Miracle-dz/UniFAR-FTUs huggingface dataset:
A Vector-Based Constrained Obstacle Avoidance Scheme for Wheeled Mobile Redundant Robot Manipulator

Obstacle avoidance is an important issue when a wheeled mobile redundant robot manipulator (WMRRM) completes given tasks in complex environment. In this article, a novel vector-based constrained obstacle avoidance (VOA) scheme is designed and analyzed for motion planning of the WMRRM. Specifically, the VOA scheme is first described as a quadratic programming (QP) problem subject to an equality constraint, an inequality constraint, and a bound constraint. Compared with the traditional obstacle avoidance scheme, the proposed VOA scheme can not only achieve subtasks, such as obstacle avoidance and physical limit avoidance when the robot completes the main end-effector task, but also greatly expands the range of feasible spaces. In addition, the VOA scheme is transformed into piecewise linear projection equations (PLPEs) and solved by a linear-variational-inequality-based primal–dual neural network (LVI-PDNN), which can efficiently obtain the optimal solutions to the VOA scheme. Computer simulations demonstrate the reliability, accuracy, and effectiveness of the proposed VOA scheme.
Homonyms Discovery in Folksonomy Based on User Community Analysis

Due to the capability of reflecting social perception on semantic of resources, folksonomy has been proposed to improve the social learning for education and scholar researching. However, its actual impact is significantly influenced by the semantic ambiguity problem of tags. So, in this paper, we proposed a novel way of detecting homonyms, one of the main sources of tag's semantic ambiguity problem, in noisy folksonomies. The study is based on two hypotheses:1) Users having different interests tend to have different understanding of the same tag. 2) Users having similar interest tend to have common understanding of the same tag. Therefore, we firstly discover user communities according to users' interests. Then, tag contexts are discovered in subsets of folksonomy on the basis of user communities. The experimental results show that our method is effective and outperform the method finding tag contexts using all tags in folksonomy with overlapping clustering algorithm especially when various users having different interests are contained by the folksonomy.
Cellular Planning and Optimization for 4G and 5G Mobile Networks

Cellular planning and optimization of mobile heterogeneous networks has been a topic of study for several decades with a diversity of resources, such as analytical formulations and simulation software being employed to characterize different scenarios with the aim of improving system capacity. Furthermore, the world has now witnessed the birth of the first commercial 5G New Radio networks with a technology that was developed to ensure the delivery of much higher data rates with comparably lower levels of latency. In the challenging scenarios of 4G and beyond, Carrier Aggregation has been proposed as a resource to allow enhancements in coverage and capacity. Another key element to ensure the success of 4G and 5G networks is the deployment of Small Cells to offload Macrocells. In this context, this MSc dissertation explores Small Cells deployment via an analytical formulation, where metrics such as Carrier plus Noise Interference Ratio, and physical and supported throughput are computed to evaluate the system ́s capacity under different configurations regarding interferers positioning in a scenario where Spectrum Sharing is explored as a solution to deal with the scarcity of spectrum. One also uses the results of this analyses to propose a cost/revenue optimization where deployment costs are estimated and evaluated as well as the revenue considering the supported throughput obtained for the three frequency bands studied, i.e., 2.6 GHz, 3.5 GHz and 5.62 GHz. Results show that, for a project life time of 5 years, and prices for the traffic of order of 5€ per 1 GB, the system is profitable for all three frequency bands, for distances up to 1335 m. Carrier Aggregation is also investigated, in a scenario where the LTE-Sim packet level simulator is used to evaluate the use of this approach while considering the use of two frequency bands i.e., 2.6 GHz and 800 MHz to perform the aggregation with the scheduling of packets being performed via an integrated common radio resource management used to compute Packet Loss Ratio, delay and goodput under different scenarios of number of users and cell radius. Results of this analysis have been compared to a scenario without Carrier Aggregation and it has been demonstrated that CA is able to enhance capacity by reducing the levels of Packet Loss Ratio and delay, which in turn increases the achievable goodput.
One Picture Is Worth a Thousand Words? The Pricing Power of Images in e-Commerce

In e-commerce, product presentations, and particularly images, are known to provide important information for user decision-making, and yet the relationship between images and prices has not been studied. To close this research gap, we suggest a tailored web mining framework, since one must quantify the relative contribution of image content in describing prices ceteris paribus. That is, one must account for the fact that such images inherently depict heterogeneous products. In order to isolate the pricing power of image content, we suggest a three-stage framework involving deep learning and statistical inference. Our empirical evaluation draws upon a comprehensive dataset of more than 20,000 online real estate listings. We find that the image content describes a large portion of the variance in prices, even when controlling for location and common characteristics of apartments. A one standard deviation in the image variable is associated with a increase in price. By utilizing a carefully designed instrumental variables estimation, we further set out to obtain causal estimates. Our empirical findings contribute to theory by quantifying the hedonic value of images and thus establishing a causal link between visual appearance and product pricing. Even though a positive relationship seems intuitive, we provide for the first time an empirical confirmation. Based on our large-scale computational study, we further yield evidence of a picture superiority effect: simply put, a beneficial image corresponds to the same price change as 2856.03 additional words in the textual description. In sum, images capture valuable information for users that goes beyond narrative explanations. As a direct implication, we aid online platforms and their users in assessing and improving the multi-modal presentation of product offerings. Finally, we contribute to web mining by highlighting the importance of visual information.
Cryptanalysis and improvement of the YAK protocol with formal security proof and security verification via Scyther

Hao proposed the YAK as a robust key agreement based on public‐key authentication, and the author claimed that the YAK protocol withstands all known attacks and therefore is secure against an extremely strong adversary. However, Toorani showed the security flaws in the YAK protocol. This paper shows that the YAK protocol cannot withstand the known key security attack, and its consequences lead us to introduce a new key compromise impersonation attack, where an adversary is allowed to reveal both the shared static secret key between two‐party participation and the ephemeral private key of the initiator party in order to mount this attack. In addition, we present a new security model that covers these attacks against an extremely strong adversary. Moreover, we propose an improved YAK protocol to remedy these attacks and the previous attacks mentioned by Toorani on the YAK protocol, and the proposed protocol uses a verification mechanism in its block design that provides entity authentication and key confirmation. Meanwhile, we show that the proposed protocol is secure in the proposed formal security model under the gap Diffie‐Hellman assumption and the random oracle assumption. Moreover, we verify the security of the proposed protocol and YAK protocol by using an automatic verification method such as the Scyther tool, and the verification result shows that the security claims of the proposed protocol are proven, in contrast to those of the YAK protocol, which are not proven. The security and performance comparisons show that the improved YAK protocol outperforms previous related protocols.
Learning From Web Data With Self-Organizing Memory Module

Learning from web data has attracted lots of research interest in recent years. However, crawled web images usually have two types of noises, label noise and background noise, which induce extra difficulties in utilizing them effectively. Most existing methods either rely on human supervision or ignore the background noise. In this paper, we propose a novel method, which is capable of handling these two types of noises together, without the supervision of clean images in the training stage. Particularly, we formulate our method under the framework of multi-instance learning by grouping ROIs (i.e., images and their region proposals) from the same category into bags. ROIs in each bag are assigned with different weights based on the representative/discriminative scores of their nearest clusters, in which the clusters and their scores are obtained via our designed memory module. Our memory module could be naturally integrated with the classification module, leading to an end-to-end trainable system. Extensive experiments on four benchmark datasets demonstrate the effectiveness of our method.
Automated Detection of Printed Circuit Boards (PCB) Defects by Using Machine Learning in Electronic Manufacturing: Current Approaches

The manufacturing of a printed circuit board in the SMT assembly line goes through multiple phases of automatic handling. To ensure the quality of the board and reduce the number of defects, inspection tasks such as solder paste inspection and automatic optical inspection are conducted. The inspection tasks are carried out at various phases of the assembly line. The paper aims to answer the questions of how machine learning technology can contribute for better PCB fault detection in the assembly line and at which parts of the assembly line this technology has been applied. The paper discusses the PCB defect detection by using machine learning and other approaches. The current research shows that PCB defect detection using machine learning are miniscule. Early detection is still unexplored and experimented in the industry.
A MULTILEVEL INVERTER WITH REDUCED SWITCH COUNT ANALYSED BY VARIOUS SWITCHING TECHNIQUES

This paper is mainly aims at comparing the reduced switch inverter with general switching and three PWM techniques. Reducing switch count helps to gets cost cut down of inverter by large scale. This also makes compact, logic easy and also easy control. These kinds of inverters are very useful for low power rating or medium power ratings[1]. But high power rating requirements we have to use normal inverters. Here in this paper we are testing the proposed model by 4 different techniques. Such as normal switching and 3 of level shift PWM techniques.
Solutions and Recent Challenges Related to Energy in Wireless Body Area Networks with Integrated Technologies: Applications and Perspectives

في هذه الورقة، بحثنا في بعض أحدث بروتوكولات التوجيه الموفرة للطاقة لشبكات منطقة الجسم اللاسلكية. شهدت هذه التكنولوجيا تطورات في الآونة الأخيرة حيث يتم حقن أجهزة استشعار لاسلكية في جسم الإنسان لاستشعار وقياس معايير الجسم مثل درجة الحرارة ونبض القلب ومستوى الجلوكوز. تقوم هذه المستشعرات اللاسلكية الدقيقة بجمع معلومات بيانات الجسم وإرسالها عبر شبكة لاسلكية وإلى المحطة الأساسية. يتم فحص قياسات البيانات من قبل الطبيب أو الطبيب ويقترح العلاج المناسب. يتم إجراء الاتصال بالكامل من خلال بروتوكولات التوجيه في بيئة الشبكة. يستهلك بروتوكول التوجيه الطاقة أثناء المساعدة في الاتصال دون توقف في البيئة اللاسلكية. بسبب الحجم الصغير جدا ، فإن استهلاك الطاقة مهم جدا. لقد ركزنا على البروتوكولات التي توفر كفاءة استخدام الطاقة وتحسين عمر الشبكة، والإنتاجية، وفقدان الحد الأدنى من المسار. سنناقش بعض أحدث بروتوكولات التوجيه الموفرة للطاقة في هذه الورقة، في وقت لاحق يتم تقديم مقارنة مع جدول مناسب. بالإضافة إلى ذلك، تمت مناقشة التحديات الأخيرة والتطبيق المحتمل ووجهات النظر.
Model Predictive Full-Torque Control for the Open-Winding PMSM System Driven by Dual Inverter With a Common DC Bus

To eliminate the weighting factor, suppress the zero-sequence current, and reduce the computation burden in the conventional model predictive torque control (MPTC) of the open-winding permanent magnet synchronous motor (OW-PMSM) system with a common dc bus, a vector partition selection-based full-torque control is proposed. The instantaneous power theory is introduced to make the control of the torque, the flux linkage, and the zero-sequence current, which is equivalent to the control of the real torque, the virtual torque, and the zero torque. Then, the full-torque-based cost function is designed to avoid the weighting factor in the proposed method. In addition, to reduce the calculation burden caused by the large number of candidate voltage vectors, a vector partition selection strategy in 3-D space is presented. Then, the candidate vectors are reduced from 27 to 6 or 7 in every control period. Finally, the experimental results verify the effectiveness of the proposed method.
Role of Shear Wave Elastography in the Assessment of Fibrosis in Chronic Liver Disease

Objective: To evaluate the diagnostic performance of shear wave elastography in the estimation of fibrosis in patients with chronic liver disease by using biopsy and/or serum markers as reference standard. Materials and methods: 100 patients underwent point quantification-shear wave elastography, for whom noninvasive serum fibrosis indices like APRI, FIB-4, and King’s score were calculated. The receiver-operator characteristic (ROC) curve analysis was performed. Results: The shear wave elastography measurements showed moderate agreement with APRI, FIB-4, and fair agreement with King’s score. The AUROC for differentiating F0-F1, F2-F3, F2-F3, and F4 are 0.873 and 0.504 respectively using APRI as reference standard. The cutoff values derived for differentiating F0-F1 and F2-F3 was 7.07 and for differentiating F2-F3 and F4 was 11.94. Conclusion: The diagnostic performance of shear wave elastography is comparable with that of serum fibrosis indices APRI and FIB-4.
Engraftment and survival after unrelated-donor bone marrow transplantation: a report from the National Marrow Donor Program

We analyzed engraftment of unrelated-donor (URD) bone marrow in 5246 patients who received transplants facilitated by the National Marrow Donor Program between August 1991 and June 1999. Among patients surviving at least 28 days, 4% had primary graft failure (failure to achieve an absolute neutrophil count > 5 × 108/L before death or second stem-cell infusion). Multivariate logistic regression analysis showed that engraftment was associated with marrow matched at HLA-A, HLA-B, and DRB1; higher cell dose; younger recipient; male recipient; and recipient from a non–African American ethnic group. More rapid myeloid engraftment was associated with marrow serologically matched at HLA-A and HLA-B, DRB1 match, higher cell dose (in non-T-cell–depleted cases), younger recipient, recipient seronegativity for cytomegalovirus (CMV), male donor, no methotrexate for graft-versus-host disease prophylaxis, and transplantation done in more recent years. A platelet count higher than 50 × 109/L was achieved by 47% of patients by day 100. Conditional on survival to day 100, survival at 3 years was 61% in those with platelet engraftment at day 30, 58% in those with engraftment between day 30 and day 100, and 33% in those without engraftment at day 100 ( P < .0001). Factors favoring platelet engraftment were higher cell dose, DRB1 allele match, recipient seronegativity for CMV, HLA-A and HLA-B serologically matched donor, and male donor. Secondary graft failure occurred in 10% of patients achieving initial engraftment, and 18% of those patients are alive. These data demonstrate that quality of engraftment is an important predictor of survival after URD bone marrow transplantation.
Traffic Flow Prediction via Spatial Temporal Graph Neural Network

Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transportation networks. The dynamic of the traffic flow on one road not only depends on the sequential patterns in the temporal dimension but also relies on other roads in the spatial dimension. Although there are existing works on predicting the future traffic flow, the majority of them have certain limitations on modeling spatial and temporal dependencies. In this paper, we propose a novel spatial temporal graph neural network for traffic flow prediction, which can comprehensively capture spatial and temporal patterns. In particular, the framework offers a learnable positional attention mechanism to effectively aggregate information from adjacent roads. Meanwhile, it provides a sequential component to model the traffic flow dynamics which can exploit both local and global temporal dependencies. Experimental results on various real traffic datasets demonstrate the effectiveness of the proposed framework.
Boundary Detection in Dynamic Wireless Sensor Networks using Convex Hull Techniques

Perimeter protection aims to detect intrusions: these intrusions may range from agricultural fields to army base camps. The main problem concerning perimeter protection is edge or boundary detection. In this paper, two boundary detection algorithms have been proposed that use convex hull techniques to find the boundary of a static and dynamic wireless sensor network (WSN). Whenever a boundary node on a static WSN has to be replaced, our method chooses a new neighbor such that the overall area covered by the boundary is maximized. In the dynamic case, we demonstrate that the boundary of a dynamic WSN can be maintained in O(lig2n) time, where n is the number of sensor nodes in the network, maximizing the coverage area of the network at all times.
Mitigation Techniques for Impulsive Noise With Memory Modeled by a Two State Markov-Gaussian Process

Impulsive noise, a common impediment preventing the system from achieving error-free transmission, is significant in many wireless and power line communication environments. Although the performance of several mitigation techniques for orthogonal frequency division multiplexing (OFDM)-based multicarrier communication systems impaired by memoryless impulsive noise are widely acknowledged, we note that OFDM is outperformed by its single-carrier counterpart when the impulses are very strong and/or they occur frequently, which is likely to exist in contemporary communication systems including smart grid communications. On the other hand, many communication technologies used in the smart grid do not employ OFDM and likewise, the assumption of memoryless noise is not valid for such communication scenarios. Memoryless noise models cannot take into account one of the main features of the actual noise, i.e., the time-correlation among the noise samples. The aim of this article is to compare and analyze several mitigation techniques such as clipping, blanking, and combined clipping-blanking to mitigate the noxious effects of bursty impulsive noise for low-density parity-check coded single-carrier communication systems. Moreover, we propose a log-likelihood ratio (LLR)-based impulsive noise mitigation for the considered scenario. In this context, provided simulation results highlight the superiority of the LLR-based mitigation scheme over the clipping/blanking schemes.
ETHERACTION: PLAYING A MUSICAL PIECE USING GRAPHICAL INTERFACES

This paper introduces the use of graphical interfaces to interpret an electroacoustic piece, Etheraction. Electroacoustic pieces, commonly created for tape, can now be interpreted in live performance with dedicated interactive systems; the interaction between the performers and these systems can use graphical interfaces, largely implemented in nowadays computers. When using graphical interfaces for real time sound control, the tasks consist in controlling sound parameters through the manipulation of graphical objects, using pointing techniques or direct control with additional devices. The paper presents how I have designed two interactive systems dedicated to interpret in live Etheraction, a multichannel piece I have initially composed for tape. The piece is based on the motion of physical models of strings that control sound parameters. The two devices control both synthesis parameters and spatialisation parameters, are based on interactions with graphical interfaces, and use specific controllers.
Urban Scene Vectorized Modeling Based on Contour Deformation

Modeling urban scenes automatically is an important problem for both GIS and nonGIS specialists with applications like urban planning, autonomous driving, and virtual reality. In this paper, we present a novel contour deformation approach to generate regularized and vectorized 3D building models from the orthophoto and digital surface model (DSM). The proposed method has four major stages: dominant directions extraction, find target align direction, contour deformation, and model generation. To begin with, we extract dominant directions for each building contour in the orthophoto. Then every edge of the contour is assigned with one of the dominant directions via a Markov random field (MRF). Taking the assigned direction as target, we define a deformation energy with the Advanced Most-Isometric ParameterizationS (AMIPS) to align the contour to the dominant directions. Finally, the aligned contour is simplified and extruded to 3D models. Through the alignment deformation, we are able to straighten the contour while keeping the sharp turning corners. Our contour deformation based urban modeling approach is accurate and robust comparing with the state-of-the-arts as shown in experiments on the public dataset.