Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint D2D Group Association and Channel Assignment in Uplink Multi-Cell NOMA Networks: A Matching-Theoretic Approach

This paper studies joint device-to-device (D2D) group association and channel assignment in uplink multi-cell non-orthogonal multiple-access (NOMA) networks. Particularly, the goal is to assign D2D groups to cellular user channels at each base-station, while accounting for negative network externality due to the interference caused by pairing a user with a D2D group. To that end, a multi-objective signal-to-interference-plus-noise ratio (SINR)-maximizing power allocation solution procedure is proposed to determine the optimal power allocation for each (D2D group, user) pair, while meeting quality-of-service (QoS) requirements. After that, the joint D2D group association and channel assignment problem is modeled as a student-project allocation with preferences over (student, project) pairs matching problem. More specifically, two polynomial-time complexity stable matching algorithms are proposed to pair D2D groups with users, and associate them with base-stations. Simulation results are presented to evaluate the proposed matching algorithms when combined with the devised solution procedure, and compare them to a joint D2D group association, channel assignment and power allocation (J-GA-CA-PA) scheme. More importantly, the proposed algorithms are shown to efficiently yield comparable SINR—per user and D2D receiver—to the J-GA-CA-PA scheme, while maintaining QoS requirements.
Medium-Term DEXA Analysis of an Uncemented Femoral Component

We investigated the bone remodelling around the AML uncemented femoral component using DEXA analysis and plain radiography in 19 patients at a mean of 89 months from operation. All patients had previously been analysed at a mean of 32 months after surgery. We found a decrease in bone mineral density around the AML implant between the two scans in Gruen zones 1,2 and 6 of up to 8%, but an increase in zones 4,5 and 7 of up to 36%. The increase in zones 4 and 5 were statistically significant (P<0.05). The relative difference between the operated and unoperated hips also showed a reduction in the bone density ratio with time, except in zones 4 and 5 where an increase was seen. We confirm the results of previous studies of proximal stress shielding and distal loading around the AML implant and demonstrate that bone remodelling continues in the medium term.
An Approximate Bufferless Network-on-Chip

Bufferless network-on-chip (NoC) designs have drawn research attention in massively parallel multicore systems via their significant benefits in power and area savings. However, it shows poor throughput and low bandwidth in current bufferless designs due to complex bufferless routing and arbitration. Especially in the NACK-based bufferless network, the network performance will be affected significantly as the network conflicts increased caused by packet retransmissions. To this end, we proposed a novel approximate bufferless network, ABNoC. ABNoC lessens network conflicts and packet retransmissions via an approximate allocation mechanism (AAM) and a packet approximation method. We show that, compared with SCARAB, ABNoC improved the bandwidth up to 1.92 times and achieved 1.2 times faster in runtime. Besides, ABNoC accomplished 83.6% retransmission reduction and 46.7% latency reduction, while maintaining low application error.
Improvement of E-learning Based via Learning Management Systems (LMS) Using Artificial Neural Networks

E-Learning nowadays is one of the learning system which uses the latest technologies in the field of innovative learning, it has been an extension of traditional education. The effectiveness of E-Learning lies in achievement of education and improving the student's performance and its reflection on the demands of students by discovering the weaknesses and strengths of the factors affecting distance learning. In this research we have used the multilayered neural networks (feedforward neural network) with an input of five neurons which represent the five criteria (virtual class presence, Discussion during semester, Solving Quiz, Mid-term examination, Assignment), hidden layer has two neurons and the output layers have one neuron. to estimate the performance of the students attending an E-Learning course, feedforward neural network was applied to real data )400 student records (80%) are used for training data and the remaining 100 records (20%) are used as test data, performance = 0.0699), to predict the performance of the students that reflect their real grades.
A map traceability management scheme for security control

ABSTRACT A map traceability management scheme for security control is proposed utilising visual cryptography and Quick Response Code. The map information is classified into two categories: the public information MPI and the traceable map information MTI, and they are both converted into QR codes. Map shares MS and control shares CS are generated according to extended multiple visual cryptographic scheme. MPI can be obtained by scanning the MS, while MTI can be got by overlaying the MS on CS which is only accessible to management units, so it can identify every map and provide the basis for the traceability management.
Authentication of GOOSE Messages under Timing Constraints in IEC 61850 Substations

For the future generation of energy systems, secure communication is a key component in ensuring a reliable and stable operation. The actual respective standard to define the communication network architectures for substation automation is the IEC 61850. In order to address the shortcomings of IEC 61850 w.r. t. communication security, IEC 62351-6 introduces respective recommendations. However, a thorough analysis of these recommendations shows that the authenticity and integrity of time-critical protocols such as Generic Object Oriented Substation Event (GOOSE) messages are not entirely covered by the proposed security measures. Therefore, in the present contribution, implementation of the RSASSA-PSS and HMAC-SHA256 authentication are investigated for the given context. Comparison with previous works is provided and obtained results show that the HMAC scheme has a better computational time than the recommended RSASSA-PSS. Thus, adjustment of the IEC 62351-6 considering the authentication of GOOSE messages shall be considered in the next edition of the standard.
A Convolutional Neural Network (CNN) Approach to Detect Face Using Tensorflow and Keras

Face recognition is used in a variety of aspects in the modern world. Face detection means to identify the face from a digital image. The deep neural network is considered a powerful tool as it can handle huge amounts of data . conventional neural network is one most popular tool to detect face detection. In this paper, a deep convolutional neural network (CNN) to extract features from input images. Keras is used for implementing CNN also D'lib and OpenCV for aligning faces on input images. Face recognition performance is evaluated using a custom dataset.
Implementing SHA-3 candidate BLAKE algorithm in Field Programmable Gate Arrays

BLAKE is a cryptographic hash function proposed as a candidate in SHA-3 contest where he successfully qualified to the final round with other 4 candidates. Although it eventually lost to KECCAK it is still considered as a suitable solution with good cryptographic strength and great performance especially in software realizations. For these advantages BLAKE is commonly selected to be a hash function of choice in many contemporary IT systems in applications like digital signatures or message authentication. The purpose of this paper is to evaluate how the algorithm is suitable to be implemented in hardware using low-cost Field Programmable Gate Array (FPGA) devices, particularly to test how efficiently its complex internal transformations can be realized with FPGA resources when overall size of the implementation grows substantially with multiple rounds of the cipher running in parallel in hardware and capacity of the configurable array is used up to its limits. The study was made using the set of 7 different architectures with different loop unrolling factors and with optional application of pipelining, with each architecture being implemented in two popular families of FPGA devices from Xilinx. Investigation of the internal characteristic of the implementations generated by the tools helped in analysis how the fundamental mechanism of loop unrolling with or without pipelining works in case of this particular cipher.
Efficient and Fair Resource Allocation Scheme for Cognitive Satellite-Terrestrial Networks

In this paper, we consider the resource allocation problem in uplink cognitive satellite-terrestrial networks, where the cognitive satellite users reuse the frequency band of terrestrial cellular networks. In this scenario, the joint radio resource allocation strategy has to be reasonably designed to satisfy the interference constraint required by the incumbent terrestrial network (ITN). We formulate the optimization model of joint resource allocation with the perspective of efficiency and fairness, where the interference constraint is defined as the maximum tolerable interference outage probability of the ITN. Then, we simplify the proposed optimization problem into a tractable convex optimization form by some necessary mathematical simplification, wherein the simplification of the probability constraint takes the outdated channel state information into account. By analyzing the conditions of the optimal solution for the joint resource allocation problem in detailed, we derive the closed-form expression for both power allocation and subchannel allocation. Besides, we design an iterative algorithm for the joint resource allocation based on these closed-form solutions. Finally, some supporting results are given to verify the correctness and efficiency of the proposed algorithm.
Methodological improvements for the analysis of domain movements in large biomolecular complexes

Domain movements play a prominent role in the function of many biomolecules such as the ribosome and F0F1-ATP synthase. As more structures of large biomolecules in different functional states become available as experimental techniques for structure determination advance, there is a need to develop methods to understand the conformational changes that occur. DynDom and DynDom3D were developed to analyse two structures of a biomolecule for domain movements. They both used an original method for domain recognition based on clustering of “rotation vectors”. Here we introduce significant improvements in both the methodology and implementation of a tool for the analysis of domain movements in large multimeric biomolecules. The main improvement is in the recognition of domains by using all six degrees of freedom required to describe the movement of a rigid body. This is achieved by way of Chasles’ theorem in which a rigid-body movement can be described as a screw movement about a unique axis. Thus clustering now includes, in addition to rotation vector data, screw-axis location data and axial climb data. This improves both the sensitivity of domain recognition and performance. A further improvement is the recognition and annotation of interdomain bending regions, something not done for multimeric biomolecules in DynDom3D. This is significant as it is these regions that collectively control the domain movement. The new stand-alone, platform-independent implementation, DynDom6D, can analyse biomolecules comprising protein, DNA and RNA, and employs an alignment method to automatically achieve the required equivalence of atoms in the two structures.
Learning to Identify Chinese Comparative Sentences

Comparison is a common kind of expression,and it is novel and substantial research to extract comparative relations between objects.Identifying comparative sentences in natural language is an important step in extracting comparative relations.To our knowledge,there is no research on identifying Chinese comparative sentences automatically.This paper first defines the problem of Chinese comparative sentence identification,and then proposes to use SVM to classify a Chinese sentence into either "comparative" or not.Various linguistic and statistical features have been explored,such as keywords and sequential patterns.Experimental results demonstrate the effectiveness of the sequential patterns,i.e.the classifier with sequential patterns can significantly outperform the traditional term-based classifier.We also empirically investigate the important factors that affect classification performance.
EMDS has a GeNIe with a SMILE

The Ecosystem Management Decision Support (EMDS) system has been further enhanced with an analytical engine—BayesFusion’s SMILE (Structural Modeling, Inference, and Learning Engine) that comes with the GeNIe (Graphical Network Interface) software—for creating Bayesian network (BN) models. BNs are graphical networks of variables linked by probabilities, that have proven useful in decision-aiding for risk analysis and risk management.
AESGRU: An Attention-Based Temporal Correlation Approach for End-to-End Machine Health Perception

Accurate and real-time perception of the operating status of rolling bearings, which constitute a key component of rotating machinery, is of vital significance. However, most existing solutions not only require substantial expertise to conduct feature engineering, but also seldom consider the temporal correlation of sensor sequences, ultimately leading to complex modeling processes. Therefore, we present a novel model, named Attention-based Equitable Segmentation Gated Recurrent Unit Networks (AESGRU), to improve diagnostic accuracy and model-building efficiency. Specifically, our proposed AESGRU consists of two modules, an equitable segmentation approach and an improved deep model. We first transform the original dataset into time-series segments with temporal correlation, so that the model enables end-to-end learning from the strongly correlated data. Then, we deploy a single-layer bidirectional GRU network, which is enhanced by attention mechanism, to capture the long-term dependency of sensor segments and focus limited attention resources on those informative sampling points. Finally, our experimental results show that the proposed approach outperforms previous approaches in terms of the accuracy.
Reliability of a low-cost webcam recording system for three-dimensional lower limb gait analysis

ABSTRACT The purposes of this study were to develop and evaluate the test-retest reliability of a specific low-cost three-dimensional webcam recording system (3D-WCRS) and compare its reliability to a standard motion analysis system. Twenty healthy volunteers comprised of 5 males and 15 females with a mean age of 22.90 years and mean BMI of 22.72 kg/m2 were investigated for angles of hip, knee and ankle joints in three planes while walking at a self-selected speed. Intraclass correlation coefficients (ICCs) were used to evaluate as well as compare the test-retest reliability of the 3D-WCRS and standard motion analysis system. Standard error of measurement (SEM) was also analyzed for the purposes of the study. The results exhibited excellent test-retest reliability for the 3D-WCRS (ICCs ranged between 0.93 and 0.99, p = 0.001) in the three joints and planes. The standard motion analysis system demonstrated excellent reliability for all joints and planes (ICCs ranged between 0.99 and 1.00, p = 0.001). Minimal SEM values were observed in both the 3D-WCRS and standard motion analysis systems. Therefore, the developed low-cost 3D-WCRS exhibits good to excellent test-retest reliability. The test-retest reliability of the 3D-WCRS is likely to be comparable to a standard motion analysis system.
Improved Computer-Generated Simulation Using Motion Capture Data

Improved Computer-Generated Crowd Simulation Using Motion Capture Data Seth A. Brunner Department of Computer Science, BYU Master of Science Ever since the first use of crowds in films and videogames there has been an interest in larger, more efficient and more realistic simulations of crowds. Most crowd simulation algorithms are able to satisfy the viewer from a distance but when inspected from close up the flaws in the individual agent’s movements become noticeable. One of the bigger challenges faced in crowd simulation is finding a solution that models the actual movement of an individual in a crowd. This paper simulates a more realistic crowd by using individual motion capture data as well as traditional crowd control techniques to reach an agent’s desired goal. By augmenting traditional crowd control algorithms with the use of motion capture data for individual agents, we can simulate crowds that mimic more realistic crowd motion, while maintaining real-time simulation speed.
Multimodal person recognition in audio-visual streams

Multimedia databases are growing rapidly in size in the digital age. To increase the value of these data and to enhance the user experience, there is a need to make these videos searchable through automatic indexing. Because people appearing and talking in the videos are often of high interest for end users, indices that represent the location and identity of people in the archive are indispensable for video search and browsing tools. On the other hand, multimedia videos contain resourceful data of people in both visual and auditory domains. This offers a potential for multimodal learning in the task of human identification. Hence, the main theme of this thesis is on algorithms to create indexes and exploit the audio-visual correspondence in large multimedia corpuses based on person identities. First, this thesis deals with algorithms to create indexes through person discovery in videos. It involves several components: face and speaker diarization, face-voice association, and person naming. To obtain face clusters, we propose a novel face tracking approach that leverages face detectors with a tracking-by-detection framework relying on long term time-interval sensitive association costs. We use also shot context to further accelerate and improve face clustering. Face clusters are then associated to speaker clusters using dubbing and talking detection, in which a multimodal framework is introduced to represent the temporal relationship between the auditory and visual streams. We also improve speaker embeddings for recognition and clustering by using a regularizer called intra-class loss. In the second half, the thesis focuses on multimodal learning with face-voice data. Here, we aim to answer two research questions. First, can one improve a voice embedding using knowledge transferred from a face representation? We investigate several transfer learning approaches to constrain the target voice embedding space to share latent attributes with the source face embedding space. The crossmodal constrains act as regularizers helping voice models, especially in the low-data setting. The second question is can face clusters be used as training labels to learn a speaker embedding? To answer this, we explore the tolerance of embedding losses under label uncertainty. From the risk minimization perspective, we obtain the analytical results that provide the heuristics in strategies to improve the tolerance against label noise. We apply the findings into our task of learning speaker embeddings using face clusters as labels. While the experimental results agree with the analytical heuristics, there is still a large gap in performance between the supervised and the weakly supervised models, which requires further investigation in the future.
Plastic bottle cap detection - Utilizing artificial intelligence

To interpret their surroundings, machines utilize sensors. Sensors provide information of the surrounding environment in the form of numeric values that are interpretable for computers. Computers can process the information received from the sensors. How the computers choose to process this information depends on the problem at hand. After processing the information, it is common for the computer to make a decision. Systems that are put together with sensors, a processing unit and other hardware, and with the purpose of solving specified tasks are called embedded systems. Reverse vending machines (RVMs) are embedded systems that are designed to solve specific tasks. This masters thesis will explore the possibility of designing a solution that detects if the cap is attached to the plastic bottles that are inserted into the RVMs. The RVMs are equipped with six cameras. These cameras captures digital images as the plastic bottles are inserted into the RVMs. The digital images consists of numeric values which makes it possible for a computer to perform digital image processing. The cap detection problem is a binary classification problem which consists of two classes; Cap and No-cap. There are three important criteria for this classification problem; Accuracy, classification time and size of classification algorithm. Accuracy is important to have a reliable classification. Classification time is important because the RVMs demands real-time classification. Size of the classification algorithm is important because RVMs have limited memory. Pattern recognition, machine learning and deep learning are different approaches that are applied for the classification problem. In this study, the different approaches applied will be presented and compared. The results of the different approaches applied vary. The best performance was achieved when deep learning was applied, more precisely convolutional neural networks (CNNs). CNNs finds relevant features in digital images on its own, unlike the other approaches where these features must be found manually. The features found in the CNNs proved to be good features and the networks managed to separate cap images from no-cap images with high accuracy. Some of the networks provided high accuracy as well as low classification time and small network size.
MODELING OF CONCENTRATION AND TEMPERATURE DEPENDENCIES OF INCUBATION TIME AT DECOMPOSITION OF SOLID SOLUTION BY MONTE CARLO METHOD

ORCID: 0000-0002-2594-5559 A. M. Gusak Doctor of physical and mathematical sciences, professor, Honored Worker of Science and Technology of Ukraine, Leading Researcher, Laboratory of Mathematical Physics, Department of Physics of EducationalScientific Institute of Informational and Eduational Technologies, The Bohdan Khmelnytsky National University of Cherkasy, Cherkasy, Ukraine, amgusak@ukr.net
Real-Time Photo-Realistic Augmented Reality Under Dynamic Ambient Lighting Conditions

Despite all major developments in graphics hardware, realistic rendering is still a computational challenge in real-time augmented reality (AR) applications deployed on portable devices. We have developed a real-time photo-realistic AR system which captures environment lighting dynamically, computes second order spherical harmonic (SH) coefﬁcients of it on the CPU and the resulting nine coefﬁcients on the AR device for real-time rendering. Our technique provides a very computationally efﬁcient rendering procedure for diffuse objects in real-time rendering scenarios. We use two options for dynamic photometric registration of environments: a Ricoh Theta S 360 ◦ camera, and a Raspberry Pi Zero mini PC with a camera with a 180 ◦ ﬁsheye lens. We tested our system successfully with software developed in Unity 3D with the Vuforia AR package, running on a Microsoft Hololens, and also an Oculus Rift DK2 with a stereo camera add-on.