.
A Novel Deep Learning Approach for Lung Cancer Recognition Based on 1-D Deep Convolutional Neural Network

Pulse diagnosis has been a requisite facet in traditional Chinese medicine as well as in western medicine, yet the prognosis of lung cancer hinged on wrist pulse analysis entails the quotidian approaches. In spite of diagnosing the lung cancer, in traditional methods, the identification stratagem is divaricated into assorted steps: analysis of signals procured, synthetic extraction and selection of features, and subsequently the classification. However, the vague and mundane feature selection and signal analysis steers to the inadequate classification accuracy due to intrinsic deficiencies. In this study, we have proposed a novel deep convolutional neural network (DCNN) based approach to discern the lung cancer against the acquired wrist pulse signals. In order to ensnare the features, vanquishing the overfitting, a 1-dimensional 15-layers DCNN model is devised hinged on 1-D convolutional, batch normalization, and pooling layers. Considering the instinctive feature extraction, from the experimental data comprised of 45,969 samples of 16 lung cancer and 20 healthy individuals, assorted units are heaped in the lodged DCNN. The experimental comparison with the state-of-art deep neural networks (DNNs) and conventional methods evinced that our lodged approach conquer the deficiencies of conventional signal processing and manual feature selection approaches. Finally, the results, with the validation precision of 97.67%, outperform the recent existing approach for lung cancer recognition.
Research on Robotic Arm Recognition and Grasping Based on RGB-D Sensor

As people's demand for service robots continues to grow, how to solve the problem of robot interaction with the physical world, especially solving the problem of robot crawling, has become a hot issue in the world. In this paper, the algorithm of target recognition and 6D pose estimation is studied based on RGB-D sensor Kinect. The target recognition, which The object to be captured is determined in a complex environment, is performed by ORB algorithm and template matching. The point cloud registration is performed by LM-ICP algorithm to calculate the position and attitude of the object relative to the robot. Finally, the feasibility of the capture experiment verification algorithm in the Gazebo physics simulation environment is carried out.
A Study for Parallelization of Multi-Objective Evolutionary Algorithm Based on Decomposition and Directed Mating

This work proposes a parallel multi-objective evolutionary algorithm based on decomposition for solving constrained multi-objective optimization problems. A representative decomposition-based algorithm, MOEA/D, decomposes multi-objective problems into a number of single-objective sub-problem using weight vectors and a scalarizing function. It keeps only the best solution for each sub-problem and neighbor solutions are used to generate offspring. Therefore, to independently execute solution generation in parallel by using multi-core, at least two solutions have to be included in a core. Hence, maximum parallel number of MOEA/D-based parallel algorithm is the population size over 2. However, in proposed parallel algorithm, it can be the population size since it keeps not only the best feasible solution but also an archive population of useful infeasible solutions for each sub-problem. The experimental results using discrete knapsack problems with 2 objectives and {2, 6, 10} constraints show that the proposed parallel algorithm achieves higher search performance by utilizing infeasible solutions even if the number of parallelization is higher than a parallel decomposition-based algorithm.
Planar Homography based Monocular SLAM Initialization Method

Simultaneous Localization and Mapping (SLAM) is a popular topic in autonomous robots navigation. It has been studied for decades in both computer vision and robotics communities. Monocular systems is more cost effective compared to RGBD or Stereo systems; however, it is relatively complicated to initialize due to scale uncertainty. Under certain conditions, it is assumed that the camera only moves in a planar scene, which provides us with homography constraints. In this paper, the efficiency of monocular initialization was improved based on the open source platform ORB-SLAM2, employing the algorithm based on planar homography constraints. We compared the improved algorithm with the source code of ORB-SLAM2 on the public datasets. It showed that our algorithm has better stability and robustness in the planar scene dataset and more initializing map points.
An Intelligent Deadlock Locating Scheme for Multithreaded Programs

Deadlock occurs when all threads of a program remain in their current state and cannot move forward. These threads execute concurrently in multi-core CPUs. As the execution order of their code lines is uncertain, it is extremely difficult to locate the accurate position that deadlock occurs without modifying the source code. C/C++, Qt and Java are three commonly used programming languages in Linux. This paper presents an intelligent scheme of deadlock locating for these languages. By modifying the kernel of pthreads, Qt and OpenJDK, we redesign three kinds of resource functions: mutex, lock and semaphore. At runtime, the file names and line numbers of these functions which a user's program calls are written to a shared memory database called Redis. The data in Redis can be fetched by two tools. One graphical tool is responsible for displaying the usage of resources and do deadlock analysis. Another is used to detect deadlock periodically and write deadlock to a journal file, or notify users by mail or short message. A plugin is also developed respectively for QtCreator and Eclipse. Both tools can be started from either plugin. The deadlock detection method does not need to modify the source code of a user program, which greatly facilitates the user to determine the location of deadlock.
How Does a Nation Walk?

Activity trackers are being deployed in large-scale physical activity intervention programs, but analyzing their data is difficult due to the large data size and complexity. As such large datasets of steps become more available, it is paramount to develop analysis methods to deeply interpret them to understand the variety and changing nature of human steps behavior. In this work, we explored ways to analyze the heterogeneous steps activity data and propose a framework of dimensions and time aggregations to interpret how providing a city-wide population with activity trackers, and monetary incentives influences their wearing and steps behavior. We analyzed the daily step counts of 140,000 individuals, walking a combined 74 billion steps in 305 days of a city-wide public health campaign. We performed data mining clustering to identify 16 user segments, each with distinctive weekly streaks in patterns of device wear and recorded steps. We demonstrate that these clusters enable us to interpret how some users increased their steps level. Our key contributions are: a new analytic method to scalably interpret large steps data; the insights of our analysis about key user segments in our large intervention; demonstrating the power to predictive user outcomes from their first few days of tracking.
The leaky integrate-and-fire neuron model for a rigid and a non-rigid object tracking

Spiking neural networks (SNNs) fall into the third generation of artificial neural network models, increasing the level of realism in a neural simulation. In this paper, a spiking neural network is presented for detecting and tracking of a moving object in video sequences with a static camera. The motion estimation of the object is carried out by minimizing a Hausdorff distance measure. The system has been successfully tested with various real video sequences. The results showed that our system can track the identified target over subsequent video frames even in occlusion case.
Contribution to the development of an energy management solution in a green smart home (EMSGSH)

This document offers a smart solution for managing green energy for a home (EMSGSH). It offers services such as optimizing the energy consumed while assuming green energy available at home. However, the random nature of this type of energy in the power generation, we require using a hybrid energy system (HES) accompanied by energy storage in the form of a battery. The home is considered a distributed system, it is composed of a set of elements that are geographically distributed and in permanent interaction. Following all its features, we chose two approaches for this solution, the multi-agent systems (MAS) for the control of the elements and the domain ontology to ensure a good formal representation of the data associated with the system. We propose a master-slave architecture. A slave agent is proposed for each device to control its local consumption and a single master agent is proposed to control all agents. The objective is to optimize the use of green energy and minimize consumption costs by exploiting the offers of electric power suppliers. The last part of this work was reserved to present the agents (tasks and responsibilities, interactions .) and to model the agent's society using a unified modeling language (UML), also the ontology elements are presented and edited in the Protégé software.
BoostMeUp

A person's emotional state can strongly influence their ability to achieve optimal task performance. Aiming to help individuals manage their feelings, different emotion regulation technologies have been proposed. However, despite the well-known influence that emotions have on task performance, no study to date has shown if an emotion regulation technology can also enhance user's cognitive performance in the moment. In this paper, we present BoostMeUp, a smartwatch intervention designed to improve user's cognitive performance by regulating their emotions unobtrusively. Based on studies that show that people tend to associate external signals that resemble heart rates as their own, the intervention provides personalized haptic feedback simulating a different heart rate. Users can focus on their tasks and the intervention acts upon them in parallel, without requiring any additional action. The intervention was evaluated in an experiment with 72 participants, in which they had to do math tests under high pressure. Participants who were exposed to slow haptic feedback during the tests decreased their anxiety, increased their heart rate variability and performed better in the math tests, while fast haptic feedback led to the opposite effects. These results indicate that the BoostMeUp intervention can lead to positive cognitive, physiological and behavioral changes.
GM(1,N) Analysis of Effect of Consumer Confidence and Transaction Security on Vietnamese Online Shopping Intention

The purpose of this research is to investigate the effect of consumer confidence and transaction security on online shopping intention in Vietnam. Consumer confidence, transaction security, and online shopping intention, with questions of four, four and one respectively, were employed to design a questionnaire. All the questions were measured with five-point Likert scale with 1 denoting strongly disagreement, 2 for disagreement, 3 for neutral 4 for agreement and 5 for strongly agreement. An online survey by convenience sampling was employed. As a result, 206 of 220 respondents were obtained. The respondent data were analyzed by GM(1,N) model to explore the weight of each factor of consumer confidence and transaction security on online shopping intention. As the results of GM(1, N) analysis suggested, trust of the quality of goods rank, online prestige, and quality of service as the first to third most significant factors in terms of their highly-flavored weighting towards online shopping intention while trust of the security system, the transact system, the payment system and the information as fifth to eighth place. That means consumers would like to believe online commodities even they do not see the actual goods. However, on the contrary, no matter how many efforts have been made to improve the policy and function of security system, transact system, payment system and the information consumers' trust on these function still remain in the low confidence, especially for information correctness. It is suggested that online shopping should pay more attention to the promotion of information provided to increase customers' confidence.
Evaluation of Hardware Data Prefetchers on Server Processors

Data prefetching, i.e., the act of predicting an application’s future memory accesses and fetching those that are not in the on-chip caches, is a well-known and widely used approach to hide the long latency of memory accesses. The fruitfulness of data prefetching is evident to both industry and academy: Nowadays, almost every high-performance processor incorporates a few data prefetchers for capturing various access patterns of applications; besides, there is a myriad of proposals for data prefetching in the research literature, where each proposal enhances the efficiency of prefetching in a specific way. In this survey, we evaluate the effectiveness of data prefetching in the context of server applications and shed light on its design trade-offs. To do so, we choose a target architecture based on a contemporary server processor and stack various state-of-the-art data prefetchers on top of it. We analyze the prefetchers in terms of their ability to predict memory accesses and enhance overall system performance, as well as their imposed overheads. Finally, by comparing the state-of-the-art prefetchers with impractical ideal prefetchers, we motivate further work on improving data prefetching techniques.