Miracle-dz/UniFAR-FTUs huggingface dataset:
The Uncompress Application on Distributed Communications Systems

—The most popular compressors are based on Lempel-Ziv coding methods. Zip compressors and Unzip de-compressors apply the sliding window method, while other applications as Compress and Uncompress under Unix and Linux platforms use the so-called LZW compressor and decompressor. LZW compression is less effective but faster than the zipping applications. We face the problem of how to implement Lempel-Ziv data compression on today’s large scale distributed communications systems. Zipping and unzipping ﬁles is parallelizable in theory. However, the number of global computation steps is not bounded by a constant and a local computation approach is more advantageous on a distributed system. Such approach might cause a lack of robustness when scalability properties are required. Differently from the Zip compressors, the LZW encoder/decoder presents an asymmetry with respect to global parallel computation since the encoder is not parallelizable while the decoder has a very efﬁcient parallelization. We show that, in practice, the number of iterations of the LZW parallel decoder (Uncompress) is much less than ten units. Since scalability and robustness are generally guaranteed if bounding the number of global computation steps is possible, LZW is more attractive than Zip for distributed communications systems in those cases (which are the most common in practice) where compression is performed only once or very rarely, while the frequent reading of raw data needs fast decompression.
Towards an Accurate Prediction of the Question Quality on Stack Overflow using a Deep-Learning-Based NLP Approach

Online question answering (Q&A) forums like Stack Overflow have been playing an increasingly important role in supporting the daily tasks of developers. Stack Overflow can be considered as a meeting point of experienced developers and those who are looking for a solution for a specific problem. Since anyone with any background and experience level can ask and respond to questions, the community tries to use different solutions to maintain quality, such as closing and deleting inappropriate posts. As over 8,000 posts arrive on Stack Overflow every day, the effective automatic filtering of them is essential. In this paper, we present a novel approach for classifying questions based exclusively on their linguistic and semantic features using deep learning method. Our binary classifier relying on the textual properties of posts can predict whether the question is to be closed with an accuracy of 74% similar to the results of previous metrics-based models. In accordance with our findings we conclude that by combining deep learning and natural language processing methods, the maintenance of quality at Q&A forums could be supported using only the raw text of posts.
Research on signals extrapolation in the time domain based on wavelet analysis

Characteristic of modulation spectrum is important for radar targets classification and recognition, while signals with high occupation ratio cause low spectral resolution. For this case, the authors present a method for splicing sub-band based on the Mallat algorithm. This method extracts the low-frequency parts in the different periods of the signals, and using the state-space method to extrapolate the signal between pulse sequence. This method sacrifices spectrum width to improve the local spectral resolution in the case of less sample data. The simulation results demonstrate that the method is effective.
An Evaluation and Comparative study of massive RDF Data management approaches based on Big Data Technologies

Today, many researches are focused on the use of Big Data technologies for managing large volumes of Semantic Web data. The use of Big Data technologies such as NoSQL database management systems guarantees the scalability and high availability of Web data. The amount of data Web increases day after day and in a way excessive. To fully exploit these data and its metadata. This requires this technology of the Semantic Web to be a scalable and high performance at the level of the storage of these Web data. Many research efforts have been devoted to create and develop a distributed RDF data management system. To achieve the scalability and high performance, these systems are implemented on the basis of the Management technology of Big Data called NoSQL. In this paper, we present a comparative study of existing systems dedicated to managing large volumes of RDF data, which are generally based on NoSQL database management systems
npm Packages as Ingredients: A Recipe-based Approach

: The sharing and growth of open source software packages in the npm JavaScript (JS) ecosystem has been exponential, not only in numbers but also in terms of interconnectivity, to the extend that often the size of dependencies has become more than the size of the written code. This reuse-oriented paradigm, often attributed to the lack of a standard library in node and/or in the micropackaging culture of the ecosystem, yields interesting insights on the way developers build their packages. In this work we view the dependency network of the npm ecosystem from a “culinary” perspective. We assume that dependencies are the ingredients in a recipe, which corresponds to the produced software package. We employ network analysis and information retrieval techniques in order to capture the dependencies that tend to co-occur in the development of npm packages and identify the communities that have been evolved as the main drivers for npm’s exponential growth.
Localized Tangential Vector Fields for the use in Tangent Vector Field Design: A Vector Field Design Approach with the use of Localized Basis Fields

The use of tangential vector fields and thus the need for designing them has steadily been increasing over the years. In this master thesis, a method is proposed and implemented that defines localized tangential vector fields on a mesh surface, which allows for the designing of vector fields on the triangulated surfaces of these meshes. Similarly, as with 2D radial basis functions for function values, it is possible to accurately approximate, compress and design vector fields using the localized tangential vector fields with an applied radial basis function scalar. The proposed system constructs multiple localized tangential vector fields on the surface and creates a basis from them that covers the entire surface. Other works have already suggested methods for the designing of tangential vector fields but most did not allow for both soft and hard constraints to be used when designing fields in real-time. One approach was able to design fields in real-time but had the bottleneck that it required a lot of precomputation time as it was using spectral processing techniques and a spectral transform to work in the spectral representation. Using the proposed localized tangential vector fields basis in this work also allows for the real-time modeling of tangential vector fields without to much precomputation time. However, it should be noted to fully optimize the proposed system several improvements on the current techniques used in this work have to be made to compete with the spectral representation. The proposed system is the first to our knowledge that uses localized tangential vector fields on meshes for the designing of tangential vector fields.
Partition of Online Social Networks using 1 Simulations based on Chainable Processing 2 Modules

11 Working with Complex Networks simulations focused on Online 12 Social Networks (OSNs), we reached the limits of the architectural 13 features of our software tool, because its project is not suitable to the 14 inclusion of new resources as dynamic topological operations, on-the-15 ﬂy visualizations, recognition of yet-to-be-developed algorithms, and 16 concurrency programming support. We analyzed our current simulator 17 and prepared a requirement list targeting to the development of a 18 new one, privileging two features: ﬂexibility and extensibility. We 19 developed and applied the concept of independent processing modules 20 that can form a processing chain according to Design Patterns and 21 Java interface standards. These Chainable Processing Modules are the 22 novelty and foundation of our architecture, that complies very well with 23 the new simulator requirements. In this work, we present our software 24 architecture and highlight its potential to help the development of open 25 projects of simulators of any type of Complex Networks. Furthermore, 26 we have implemented part of the proposed architecture and shown that 27 new researches were fast carried out because of the reached ﬂexibility. 28
EFFICIENT DETECTION OF HANG BUGS IN MOBILE APPLICATIONS

ii Dedication . iii Acknowledgments. iv Vita . v List of Tables . ix List of Figures . x Chapter 1: Introduction . 1 Chapter 2: Literature Survey and Related Work . 3 Chapter 3: Background . 5 3.1: Andriod . 5 3.2: Andriod Applications . 5 3.3: Hang Problem. 6 3.3.1: Reasons for Hang . 6 3.4: Soft Hang . 7 Chapter 4: Algorithm Design . 9 vii 4.1: Overview . 9 4.2: Algorithm Development . 10 4.2. 1: Event Detector . 10 4.2. 2: Trace Scheduler . 10 4.2. 3: Trace Collector . 11 4.3: Implementation . 12 Chapter 5: Algorithm Evalutaion . 25 5.1: Evaluation . 25 5.2: Experimental Set-up . 25 5.3: Hang Bug Detection . 26 5.3.1: Delay. 28 5.3.2: Number of Hangs Detected . 28 5.3.3: Overhead Analysis. 29 5.3.4: Main Thread Overhead . 29 5.3. 5: Whole App Overhead . 29 Chapter 6: Results and Conclusions . 30 6.1: Delay . 30 6.2: Number of Hangs Detected . 32 6.3: Main Thread Overhead . 34 viii 6.4: Whole App Overhead . 36 6.5: Conclusions . 38 References . 39
Improving Job Scheduling on a Heterogeneous Cluster by Predicting Job Execution Times Using Heuristics

In this paper, we propose the scheduling system for the Bioinformatics Resource Facility Hagenberg (BiRFH). This system takes advantage of the fact that the facility offers tailored solutions for the customers, which includes having a limited amount of different programs available. Additionally, the BiRFH system provides access to different hardware platforms (standard CPU, GPGPU on NVIDIA Cuda, and IMB Cell on Sony Playstation machines) with multiple versions of the same algorithm optimized for these platforms. The BiRFH scheduling system takes these into account and uses knowledge about past runs and run times to predict the expected run time of a job. That leads to a better scheduling and resource usage. The prediction and scheduling use heuristic and artificial intelligence methods to achieve acceptable results. The paper presents the proposed prediction method as well as an overview of the scheduling algorithm.
Using Automatic Anomaly Detection to Identify Faults in Sewers

CCTV surveys are a common practice within water companies worldwide, and necessary to ensure the effective maintenance of all sewer pipes. Currently surveys are time consuming, requiring a trained technician to watch and label hours of video footage. This paper presents an automated anomaly detection approach for the detection of sewer faults using only raw CCTV sequences. A One-Class Support Vector Machine (OCSVM), is trained using images of normal pipe, and highlights any abnormalities or faults within a sewer survey for further investigation. This technique is validated and demonstrated on a comprehensive dataset of ~8000 still CCTV images, achieving an accuracy of 75% and an accuracy of 85% when applied to a video sequence. Overall the OCSVM proves itself to be an effective anomaly detection technique.
Toward a Flexible Design of SDN Dynamic Control Plane: An Online Optimization Approach

With a centralized control over the forwarding devices and the embedded flows, Software Defined Networking promises to increase the flexibility of communication networks. Meanwhile, a dynamic control plane would adapt itself in a timely manner to sustain flow setup performance in the face of traffic variations. Such adaptation depends on a careful decision of the controller placement, which is challenging because we need to consider two contradictory objectives, namely the cost of operating the control plane and the cost of its adaptation. In this work, we model the problem of operating the control plane as a multi-period offline optimization problem to minimize the total cost induced by the flow setup performance and the control plane adaptation. We leverage the lookahead control scheme and decompose the intractable offline problem into smaller instances, which are solved in an online fashion efficiently with an algorithm based on simulated annealing. We perform extensive simulations on real world topologies and show that our proposed algorithm can reduce the total cost by up to 20% compared with the reference algorithms. Further, we analyze the need of frequent control plane adaptation, and compare different control plane design choices according to a novel flexibility measure.
A Soft-Rough Set Based Approach for Handling Contextual Sparsity in Context-Aware Video Recommender Systems

Context-aware video recommender systems (CAVRS) seek to improve recommendation performance by incorporating contextual features along with the conventional user-item ratings used by video recommender systems. In addition, the selection of influential and relevant contexts has a significant effect on the performance of CAVRS. However, it is not guaranteed that, under the same contextual scenario, all the items are evaluated by users for providing dense contextual ratings. This problem cause contextual sparsity in CAVRS because the influence of each contextual factor in traditional CAVRS assumes the weights of contexts homogeneously for each of the recommendations. Hence, the selection of influencing contexts with minimal conflicts is identified as a potential research challenge. This study aims at resolving the contextual sparsity problem to leverage user interactions at varying contexts with an item in CAVRS. This problem may be investigated by considering a formal approximation of contextual attributes. For the purpose of improving the accuracy of recommendation process, we have proposed a novel contextual information selection process using Soft-Rough Sets. The proposed model will select a minimal set of influencing contexts using a weights assign process by Soft-Rough sets. Moreover, the proposed algorithm has been extensively evaluated using “LDOS-CoMoDa” dataset, and the outcome signifies the accuracy of our approach in handling contextual sparsity by exploiting relevant contextual factors. The proposed model outperforms existing solutions by identifying relevant contexts efficiently based on certainty, strength, and relevancy for effective recommendations.
MI-based optimal jamming waveform design

In order to improve the estimation performance of radar system, previous studies on radar waveform design mostly focused on maximizing the mutual information between the target and the echo to design the transmitted waveform. However, these studies neglect the existence of jammer in the actual electromagnetic environment, so this paper introduces a jammer to design the corresponding jamming waveforms according to whether there are signal-related clutter or not, which weakens the estimation performance of radar system. Simulation results show that the energy allocation strategies of the two jamming waveforms are different.
An MRI scanner‐independent radiofrequency dosimeter for the estimation of RF power deposition with a human torso phantom

An assessment of radiofrequency (RF) power deposition independent of the information provided by MRI scanners is thus desirable. We developed a novel scanner-independent RF dosimeter based on measurements of the resistance of a thermistor that dissipates the RF power during scanning. With the RF dosimeter, the RF power deposition for four MRI sequences with specific absorption rate (SAR) values (0.1-3.3 W/kg) was measured on five different scanners and the correlation between the RF dosimeter reading and the SAR levels calculated by the scanners was investigated. The novel RF dosimeter showed a linear relationship between the RF power deposition and the scanner-reported whole-body averaged SAR for each scanner. However, there was a variability in the reading among different scanners. The RF dosimeter readings were 9.7 and 9.5 mW on GE 1.5 T (SAR=2.6 W/kg), 3.6 and 3.7 mW on Philips 1.5 T (SAR=3.3 W/kg), 9.5 and 8.6 mW on Siemens 3 T (SAR=3.0 W/kg), and 4.7 and 3.9 mW on Philips 3 T (SAR=2.6 W/kg), respectively. The scanner-independent RF dosimeter developed in this study can play a significant role in checking the accuracy of scanners’ SAR values as a standardized method for measuring the RF power deposition for MR safety.
Probabilistic calibration of material models from limited data and its influence on structural response

Most structural-scale material models are phenomenological or empirical in nature and require calibration from limited amounts of experimental data. In this paper, we present a Bayesian approach for material model calibration from limited data. The approach incorporates the uncertainties associated with both the form and the parameters of probability models for material constitutive parameters. Incorporating these effects results in an imprecise probabilistic material model that gives a more realistic assessment of the uncertainties associated with the material performance than existing calibration practices based on, for example, maximum likelihood estimation. The proposed calibration process is applied to probabilistic parameter estimation of a viscoplastic damage model for ductile metals. Specifically, the model is calibrated for aluminum alloy Al-6061 T6 at various temperatures to model structural response during fire. An experimental investigation of Al-6061 T6 at temperatures ranging from room temperature to 300◦C has been conducted and the data are used for material model calibration. The material model has been implemented in Abaqus and we assess uncertainties in the tensile response of aluminum square hollow sections with an aim at placing realistic bounds on their reliability. Probabilistic structural modeling is conducted using an efficient simulation-based method for propagating the imprecise probabilities associated with material parameters.
A Survey on Device-to-Device Communication in 5G Wireless Networks

The Device-to-Device (D2D) communication model in 5G networks provides a useful infrastructure to enable different applications. D2D communication, with use of cellular or ad-hoc links, improve the spectrum utilization, system throughput, and energy efficiency of the network thereby preparing the ability for the user equipment to start communications with each other in proximity. The purpose of this paper is preparing a survey based on the D2D communication and review the available literature that in a widespread way research about the D2D paradigm, different application scenarios, and use cases. Moreover, new suspicion in this area that leads to identifying open research problems of D2D communications in cellular networks. A Survey on Device-to-Device Communication in 5G Wireless Networks Amir Hossein Farzamiyan CISTER Research Centre,ISEP Polytechnic Institute of Porto
Thread Isolation to Improve Symbiotic Scheduling on SMT Multicore Processors

Resource sharing is a critical issue in simultaneous multithreading (SMT) processors as threads running simultaneously on an SMT core compete for shared resources. Symbiotic job scheduling, which co-schedules applications with complementary resource demands, is an effective solution to maximize hardware utilization and improve overall system performance. However, symbiotic job scheduling typically distributes threads evenly among cores, i.e., all cores get assigned the same number of threads, which we find to lead to sub-optimal performance. In this paper, we show that asymmetric schedules (i.e., schedules that assign a different number of threads to each SMT core) can significantly improve performance compared to symmetric schedules. To leverage this finding, we propose thread isolation, a technique that turns symmetric schedules into asymmetric ones yielding higher overall system performance. Thread isolation identifies SMT-adverse applications and schedules them in isolation on a dedicated core to mitigate their sharp performance degradation under SMT. Our experimental results on an IBM POWER8 processor show that thread isolation improves system throughput by up to 5.5 percent compared to a state-of-the-art symmetric symbiotic job scheduler.
A Simple, Accurate and Highly Secure Method to Encrypt-Decrypt Digital Images

The digital image may be important and has a secret character, which requires not understanding it when looking at the naked eye or not understanding the contents. So seeking a method of digital image encryption-decryption is a very important task.In this paper we will introduce a new method of digital image encryption-decryption, which will be very simple, highly secure and accurate and highly efficient.
A Dual-Output Step-Down Switched-Capacitor Voltage Regulator With a Flying Capacitor Crossing Technique for Enhanced Power Efficiency

This paper presents a dual-output switched-capacitor voltage regulator (DOSCVR) with a new flying capacitor crossing technique (FCCT) to improve power efficiency. With the FCCT, the power loss of the DOSCVR is reduced by 28%, increasing the power efficiency by 7% compared with the power efficiency of the conventional method that generates two voltages using independent single-output SCVRs. This enhanced efficiency results in no area penalty since the total flying capacitance remains unchanged. A prototype is then implemented in a 0.13- $\mu \text{m}$ CMOS process to validate the proposed scheme. The prototype simultaneously produces two regulated voltages of 0.44 and 0.88 V from an input voltage of 1.5 V. The output voltages are regulated by low-bound regulation with frequency modulation. The maximum output current is 12 mA with a total flying capacitance of 0.98 nF, and the measured peak efficiency is 78%.
Mobility study based on the analysis of Bluetooth data in the city of Brisbane

In the field of transport planning, many studies have examined the effectiveness of different algorithms related to the problem of transport network design. However, no study has considered the alternative of a problem of transport network modification, which consists of making small alterations to the current network without having to entirely design it. The focus of this research is on designing an algorithm able to find the optimal route for placing a new public transport line, understanding optimal as capable of minimizing the traffic of private vehicles around the city. As observed, it corresponds to one case of transport network modification. Data used in this project were collected by the detection of devices through the Bluetooth sensors spread over the city of Brisbane, Australia. The use of real data led to the need of a previous analysis and cleaning of the database, in order to obtain results which are feasible and reliable. Since it is a non-linear optimization problem, heuristic solution algorithms are used in the design procedure. The algorithm proposed is based on genetic algorithms, and it has been adjusted to this case of study by adapting the functions that compound the algorithm. The proposed model was tested and the results obtained analysed, concluding that the designed algorithm is effective for generating routes able to minimize the car flow in a transport network.