Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Human Activity Identification Based on Image Processing and Artificial Intelligence

Recognizing the activities of humans through computer vision techniques is an important area of research. This area of research leads to various applications such as patient monitoring, fall detection, surveillance and human-computer interface. The capability for recognizing these acts lays foundation for developing highly intelligent and decision making systems. Generally, most of the mentioned applications requires automatic recognition of high-level activities, consisting of simple actions of multiple persons. Usually, the intelligence to the system is delivered only if these activities are properly classified. This paper addresses various machine learning algorithms used in classifying various activities such as Multi-Layer Perceptron, Random Forest, Naïve Bayes and SVM algorithms. This paper provides classification of general to complex human activities through comparison study and performance evaluation of these mentioned algorithms using very large set of images. This review will provide much needed information for further research in more productive areas.
Analysis of Decadal Climate Predictions with User‐guided Hierarchical Ensemble Clustering

In order to gain probabilistic results, ensemble simulation techniques are increasingly applied in the weather and climate sciences (as well as in various other scientific disciplines). In many cases, however, only mean results or other abstracted quantities such as percentiles are used for further analyses and dissemination of the data. In this work, we aim at a more detailed visualization of the temporal development of the whole ensemble that takes the variability of all single members into account. We propose a visual analytics tool that allows an effective analysis process based on a hierarchical clustering of the time‐dependent scalar fields. The system includes a flow chart that shows the ensemble members' cluster affiliation over time, reflecting the whole cluster hierarchy. The latter one can be dynamically explored using a visualization derived from a dendrogram. As an aid in linking the different views, we have developed an adaptive coloring scheme that takes into account cluster similarity and the containment relationships. Finally, standard visualizations of the involved field data (cluster means, ground truth data, etc.) are also incorporated. We include results of our work on real‐world datasets to showcase the utility of our approach.
An All-to-All Multicast Routing Protocol in a Wireless Ad-Hoc Network to Improve Channel Utilization

ALL-TO-ALL MULTICAST ROUTING PROTOCOL IN A WIRELESS AD-HOC NETWORK Zhong-Hua Zhou, M.S. Columbus State University, 2001 Supervising Professor: Seong-Moo Yoo Wireless Ad-hoc network has become more and more popular. Several different multicasting schemes for this type of network have been developed. But most of them are targeting a situation where the total number of senders is much less than the total number of receivers. This thesis analyzes different wireless communication patterns in ad hoc network, the trend for wireless communication and presents a new multicast scheme. It targets an ad-hoc network where all mobile hosts act as both senders and receivers. The proposed scheme in this thesis will reduce overheads by combining data packets that are destined for the same mobile hosts. The action of combining jobs will be done at mobile hosts which are not joined in the multicasting group and only functioned as forwarders.
Design Rules, Volume 2: How Technology Shapes Organizations; Chapter 6 The Value Structure of Technologies, Part 1: Mapping Functional Relationships

Organizations are formed in a free economy because a person or group perceives value in carrying out a technical recipe that is beyond the capacity of a single person. Technology specifies what must be done, what resources must be assembled, what actions taken, what transfers made in order to convert stocks of material, energy and information into products of value to someone. The purpose of this chapter to build a robust and versatile language that is capable of representing large technical systems. The language is based on elements I have labeled functional components. The language is more abstract than the language of technical recipes and task structures, thus it is capable of hiding details that may be distracting. However, the language also makes it possible to “track back�? from each named functional component to a technical recipe (or the lack of one).
Toward a Scalable, Robust, and QoS-Aware Virtual-Link Provisioning in SDN-Based ISP Networks

In this paper, we propose a control framework for virtual-link provisioning in software defined networking (SDN)-based Internet service provider (ISP) networks. Within the problem’s scope, we have analyzed the traffic engineering (TE), quality of service (QoS) provisioning, network stability, and failure recovery issues. The proposed solution aims to optimize the network throughput subject to multiple QoS constraints and link-failure robustness constraints that are defined in service level agreements (SLAs), while minimizing the time-average rate of routing updates according to ISP preferences. In contrast to the conventional TE mechanisms which periodically optimize all the routing rules, our solution is based on a bi-objective optimization model which jointly optimizes the routing and the number of flow-table updates. In this way, the reconfiguration overhead of TE is reduced, thus the SDN controller could perform TE more frequently, in order to quickly and efficiently respond to traffic and topology changes. To generate a set of Pareto optimal solutions for the proposed optimization model we use the augmented $\epsilon $ -constrained method. The most efficient trade-off solution from the Pareto set is chosen by the Lypunov drift-plus-penalty algorithm. In order to make the proposed approach scalable enough for large network topologies, we also propose a heuristic TE algorithm that runs in polynomial time. The proposed solution is validated via simulations and experimentally, and it is shown that it outperforms conventional periodic TE techniques. The advantages of our proposal over a state-of-the-art TE technique in terms of the capability to adjust the reconfiguration rate based on the threshold set by ISP is demonstrated. In the analysis, a special attention is paid to the impact of proactive failure protection mechanisms on the network service capacity.
Nonlocal Means Filtering Based Speckle Removal Utilizing the Maximum a Posteriori Estimation and the Total Variation Image Prior

In this paper, the problem of image speckle removal is addressed. To alleviate the pepper–salt remainder in the speckled image, we propose to utilize the nonlocal means filtering, where the weighting coefficients are derived based on the maximum a posteriori estimation with the total variation image prior. As a result, the objective function of the pixel fitting term plus the total variation regularizer is formulated, and it is solved with the majorization–minimization approach. To avoid the computationally intractable step size selection in the huge-scale gradient-based optimization, we split and solve the variables in the pixel fitting term and regularizer by means of the alternating direction method of multipliers. Performance analysis is performed for the Rayleigh and Gamma distributed signal models. The simulation and experimental results show the superior performance compared with other image despeckling methods in terms of various metrics and visual perception.
R-RNN: Extracting User Recent Behavior Sequence for Click-Through Rate Prediction

Click-through rate (CTR) prediction is of great importance in such web applications as recommender systems, web search, and online advertising. Exploring the interest of a user through feature interactions behind massive behaviors of users is essential for CTR prediction. Recently, deep learning-based models have been proposed, which follow a similar embedding and multi-layer perception (MLP) paradigm and achieve great success in both business and research fields. However, existing studies ignore the sequential characteristics of click-through behaviors of users. To overcome this limitation, we propose a novel model named recent recurrent neural network (R-RNN) to adaptively learn the representation of the interest of a user from his or her overall historical click-through behaviors. R-RNN puts an emphasis on recent click-through behaviors via a novel neural network architecture. Compared to the state-of-the-art methods including the deep interest network (DIN) model recently proposed by Alibaba, R-RNN not only applies the attention mechanism to help capture the representation of the main interest of a user but also incorporates a long short-term memory (LSTM) unit for exploring the trend of the change of the interest of a user behind his or her recent click-through behaviors. Comprehensive experiments are conducted to demonstrate the effectiveness of R-RNN for CTR prediction on a benchmark data set. The results show that R-RNN significantly outperforms existing deep learning models. The results also demonstrate that the length of the recent click-through behavior sequence has an important effect on the prediction performance of the model.
Absolute humanoid localization and mapping based on IMU Lie group and fiducial markers

Current locomotion algorithms in structured (in-door) 3D environments require an accurate localization. The several and diverse sensors typically embedded on legged robots (IMU, coders, vision and/or LIDARS) should make it possible if properly fused. Yet this is a difficult task due to the heterogeneity of these sensors and the real-time requirement of the control. While previous works were using staggered approaches (odometry at high frequency, sparsely corrected from vision and LIDAR localization), the recent progress in optimal estimation, in particular in visual-inertial localization, is paving the way to a holistic fusion. This paper is a contribution in this direction. We propose to quantify how a visual-inertial navigation system can accurately localize a humanoid robot in a 3D indoor environment tagged with fiducial markers. We introduce a theoretical contribution strengthening the formulation of Forster's IMU pre-integration, a practical contribution to avoid possible ambiguity raised by pose estimation of fiducial markers, and an experimental contribution on a humanoid dataset with ground truth. Our system is able to localize the robot with less than 2 cm errors once the environment is properly mapped. This would naturally extend to additional measurements corresponding to leg odometry (kinematic factors) thanks to the genericity of the proposed pre-integration algebra.
Ταξινόμηση βιοϊατρικών εικόνων με τεχνικές ημι-επιβλεπόμενης μάθησης

This thesis is about bio-medical image classification. Our goal was to train a machine learning model that assigns Unified Medical Language System concepts to bio-medical images using semi-supervised learning techniques.We experimented with an algorithm that does not need a labelled dataset but uses distant supervision to label each image based on its caption. Since finding data is one of the biggest problems in machine learning, we took advantage of the open access to biomedical texts provided by PubMed, from which we extracted images and their captions. To evaluate the performance of our model, we experimented with 2 datasets, one with 21.000 images and one with 800.000, using distant supervision to produce a test set.
A Lightweight Residual-Inception Convolutional Neural Network

Deep convolutional neural networks have become a powerful tool to solve practical problems, especially in the field of image recognition and machine learning. This paper introduces a lightweight convolutional neural network model which named RINet, based on a combination of the Inception structure and blocks inspired by ResNet. This model achieves high accuracy while reducing the number of parameters and increases the training speed. It has reached the correct rate of 93.7% on the dataset of ISIC and the recognition accuracy improves 6.3% and 1.5% compared to deeper networks called InceptionV1 and transfer learning of InceptionV3.
Wireless Channel Induced Coding

In this letter, we present a new wireless channel induced coding (WiCiC) scheme over the binary field in order to eliminate the performance loss which occurs due to not taking the channel conditions into account while determining network code coefficients. We utilize the composite channel-state matrix composed of source-to-relay and relay-to-destination channel-state matrices, as a result of the transition from the tripartite graph model to the bipartite graph model of the network. Through the use of this composite channel-state matrix, the encoding problem can be handled as a maximum flow problem owing to the max-flow min-cut theorem. The proposed WiCiC scheme has a lower decoding complexity and decoding delay compared with the benchmark studies while achieving the optimum decoding failure performance and providing user fairness. Extensive simulation results are presented to make reasonable inferences about the decoding performance of the WiCiC scheme.
Neonatal Incubator Embedded Temperature Observation and Monitoring Using GSM

In order to reduce the risks of death rates, neglecting by the observers, and sometimes the little number of the medical staff in hospital, A monitoring system based microcontroller in neonatal incubator was developed in this article. An interfacing process is made between the infant, infant incubator, and the computer, by taking the temperature readings and send the results to doctor's or the person who in- charge's phone through Short Messages Service, and to a personal computer PC, with sound alarm on the incubator, every given period of time; A temperature sensor is interfaced with a configured single inline module SIM of phone, the temperature reading that obtained from interfacing method is combined with the readings of the incubator sensor, then the final result will be taken from the microcontroller because it will be more accurate than the incubator’s thermometer. This system was designed only to check and give an alarm if there was any unaccepted change in temperature due to the normal temperature ranges.
Study of a Full-View 3D Finger Vein Verification Technique

Finger vein modality has unique advantages, allowing it to play an important role in biometrics. However, the approach to vein imaging and information acquisition typically adopted in current vein verification systems employs a monocular camera to acquire a single-view 2D vein image from only one side of the finger, which causes two problems: it acquires limited vein pattern information for verification, and it causes clear differences among samples of the same subject captured from different finger positions in contact-free mode. Both of these problems have adverse effects on system performance. In general, existing systems are more sensitive to positional variations of the finger, particularly those caused by pitch and roll movements. This concern remains a challenge despite considerable efforts to address it in recent years. To provide a fundamental solution to the above issues, we propose an entirely new system, which includes a software and hardware platform that collects a full-view of the vein pattern information from whole fingers with three cameras, a novel 3D reconstruction method to build the full-view 3D finger vein image, and a corresponding 3D finger vein feature extraction and matching strategy based on a lightweight convolutional neural network (CNN) with depthwise separable convolution. Experimental results demonstrate the potential of our proposed system and show that compared to the traditional single-view 2D mode of finger vein recognition, the new system both efficiently improves the recognition performance and simultaneously takes full advantage of additional valid information provided by the finger vein biometrics.
Inverse Kinematic Modeling of a 6-PSS Compliant Parallel Platform for Optoelectronic Packaging

This paper presents an inverse kinematic analysis of a 6-Prismatic-Spherical-Spherical (6-PSS) compliant parallel platform for optoelectronic packaging. The platform is driven by the piezoelectric motors, which combines the advantages of parallel platforms and wide-range flexure hinges. Since the rotation center of the wide-range flexible hinges constantly varies with the motion of the platform, it causes much error for positioning accuracy of the platform in applications. In this paper, an inverse kinematic solution based on elastokinematic analysis is proposed, in which the deformation of the wide-range flexure hinges is considered. And finally, several different cases based on the elastokinematic model are given, the results prove this method effective.
Pengamanan Internet of Things untuk Tanda Tangan Digital Menggunakan Algoritme Elgamal Signature Scheme

Internet of Things (IoT) memungkinkan suatu objek menghasilkan data dan bertukar data. Pengaplikasian IoT menggunakan mikrokontroler seperti Arduino belum memiliki fitur untuk menjaga keamanan data di dalamnya. Selain itu, Arduino memiliki kapabilitas komputasi terbatas. Oleh karena itu, perlu diterapkan kriptografi dengan algoritme yang memiliki komputasi rendah pada Arduino untuk menjaga keamanan data. Penjagaan keamanan data terutama pada keaslian asal data, dilakukan dengan menggunakan tanda tangan digital. Penerapan tanda tangan digital dapat dilakukan salah satunya dengan algoritme Elgamal signature scheme. Penerapan tanda tangan digital menggunakan algoritme Elgamal signature scheme berhasil diterapkan pada perangkat Arduino Uno untuk melakukan tanda tangan digital dan verifikasi. Kinerja algoritme Elgamal signature scheme dilihat dari analisis waktu eksekusi dan analisis keamanan algoritme. Waktu eksekusi proses tanda tangan digital membutuhkan waktu lebih lama dibandingkan dengan waktu eksekusi proses verifikasi. Algoritme Elgamal signature scheme membutuhkan waktu dua kali lebih lama karena banyaknya perhitungan sistematis pada perangkat Arduino Uno. Proses verifikasi terbukti gagal jika ada perubahan data dan pasangan tanda tangan digital. Kata Kunci: Arduino, Elgamal signature scheme, internet of things, kriptografi, tanda tangan digital.
Quantification of emotional values for attributes from sentiment analysis based on mobile phone reviews

E-commerce online reviews are commonly short in length, lack of contextual semantics, and are mixed with explicit emotions and implicit emotions. The subjects of implicit emotions are not easy to be extracted and be defined. Now product-oriented attributes are used to define implicit and fine-grained emotions with the help of the domain emotion dictionary that is constructed by extracted explicit attribute-emotion pairs through using a variety of part-of-speech rules. And implicit emotion and attribute pairs for product “mobile phone” are mined with the help of explicit attribute-emotion pairs and probability calculation method. At last, explicit and implicit emotion values are calculated by considering degree and negative modifiers. The experimental results show that proposed method is effective and has certain commercial value. And the process method is easy to extend to other product fields.
Design of concert hall in uremia based on notification to hidden role of music in architect

The growth of sound in a concert hall is reviewed with emphasis on hearing the direct sound clearly. Architecture in its daily means provides renewed space and is replaced within a larger space and people are moving in it and in it’s around. Music is purely mechanical conception, organized and deliberate scattering of sound waves in space. The aim of current research is design of concert hall based on notification to hidden role of music in architect in Uremia city. Methodology is based on librarian, descriptive-analytical method that is of applied kind. Results show that architecture and Music have practical visuals that no one can reject these characters. Also, we can presented three themes of foundation and basis physical creation, architecture and music for comparison (first: the physical essence; second: the evaluation tools for; Third: change the essence to the body with including of the idea and expand it).
Public Service Chatbots: Automating Conversation with BBC News

Abstract Automation of journalistic tasks is growing with the development of increasingly sophisticated software for newsgathering, production, and distribution. Bots are one form of algorithmic technology that has found a place in the modern newsroom, with chatbots leading the way as news organisations seek to attract new audiences using conversational forms of journalism. Recent advances in artificial intelligence (AI) and machine learning (ML) have fuelled increasing experimentation with machine autonomy and there has been much hyperbole in the press about the extent and impact of this on journalism. Looking at on-the-ground trials in audience-facing bots at the UK’s largest public broadcaster, we find a significantly more restricted picture. News bots at The BBC to-date have been basic, do not use ML, and have rarely been integrated into news production. The organisation is laying groundwork for development of more interactive news formats with an increasingly conversational tone and individual mode of address as part of a strategy for increased personalisation, which is likely to involve growing levels of ML. In the process, bots are reconfiguring working practices and infrastructure, posing new editorial and technical challenges, and redefining relationships with audiences. We discuss the implications of this for public service media.
Integrated System for easier and effective Drug Information

With the advent of new technologies, a large amount of biological data is easily generated in comparatively cheaper cost. Prior to this data integration was done by simple means of database addition, with less complexity due to lesser data generated in a standardized format. Understanding a complete biological phenomenon, such as disease, need a comprehensive understanding of many dimensions associated with it. This information cannot be captured in a single data type format. Mandating the use of a single data type study would leave us with incomplete answers to various biological questions. Thus the development of an effective integration technique with effective visualization platform is the need of the hour. One such framework requires the identification of relevant data from the input system, storing and transforming data into the intermediary level and then mapping these data into an appropriate position in the output systems. This intermediate level helps in reducing the number of connection and repeated specification creation. Integration of drug dataset would not only reduce the propagation of incorrect and not-updated medicinal information among doctors, but it would also help build better treatment strategies. Integration of drug data and visualization technique would be a novel approach to study drugs and effect on one platform. In this work, we tried to integrate the Adverse Effects, Drug Enforcement and Drug Label data from openFDA. This integrated database is coupled with a visualization platform IDEALS, an abbreviation for Integrated Drug Events, Adverse Effect and Label System.