Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal stealthy attacks on actuators for strictly proper systems

In this paper, we consider stealthy data injection attacks against control systems, and develop security sensitivity metrics to quantify their impact on the system. The final objective of this work is to use such metrics as objective functions in the design of optimal resilient controllers against stealthy attacks, akin to the classical design of optimal ℋ∞ robust controllers. As a first metric, the recently proposed ℓ2 output to output gain is first examined, and fundamental limitations of this gain for systems with strictly proper dynamics are uncovered and characterized. To circumvent such limitations, a new security sensitivity metric is proposed, namely the truncated ℓ2 gain. Necessary and sufficient conditions for this gain to be finite are derived, which we show can cope with strictly proper systems. Finally, we report preliminary investigations on the design of optimal resilient controllers, which are supported and illustrated through numerical examples.
PREVALENCE AND RISK FACTORS OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE IN PAST PULMONARY TUBERCULOSIS PATIENTS, KHARTOUM STATE, SUDAN

PREVALENCE AND RISK FACTORS OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE IN PAST PULMONARY TUBERCULOSIS PATIENTS, KHARTOUM STATE, SUDAN R K Osman 1 , G Bjune 2 , and A El Sony 3 1 MD, Epi-Lab, Khartoum, Sudan, MPhil International Community Health, University of Oslo, Norway. 2 Professor, Institute of Health and Society, Faculty of Medicine, University of Oslo, Norway. 3 Professor, Epi-Lab, Khartoum, Sudan. Past-president of the IUATLD Background: Chronic Obstructive Pulmonary Disease (COPD) and Pulmonary Tuberculosis (PTB) constitute a significant public health problem, especially in low and middle income countries. Although, smoking is considered to be a significant risk factor for COPD, further research is necessary to identify risk factors other than smoking, especially in low and middle income countries where the greatest impact of COPD is expected to occur. Not only there is an increasing interest for non-tobacco risk factors, but also there is an emerging evidence that past PTB is a significant risk factor for COPD. Objectives: The study major objective is to measure the prevalence and risk factors of COPD among Past Pulmonary Tuberculosis (PPTB) patients. Furthermore, to identify the effect of smoking and delayed TB treatment in the development of COPD. Methods: This is a retrospective cross-sectional study conducted among PPTB subjects and their age and sex matched controls. A total of 136 patients with PPTB was selected from the chest referral clinic at Omdurman teaching hospital after meeting the inclusion and exclusion criteria and matched with 136 controls. Data was collected using structured questionnaires during face to face interview. Then the study participants underwent lung function assessment using spirometry. PPTB diagnosis was based on positive sputum smear results, while COPD was defined as a postbronchodilator FEV1/FVC <0.7 Results: Prevalence of COPD among PPTB subjects (mean age, 43.97 ± 8.51 years) was 8.8%. PPTB was an independent risk factor and the strongest predictor for COPD with an odds ratio of 12.39 after adjusting for confounding by age, gender, occupational exposure, biomass fuel and smoking did not affect the association. Delayed TB treatment was a risk factor for COPD [OR: 4.09 (95% CI 1.17-14.32)].
Product Reviews based on Location using N-gram model

Web-based social networking information as client created content on blog surveys, smaller scale blogging like Twitter, dialog gatherings, diverse sorts of social destinations, item audit and interactive media sharing sites present numerous new chances and difficulties to the two makers and buyers. A model is available to examine Twitter information of e-clients and concentrate data and make forecasts about their shopping conduct on a web based business commercial centre. In the wake of gathering information from a web based business showcase, played out an information mining application and extricated online clients' personal conduct standards about purchasing or not. The model that is available predicts whether clients will or won't purchase their things included to shopping crates a commercial centre. The enormous development of online informal communities (OSN) like Twitter, Facebook and other person to person communication gateways have made a need to decide individuals' sentiment and dispositions. Posting client criticism on items has turned out to be progressively prevalent for individuals to express their assessments toward items and administrations. The organizations feel that there is an opportunity to enhance the market of an item in the event that they know about how individuals feel about it. In this examination, there is utilization of machine learning procedures to discover about web based business website that is more valuable and useful for e-clients by foreseeing and breaking down through their audits.
Internet of Things Based Wireless Garbage Monitoring System

Internet of things [IoT] enables realization of the development of various intelligent systems and devices. In this scenario, intelligent waste management occupies an indispensable part in building smart cities. Furthermore, for improving the intensity and efficiency of waste monitoring system, we investigate the ways by which the garbage can be collected in the city and imitating new techniques to monitor all these processes. This paper proposes an idea of wireless garbage monitoring system with IoT as an underlying architecture. We can use this system to meet the actual garbage monitoring needs, and use of corresponding software system to achieve remote monitoring, that aims to minimize human intervention and improve the efficiency of human activities. Meanwhile, we have proposed a technical solution to build a simple garbage monitoring hardware and software system as a reference. It is believed that this trivial reference will give the platform to build a better design and its implementation.
A Method of Differences for Determining the Propagation Constant from Multiline Measurements

Propagation constant versus frequency curves are obtained after processing S-parameters measured to several lines differing only in length. The proposed method can be easily implemented once the thru-reflect-line (TRL) eigenvalues are calculated considering different combination of line pairs. The noticeable simplicity of the approach when compared to the original multiline algorithm makes it an attractive alternative to improve the determination of the complex propagation constant in practical applications by using experimental data collected to more than two lines. Here, applicability is demonstrated up to 110 GHz for printed circuit board (PCB) microstrip lines. Furthermore, it is provided an improved equation to determine the effective relative permittivity once the complex propagation constant is determined.
Satellite Image Enhancement Using Neural Networks

Satellite imagery has revolutionized earth's civilization. It allowed human beings to explore many hidden wonders of earth, it allowed to uncover many unexplored places and discovered Iceland's, volcanoes, forests, natural resources and many more. Satellite imagery has improved exponentially in the millennium. Satellites are used to take images of earth and stitch it to form maps and helped people to navigate, many technologies used in imaging and positioning to improvise and sensors are used to accurate imaging like LISS, however sensors have a hardware limitation. There are many factor which affect the clarity of imaging like mist, light pollution, air pollution, clouds, which masks the actual image, and that's where image enhancement comes into picture. Existing systems use neural networks to enhance the image which uses model VGG19 which has error rate up to 7.5% and it has only 60% accuracy. Satellite image enhancement is divided into 2 segments, upscaling and de-hazing, image is loaded and pixels are segmented to have compatibility and work optimization. Earth image is very vast and complex, dedicated image enhancement for particular place is not possible so neural network is selected which can detect the place automatically and enhance it. Resnet is the model which can be used efficiently in this context, it results in better accuracy 70%-80% and has very less error rate. And for haze removal dark channel prior is used which is efficient and works faster without mat lab. The results shows positive improvement for image enhancement for almost all type of image with more efficiency. This can be done by comparing with the trained datasets present in landsat and present data. The landsat dataset consists of multiple satellite imagery which has been cropped. The result has showed increased accuracy and also haze level has been reduced from 43% to 35% in de-hazing. The proposed system has over 80% efficiency.
Low Power High Stability SRAM Cell with Combined Effect of Sleep- Stack and Diode Gated Technique

In this work, Static Random Access Memory (SRAM) is designed on 0.18 micron by using CADENCE virtuoso tools. It focuses on the power consumption and leakage power improvement since SRAM has high leakage power consumption. Leakage power and performance of SRAM cell is greatly affected by the development of technology and speed of operation. The total leakage power is greatly affected by the combined effect of subthreshold leakage current and gate leakage current. With the fall of oxide thickness the gate leakage current increases exponentially. Hence efficient techniques are required which will address the gate leakage component. In our design, with sleep- stack two more transistors are used in conventional SRAM cell to minimize the gate leakage current and to improve the performance of the memory cell.
Asynchronous Multi-rate Sampled-data Control: an Embedded Model Control Perspective

This study presents an estimation and control method for systems with asynchronous measurements with time-stamp. This approach is developed within the framework of Embedded Model Control (EMC), a control design methodology based on the estimation and the active rejection of the uncertainties affecting the plant to be controlled. The proposed control approach allows us to deal with control applications in which the measurement or the control law are characterized by a variable sampling time. Most notably, the proposed algorithm is capable to manage delays and package dropouts, potentially affecting networked control systems, by leveraging not only the sensors measurement information but also their time-stamp.
Maximum Efficiency Control of a Wireless EV Charger with On-Line Parameter Calculation

Dynamic wireless power transfer (WPT) systems are considered as a viable option for EV charging applications. However, maintaining high efficiency and fixed output voltage of the systems is of great concern due to the variations of driving conditions and system parameters. This paper proposes a control method for maximizing efficiency and regulating load voltage of a WPT system simultaneously, considering the variations. In the proposed method, the secondary circuit parameters and mutual inductance are calculated on-line, which cancels system dependency on the parameter variations. Hence, employing the control method makes the system compatible with different EVs with different parameters. Furthermore, the system keeps its optimal operation even with abrupt misalignments caused by movement of EV. Simulation results validate the proposed method.
Breathiness Indices for Classification of Dysarthria Based on Type and Speech Intelligibility

Dysarthria classification based on intelligibility level is useful for speech pathologists for deciding the therapy. However, intelligibility assessment also depends on perceptual attributes like hypernasality, breathiness, slow rate, short pauses etc. These perceptual attributes vary depending on the cause for dysarthria giving rise to different types of dysarthria. In this work, we explore the use of breathiness features for intelligibility assessment of dysarthria and for distinguishing type of dysarthria. Voiced segments from two controlled speakers, two dysarthric speakers with low and mid intelligibility level each from UA database are used in the work. Features were analysed for use in dysarthria intelligibility assessment vs. distinguishing type of dysarthria.
First Level Approximation of Measured Neuronal Response using a Leaky Integrate and Fire Model

Modeling of a spiking bio-neuron is still an important exercise in view of possible applications of the underlying features in the areas of neuromorphic engineering, cognitive radio and spiking neural networks. An approximate but low-complexity mathematical model on membrane potential [1] is used as a possible candidate to assess multiple model parameters from a selected measured data set [2]. The first hand assessment of the model parameters are presented in this paper. Estimates of the model parameters will help in tuning the model and thus generating spike trains closer to what are generated by biological neurons.
DOCKERANALYZER : Towards Fine Grained Resource Elasticity for Microservices-Based Applications Deployed with Docker

This article deals with anomaly detection for microservices-based applications during elastic treatment. In elastic treatment, scaling-up resources is based on threshold. Many studies consider that threshold exceeding is caused by the increase in requests number. However, this exceeding may be caused by many problems such as specific requests requiring a lot of resources or issues related to VMs and containers. That's why, when thresholds are exceeded we propose to apply an analysis treatment that detects and identifies the root cause of the threshold exceeding, either it's caused by a problem such as specific request, VM issue, container issue or it's caused by a normal increase in request's number. This paper presents "DOCKERANALYZER" a software module that detects and identifies execution problems in microservices context. Experimental measurements have been conducted on an IOT platform as a real use-case presenting realistic problems and demonstrating the effectiveness of our proposed solution.
Targeted Influence Maximization Based on Cloud Computing Over Big Data in Social Networks

This paper focuses on the targeted influence maximization based on cloud computing in social networks. Most of existing influence maximization works assume that the influence diffusion probabilities on edges are fixed, and identify the Top- $k$ users to maximize the spread of influence assuming the knowledge of the entire network graph. However, in real-world scenarios, edge probabilities are typically different based on various topics, and may be affected by information received. Meanwhile, obtaining complete network data is difficult due to privacy and computational considerations. Moreover, existing influence maximization algorithms considering target users do not discuss cloud computing which lead to low computational efficiency when dealing with big datasets in social networks. To this end, this paper proposes a targeted influence maximization solution based on cloud computing. First, a new topic-aware model called tag-aware IC model is presented, which takes into account users’ interests, characteristics of the item being propagated, and the similarity between users and the related information. Then, efficient algorithms with approximation guarantee are provided using a bounded number of queries to the graph structure. These methods aim to find a seed set that maximizes the expected influence spread over target users who are relevant to given topics. Finally, empirical studies of the proposed algorithms are designed and performed on real datasets. The experimental results show that the techniques in this paper achieves speedup and savings in storage compared with the state-of-the-art methods.
Designing and Prototyping the Human-centered ELectric Prosthetic (HELP) Hand

The majority of amputations in India are lower arm amputations. Due to stigma in Indian society, many amputees hide their amputation even at the cost of functionality, wearing purely cosmetic prostheses. Partnering with the Indian non-profit, BMVSS, the goal of this project is to design a low cost, durable, and culturally-appropriate electric prosthetic hand to promote equal opportunity for amputees socially and economically.
Multi-UAV Coverage Scheme for Average Capacity Maximization

Unmanned aerial vehicle (UAV) can act as flying base station to provide ground user (GU) with ubiquitous service in crowded or remote areas. The objective of this letter is to obtain the multi-UAV coverage scheme for maximizing the average capacity of UAV, while simultaneously ensuring that the deployed UAVs can cover all the massive or scattered GUs. We propose the dense boundary prioritized coverage (DBPC) scheme, the main idea of which is the high priority given to cover the dense boundary GU, to deploy multiple UAVs for the average UAV capacity maximization. Particularly, in DBPC scheme, the feasible region division (FRD) algorithm, which utilizes the Lagrange method, is obtained to derive the optimal placement of single UAV for maximizing the UAV capacity. Simulation results are presented to validate the superiority of proposed DBPC scheme.
Predicting and Reducing Dropout in Virtual Learning using Machine Learning Techniques: A Systematic Review

Context: This Research to Practice Full Paper presents a systematic review of methodologies that propose ways of reducing dropout rate in Virtual Learning Environments (VLE). This generates large amounts of data about courses and students, whose analysis requires the use of computational analytical tools. Most educational institutions claim that the greatest issue in virtual learning courses is high student dropout rates. Goal: Our study aims to identify solutions that use Machine Learning (ML) techniques to reduce these high dropout rates. Method: We conducted a systematic review to identify, filter and classify primary studies. Results: The initial search of academic databases resulted in 199 papers, of which 13 papers were included in the final analysis. The review reports the historical evolution of the publications, the Machine Learning techniques used, the characteristics of data used, as well as identifies solutions proposed to reduce dropout in distance learning. Conclusion: Our study provides an overview of the state of the art of solutions proposed to reduce dropout rates using ML techniques and may guide future studies and tool development.
Reinforcement Learning Augmented Optimization for Smart Mobility

Many mobility applications in smart cities are addressed as optimization problems. However, often, these problems are fragile due to their large-scale and non-convex nature, and also due to uncertainties arising because of human activity. In this paper, we apply a model-based Markov-decision-process (MDP) closed-loop identification algorithm to augment classical optimizers, with a view to alleviating this fragility. Specifically, we use deterministic optimal solutions provided by classical optimizers as initial guesses for MDP’s policies, which are then "amended" as a result of online interaction with the environment to cope with uncertainty. Applications are described from niche of smart mobility problems, and numerical results are provided.
Performance of 60GHz mm-Wave Communication System Overcomes Occlusion Problems by Adding Relays

6IEEE 802.11ad standardized 60GHz millimeter wave technology brings us super data rate. However, the millimeter wave link's performance is seriously affected by transmission distance and blocking. In this paper, we built a relay system model under the IEEE802.11ad standard channel, studied the analytical expression of the system's signal-to-noise ratio (SNR), the probability density function (PDF), cumulative density function (CDF) and the moment generating function(MGF). Then we first discuss the bit error rate (BER) between relay links and direct transmission links using this system model. The second, we discuss the AF opportunity relay with fixed rate transmission to describe the benefits brought by the relay, and then compare the simulation performance of the system model, including average BER and the average channel capacity with different number of relays. We finally summarize that using relay in millimeter wave communication system can significantly improve the transmission performance of millimeter wave communication system.
Automatic epileptic seizure detection in a mixed generalized and focal seizure dataset

Detection of seizure periods in an epileptic patient is an important part of health care. However, due to the variety in types of seizures and location of them, real-time seizure detection is not straight forward. In this paper, we propose a method for seizure detection from EEG signals in datasets which have both generalized and focal seizures. The proposed method is useful in the situations that we have no prior knowledge about the location of the patient’s seizure and the pattern of evolution of seizure location. In the proposed method, first, the artifacts are automatically reduced by Blind Source Separation (BSS) methods. Then, the channels are clustered into two clusters. After that, the channels of each cluster are classified into the seizure and non-seizure groups. The final decision is made based on voting in each cluster. If at least one cluster shows seizure behavior, we assign the corresponding epoch to seizure class. Results show that our method achieved 80.72% accuracy, 80% sensitivity, 81.08% specificity, and 67.55% precision in a mixed generalized and focal seizure dataset.
The Role of Implicit Theories in Computer Science and Engineering Studies: First Findings and Review

This Full Paper in the Research-category presents our initial findings in regards of students' implicit theories. Beliefs that people associate to themselves, often called implicit theories, self-theories, or mindsets, are a fundamental source of bias in human thinking. Research shows that implicit theories radically shape human thinking, behaviour, and progress. In the domain of education, implicit theories have significant associations to academic performance. In social domains, implicit theories are associated with development of social and soft skills, and groupwork behaviour. This article reviews the most significant research on implicit theories in educational and social domains, and shows significant research gaps in computing education. This article also summarises our previous findings, and presents new results from a sample of $n =39$ second year CS or computer engineering (CE) students in University of Turku, Finland, Department of Future Technologies. We investigated students' implicit theories and related association with academic achievement in first CS courses. Contrary to a lot of research, but in line with our own previous findings, we found that fixed implicit theories were connected with academic achievement. However, more data is needed to add reliability and confirm the results either way. The implications of implicit theories to development of soft skills are discussed, and a future research agenda is laid out.