Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on 5G Millimeter Wave Communications for UAV-Assisted Wireless Networks

In recent years, unmanned aerial vehicles (UAVs) have received considerable attention from regulators, industry and research community, due to rapid growth in a broad range of applications. Particularly, UAVs are being used to provide a promising solution to reliable and cost-effective wireless communications from the sky. The deployment of UAVs has been regarded as an alternative complement of existing cellular systems, to achieve higher transmission efficiency with enhanced coverage and capacity. However, heavily utilized microwave spectrum bands below 6 GHz utilized by legacy wireless systems are insufficient to attain remarkable data rate enhancement for numerous emerging applications. To resolve the spectrum crunch crisis and satisfy the requirements of 5G and beyond mobile communications, one potential solution is to use the abundance of unoccupied bandwidth available at millimeter wave (mmWave) frequencies. Inspired by the technique potentials, mmWave communications have also paved the way into the widespread use of UAVs to assist wireless networks for future 5G and beyond wireless applications. In this paper, we provide a comprehensive survey on current achievements in the integration of 5G mmWave communications into UAV-assisted wireless networks. More precisely, a taxonomy to classify the existing research issues is presented, by considering seven cutting-edge solutions. Subsequently, we provide a brief overview of 5G mmWave communications for UAV-assisted wireless networks from two aspects, i.e., key technical advantages and challenges as well as potential applications. Based on the proposed taxonomy, we further discuss in detail the state-of-the-art issues, solutions, and open challenges for this newly emerging area. Lastly, we complete this survey by pointing out open issues and shedding new light on future directions for further research on this area.
A Real-time Character-level Subtitle Detector based on Faster R-CNN

Faced with a deluge of video data, automatic subtitle text detection and recognition using computer vision technology can be extremely useful. This paper proposes a real-time character-level subtitle detector, which detects subtitle text with high accuracy and efficiency in a single forward pass. It also provides an efficient method to build dataset aimed at video subtitle text detection problem. This paper’s method can achieve both 99.5% precision and recall for text detection, and the whole detection and recognition process can achieve 45 fps.
Randomization for Safer, more Reliable and Secure, High-Performance Automotive Processors

Editor’s note: This article presents a future computing platform for automotive systems, where randomization will be introduced in the nonfunctional behavior of certain hardware components. By doing so, besides ensuring the higher performance of the processors, it will enhance the security of the complex processors, provide time predictability via probabilistic analysis, and finally, enhance reliability against aging and voltage noise. —Mohammad Al Faruque, University of California, Irvine
Mass to light ratio of galaxies and gravitational lensing

We investigate the potential of constraining the mass to light ratio of field galaxies using weak lensing shear and flexions. A suite of Monte Carlo simulations are used to generate weak lensing observations with different noise models. Using mock data, we find that the inclusion of flexions can improve the estimate of foreground halo parameters, but the details are strongly dependent on noise in the model. In the intrinsic noise limit, both shear and flexions are promising tools to study the mass to light ratio of galaxies. However, if the noise model of flexions follows the form described by Rowe et al., there is only ∼5% improvement in the constraints even with next generation lensing observations.
UWB localization system for partially GPS-denied robotic applications

Most common robotic outdoor navigation systems typically rely upon global positioning system, never the less there are several scenarios in which the GPS alone is not enough. The presence of high buildings, trees or other obstacles can cause a substantial deterioration of the estimation of the position of the robot due to blockage of line-of-sight and multipath effects. So, dealing with partially GPS-denied environments makes it necessary to develop ad hoc solutions to aid navigation. This work moves the first steps towards the development of a localization system based on drones and ultra-wide band technologies. The idea is to use three or four drones hovering above the working area of the rover where they can be localized using GPS. Moreover, both the UAVs and the ground vehicle are equipped with ultra-wide band sensors in order to precisely measure the relative distance between all the agents. With this information, it is possible to compute the position of the UGV using trilateration algorithms. The first part of this thesis is a theoretical analysis of the trilateration problem. Two algorithms are compared, taking into account some simple geometries for the positioning of the anchor nodes. The first goal is to evaluate which are the points in space that minimize the positioning error. The second issue is to understand how the number of anchors affect the precision of the position estimation. In the second part of the work, several measurements are performed using two different sets of commercial sensors: Decawave TREK1000 and Pozyx. The precision of both systems is evaluated through ranging and positioning measurements. The computed parameters are then used in simulation in combination with information on the positioning of a drone hovering in fixed position. In this way it is finally possible to compute the expected error in the localization of the ground vehicle.
Joint image compression and encryption schemes

Thanks to the rapid growth of computer networks and information technology, the number of multimedia applications related to images is rapidly increasing, thus the security issue of image storage and transmission becomes much more essential. Many encryption algorithms have been proposed to ensure images’ safety. Among these encryption schemes, strategies of integrating image compression process with encryption algorithms have received much attention, since compression is a must-do step for most images we see on the Internet. Among various compression standards, JPEG is the most commonly applied method for lossy compression of digital images. This thesis presents our contributions in realizing image encryption during the JPEG compression process. In this thesis, four different joint image compression and encryption schemes are proposed by introducing encryption techniques into the intermediate stages of JPEG. All the encryption algorithms are format compliant to JPEG. The first encryption scheme is realized by applying new order-8 orthogonal transforms for blocks’ transformation, followed by block permutation on quantized 8×8 blocks. These new transforms are produced by embedding rotation angle of π to 8×8 DCT’s flow-graph structure. Through controlling the number of rotation angles embedded, we can achieve the quality control of the final encrypted images. In the second encryption scheme, we improve the first scheme by allowing more rotation angles, not just π, to be embedded into the flow-
EDAC software implementation to protect small satellites memory

Radiation is a well-known problem for satellites in space. It can produce different negative effects on electronic components which can provoke errors and failures. Therefore, mitigating these effects is especially important for the success of space missions. One of the techniques to increase the reliability of memory chips and reduce transient errors and permanent faults is Error Detection and Correction (EDAC). EDAC codes are characterised by the use of redundancy to detect and correct errors. This final project consists in the implementation of a software EDAC algorithm to protect the main memory of a microcontroller. The implementation requirements and the issues of software EDAC are described and the test results are commented.
Unsupervised Deep Feature Learning With Iteratively Refined Pseudo Classes for Scene Representation

Recently, more and more attention has been focused on the remote sensing scenes since they contain plentiful spectral and spatial information. In order to obtain good performance for scene representation, a proper model for feature extraction and large amounts of labeled training samples are required. However, in real-world applications, it usually cannot provide enough labeled samples since labeling is always time-consuming. To overcome this problem, this work develops a novel unsupervised deep feature learning framework with iteratively refined pseudo-classes for remote sensing scene representation. First, we introduce the center points to construct the pseudo-classes and assign the pseudo labels to the training samples. Then, a pseudo-center loss is developed by decreasing the intra-class variance between the learned features of the samples and the corresponding center points to iteratively refine the pseudo classes with the training samples in the training process. Moreover, to increase the inter-class variance between different pseudo classes and further improve the performance of the unsupervised learning, this work imposes the diversity-promoting priors over the center points. Finally, the unsupervised learning framework is developed by joint learning of the diversified pseudo-center loss and pseudo-class-based softmax loss where the pseudo-class-based softmax loss is to update the convolutional neural network (CNN) with the pseudo-classes and the diversified pseudo-center loss is to iteratively refine the pseudo-classes with the features learned from the CNN. Experiments are conducted over three real-world remote sensing scene datasets to validate the effectiveness of the proposed method and the experimental results show the superiority of the method when compared with other state-of-the-art methods.
A 3-D Rotation-Based Through-Silicon via Redundancy Architecture for Clustering Faults

Three-dimensional integrated circuits (3-D ICs), which feature many benefits, such as high bandwidth and a high degree of integration, have recently received considerable attention from the semiconductor industry. However, these chips feature through-silicon vias (TSVs), which vertically connect multiple dies, and these TSVs may fail, resulting in a decreased yield. Unfortunately, previously proposed methods to repair TSVs cannot handle certain failure patterns. For example, existing techniques cannot repair clustered TSV faults, which commonly occur in practice. Furthermore, the number of signal TSVs typically determines the number of redundant TSVs, which may result in wasteful and redundant TSVs. In this paper, a new TSV repair scheme is proposed that replaces defective TSVs with redundant TSVs by utilizing the architecture of a cube, which can replace any face with any of the other faces. Both signal TSVs and redundant TSVs are placed in the face of cube, so any faulted TSVs can be replaced with redundant TSVs. The experimental results indicate that the new method guarantees 100% coverage with any number of signal TSVs and redundant TSVs.
The correspondence problem: which brain maps are significantly similar?

A critical issue in many neuroimaging studies is the comparison between brain maps. How should we test the hypothesis that two or more brain maps are partially convergent or overlap to a significant extent? This “correspondence problem” affects, for example, the interpretation of comparisons between task-based patterns of functional activation, resting-state networks or modules, and neuroanatomical landmarks. In published work, this problem has been addressed with remarkable variability in terms of methodological approaches and statistical rigor. In this paper, we address the correspondence problem using a spatial permutation framework to generate null models of overlap, by applying random rotations to spherical representations of the cortical surface. We use this approach to derive clusters of cognitive functions that are significantly similar in terms of their functional neuroatomical substrates. In addition, using publicly available data, we formally demonstrate the correspondence between maps of task-based functional activity, resting-state fMRI networks and gyral-based anatomical landmarks. We provide open-access code to implement the methods presented for two commonly-used tools for surface based cortical analysis. This spatial permutation approach constitutes a useful advance over widely-used methods for the comparison of cortical maps, and thereby opens up new possibilities for the integration of diverse neuroimaging data.
A Lyapunov optimization approach to energy-efficient content delivery networks

A content delivery network (CDN) supports fast growing services (such as streaming media) for end users and uses request a routing algorithm to enhance the users’ perceived quality of experience (QoE). This working process can increase the power consumption and eventually the operational cost of the CDN system substantially. In addition, a smart grid facilitates real-time electricity pricing for the consumers as well as the integration of green energy resources into the electrical grid for improving the system efficiency. In this paper, we first take into account these key functionalities of a smart grid to propose a real-time energy management algorithm for reducing the operational cost within a CDN cluster. We then define the proposed stochastic optimization algorithm for jointly optimizing the energy cost and perceived quality within a CDN system based on the popular Lyapunov-drift methodology. Numerical results are presented to evaluate and validate the aforementioned solution. The obtained results indicate large operational and energy cost savings for a single CDN or more than one CDN providers over a conventional method that only optimizes the users’ perceived QoE.
Online Responding as an Innovative Communication Channel to Engaging Customers

The shift to a “hypermedia” environment has changed the nature of communications due to the rapid growing of marketing communications through online media. Marketers are increasingly losing control over the dialogues around their products and brands. This research investigates how online responding emerges as an innovative communication channel to engage customers. Based on the data from Tripadvisor.com, it shows that online responding with different attributes (whether to respond, the frequency, the speed, and the amount of information) exerts asymmetric impact on customer engagement in terms of WOM dimensions and voting behavior. Findings provides pragmatic guidelines to strategically respond to customer reviews.
Cuckoo Search Algorithm Based Feature Selection in Image Retrieval System

Efficiency an d retrieval time are very important issues in any content-based image retrieval system. In this study, an efficient image retrieval system was introduced depending on several features extracted from the database images, namely color moment (Mean, Standard Deviation), GLCM, and DWT( only LL-sub band). To increase the retrieval speed, Cuckoo search algorithm was used to select the important positions that contain full power features from the (LL-sub band). On using the Cuckoo search algorithm, only (50) important positions were chosen out of the total (24576) positions within (LL- sub band). These positions were stored for later use when entering a query image. Thus, the time taken to retrieve images was greatly reduced and this process also increased the efficiency of the system due to the fact that the selected positions gave the lowest distance measures between the query images and the similar images when evaluated using Manhattan distance measure. Two effectual performance measures (precision & recall) were used to calculate the accuracy of the system. The findings proved the system efficiency when compared to other previous works. Keywords: CBIR, Color Moment, GLCM, DWT, Cuckoo Search algorithm, Manhattan measure DOI : 10.7176/JEP/10-15-08 Publication date :May 31 st 2019
Use cases of lossy compression for floating-point data in scientific data sets

Architectural and technological trends of systems used for scientific computing call for a significant reduction of scientific data sets that are composed mainly of floating-point data. This article surveys and presents experimental results of currently identified use cases of generic lossy compression to address the different limitations of scientific computing systems. The article shows from a collection of experiments run on parallel systems of a leadership facility that lossy data compression not only can reduce the footprint of scientific data sets on storage but also can reduce I/O and checkpoint/restart times, accelerate computation, and even allow significantly larger problems to be run than without lossy compression. These results suggest that lossy compression will become an important technology in many aspects of high performance scientific computing. Because the constraints for each use case are different and often conflicting, this collection of results also indicates the need for more specialization of the compression pipelines.
ZoneTier

Integrating solid-state drives (SSDs) and host-aware shingled magnetic recording (HA-SMR) drives can potentially build a cost-effective high-performance storage system. However, existing SSD tiering and caching designs in such a hybrid system are not fully matched with the intrinsic properties of HA-SMR drives due to their lacking consideration of how to handle non-sequential writes (NSWs). We propose ZoneTier, a zone-based storage tiering and caching co-design, to effectively control all the NSWs by leveraging the host-aware property of HA-SMR drives. ZoneTier exploits real-time data layout of SMR zones to optimize zone placement, reshapes NSWs generated from zone demotions to SMR preferred sequential writes, and transforms the inevitable NSWs to cleaning-friendly write traffics for SMR zones. ZoneTier can be easily extended to match host-managed SMR drives using proactive cleaning policy. We implemented a prototype of ZoneTier with user space data management algorithms and real SSD and HA-SMR drives, which are manipulated by the functions provided by libzbc and libaio. Our experiments show that ZoneTier can reduce zone relocation overhead by 29.41% on average, shorten performance recovery time of HA-SMR drives from cleaning by up to 33.37%, and improve performance by up to 32.31% than existing hybrid storage designs.
Proficient Centriod Selection Process for K-Mean Bunching Algorithm in Data Mining

In estimation and data mining, k-proposes gathering is shocking for its ability in gathering wide instructive records. The truth is to get-together server farms into packs with a convincing spotlight on that relative things are lumped together in a nearby get-together. Right when all is said in done, given a blueprint of articles together with their properties, the objective is to detach the things into k parties to such an extent, to the point that things lying in one package ought to be as close as conceivable to each other's (homogeneity) and things lying in various get-togethers are additionally secluded from each other. Regardless, there exist a few imperfections in standard K-decides clustering check. As showed up by the framework, in any case, the figuring is delicate to picking beginning Centroid and can be sensibly gotten in any occasion concerning the estimation (the aggregate of squared oversights) utilized as a touch of the model. In like path, obviously, the K-incites issue the degree that finding a general superfluous aggregate of the squared botches is NP-hard regardless of when the measure of the get-together is proportionate 2 or the measure of
STEGANOGRAFI BERBASIS CITRA DIGITAL UNTUK MENYEMBUNYIKAN DATA MENGGUNAKAN METODE LEAST SIGNIFICANT BIT (LSB)

Kerahasiaan data merupakan hal yang sangat sangat penting dalam suatu organisasi maupun untuk pribadi. Apalagi kalau data tersebut berada dalam suatu sistem komputer, jaringan komputer bahkan dalam jaringan internet.kebutuhan akan keamanan dan kerahasiaan menjadi kebutuhan pokok saat ini. Karena maraknya pembajakan data maupun pencurian data. Steganografi bisa menjadi solusi dalam menjaga kerahasiaan data dan keamanan dari data yang dimiliki. Data yang ada akan disembunyikan sehingga tidak semua orang bisa melihat dan menggunakannya. Dengan metode Least Significant Bit, Steganografi bisa dilakukan dengan menyisipkan data kedalam gambar yang diinginkan. Proses yang terjadi adalah bit-bit data akan disisipkan ke dalam bit citra digital sehingga bit data akan berada di dalam bit wadah citra digital tersebut untuk disembunyikan. Dengan adanya steganografi dan metode Least Significant Bit data bisa disembunyikan kemuian diambil kembali untuk bisa dibaca oleh pemilik data.
Fault tolerance in WSN through PE-LEACH protocol

The fault is inevitable in case of the wireless sensor network (WSN) because of its remote deployment and constrained architecture. In literature, many algorithms have been proposed to address several types of fault in the WSN life-cycle. In the context of fault tolerance, the clustering method has been adopted as a proven technique. Heintzelman et al. first considered the clustering technique and proposed the seminal low energy adaptive clustering hierarchy (LEACH) algorithm for fault handling in WSN. In this study, the authors propose a LEACH variant clustering convention called partitioned-based energy-efficient – LEACH (PE-LEACH) protocol which tends to the energy-based fault-tolerant technique. They also study and present a taxonomy on LEACH variants. The execution of PE-LEACH is analysed against its predecessors; hard-computing based LEACH, energy-efficient LEACH (E-LEACH) convention and soft-computing-based energy-swarm-optimisation LEACH (ESO-LEACH). They have found through their recreation that PE-LEACH outperforms than LEACH and E-LEACH separately whereas, for ESO-LEACH the PE-LEACH is a tough competitor.
Fake Profiles Types of Online Social Networks: A Survey

Today, OSNs (Online Social Networks) considered the most platforms common on the Internet. It plays a substantial role for users of the internet to hold out their everyday actions such as news reading, content sharing, product reviews, messages posting, and events discussing etc. Unfortunately, on the OSNs some new attacks have been recognized. Different types of spammers are existing in these OSNs. These cyber-criminals containing online fraudsters, sexual predators, catfishes, social bots, and advertising campaigners etc.OSNs abuse in different ways especially by creating fake profiles to carry out scams and spread their content. The identities of all these malicious are so damaging to the service providers and the users. From the opinion of OSNs service providers, the loss of bandwidth moreover the overall reputation of the network is affected by fake profiles. Thus, needing more complex automated methods, and tremendous effort manpower to discover and stopping these harmful users.This paper explains different kinds of OSNs risk generators such as cloned profiles, compromised profiles, and online bots (spam-bots, chat-bots, and social-bots). In addition, it presents several classifications of features that have been used for training classifiers in order to discover fake profiles. We try to show different ways that used to detect every kind of these malicious profiles. Also, this paper trying to show what is the dangerous type of profile attacks and the most popular in OSNs.