Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Transmission Concepts for Indoor THz Communication Systems

Continuous research progress in THz communications including communications and networking paradigms as well as devices and implementation aspects indicate that this technology has the potential to become a key component in future beyond 5G systems. Still, some research challenges have to tackled before, e.g. related to the peculiarities of the highly frequency-selective THz channel resulting in a much longer channel impulse response (CIR) compared to channels in the lower frequency bands. In this paper, first, various equalization techniques such as minimum mean-squared error (MMSE) linear equalization and decision-feedback equalization (DFE) are studied for single-carrier transmission in order to combat the frequency selectivity of a deterministic THz channel. Furthermore, orthogonal frequency-division multiplexing (OFDM) communication with zero-forcing (ZF) equalization is considered, and the performance of single-carrier and multi-carrier transmissions are compared. Secondly, the advantages of a stochastic channel model are pointed out for the analysis of a multiple-input multiple-output (MIMO) transmission, and ZF and MMSE linear frequency-domain equalization are investigated for a single-carrier frequency-division multiple access (SC-FDMA) transmission over a MIMO THz channel. We conclude that SC-FDMA along with MMSE linear equalization is promising for practical THz systems.
LORAWAN Internet of Things Network Planning for Smart Metering Services

Optimization of energy usage is essential to minimize the waste of energy usage. Smart Metering is a solution to resolve this problem. It can measure, collect, analyze energy distribution and its consumption, and communicate with metering devices on schedule and on-demand, i.e., for electricity, water, and gas services. However, selecting the right Internet of Things (IoT) network connectivity to deploy Smart Metering services is still the main challenge, especially for the dense urban setting. LoRaWAN is one of the most popular Low Power Wide Area technologies consider as a suitable IoT network connectivity for Smart Metering because LoRaWAN has a long battery life, using unlicensed frequency and low cost of deployment. In this research, the LoRaWAN IoT network planning to deliver Smart Metering services was conducted in Jakarta City, as representative of the dense urban setting. The network planning methods are using capacity and coverage analysis as well as radio network planning simulation. It is found that to deploy Smart Metering services in the Jakarta City area, at least 23 gateways are required with Spreading Factor (SF) values of 7, Code Rate (CR) of 1/2/3/4, and can serve at the level of receiver sensitivity > -129dBm. An average received signal level of -78.84 dBm with an average of throughput distribution of 21.69 kbps were obtained based on coverage prediction simulation. Meanwhile the Signal to Noise Ratio (SNR) simulation generated the highest SNR is 30 dB, and the lowest SNR is -20dB resulting in the average SNR level of the entire region is about -1.05dB.
Skjuvhållfasthet - utvärdering i kohesionsjord

(1) Beteckningar och symboler; (2) Inledande besiktning och inhamtning av geologisk information; (3) Empirisk erfarenhet; (4) Bestamning av egenskaper genom provning; (5) Hallfasthet i silt och fri .
Preference-Based Privacy Markets

In the modern era of the mobile apps (the era of surveillance capitalism - as termed by Shoshana Zuboff) huge quantities of surveillance data about consumers and their activities offer a wave of opportunities for economic and societal value creation. ln-app advertising - a multi-billion dollar industry, is an essential part of the current digital ecosystem driven by free mobile applications, where the ecosystem entities usually comprise consumer apps, their clients (consumers), ad-networks, and advertisers. Sensitive consumer information is often being sold downstream in this ecosystem without the knowledge of consumers, and in many cases to their annoyance. While this practice, in cases, may result in long-term benefits for the consumers, it can result in serious information privacy breaches of very significant impact (e.g., breach of genetic data) in the short term. The question we raise through this paper is: Is it economically feasible to trade consumer personal information with their formal consent (permission) and in return provide them incentives (monetary or otherwise)? In view of (a) the behavioral assumption that humans are ‘compromising’ beings and have privacy preferences, (b) privacy as a good not having strict boundaries, and (c) the practical inevitability of inappropriate data leakage by data holders downstream in the data-release supply-chain, we propose a design of regulated efficient/bounded inefficient economic mechanisms for oligopoly data trading markets using a novel preference function bidding approach on a simplified sellers-broker market. Our methodology preserves the heterogeneous privacy preservation constraints (at a grouped consumer, i.e., app, level) upto certain compromise levels, and at the same time satisfies information demand (via the broker) of agencies (e.g., advertising organizations) that collect client data for the purpose of targeted behavioral advertising.
Genetic diversity and sex determination in date palms (Phoenix dactylifera L.) based on DNA markers

Genetic diversity and relatedness were measured among six date palm (Phoenix dactylifera L.) genotypes by means of RAPD and inter-simple sequence repeat (ISSR) analyses. The level of polymorphism among the six genotypes as revealed by RAPD and ISSR was 60.2% and 73%, respectively. Based on the data obtained, it was possible to discriminate between the different genotypes used and to identify the unknown genotype collected from Matroh Governorate. The data indicated that ten RAPD and six ISSR markers were found to be genotype-specific. The highest number of RAPD specific markers was scored for the unknown genotype (5 markers) while the cultivar Karamah and Frehi scored three and two markers, respectively. Four ISSR markers characterize the cultivar Karamah where Frehi and Oshkingbil cultivars were characterized by one marker each. The cluster analysis indicates that the unknown cultivar was closely related to the cultivar Frehi and Oshkingbil. The potential uses of the biochemical and molecular markers in sex identification of in vivo grown date palm were investigated. Two male specific protein bands with the molecular weight of 72 and 47 kDa and another two female specific bands with the molecular weight of 135 and 55 kDa were detected. Female specific peroxidase band (RF of 0.4) and male specific estrase band (RF of 0.5) were also detected. Random amplified polymorphic DNA (RAPD) technique was used to compare genetic material from male and female date palm trees. The sexspecific RAPD markers were determined. The data indicate that 13 RAPD markers were found to be female specific and 5 markers were male specific and can be applied to screen male and female plants at young stages.
Personalized E-Learning Content Based On Felder-Silverman Learning Style Model

A Personalization of E-learning can be done by personalize E-learning features, which are learning content and learning activities provided. In this study, E-learning content personalization was carried out based on the Felder Silverman Learning Style Model (FSLSM). FSLSM is a method of classifying learning styles into 4 dimensions, namely perception, processing, input and understanding. FSLSM classifies learning styles using the Index of Learning Styles (ILS) questionnaire, consisting of 44 questions. In the system that was built, the ILS questionnaire was digitized to map E-learning users, based on their learning style. Afterwards, content personalization algorithms were designed, so users could access content that were suitable to their learning styles while using the system, after that learning content was designed for each dimension of the FSLSM with ADDIE modelling, which was the stage for creating E-learning content. To test the designed algorithm, Delphi method was used, which is a method that collects expert opinion on a problem. For system test, System Usability Scale (SUS) method was used to measure acceptance of user. The results of this research, getting agreement on the accuracy of the design of content personalization algorithms from 3 E-learning experts, the system that was built got SUS 75.33 which was acceptable with grade B, means the system was accepted as a learning tools to help them in learning process based on SUS assessment.
IoT Object Security towards On-off Attack Using Trustworthiness Management

Internet of Things (IoT) can create the world with the integration of the physical things with the seamlessly network of information purposely to give a sophisticated and smart service for human life. A variety of threats and attacks to IoT object, however, can lead to the misuse of data or information to the IoT objects. One of the attacks is On-off Attack in which the attacker acts not only as an object with a good manner by sending the valid trust value but also sometimes as a bad object by sending invalid one. To respond this action, there is a need for the object security to such attacks. Here the writer used the Trustworthiness Management as a method to cope with this attack. Trustworthiness Management can use the aspect of trust value security as a reference for detecting an attack to the object. In addition, with the support of security system using the authentication provided by MQTT, it is expected that it can provide an additional security. The approach used in this research was the test on On-Off Attack detection directly to the object connected to the network. The results of the test were then displayed on the webpage made using PHP and MySQL database as the storage of the values sent by the object to the server. The test on the On-off Attack detection was successfully conducted with the success level of 100% and the execution to detection took 0.5518318 seconds. This then showed that Trustworthiness Management can be used as one of the methods to cope with On-off Attack.
GVF: GPU-Based Vector Fitting for Modeling of Multiport Tabulated Data Networks

Modeling of multiport data characterizing high-speed modules, such as packages, vias, and complex multiconductor interconnects is becoming increasingly important in signal and power integrity applications. Vector fitting (VF) algorithm has been widely used by designers for macromodeling and system identification from such multiport tabulated data. Since VF and strategies based on it require many iterations to arrive at an optimal number of converged poles, it is highly desired to reduce the computational cost of each VF iteration. This article advances the applicability of VF to exploit the emerging massively parallel graphical processing units (GPUs) by developing necessary parallelization strategies and investigates their performance while using different GPU libraries. For large problem sizes (an increasing number of poles and ports), numerical results demonstrate that the proposed method while using MAGMA libraries provides significant speedup compared with existing multi-CPU-based parallel VF techniques.
Team Formation in Software Engineering: A Systematic Mapping Study

Context: Software team formation is an important project management activity. However, forming appropriate teams is a challenge for most of the companies. Objective: To analyze and synthesize the state of the art on the software team formation research. Additionally, we aim to organize the identified body of knowledge in software team formation as a taxonomy. Method: Using a Snowballing-based systematic mapping study, 51 primary studies, out of 2516, were identified and analyzed. We classified the studies considering the research methods used, their overall quality, and the characteristics of the formed teams and the proposed solutions. Results: The majority of the studies use search and optimization techniques in their approaches. Also, technical attributes are the most frequent type considered to build individuals’ profiles during the team formation process. Furthermore, we proposed a taxonomy on software team formation. Conclusion: There is a predominant use of search-based approaches that combine search and optimization techniques with technical attributes. However, the adoption of non-technical attributes as complementary information is a tendency. Regarding the research gaps, we highlight the level of subjectivity in software team formation and the lack of scalability of the proposed solutions.
Inverse Kinematics of Redundant Manipulators With Dynamic Bounds on Joint Movements

Redundant manipulators are usually required to perform tasks in the operational space, but collision-free path planning is computed in the configuration space. Limiting the deviation with respect to the collision-free configuration-space trajectory may allow the robot to avoid collisions without modifying the primary task. This letter proposes a method to guarantee that the solution of the inverse kinematic problem deviates from the nominal joint-space trajectory less than a desired threshold. The excursion limitation is ensured by means of linear constraints and the automatic regulation of the weights of secondary tasks. Numerical and experimental results prove the validness of the proposed approach.
Tensor Co-clustering: A Parameter-less Approach

Tensors co-clustering has been proven useful in many applications, due to its ability of coping with high-dimensional data and sparsity. However, setting up a co-clustering algorithm properly requires the speciﬁcation of the desired number of clusters for each mode as input parameters. To face this issue, we propose a tensor co-clustering algo-rithm that does not require the number of desired co-clusters as input, as it optimizes an objective function based on a measure of association across discrete random variables that is not aﬀected by their cardinality. The eﬀectiveness of our algorithm is shown on real-world datasets, also in comparison with state-of-the-art co-clustering methods.
Robust PI Control of Interval Plants With Gain and Phase Margin Specifications: Application to a Continuous Stirred Tank Reactor

The paper is focused on robust Proportional-Integral (PI) control of interval plants with gain and phase margin specifications and on the application of this approach to a Continuous Stirred Tank Reactor (CSTR). More specifically, the work aims at the determination of PI controller parameter regions, for which not only robust stability but also some level of robust performance of the closed-loop control system is guaranteed, and this robust performance is represented by the required gain and phase margin that has to be ensured for all potential members of the interval family of controlled plants, even for the worst case. The applied technique is based on the combination of the previously published generalization of stability boundary locus method (for specified gain and phase margin under the assumption of fixed-parameter plants) with the sixteen plant theorem. This extension enables the direct application of the method to design the robustly performing PI controllers for interval plants. The effectiveness of the improved method is demonstrated on a CSTR, modeled as the interval plant, for which the robust stability and robust performance regions are obtained.
Backup or Not: An Online Cost Optimal Algorithm for Data Analysis Jobs Using Spot Instances

Recently, large-scale public cloud providers begin to offer spot instances. This type of instance has become popular with more and more cloud users in the light of its convenient access mode and low price, especially for those big data analysis jobs with high performance computation requirements. However, using spot instances may carry the risk of being interrupted and lead to extra costs for job re-executions because these instances are generally unstable. Yet, such cost can be greatly reduced if a backup can be made at the right time before interruptions. For convenience and cost efficiency, users can choose the StaaS (Storage-as-a-Service) storage provided by the same cloud provider, whose spot instances are used by the users, to store backup data files for future job execution recovery. Since making backups too often will incur increased costs, users need to make the backup decisions appropriately considering the condition when an abrupt interruption will occur in the future. However, it is hard to know or predict precisely when such an interruption will occur. For solving this problem, in this article, we propose an online algorithm to guide cloud users to make backups when using spot instances to execute big data analysis jobs, without requiring any information about future interruptions. We prove theoretically that our proposed online algorithm can guarantee a bounded competitive ratio less than 2. Finally, according to extensive experiments, we verify the effectiveness of our online algorithm in reducing the additional cost caused by interruptions in using spot instances and find that our online algorithm can still achieve a stable cost optimization even if interruptions occur frequently.
Unauthorized Access Monitoring in Optical Access Networks

The article deals with the problem of protecting information from unauthorized access. The results of comparative analysis of methods for information extracting from optical fiber lines are presented. The classification of extraction methods, physical conditions for leak channels formation is given. In addition, methods making it possible to remove part of the optical radiation to outside of the propagation medium are given. A technique is proposed for detecting the fact of the information leakage channels formation. A number of different ways for the detection is described to successfully combat unauthorized access. The analysis showed that the simplest detection method is bending of optic fiber in order to eliminate total internal reflection. In this case, to monitor and detect fiber bends it is necessary to track the introduced losses. The structure, operation principles and trends in monitoring systems development are given.
V-Sandbox for Dynamic Analysis IoT Botnet

With the increasing use of resource-constrained IoT devices, the number of IoT Botnets has exploded with many variations and ways of penetration. Nowadays, studies based on machine learning and deep learning have focused on dealing with IoT Botnet with many successes, and these studies have required relevant data during malware execution. For this, the sandbox environment and behavior collection tools play an essential role. However, the existing sandboxes do not provide adequate behavior data of IoT botnet such as the C&C server communication, shared libraries requirements. Moreover, these sandboxes do not support a wide range of CPU architectures, data is not exhaustively collected during executable file runtime. In this paper, we present a new practical sandbox, named V-Sandbox, for dynamic analysis of the IoT Botnet. This sandbox is an ideal environment for IoT Botnet samples that exhibit all of their malicious behavior. It supports the C&C servers connection, shared libraries for dynamic files, and a wide range of CPU architectures. Experimental results on the 6141 IoT Botnet samples in our dataset have demonstrated the effectiveness of the proposed sandbox, compared to existing ones. The contribution of this paper is specific to the development of a usable, efficient sandbox for dynamic analysis of resource-constrained IoT devices.
Super Resolution DOA Based on Relative Motion for FMCW Automotive Radar

Frequency modulated continuous wave (FMCW) based millimeter wave (MMW) radar systems have found widespread applications in advanced driving assistant system recently. MMW radar has clear superiority in many aspects, such as excellent range resolution, adaptability to various environments and low cost. However, its fatal weakness is the poor angular resolution which is very important in automotive radar applications. This shortcoming can be overcome by using a physically large aperture. However, achieving moderate angular resolution requires an array that is too large to be practically integrated into a vehicle. In this paper, a high resolution direction of arrival (DOA) approach is proposed by utilizing the relative motion between radar and targets, and a very large virtual aperture can be created by combining the signal vectors from multiple consecutive coherent processing intervals (CPIs). It should be pointed out that, the proposed approach is applicable for both stationary and moving targets even without motion data of vehicles. Therefore, it is completely different from the synthetic aperture radar (SAR). The effectiveness is verified by both simulation results and the measurement data. It is demonstrated that the angular resolution can be superior to 1° by a linear array consisting of only 4 elements.
SPQER: Speech Quality Evaluation Using Word Recognition for VoIP Communication in Lossy and Mobile Networks

In this paper, we introduce SPQER (pronounced speaker), a novel approach to evaluate the quality of experience for real-time Voice over IP (VoIP) communication in mobile and lossy networks. Traditional speech quality metrics, e.g., Perceptual Evaluation of Speech Quality (PESQ) or the Hearing-Aid Speech Quality Index (HASQI), directly compare frequencies and amplitudes to calculate the received signal distortions. SPQER instead uses machine learning classification to evaluate the percentage of recognizable words in conjunction with a time-based decay function to penalize delay and cross-talking. So instead of evaluating noise, SPQER directly answers the question: What percentage of words is the recipient able to understand? We presented a sensitivity analysis, which is based on testbed experiments for different packet loss rates and simulated delays, to asses the impact of challenging link conditions. A final correlation analysis to a short user study shows that SPQER can better evaluate the amount of understandable words than PESQ and HASQI, while still giving a more precise indication about the voice quality than the Word Error Rate (WER) metric.
Towards Making Unsupervised Graph Hashing Robust

Unsupervised hashing without supervision easily deteriorates in the case of grossly corrupted data. Motivated by robust optimization, this paper proposes a dual-graph regularized robust hashing (DGRH) based on both manifold smoothness and robust estimators in a more intuitive manner. Orthogonal to existing robust hashing methods, DGRH directly removes the outliers of datasets with M-estimator to exert robustness. In specific, it intends to recover low-rank representation from corrupted data via l1 loss while preserving neighborhood relationships among samples with dual-graph regularization. Although DGRH seems a simple extension of robust PCA on graphs with hashing trick, it is easy to implement yet effective. Theory analysis is provided to support our claim. Experiments of image retrieval on three popular benchmark datasets show the efficacy of DGRH as compared to several well-behaved representative counterparts.
Unsupervised Anomaly Detection Using K-Means, Local Outlier Factor and One Class SVM

Anomaly detection is one area that is still being researched today. An anomaly that occurs in data can be utilized in various ways, such as detection of money embezzlement, increasing production, improving the quality of data, preventing attacks on a network and others. This study aims to find anomalies in the data drug use in hospitals consisting of two datasets using the K-Means algorithm, Local Outlier Factor (LOF) and One Class Support Vector Machine (OC-SVM). The results of this study are that the three algorithms can find outliers. Based on its performance in both datasets, OC-SVM outperforms LOF and K-Means