Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Image Segmentation based on Full Convolutional Neural Network

Image segmentation technology is an important branch in the field of computer vision. Image segmentation is the basis of image analysis processing, and image segmentation effect directly affects image subsequent processing. Aiming at the problem that the segmentation method based on traditional machine learning is not accurate, the edge information is lost and the robustness needs to be improved, this paper proposes an image segmentation algorithm based on improved full convolution neural network. The algorithm utilizes the better feature extraction ability of the deep learning model and the sensitivity of the cluster segmentation to the edge information, and further assists the segmentation with the Ncut algorithm. The experimental results show that compared with the traditional convolutional neural network image segmentation algorithm, the algorithm finally achieves higher segmentation accuracy. From the overall analysis, the segmentation method proposed in this paper is better than other methods.
Respiratory Distress Syndrome Management in Delivery Room

The proper management of respiratory distress syndrome in the delivery room is a crucial step in the transition to extrauterine life, especially for preterm infants. In fact, it has been widely established that the optimization of the cardiovascular and the respiratory changes, which normally happen as soon as a term healthy baby is delivered, can have long-term effects. For this reason, every clinician approaching the delivery room should be aware of the consequences an inappropriate management could lead to and should know how to perform a proper resuscitation, using, where available, the most recent techniques. Regardless of the level of care provided by the hospital, there are some key interventions, which can be applied easily in every setting and are of crucial importance. In this chapter, we aim to provide a comprehensive overview of the most relevant measures to manage respiratory distress syndrome from the delivery room, starting from an explanation of the disease and moving toward the most recent evidence, from the basic concepts to the most advanced techniques to monitor fetal-neonatal transition.
An overview of methods for treating selectivity in big data sources

6 Executive summary . 7 Introduction to the study . 7 What are big data? 7 Errors impairing inference from big data sources . 8 Framework for focusing on selectivity . 8 Selectivity issues for different data sources . 9 Methods for correcting for selectivity . 10
Feature based Monocular Visual Odometry for Autonomous Driving and Hyperparameter Tuning to Improve Trajectory Estimation

In this paper, we will present an efficient feature-based localization method and use hyperparameter tuning to improve the accuracy of the method. We will first address the advantages of vision-based localization algorithms. These algorithms are more cost-efficient without GPS and are easier to integrate with vision based perception algorithms such as object detection and semantic segmentation. With camera equipped on cars, visual simultaneous localization and mapping, also called visual SLAM, improves many applications such as autonomous driving and advanced driver-assistance systems (ADAS). Currently, there are many different Visual SLAM algorithms and we will analyse the advantages of featured-based sparse methods. In the paper, we will briefly talk about the pipeline of our proposed method including feature extraction, key points matching, 8-point RANSAC algorithm and single value decomposition (SVD). Then we will use the grid search method to tune the parameters in the algorithm and improve the accuracy of the featured based visual localization method.
CLOUD COMPUTING THREATS AND RISKS: UNCERTAINTY AND UNCONROLLABILITY IN THE RISK SOCETY

: As the Fourth Industrial Revolution era is now constantly progressing, the emergence of Cloud Computing, the Internet of Things, and Big Data is fast changing the computer domain. Accordingly, the computing tasks and processes are getting bigger and more complicated than ever before. In particular Cloud Computing (hereafter abbreviated as CC) is turning out to be a labyrinth in spite of its enormous advantages that it offers to its stake holders—both the individuals and organizations--in today’s informational environment of the networked society. CC, as a popular and promising domain of technological paradigm, is now globally established following the availability of high capacity network system, less expensive computers and scalable storage tools, reliable hardware virtualization, and necessaryservice oriented architectural frameworks - all ready to instantly deliver various services on demand. Be that as it may, the character of CC is in a way dialectical and, accordingly, the main objective of the present paper is to undertake, from an interdisciplinary cyber-behavioural perspective, a modest attempt to frame a critical discourse mainly of its threats, vulnerabilities and risks that haunt CC services. These CC issues make it impossible to achieve end–to-end security, on the one hand, and, at one and the same time, further strengthen, on the other hand, the process of transforming late modern society into what is known as risk society.
Investigation of distance effect on Gaussian Mixture Models in Speaker Identification

This paper investigate the effect of distance on the Gaussian Mixture Models (GMM) for text dependent speaker identification. Three stages are used for three different distances from the microphone (1m, 2m, and 3m). The set of feature extraction used here include Mel frequency cepstral coefficient (MFCC), Bark frequency cepstral coefficient (BFCC) and linear predictive cepstral coefficient (LPCC). These features are obtained from 20 speakers (10 adults and 10 children) ;all spoke five Arabic words in 5 seconds. The set of classification includes two types GMM and multilayer perceptron neural network (MLP). Total results show that MFCC has the best performance in feature extraction, and GMM has better recognition than MLP as total recognition in GMM is 93.15% and recognition in MLP is 88.06%. The results show also that the recognition rate decreases from 93.15% to 80.82% as the distance is increased from 1m to 3m.
LOW-GRADE INFLAMMATION IN SUBCLINICAL HYPOTHYROIDISM: ROLE OF HIGHSENSITIVE C-REACTIVE PROTEIN

Objective: Subclinical hypothyroidism (SCH) patients may present with abnormal lipid profile more specifically in patients having thyroid-stimulating hormone (TSH) >10 mIU/L. Since the contradiction still lies with patients having TSH <10 mIU/L, so the role of high-sensitive C-reactive protein (Hs- CRP) may be important with the prediction of inflammatory cardiovascular risk.Methods: Recently diagnosed 30 SCH patients both male and female were recruited and compared 30 normal healthy adults. Age and body mass index (BMI) of the study population were noted. Thyroid profile including TSH, FT4, and T3 was measured by an enzyme-linked immunosorbent assay (ELISA). Total cholesterol, triglycerides, and high-density lipoprotein (HDL) cholesterol were measured by the CHOD-POD method, GPO-PAP method, and CHOD-POD/phosphotungstic method. Low-density lipoprotein (LDL) cholesterol and very low-density cholesterol were measured by Friedewald formula. Lipoprotein ratios were also calculated. An ELISA was also used for the estimation of Hs-CRP.Results: The significant results were obtained in this study. BMI was significantly (<0.01) elevated in patients’ group compared to the control group. In the thyroid profile, TSH was significantly (<0.05) different between the groups. Total cholesterol, triglycerides, and LDL-cholesterol were significantly (<0.01) elevated in the case group. A significantly lower concentration of HDL cholesterol was observed in SCH patients when compared with control subjects. There was an elevated concentration of lipoprotein ratios in patients group. The mean concentration of Hs-CRP was highly significant between the groups. The level was higher in the case group compared to the control group. In patients’ group, there was a positive association (β- 0.36) (confidence interval 95%–0.002–0.536) between TSH and Hs-CRP. This association was highly significant.Conclusion: SCH patients having TSH <10 μIU/ml were characterized by dyslipidemia and elevated Hs-CRP. Increased lipoprotein ratios and Hs-CRP may promote low-grade inflammation in SCH patients, by which cardiovascular risk can be developed.
A Novel Multi-Agent Model-Free Control for State-of-Charge Balancing Between Distributed Battery Energy Storage Systems

This article proposes a novel state of charge (SoC) balancing control strategy based on multi-agent control between distributed the battery energy storage systems (BESSs) in super-UPS. The proposed control strategy has plug and play capability. Batteries with different capacities are considered in the control system. The battery capacity degradation under long term operation is further taken into account. In addition, a model-free iPI control strategy is designed for primary current control to achieve the tracking response of BESS to the reference current. Firstly, the distributed voltage and per-unit current dynamic consensus algorithm is introduced to maintain the DC bus voltage and achieve the battery current sharing. Then, a SoC balancing algorithm based on multi-agent control strategy is proposed. Furthermore, an adaptive capacity estimation algorithm is proposed considering the battery capacity degradation. The efficiency of the proposed control strategy is demonstrated in MATLAB/Simulink.
After Vehicle Automation Fails: Analysis of Driver Steering Behavior after a Sudden Deactivation of Control

: Vehicles with SAE Level 2 or 3 automation rely on the driver to intervene and resume control when failures occur. In cases which the driver must steer upon regaining control, the initial conditions of the vehicle’s state variables can affect the success of the drivers' recovery. Hence, a model to determine the consequences of these initial states could help identify the requirements of shared control to guarantee a smooth recovery after an automation failure. Such a modeling tool should be simple, such as a two-point visual continuous control model of steering. Data to validate such a model were collected from participants driving in the NADS-1 simulator who were placed in a situation similar to an extreme case of automation failure by drifting their vehicle to a target heading angle and lane deviation. This was done while the drivers were distracted with a secondary task that kept their eyes off the road. The maximum lane deviation reached during recovery shows that the initial heading angle and steering wheel angle strongly affected the maximum lane deviation. Moreover, a slightly modified version of the two-point visual control model was used to simulate the drivers' steering profiles. The model was successful at recreating the participants heading angle and lane deviation profiles but failed to replicate the drivers' steering profile. This simple model of steering control could be used to assess the consequences of a vehicle ceding control at various initial conditions, but is not able to reproduce all aspects of steering control.
Design and development of a planning architecture for CPUs and GPUs for containers

Graphics Procesing Units (GPUs) were originally designed as coprocessing units to accelerate the creation of images intended for being displayed in a screen. Nevertheless, over the years graphics chips became increasingly programmable and developers started to create applications that used the GPU parallel architecture to accelerate calculations. Initially, it was necessary to use programming languages designed for graphics, such as OpenGL, but NVIDIA created CUDA (a platform and an application programming interface or API) for general purpose processing. Nowadays, for certain applications a GPU can utterly surpass the performance of a Central Processing Unit (CPU). Thus, many applications are being designed for its use in GPUs, but they still can use the CPU when the GPU is busy with another task. Service virtualization techniques became also essential to develop new applications that are easy to deploy, maintain and scale. For example, the microservice architecture is usually built by using virtual machines or, more frequently, containers (where the OS is virtualized and not the hardware) to execute each service. Among the diﬀerent containerization tools, Docker is one of the most popular, and the selected in this Master’s Thesis.
Judge, Jury & Encryptioner: Exceptional Device Access with a Social Cost.

We present Judge, Jury and Encryptioner (JJE) an exceptional access scheme for unlocking devices that does not give unilateral power to any single authority. JJE achieves this by placing final approval to unlock a device in the hands of peer devices. JJE distributes maintenance of the protocol across a network of "custodians" such as courts, government agencies, civil rights watchdogs, and academic institutions. Unlock requests, however, can only be approved by a randomly selected set of recently active peer devices that must be physically located by law enforcement in order to gain access to the locked device. This requires that law enforcement expend both human and monetary resources and pay a "social cost" in order to find and request the participation of random device owners in the unlock process. Compared to other proposed exceptional access schemes, we believe that JJE mitigates the risk of mass surveillance, law enforcement abuse, and vulnerability to unlawful attackers. While we propose a concrete construction, our primary goal with JJE is to spur discussion on ethical exceptional access schemes that balance privacy of individuals and the desires for law enforcement. JJE transparently reveals the use of exceptional access to the public and enforces a fixed social cost that, we believe, can be an effective deterrent to mass surveillance and abuse.
User Utterance Acquisition for Training Task-Oriented Bots: A Review of Challenges, Techniques and Opportunities

Building conversational task-oriented bots requires large and diverse sets of annotated user utterances to learn mappings between natural language utterances and user intents. Given the complexity of human language as well as the recent advances on intent recognition (especially deep-learning-based approaches), bot developers now have faced a new challenge: efficiently and effectively collecting a large number of quality (e.g., diverse and unbiased) training samples. This article studies training user utterance acquisition along several important dimensions including cost and quality. We discuss state of the art techniques, identify open issues, and inform an outlook on future research directions.
Decentralized authentication of data stored in clouds

In this paper, we propose the secure data storage in clouds for a new decentralized access. The cloud verifies the authenticity of the series without knowing the user’s identity in the proposed scheme. Our feature is that only valid users can able to decrypt the stored information. It prevents from the replay attack. This scheme supports creation, modification, and reading the data stored in the cloud and also provide the decentralized authentication and robust. It can be comparable to centralized schemes for the communication of data, computation of data, and storage of data.
Comparison of multi-robot task allocation algorithms

Multi-robot systems (MRS) are capable of performing a set of tasks by dividing them among the robots in the fleet. One of the challenges of working with multirobot systems is deciding which robot should execute each task. Multi-robot task allocation (MRTA) algorithms address this problem by explicitly assigning tasks to robots with the goal of maximizing the overall performance of the system. The indoor transportation of goods is a practical application of multi-robot systems in the area of logistics. The ROPOD project works on developing multi-robot system solutions for logistics in hospital facilities. The correct selection of an MRTA algorithm is crucial for enhancing transportation tasks. Several multi-robot task allocation algorithms exist in the literature, but just few experimental comparative analysis have been performed. This project analyzes and assesses the performance of MRTA algorithms for allocating supply cart transportation tasks to a fleet of robots. We conducted a qualitative analysis of MRTA algorithms, selected the most suitable ones based on the ROPOD requirements, implemented four of them (MURDOCH, SSI, TeSSI, and TeSSIduo), and evaluated the quality of their allocations using a common experimental setup and 10 experiments. Our experiments include off-line and semi on-line allocation of tasks as well as scalability tests and use virtual robots implemented as Docker containers. This design should facilitate deployment of the system on the physical robots. Our experiments conclude that TeSSI and TeSSIduo suit best the ROPOD requirements. Both use temporal constraints to build task schedules and run in polynomial time, which allow them to scale well with the number of tasks and robots. TeSSI distributes the tasks among more robots in the fleet, while TeSSIduo tends to use a lower percentage of the available robots. Subsequently, we have integrated TeSSI and TeSSIduo to perform multi-robot task allocation for the ROPOD project.
Blockchain-based Cross-Organizational Execution Framework for Dynamic Integration of Process Collaborations

Cross-organizational business processes involving multiple participants are choreographed, thus rely on mutual trust of collaborators or need to be coordinated by a central instance. Using Smart Contracts, business processes can be executed without a mutually trusted and centralized orchestrating authority. Former Blockchain-based execution framework proposals focus on orchestration diagrams as a basis for execution. Contrary, this work focuses on BPMN process collaboration diagrams as implementation basis and makes additional transformation steps obsolete. With the herein proposed framework for execution of cross-organizational process collaborations, another approach for the implementation and execution of inter-organizational processes on a Blockchain is presented, including a voting mechanism for process deployment as well as a subscription service to facilitate process handovers between participants more efficiently. The framework is exemplified and evaluated with a use case from a large German industrial manufacturing company.
A Proposed Model for Health Management and Expert Diagnosis System for the Prediction of Common Diseases for Ethiopia (East Africa)

Many rural & pastoral communities in Ethiopia have extremely limited access to medical advice. People are traveling long distances to avail medical facilities, and there is a shortage of medical experts in most of the health institutions. This results in slow service, and ends up waiting long hours without receiving any attention for the patient. Expert on particular diseases is also not available at the health centers, in that case, patients are misdiagnosed and abused medically. Hence medical expert systems can play a significant role in such scenarios where medical experts are not readily available. This work presents the patients registration system and design of a knowledge-based expert system that aims to provide the prediction of the diseases to the patients and medical advisors to expedite their non-surgical diagnostic system.
Towards a system for predicting the category of educational and vocational guidance questions using bidirectional encoder representations of transformers (BERT)

Educational and vocational guidance is a particularly important issue today, as it strongly determines the chances of successful professional integration into the increasingly difficult labor market. Families have understood this well since they have been interested, in the educational orientation of their child. In this sense, we have set up a system for classifying questions of educational and professional orientation in based on Holland's test using the BERT method. Text classification, particularly the classification of questions is a basic task in natural language processing which is a very profound concept in the field of artificial intelligence. As the most abundant data in the world today is in the form of texts, having a powerful word processing system is essential and is more than just a necessity. Recently, Transformers models such as Bidirectional encoder representations of transformers or BERT are a very popular NLP models known for producing remarkable results compared to other methods in a wide variety of NLP tasks. In this article, we demonstrate how to implement a multi-class classification using BERT. In particular, we explain the classification of questions concerning the field of educational and vocational guidance following the RIASEC typology of Holland. Our model allows us to obtain the category of each input question. In our case, we define four classes (Activity, Occupations, Abilities, and Personality) for the set of questions, which constitute our data set. The results of this approach demonstrate that our model achieves competitive performance.
Automated Program Hardening via Hoisted Privilege Reductions

Privilege based security policies for programs are effective as a first line of defense against attacks. They are able to mitigate broad classes of attacks against programs, potentially saving the costs of searching for and mitigating specific vulnerabilities. Deploying such techniques, however requires expert knowledge and manual analysis of programs. We propose Passive Privilege Inference and Reducer (PPIR), a technique driven by a novel static analysis that automates the process of inferring the privileges required by a program. We develop a tool that uses this technique to infer the privileges required by a program and instrument it with a security policy to enforce the Principle of Least Privilege. We show that PPIR performs on par with handcrafted security measures while eliminating the manual burden of investigating and inserting privileges. PPIR further enables the potential to progressively reduce privileges as a program executes.
OPAL: An extensible framework for ontology‐based program analysis

The syntactic information of a program can be represented as resource description framework (RDF) triples called program triples. We propose an extensible static analysis framework, called OPAL—Ontology‐based Program AnaLysis. The framework enables formal representation of external knowledge, such as usage knowledge of libraries and domain knowledge. Utilizing this knowledge and the program triples, we compute the semantic information, called static trace of the program. It is generated through path‐sensitive intraprocedural traversal of the program. We approximate information in case of loops by unrolling them a fixed number of times. The main contribution of the framework is to store the static trace as RDF triples called semantic triples. They are described using the Program Analysis ontology proposed in this article. The program triples and the semantic triples are together called consolidated program triples. These triples are stored and used to accelerate the execution of client‐analyses specified by the end user. In the framework, a client‐analysis is specified by a set of conjunctive expressions that use SPARQL (W3C RDF query language) queries. The framework is effective for the client‐analyses that warrant sound and approximate information. The effectiveness is assessed first, using two source‐code‐analyses that require only the program triples, and then 10 intraprocedural path‐sensitive analyses that require the consolidated program triples. Using NPB and SPEC 2006 benchmarks, we achieve an improvement in the conciseness of analysis specifications. Also, the execution time using OPAL is competitive to LLVM's clang for individual analysis and outperforms clang over a series of analyses because of the reuse of consolidated program triples.