Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutrition Monitoring and Calorie Estimation using Internet of Things (IoT)

Diet observation is one of the principal aspect in precautionary health care that aims to cut back varied health risks. The various recent advancements in smartphone and wearable sensing element technologies have paved way to a proliferation of food observation applications that are based on automated image processing and intake detection, with an aim to beat drawbacks of the standard manual food journaling that's time overwhelming, inaccurate, underreporting, and low adherent. The currently developed food logging methods are very much time consuming and inconvenient that limits their effectiveness. The proposed work presents an Internet of Things (IoT) based mobile-controlled calorie estimation system to make technical advancements in healthcare industry. The proposed system operates on mobile environment, which allow the user to acquire the food image and quantify the calorie intake mechanically. The Mqtt protocol based MyMqtt broker is used to connect the application and the edge device and also to store the data in the Thingspeak cloud. A deep convolutional network is employed to classify the food accurately within the system. The volume estimation is done using sensors and the calorie approximation is done using formula
Insulation fault identification of vacuum circuit breakers based on improved MFCC and SVM

Insulation faults in vacuum circuit breakers can produce physical phenomena such as visible sound, ultrasound, and electromagnetic waves. Through the real-time collection and analysis of the discharge sound during the internal insulation failure of the circuit breaker, the insulation state of the circuit breaker can be judged. This article proposes a method. The improved MFCC algorithm is used to extract the characteristic parameters of the discharge signal.The recognition of the flashover discharge sound signal is achieved by one-class SVM. The one-class SVM is constructed, so one-class SVM can be used to identify whether the signal is a surface discharge or not. The characteristic vectors of the audio signal of discharge have intrinsic similarity, and the distribution can be concentrated, which is significantly different from other abnormal sounds. The conclusion is verified by experimental data. The calculation results show that the it can effectively identify the insulation state of vacuum circuit breaker by the method of using of MFCC feature extraction based on Fisher criterion and one-class support vector machine.
CoClust: A Python Package for Co-Clustering

Co-clustering (also known as biclustering), is an important extension of cluster analysis since it allows to simultaneously group objects and features in a matrix, resulting in row and column clusters that are both more accurate and easier to interpret. This paper presents the theory underlying several effective diagonal and non-diagonal co-clustering algorithms, and describes CoClust, a package which provides implementations for these algorithms. The quality of the results produced by the implemented algorithms is demonstrated through extensive tests performed on datasets of various size and balance. CoClust has been designed to complete and easily interface with popular Python machine learning libraries such as scikit-learn.
Neural Network based Heart Disease Prediction

Machine learning is widely used to make the machine to learn and to predict when exposed to new data. Due to many advancements in machine learning, there are various methods that can be adopted to predict the heart disease of an individual. Heart Disease is one among the major diseases affecting the individual around the world. There are several risk factors which leads to heart disease. The combination of logistic regression analysis and neural network provides a novel approach in predicting the heart disease. Initially logistic regression is applied to select the major risk factors for predicting the disease. It produces the significant risk factors that are useful in predicting the heart disease based on statistical p-value. The risk factors which are not having the significant impact are identified and removed. The resultant significant factors are provided as input to the neural network. And neural network is trained for the risk factors that is obtained from logistic regression and used to test whether the person is having the heart disease or not. Thus, the integration of logistic regression and neural network is applied in predicting the heart
A Framework for Adaptive Personalized E-learning Recommender Systems

With the undergoing technological revolution in education, adapting recommender systems to the personalized e-learning is an emerging topic in the education sector. Detecting the student model offers a potential to recommend a learning material that is adequate to the student progress. Accordingly, the learning objects and hypermedia can be adapted to each individual student to meet the personalized learning needs. This paper proposes a framework for applying recommender systems in personalized e-learning domain. Furthermore, the recommender system previous examples, opportunities, and associated challenges are discussed.
A Temporal Distributed Group Decision Support System Based on Multi-Criteria Analysis

Decision support consists of proposing tasks and projects by taking into account temporal constraints and the use of resources with the aim of finding a compromise solution between several alternatives. Indeed, on the one hand, centralized resolution systems and methods are generally inappropriate to the real case because of the local unavailability of decision makers. On the other hand, the data of the decisional problem are generally poorly expressed in a negotiation environment. Other techniques and approaches treat the same decision-making problem and impose a distributed vision for coherent decisions. For this purpose, Multi-Agent Systems (MAS) allow modeling a distributed resolution of the group decision support problem. In this article, we propose a new model of a multi-criteria group decision support system based on a multi-agent system modeling a spatial problem. We consider that each decision maker is assimilated to an agent that has a decision-making autonomy, in which he interacts with other agents in the debate through a negotiation process in order to reach an acceptable compromise. In this study, we propose coordination mechanisms among agents to highlight the simulated negotiation. Therefore, the proposed system finds a solution before fixed deadlines’ time expire. We experiment the suggested negotiation model to solve the decisional problem of spatial localization in territory planning.
Transmission Performance Analysis of TDMA Radio and MAC Communication of TDMA Protocol

Radio is one of the wireless network system devices that is still widely used today. Transceiver was used because the radio can function as a transmitter and receiver. In this paper, half-wave dipole antenna for radio communication as a transceiver on TDMA Digital Radio was simulated. The antenna was used at 167.5 MHz and radio communication channel. In collecting the data, spectrum analyzer was used as the measuring tool. To analyze the output signal generated by TDMA Digital Radio, the data sample on both measurements. This paper compared the TDMA Digital Radio communication with simulation by using MATLAB. Analyze the Channel Power that has become the output of Spectrum Analyzer, and the measurement of Spectrum Analyzer Measurement was using MATLAB.
ACO-Based Dynamic Decision Making for Connected Vehicles in IoT System

With the rapid development of the internet of things (IoT), connected vehicles are set to become a huge industry over the next few years. In this study, we take an investigation of the distributed intelligent traffic system by pushing intelligence into connected vehicles in terms of dynamic decision making for traversing a certain area (e.g., roundabout and intersection). In particular, we propose a model for the next generation of intelligent transportation system, which focuses on dynamic decision making of connected vehicles based on Ant Colony Optimization, a typical Swarm Intelligence (SI)-based algorithm. Specifically, we first present a communication framework among connected vehicles for sharing information of traffic flow. Then, by applying the concept of SI, connected vehicles are regarded as artificial ants which are able to self-calculate to make an adaptive decision following the dynamics of traffic flow. Furthermore, for evaluating the effectiveness of the proposed approach, we have constructed a framework to model and simulate the traffic system in IoT environment. Simulations with different scenarios of transportation systems indicate promising results comparing with previous works.
Implementation of Deduplication on Encrypted Big-data using Signcryption for cloud storage applications

As Big Data Cloud storage servers are getting widespread the shortage of disc space within the cloud becomes a major concern. The elimination of duplicate or redundant data, particularly in computer data is named deduplication. Data deduplication is a method to regulate the explosive growth of information within the cloud storage, most of the storage providers are finding more secure and efficient methods for their sensitive method. Recently, a noteworthy technique referred to as signcryption has been proposed, in which both the properties of signature (ownership) and encryption are simultaneously implemented with better performance According to deduplication, we introduce a method that can eliminate redundant encrypted data owned by different users. Furthermore, we generate a tag which will be the key component of big data management. We propose a technique called digital signature for ownership verification. Convergent encryption also called for a content hash key cryptosystem. Convergent encryption is an encryption approach that supports deduplication. With this encryption technique, the encryption key is generated out of a hash of plain text. Therefore applying this technique, identical plaintexts would turn out the same ciphertext.
Digital Transformation of the Government: A Case Study in Indonesia

The world’s Industrial Revolution 4.0 and Society 5.0 are massively utilising the Internet of Things, Big Data, Artificial Intelligence and Robotic technology to solve various challenges and social problems. The challenge for the government now is to fully utilise these technologies to improve public services and government administrations. This study focuses on the transformation process of an e-government to become a digital government. The study aims to analyse the current development of e-government in Indonesia and the barriers to implement it as well as to propose how to transform from being an e-government to becoming a digital government. It uses a qualitative approach supported by secondary data. Focus Group Discussion was held in May 2019 to identify e-government barriers factors. The secondary data, meanwhile, was collected through e-government surveys published by the United Nations and E-government Evaluation issued by Indonesia’s Ministry of Empowerment Apparatus and Bureaucracy Reform. Data obtained was analysed using descriptive analysis techniques. Study shows that the development of Indonesia’s e-government is slow and lags behind other ASEAN countries. The E-government index in government institutions is not on target. There is a gap between the e-government indexes and central institutions and gaps between the Provincial and Regency / City Governments. The barriers factors of e-government are: (1) Regulations are not sufficient enough to encourage and guide e-government (2) Lack of data integration; (3) Gaps in the availability of ICT infrastructure between regions; (4) Limited ICT competence and, (5) Bureaucratic culture and leadership. Keywords: Indonesia, e-government, digital transformation, barrier factor, qualitative methodology.
The Community College Presidency Demystified: Creating a Pipeline of Latino Leaders

Community colleges are designed to serve populations largely drawn from a local base. In an increasing number of cases, the student populations are diverse, while both the administration and faculty, particularly in positions of leadership, are overwhelmingly white. Because of changing demographics, many community colleges serve predominately Latino communities. The present article describes the impact of a national culturally tailored leadership development program for Latino administrators in community colleges. Using data from extensive, semi-structured interviews, this qualitative research study demonstrates that participants in the leadership fellows program found their experience empowering and transformational. Research findings indicate that as a direct result of their participation in a culturally relevant leadership program, participants felt increased confidence and sought out executive community college leadership positions. Additionally, participants benefitted from a national network of peers and mentors.
Smart Anti-jamming Mobile Communication for Cloud and Edge-Aided UAV Network

The Unmanned Aerial Vehicles (UAV) networks consisting of low-cost UAVs are very vulnerable to smart jammers that can choose their jamming policies based on the ongoing communication policies accordingly. In this article, we propose a novel cloud and edge-aided mobile communication scheme for low-cost UAV network against smart jamming. The challenge of this problem is to design a communication scheme that not only meets the requirements of defending against smart jamming attack, but also can be deployed on low-cost UAV platforms. In addition, related studies neglect the problem of decision-making algorithm failure caused by intermittent ground-to-air communication. In this scheme, we use the policy network deployed on the cloud and edge servers to generate an emergency policy tables, and regularly update the generated policy table to the UAVs to solve the decision-making problem when communications are interrupted. In the operation of this communication scheme, UAVs need to offload massive computing tasks to the cloud or the edge servers. In order to prevent these computing tasks from being offloaded to a single computing resource, we deployed a lightweight game algorithm to ensure that the three types of computing resources, namely local, edge and cloud, can maximize their effectiveness. The simulation results show that our communication scheme has only a small decrease in the SINR of UAVs network in the case of momentary communication interruption, and the SINR performance of our algorithm is higher than that of the original Q-learning algorithm.
INFORMATION SYSTEM OF THE DIAGNOSTICS OF A FUNCTIONAL RELIABILITY OF PIPELINE SYSTEMS

The information system of the diagnostics of the reliability of pipeline systems is developed in the paper. It is intended to provide a single information space for prompt and coordinated support of management decisions in the workplaces of technical specialists and repair personnel of structural subdivisions of a pipeline system. The task of minimizing costs is formulated, which is solved by numerical optimization methods and the graphical method. The information system makes accessible comparative analyses of alternative structures of a pipeline network by criterion of functional reliability. Keywords: information system, diagnostics, model, reliability, pipeline system.
Command Guidance Missile Tracking Algorithms Evaluation Based on Visual Simulation

— Visual simulation has proven to be a conclusive part of any system development cycle. visual simulation first necessitates that a model to be developed to represent the characteristics of the selected physical system or process. The model represents the system itself but in a simpler manner to avoid the complexity and to be trackable, while the simulation represents the operation of the system over time. This paper uses 3D visualization to model real-life situations to observe how different tracking algorithms react. By changing the simulation conditions, the real system behavior is able to be predicted. The Simulation involves both missile and target objects which are created by Multigen-Creator, 6-DOF (degree of freedom) mathematical model of the missile to derive its dynamical parameter during its flight trajectory implemented with Visual C++ all integrated in an infrared simulation environment which is implemented using Vega and finally the tracking algorithm needed to be evaluated.
Solving Travelling Salesman Problem(TSP) Using Ant Colony Optimization(ACO)

- Vehicle traffic congestion leads to air pollution, driver frustration, and costs billions of dollars annually in fuel consumption. Finding a proper solution to vehicle congestion is a considerable challenge due to the dynamic and unpredictable nature of the network topology of vehicular environments, especially in urban areas. Ant colony optimization (ACO) has been widely used for different combinatorial optimization problems. Ant Colony Optimization (ACO) as a heuristic algorithm has been proven a successful technique and applied to a number of combinatorial optimization (CO) problems. The traveling salesman problem (TSP) is one of the most important combinatorial problems. We present a bio-inspired algorithm, food search behavior of ants, which is a promising way of solving the Travel Salesman Problem. in this paper, we investigate ACO algorithms with respect to their runtime behavior for the traveling salesperson (TSP) problem. Simulation results conducted using MATLAB suggests that the proposed system would perform consistently despite increase of vehicles with in the given area.
Photoacoustic Detection and Optical Spectroscopy of High-Intensity Focused Ultrasound-Induced Thermal Lesions in Biologic Tissue

In this study, the capability of a photoacoustic (PA) method in detecting high-intensity focused ultrasound (HIFU) thermal lesions was investigated in chicken breast tissue in vitro and the optical properties of the HIFU-treated and native tissues were determined. Created with a 1-MHz HIFU transducer, the detectability of the induced thermal lesions was assessed photoacoustically at 720 and 845 nm and their optical properties were characterized in the wavelength range 500-900 nm. The results show that the averaged ratio of the peak-to-peak PA signal amplitude of HIFU-treated tissue to that of native tissue is more than 3 fold. The optical spectroscopy investigation revealed that the absorption and reduced scattering coefficients are higher for HIFU-treated tissues than native tissues. This work demonstrates the capability of the PA method in detecting HIFU-induced thermal lesions due, in part, to the increase in their optical absorption coefficient, reduced scattering coefficient, and deposited laser energy fluence.
Directing a Search Towards Execution Properties with a Learned Fitness Function

Search based software testing is a popular and successful approach both in academia and industry. SBST methods typically aim to increase coverage whereas searching for executions with specific properties is largely unresearched. Fitness functions for execution properties often possess search landscapes that are difficult or intractable. We demonstrate how machine learning techniques can convert a property that is not searchable, in this case crashes, into one that is. Through experimentation on 6000 C programs drawn from the Codeflaws repository, we demonstrate a strong, program independent correlation between crashing executions and library function call patterns within those executions as discovered by a neural net. We then exploit the correlation to produce a searchable fitness landscape to modify American Fuzzy Lop, a widely used fuzz testing tool. On a test set of previously unseen programs drawn from Codeflaws, a search strategy based on a crash targeting fitness function outperformed a baseline in 80.1% of cases. The experiments were then repeated on three real world programs: the VLC media player, and the libjpeg and mpg321 libraries. The correlation between library call traces and crashes generalises as indicated by ROC AUC scores of 0.91, 0.88 and 0.61. The produced search landscape however is not convenient due to plateaus. This is likely because these programs do not use standard C libraries as often as do those in Codeflaws. This limitation can be overcome by considering a more powerful observation domain and a broader training corpus in future work. Despite limited generalisability of the experimental setup, this research opens new possibilities in the intersection of machine learning, fitness functions, and search based testing in general.
Improving Autonomous Driving Safety through a better Understanding of Traffic Scenes and of Potential Upcoming Collisions : A Bayesian & Machine Learning Approach (Invited Plenary Speech)

Motion Autonomy and Safety issues in Autonomous Vehicles are strongly dependent upon the capabilities and performances of Embedded Perception and Situation Awareness systems. Recent benign and severe accidents (e.g. Tesla or Uber) have shown that the level of safety obtained using currently tested autonomous driving systems is still insufficient. This speech addresses this important perception and safety issue, and presents how it can be addressed using Bayesian and Machine Learning approaches. The approach is illustrated using results obtained in the scope of several Research and Development projects conducted in cooperation with the French IRT Nanoelec and with several industrial companies such as Toyota and Renault.
SDN implementation in regard to network elements security

The article presents the process of creating an example multi-domain Software Defined Network based on open source solutions. Significant threats to the SDN and currently used protection practices were discussed. Authors proposed a solution to the problem of resource authorization and authentication by introducing a Hidden Data Layer based on steganographic methods to SDN. Hidden Data Layer consists of three essential elements, i.e. a Head Resident which doubles the functions of a public SDN controller and is a superior unit concerning public one, a Hidden Layer Protocol realising communication between individual network elements. The third element is Hidden Protocol Interpreter as an interpretive and executive function which carries out tasks sent by secret layer protocol. This type of solution assures at first fast and effective detection of threats and secondarily redundancy of the control layer in the case of disabling or debilitating the use of the public controller.
NLOS Identification and Correction Based on Multidimensional Scaling and Quasi-Accurate Detection

In wireless sensor networks, most of the previous NLOS identification is based on error estimation model established by raw data. In this paper, we propose a method of NLOS identification named NIMQ based on multidimensional scaling (MDS) and Quasi-Accurate detection (QUAD). In this method, we first map NLOS information into gross error information by MDS, then we use QUAD to identify the gross errors which contain the NLOS information. This method relies only on distance measurements and is independent of the measured error estimation model. In addition, using the network topology constraints in higher dimensional space, the identified distance can be corrected by multiple iterations. Finally, an NLOS iterating correction algorithm (NICA) is proposed. Simulations show that in different scenarios our proposed NIMQ and NICA can well identify and correct NLOS measurement.