Miracle-dz/UniFAR-FTUs huggingface dataset:
Primal-dual algorithm to solve the constrained second-order total generalized variational model for image denoising

Abstract. The variational method, which is a popular approach for image denoising, aims to estimate the original image from a noisy or corrupted image. To consider the constraints of image pixel values fully, our study investigates a constrained second-order total generalized variational (TGV) model, which includes non-negative and bounded constraints as a special case. By adopting an equivalent definition of the second-order TGV, we transform the proposed constrained minimization problem into a minimization of the sum of two convex functions, where one is composed of a linear transformation. Subsequently, we employ the relaxed primal-dual proximity algorithm to solve it. The advantage of the obtained algorithm is that it is matrix-inversion free and does not involve any subproblem. Numerical results demonstrate that the performance of the constrained TGV model is slightly better than that of the unconstrained model.
Towards the Detection of Mobile DDoS Attacks in 5G Multi-Tenant Networks

The fifth-generation (5G) mobile networks target a variety of new use cases that involve a massive amount of heterogeneous devices connected to the same infrastructure. This trend also brings new security threats, and one of the most critical ones for the availability of network services is a Distributed Denial of Service (DDoS) attack. A small portion of the billions of connected devices can be employed as a botnet to trigger a massive DDoS flooding attack that can bring down important services or affect the complete infrastructure. Traditional security systems against DDoS attacks are generally designed to work in infrastructures with a particular topology. However, the mobility of many devices subscribed to the network should be taken into account when designing defence systems. Otherwise, both the detection and the trace back of the attacker will be limited to non-mobile devices as the source of the attack. This is specially relevant when security needs to be part of the definition of the network slices associated to the 5G networks. This paper presents a novel approach to overcome the limitation of traditional detection systems. A novel sensor provides the required information to trace back an attacker even if it is moving among different locations. The proposed approach is suitable to be deployed in almost all 5G network segments including the Edge. Architectural design is described and empirical experiments have validated the proposed approach.
SODALITE: SDN Wireless Backhauling for Dense 4G/5G Small Cell Networks

Dense deployments of small cells (SCs) are key to fulfill the capacity requirements of future 5G networks. However, two roadblocks to the adoption of SCs are: 1) the limited availability and the cost of sites with wired backhaul resources and 2) the complexity to manage a dense deployment of wireless backhaul nodes. Towards these challenges we propose SODALITE, a novel system that applies software-defined networking (SDN) to a wireless backhaul network. We present, how SODALITE can be integrated to 3GPP’s 4G and 5G architectures, and show the feasibility of SODALITE through LTE network testbed experiments. We substantiate the scalability of SODALITE through stochastic studies using real-life traffic traces from an LTE network and discuss the effects of cell densification and 5G system architecture on these studies. Further, a reliable backhauling solution for wireless links is introduced in SODALITE through SDN-enabled mechanisms that are capable of reconfiguring the data plane upon a link failure detection. Its reliability is shown through experiments on an LTE network testbed, and studied thoroughly via rigorous simulations and network emulator evaluations. As a result, we claim that SODALITE is a promising carrier-grade system to manage a wireless SC backhaul.
A New Normalized Method of Object Shape-based Recognition and Localization

This paper introduces a new normalized measure for the assessment of a contour-based object pose. This algorithm allows a supervised assessment of recognition and localization of known objects, the differences between a reference edge map and a candidate image are quantified by computing a performance measure. This measure is normalized and is able determine the degree to which an object shape differs from a desired position. Compared to 6 other approaches, experiments on real images at different sizes/scales exhibit the suitability of the new method for an object pose or shape matching estimation.
Trajectory Planning and Safety Assessment of Autonomous Vehicles Based on Motion Prediction and Model Predictive Control

Security problem is a fundamental issue for autonomous vehicles. Trajectory planning is a significant component of autonomous vehicle system, which directly influences the automated traffic safety. In this paper, the motion prediction of other traffic participants is considered. We use Monte Carlo simulation to predict the probabilistic occupancy of the object and give a map from probability statistics to actual scenarios. The non-time-based reference trajectory can be obtained by using high-definition map and lane detection. Then model predictive control is utilized to optimize the reference trajectory according to the current state of autonomous vehicle. Different prediction horizons and coordinate transformation are adopted to optimize the planning. By doing so, the constraint conditions can be easily involved and the result is more intuitive. The probabilistic occupancy of other traffic participants are computed offline and then the obtained results are used in real-time application. Therefore, the real-time computational burden is reduced. The crash probability is put forward to verify the feasibility of real-time trajectory in safety assessment module. Two typical scenarios are analyzed: lane change on the straight road and turning at the intersection. The simulation results illustrate the efficiency of our method.
Smart Hospital for Heart Disease Prediction Using IOT

The Internet of Things (IoT) is inter communication of embedded devices using various network technologies. The IoT technology is all set to become the upcoming trend in the future. We are proposing a healthcare monitoring system consisting of ECG Sensors. The parameters which are having a significant amount of importance are sensed by the ECG sensors which are vital for remote monitoring of patient. A mobile app observation is used to continuously monitor the ECG of the patient and various data extraction techniques are performed on the ECG wave to extract attributes to correctly predict heart diseases. Data mining with its various algorithms reduce the extra efforts and time required to conduct various tests to detect diseases. Data is collected from ECG sensors. The data is stored onto s storage medium where data mining algorithms are performed on the data collected. These algorithms predict whether the patient has any heart disease. The results can be referred by the doctors for diagnosis purpose. By using IOT technology and data mining algorithms the predication of heart disease is going to do in system
Incremental and Parallel Learning Algorithms for Data Stream Knowledge Discovery

Incremental and parallel are two capabilities for machine learning algorithms to accommodate data from real world applications. Incremental learning addresses streaming data by constructing a learning model that is updated continuously in response to newly arrived samples. To solve the computational problems posed by large data sets, parallel learning distributes the computational efforts among multiple nodes within a cloud or cluster to speed up the calculation. With the rise of BigData, data become simultaneously large scale and streaming, which is the motivation to address incremental and parallel incremental (PI) learning in this work. This research first considers the incremental learning alone, in the graph max-flow/min-cut problem. An augmenting path based incremental max-flow algorithm is proposed. The proposed algorithm handles graph changes in a chunking manner, updating residual graph via augmentation and de-augmentation in response to edge capacity increase, decrease, edge/node adding and removal. The theoretical guarantee of our algorithm is that incremental max-flow is always equal to batch retraining. Experiments show the deterministic computational cost save (i.e., gain) of our algorithm with respect to batch retraining in handling graph edge adding. The proposed incremental max-flow is then applied to upgrade an existing batch semi-supervised learning algorithm known as graph minicuts to be incremental. In batch graph minicuts, a graph is learned from input labeled and unlabeled data, and then a min-cut is conducted on that graph to make the classification decision. In the proposed modification, the graph is updated dynamically for accommodating online data adding and retiring. Then the proposed incremental max-flow algorithm is adopted to learn min-cut from the resulting
Infotainment Enabled Smart Cars: A Joint Communication, Caching, and Computation Approach

Remarkable prevalence of cloud computing has enabled smart cars to provide infotainment services. However, retrieving infotainment contents from long-distance data centers poses a significant delay, thus hindering to offer stringent latency-aware infotainment services. Multi-access edge computing is a promising option to meet strict latency requirements. However, it imposes severe resource constraints with respect to caching, and computation. Similarly, communication resources utilized to fetch the infotainment contents are scarce. In this paper, we jointly consider communication, caching, and computation (3C) to reduce infotainment content retrieval delay for smart cars. We formulate the problem as a mix-integer, nonlinear, and nonconvex optimization to minimize the latency. Furthermore, we relax the formulated problem from NP-hard to linear programming. Then, we propose a joint solution (3C) based on the alternative direction method of multipliers technique, which operates in a distributed manner. We compare the proposed 3C solution with various approaches, namely, greedy, random, and centralized. Simulation results reveal that the proposed solution reduces delay up to $\text{9}\%$ and $\text{28}\%$ compared to the greedy and random approaches, respectively.
A zero-forcing beamforming based time switching protocol for wireless powered internet of things system

In this paper, a time switching (TS) protocol for the wireless powered communications system with per-antenna power constraints is considered. To eliminate the multi-user interference, we adopt the zero-forcing beamforming scheme to maximize the sum rate performance. A two-step algorithm is proposed to solve the sum rate maximization problem with per-antenna power constraints. More specifically, golden section search method is used to find optimal time switching factor in the first step. For each given TS factor, the sub-problem in the second step is convex, which can be efficiently solved by standard software package. Numerical results are provided to demonstrate the effectiveness of the proposed methods, and some interesting results are also observed.
Multiobjective fuzzy mathematical model for a financially constrained closed‐loop supply chain with labor employment

This paper addresses the multiobjective, multiproducts and multiperiod closed‐loop supply chain network design with uncertain parameters, whose aim is to incorporate the financial flow as the cash flow and debts' constraints and labor employment under fuzzy uncertainty. The objectives of the proposed mathematical model are to maximize the increase in cash flow, maximize the total created jobs in the supply chain, and maximize the reliability of consumed raw materials. To encounter the fuzzy uncertainty in this model, a possibilistic programming approach is used. To solve large‐sized problems, the multiobjective simulated annealing algorithm, multiobjective gray wolf optimization, and multiobjective invasive weed optimization are proposed and developed. The numerical results demonstrate that these algorithms solve the problems within about 1% of the required solving time for the augmented ε‐constraint and have similar performance and even better in some cases. The multiobjective simulated annealing algorithm with a weak performance takes less time than the other two algorithms. The multiobjective gray wolf optimization and multiobjective invasive weed optimization algorithms are superior based on the multiobjective performance indices.
Improving architecture efficiency of SoftCore processors

The growing complexity of applications increases the challenge of the System-On-Chip design. The most efficient way to produce area and power efficient circuits is to design fully dedicated architectures for ASIC or FPGA technologies. However, such approach is unsuited with the “time to market” constraint due to times for design and verification. Moreover, this approach has a lack of flexibility: a modification in the application specification can require new design flow iterations. Another way to implement applications under “time to market” pressure is based on high-end processor usage. Nowadays, General-Purpose Processors (GPP) or Digital Signal Processors (DSP) provides high computation performance. Moreover, programming languages and compiler tools provide high flexibility degree for these approaches. However, general processors are not relevant in the embedded market.
GMSK Coherent Demodulation Technology based on Laurent Decomposition

Abstract. Using the Laurent method, the GMSK signal can be expressed as the sum of a series of pulse amplitude modulation (PAM) signals. The GMSK signal suboptimal coherent detection technology based on Laurent decomposition is improved. Under different phase correlation lengths, the BER performance and implementation complexity of GMSK signal optimization, suboptimization and improved coherent detection algorithms are simulated. The results show that compared with the suboptimal detection algorithm, the proposed algorithm improves the demodulation error performance without increasing the complexity of the demodulation module.
Upward social comparison and Facebook users' grandiosity

Purpose The purpose of this paper is to confirm the causal relationship, in an upward social comparison, of envy, loneliness and subjective well-being (SWB). Particularly, the authors address the mediating roles, each, of benign envy (BE) and malicious envy (ME) as different types of envy. In addition, the authors explore the grandiosity of users, in terms of narcissistic personalities, and whether it has discriminatory impacts on this causal relationship. Design/methodology/approach The authors re-enacted a situation that users confront on Facebook as a quasi-experiment to determine if there is an effectual relationship among variables in the path of upward comparison, envy, loneliness and SWB. First, the authors divided envy into BE and ME to examine its mediating role in the path of upward comparison and loneliness. Second, the authors examined the differentiated effects of both kinds of envy and loneliness on SWB. Finally, the authors determined if users’ grandiose, narcissistic behaviour has moderating effects on the path of each variable. Findings The results revealed that upward comparison has a positive effect on both kinds of envy; however, in the path of loneliness, only ME operated and played a mediating role. Furthermore, grandiosity had a partially significant moderating effect. Research limitations/implications This study has the following theoretical implications. The mediating effect of envy was identified in the path of upward comparison, loneliness and SWB. Research limitation is as follows: this study could not effectively reflect individual differences. It is necessary to include individual difference variables in later research, including characteristics of social comparison. Practical implications This study has the following practical implications. Social comparison on Facebook poses a more serious problem than it does offline; therefore, users need to protect their own SWB. If users can actively cope with the information of others and selectively choose their upward comparison targets, they can reduce their loneliness and improve their SWB as expected in the hypotheses. Social implications The “unfriending” events that occur on Facebook may be explained by the mediating phenomenon of ME. The research showed that the excessive narcissism of users on Facebook is an inconsistent information with real selves of users, thus triggering the ME, which causes avoidance from other Facebook users. Originality/value The authors have proven that social comparison and envy emotion are the causes of the loneliness, while the authors are on Facebook. Especially, the mediation role of BE and ME are discussed in a distinguished manner. Also, the authors confirmed that the influence of narcissism could further aggravate the problem of loneliness. Finally, the authors found that the variables of the study also affect the SWB of the Facebook user.
Alignment control technology of liquid crystal molecules

Liquid crystal displays are now indispensable in everyday life. The display characteristics considerably depend on the configuration of liquid crystal (LC) molecules and interactions between the LC molecules and an alignment film surface. In this paper, we introduce various methods to control parameters that dominate the LC alignment. These parameters include order parameters, the pretilt angle, the director direction, and surface anchoring strength. We also introduce their evaluation methods. In particular, recent alignment film‐free technology is explained in detail. In addition, details of how these parameters are related to the display characteristics, particularly wide viewing angles and fast response characteristics, are described primarily with reference to recent technologies.
Lecturers' Perceptions of the Use of Learning Management Systems: A Case Study in Open Distance Learning

Learning management systems (LMSs) provide possibilities to facilitate online teaching and learning; to create communities of inquiry, support dialogue and collaborative learning; and reduce students' sense of disconnectedness in distance learning. Nevertheless, studies show that LMSs are often under-utilized. This article reports on lecturers' perceptions of the use of an LMS in an open distance learning (ODL) institution in South Africa. The authors used a phenomenological case study and followed a qualitative approach. Interviews were conducted with ten lecturers from eight academic departments. The results reveal that participants used the LMS mainly for administration and to a lesser extent for teaching and learning, with a limited presence of a community of inquiry. The challenges reported by interviewees had poor access to the internet, a lack of skills, fear, and workload. The study suggests that if lecturers used the LMS for teaching and learning, it will yield many benefits for students.
Robust Energy Efficiency Maximization in Multicast Downlink C-RAN

In this paper, we investigate the beamformer design of the energy-efficient downlink multicast cloud radio access network (C-RAN) with the compression and data-sharing strategies, respectively. With the imperfect channel state information, we aim to maximize the worst-case energy efficiency (EE), which is defined as the ratio of the sum of worst-case group rates to the total power consumption of the network. The formulated problems for both strategies are non-convex and challenging to solve. In order to find the solutions, we develop a two-layer iterative algorithm that recast the formulated problems as a series of convex quadratic matrix inequality problems which can be solved by the generated Augmented Lagrangian method. The convergence of the proposed algorithms is proved via both analytical and simulated ways, and the effectiveness of them on improving EE is also validated. Moreover, through the comparison of the two transmission strategies considered, we find that the compression strategy can achieve higher worst-case EE than the data-sharing strategy in most scenarios, except for the case with low rate requirement in small-size network. Our conclusions can be used as meaningful references for the practical system design.
THE ADOPTION OF M-COMMERCE APPLICATIONS: RURAL DWELLERS' PERSPECTIVES

In recent years, mobile applications (mobile apps) have gained widespread acceptance. Mobile apps services have effectively encircled most of the human activities, with the purpose of improving the living standard of the entire public. The adoption and diffusion of a mobile app are influenced by its characteristics. This study examines the adoption and diffusion of mobile commerce among the people living in a remote area of South Africa to determine the relationship between the characteristics of innovations and the adopters' attitudes. Through a survey that used diffusion of innovation as the basic framework, the opinions of the participants living in Mbongolwane in the Uthungulu District Municipality in the KwaZulu-Natal province of South Africa were gathered. A total of 120 out of 300 questionnaires distributed, were returned, giving a response rate of 40%. It was found that the perceived attitudes of the rural dwellers are closely related to the characteristic of innovations as well as the adoption of mobile commerce. Also, the findings indicate that though the people are very keen to receive a new technological invention, they are still late adopters. This research work offers a support in improving the adoption and diffusion of mobile apps among the rural inhabitants.
SocialTree: Socially Augmented Structured Summaries of News Stories

News story understanding entails having an effective summary of a related group of articles that may span different time ranges, involve different topics and entities, and have connections to other stories. In this work, we present an approach to efficiently extract structured summaries of news stories by augmenting news media with the structure of social discourse as reflected in social media in the form of social tags. Existing event detection, topic-modeling, clustering and summarization methods yield news story summaries based only on noun phrases and named entities. These representations are sensitive to the article wording and the keyword extraction algorithm. Moreover, keyword-based representations are rarely helpful for highlighting the inter-story connections or for reflecting the inner structure of the news story because of high word ambiguity and clutter from the large variety of keywords describing news stories. Our method combines the news and social media domains to create structured summaries of news stories in the form of hierarchies of keywords and social tags, named SocialTree. We show that the properties of social tags can be exploited to augment the construction of hierarchical summaries of news stories and to alleviate the weaknesses of existing keyword-based representations. In our quantitative and qualitative evaluation the proposed method strongly outperforms the state-of-the-art with regard to both coverage and informativeness of the summaries.