Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Scheduler for CUDA Programming in Heterogeneous CPU/GPU Environment

The demand for high performance has driven the technology to grow exponentially requiring the computer systems to work as effectively as possible for a valuable output. Substantial innovation in technology with time has made the use of GPUs working together with CPUs in order to make the system more efficient in performing computations optimally. This paper presents design of a scheduler for heterogeneous CUDA environment which ensures that while the task is fetched into the system, all the nodes participate fully in scheduling, thereby completing the task in less span of time as compared to normal schedulers resulting in efficient results. Tasks have been divided amongst the computing nodes according to the availability of the nodes giving maximum possible throughput of the system according to the workload. The scheduler has the potential of running the GPU code in parallel on different computing nodes within the High Performance Computing environment improving the overall performance of the applications. As a result, it turned out that the Smart scheduler gives better throughput in comparison to SLURM's existing schedulers which indicates that there was a room in SLURM's existing schedulers to increase the number of jobs within less span of time. The overall improvement in the throughput was observed to be up to 70 percent which is also shown in Figure 4.
Resilient Edge: A Scalable, Robust Network Function Backend

Increasing hardware resources, such as multi-core and multi-socket CPUs, memory capacity and high-speed NICs, impose significant challenges on Network Function Virtualization (NFV) backends. They increase the potential numbers of per-server NFs or tenants, which requires a packet switching architecture that is not only scalable to large number of virtual ports, but also robust to attacks on the data plane. This is a real problem; a recent study has reported that Open vSwitch, a widely used software switch, had a buffer-overflow bug in its data plane that results the entire SDN domain to be hijacked by worms propagated in the network. In order to address this problem, we propose REdge. It scales to thousands of virtual ports or NFs (as opposed to hundreds in the current state-of-the art), and protect modular, flexible packet switching logic against various bugs, such as buffer overflow and other unexpected operations using static program checking. When 2048 NFs are active and packets are distributed to them based on the MAC or IP addresses, REdge achieves 3.16 Mpps or higher packet forwarding rates for 60 byte packets and achieves the wire rate for 1500 byte packets in the 25 Gbps link. key words: software switch, NFV, security, operating system
Efficient k-means Using Triangle Inequality on Spark for Cyber Security Analytics

With the advancement in technology and the increase in the number of digital sources, data quantity increases every day and, consequently, the cyber security related data quantity. Traditional security systems such as Intrusion Detection Systems (IDS) are not capable of handling such a growing amount of data set in real time. Cyber security analytics is an alternative solution to such traditional security systems, which can use big data analytics techniques to provide a faster and scalable framework to handle a large amount of cyber security related data in real time. k-means clustering is one of the commonly used clustering algorithms in cyber security analytics aimed at dividing security related data into groups of similar entities, which in turn can help in gaining important insights about the known and unknown attack patterns. This technique helps a security analyst to focus on the data specific to some clusters only for the analysis. To improve performance, k-means can exploit the triangle inequality to skip many point-center distance computations, without affecting the clustering results. In this paper, we re-formulate the parallel version of Elkan's k-means with triangle inequality (k-meansTI) algorithm, implement this algorithm on Apache Spark, and use it to classify Web attacks in different clusters. The paper also provides the speed comparison of our parallel k-meansTI on Spark with the Spark ML k-means clustering algorithm.
IoT-enabled highway safety pre warning system

We propose to demo an IoT-enabled highway safety pre warning system which can inform the drivers of the danger ahead. In this system, we use road side unit (RSU) installed on highway guardrail to collect motion information from the on board unit (OBU) mounted on vehicles. Once accident detected, all RSUs and OBUs within a pre-defined range will receive a broadcast message indicating emergence. RSUs and OBUs are inter-connected using DSRC and 4G/5G network.
Improved hop-based localisation algorithm for irregular networks

The hop-based localisation algorithm uses hop-by-hop propagation to establish node-to-anchor distance estimation, which does not require costly and complicated ranging hardware. This helps boost system performance, while minimising the cost of localising the nodes within the network. However, the application of hop-based localisation algorithms is restricted due to their dramatic accuracy degradation in irregular network, which is mainly caused by the large error of distance estimation. The authors find that the error variance of the estimated distance increases as the hop count increases, i.e. there is a heteroscedasticity problem in the distance estimation process, which will affect the location estimation. In this study, by exploring the error during the location estimation, they aim to find and employ the optimal weighted function to improve localisation accuracy. A geometric constraint algorithm is also devised to correct the incorrectly estimated location by mitigating the adverse effects from flip ambiguity. By combining the optimal weighted function and the geometric constraint algorithm, a novel hop-based localisation algorithm is proposed in this study. Both the theoretical analysis and experimental results show that the proposed method has not only maintained the economic characteristics of hop-based localisation, but also has the high localisation accuracy where it can be adapted to various networks with different node distributions.
Compressed channel estimation for FDD massive MIMO systems without prior knowledge of sparse channel model

In this study, a new greedy-based algorithm for sparse channel estimation in frequency-division duplexing massive multiple-input multiple-output (MIMO) systems is introduced. The proposed algorithm is able to acquire the channel with no information about the channel model such as the sparsity order, statistical bound on sparsity support, and special features of the channel. In other words, the authors assume that the massive channel is totally unknown and then exploit the inherent property of correlation between measurements and the sensing matrix to estimate the channel, which is available at the user side. By utilising this property, a halting parameter is defined as the halting threshold instead of known prior information assumed in the conventional greedy algorithms. Furthermore, the lower and upper bounds of the halting parameter are obtained that helps the algorithm to take the suitable values of this parameter and by considering the lower bound as the values of the halting parameter, the algorithm can prevent from missing the original channel bins. Simulation results demonstrate that the proposed algorithm outperforms its counterparts in terms of normalised mean square error while it faces a completely unknown massive MIMO channel.
FF-SMOTE: A Metaheuristic Approach to Combat Class Imbalance in Binary Classification

ABSTRACT Nature inspired intelligent computation-based algorithms have been grown remarkably over the previous years. These algorithms are applied for optimizing the values to a number of problem areas ranging from scientific research to industry or commerce. Class imbalance is a challenging problem of classification to identify smaller class when dealing with skewed distributions. This paper proposed a firefly-based oversampling technique to combat class imbalance in binary classification. The proposed technique is applied on 10 UCI data sets with the imbalance ratio ranging from high to low and is compared with the other state-of-the art oversampling techniques. The performance of the proposed method is assessed through performance metrics area under the curve and geometric mean. The techniques are also analyzed statistically using Friedman and Wilcoxon matched signed rank Test. Through experimental and statistical analysis, it is reported that the proposed technique outperformed other oversampling techniques.
Evasive Malicious Website Detection by Leveraging Redirection Subgraph Similarities

SUMMARY Many users are exposed to threats of drive-by download attacks through the Web. Attackers compromise vulnerable websites discovered by search engines and redirect clients to malicious websites created with exploit kits. Security researchers and vendors have tried to prevent the attacks by detecting malicious data, i.e., malicious URLs, web content, and redirections. However, attackers conceal parts of malicious data with evasion techniques to circumvent detection systems. In this paper, we propose a system for detecting malicious websites without collecting all malicious data. Even if we cannot observe parts of malicious data, we can always observe compromised websites. Since vulnerable websites are discovered by search engines, compromised websites have similar traits. Therefore, we built a classiﬁer by leveraging not only malicious but also compromised websites. More precisely, we convert all websites observed at the time of access into a redirection graph and classify it by integrating similarities between its subgraphs and redirection subgraphs shared across malicious, benign, and compromised websites. As a result of evaluating our system with crawling data of 455,860 websites, we found that the system achieved a 91.7% true positive rate for malicious websites containing exploit URLs at a low false positive rate of 0.1%. Moreover, it detected 143 more evasive malicious websites than the conventional content-based system.
Superposition coded OFDM transmissions in a downlink cooperative relay network based on statistical channel state information

Non-orthogonal multiple access has recently emerged as a promising multiple access technique for future wireless technology. In this study, the authors investigate superposition coding in a downlink cooperative cellular system based on the orthogonal frequency division multiplexing (OFDM) modulation in the presence of frequency selective multipath fading channels, a setup that has not been studied before. Specifically, the base station communicates with two paired mobile users simultaneously via the OFDM modulation with the help of a half-duplex relay under either the decode-and-forward (DF) or amplify-and-forward (AF) scheme, where the power allocation between two mobile users depends on the statistical channel state information (CSI) rather than perfect CSI. They derive the ergodic rate regions under Rayleigh fading channels and present an ad-hoc approach to determine the power splitting parameters when the target data rates are given. Simulation results based on the outage probabilities show that superposition coding consistently outperforms frequency division, however, the relative advantage of the DF and AF schemes in a superposition-coded OFDM relay system depends on the system geometry.
Compute node communication in the fog: survey and research challenges

Fog and edge computing have given rise to applications that utilize cloud services at the edge of the network. To enable services at the edge, compute nodes are provisioned at strategic positions throughout the network in order to avoid bottlenecks and reduce latency. A lot of research has been conducted in this context, resulting in multiple computing platforms which organize the compute nodes using different communication types, i.e., hierarchical, peer to peer or hybrid. To better understand the role communication plays in fog and edge computing, this paper reviews key literature on fog computing platforms and identifies research challenges that emerge. The outcomes of this review also suggest that the communication type of a computing platform affects the functionality of the final applications.
QUILT: quality inference from living digital twins in IoT-enabled manufacturing systems

A Digital twin is the virtual replica of a physical system. Digital twins are useful because they provide models and data for design, production, operation, diagnostics, and prognostics of machines and products. Traditionally, building a digital twin requires many built-in sensors to monitor various physical phenomena associated with cyber-physical systems such as vibration, energy consumption, etc. However, many legacy manufacturing systems do not have multi-physics sensors built-in by default. Moreover, it might not be feasible to intrusively place sensors in these systems after they are manufactured. To bring the advantages of digitalization to legacy manufacturing systems, this paper contributes with an Internet-of-Things (IoT) based methodology to build digital twins using an indirect medium such as side-channels, which can localize anomalous faults and infer the quality of the products being manufactured while keeping itself up-to-date. We achieve this by exploring and utilizing the side-channels (emissions such as acoustics, power, magnetic, etc.) of the system that unintentionally reveal the cyber and physical state of the system. To validate our methodology, in this paper, we focus on building a digital twin model of a Fused-Deposition Modeling (FDM) based Cartesian additive manufacturing system. The proposed methodology achieves 83.09% accuracy in anomaly localization. To the best of our knowledge, this is the first work demonstrating the possibility of modeling and maintaining a living digital twin of a manufacturing system by extracting information from the side-channels using low-end IoT sensors.
Integration of spatialization and individualization: the future of epidemic modelling for communicable diseases

ABSTRACT In the past several decades, epidemic modelling for communicable diseases has experienced transitions from treating the entire study area as a whole to addressing spatial variation within the area, and from targeting the entire population to incorporating characteristics of categorized subpopulations and finally going down to the individual level. These transitions have been first driven by the recognition that generalizations of space and population in conventional epidemic modelling may have hampered the effectiveness of the modelling; they then have been supported by increasingly available data that allow depiction of detailed spatiotemporal characteristics of an epidemic, as well as those characteristics of the environment in both human and natural aspects; and finally they have been facilitated by developments in geographic information science, data science, computer science, and computing technologies. Based on a review of a variety of recently developed communicable disease models, we explicitly put forward the notions of spatialization and individualization in this area, and point out that the integration of the two is the future of communicable disease modelling. We also point out that in this area models based on the object conceptualization are good at modelling spatiotemporal process, whereas models based on the field conceptualization are good at representing spatialized information. We propose a procedural framework of epidemic modelling that implements the integration of individualization and spatialization, integration of object-based process and field-based representation, and integration of modelling that retrospectively traces infection relationships based on historical patient data and modelling that prospectively predicts such relationships of future epidemics.
Birefringence of rotary photon drags through induced chiral atomic medium

The birefringence of rotary photon drags is investigated through induced chiral atomic medium. The birefringence of refractive and group indices, divergent angle and rotary photon drags were controlled and modified with detuning, phases and strength of Rabi frequencies of the driving fields. The value of divergent angle oscillated between ±0.01 radian with phases of control fields φ3,4 and is minimum at φ3 = π, while maximum at φ4 = π. The right and left circularly polarized beams are drag clockwise due clockwise spinning of the medium in the normal dispersive region, while the right and left circularly polarized beams are drag clockwise due anticlockwise spinning of the medium in anomalous dispersive region. The maximum rotary photon drags of birefringent beams of light is calculated to ±5 milli radian. The results show advancement in molecular structure, dual image coding and image designing technology.
Fast proximal splitting algorithm for constrained TGV-regularised image restoration and reconstruction

A fast algorithm is proposed to tackle the constrained total generalised variation (TGV)-based image-restoration and reconstruction problems. The proposed algorithm proceeds by splitting: the non-smooth constrained TGV model is first decomposed into several sub-problems easier to solve, and then the linear gradient or proximity operators, including projections and shrinkages, of the sub-problems are individually called without inner iteration. The algorithm is highly parallel since most of its steps can be executed simultaneously. Image-restoration and reconstruction experiments demonstrate that the proposed algorithm outperforms several state-of-the-art TV-based methods both in accuracy and high-speed efficiency. Besides, the proposed method efficiently suppresses staircase effects and presents better visual impression.
Grapple: A Graph System for Static Finite-State Property Checking of Large-Scale Systems Code

Many real-world bugs in large-scale systems are related to object state that is supposed to obey a specified finite state machine (FSM). They are triggered when unexpected events occur on objects in certain states, making these objects transition in a way that violates their specifications. Detecting such FSM-related bugs with static analysis is challenging, especially in distributed systems that have large codebases. This paper presents a single-machine, disk-based graph system, called Grapple, which was designed to conduct precise and scalable checking of finite-state properties for very large codebases. Grapple detects bugs through context-sensitive, path-sensitive alias and dataflow analyses, which are both formulated as dynamic transitive-closure computations and automatically parallelized by the system. We propose a novel path constraint encoding/decoding algorithm to attach a path constraint to a graph edge, allowing the graph engine to efficiently recover a path and compute its constraint during the computation. We have implemented Grapple and conducted a comprehensive evaluation over widely deployed distributed systems. Grapple reported a total of 376 warnings, of which only 17 are false positives. Our results also demonstrate the scalability of Grapple: it took between 51 minutes and 33 hours to finish all the analyses on a low-end desktop with 16G memory and 1T SSD space, while the traditional approaches ran out of memory in all cases.
Regulation Function for Agent Adaptation Issues in Ambient Environment

In this work we deal with action selection issues for software agent operating in ambient environment which is highly dynamic. Unpredictable events may occur and inappropriate actions may damage the software and its environment. Adaptation ability is a key requirement for such issues. We use multi-agent paradigm to address them. We propose a regulation function within agent architecture. It is a filter which acts on the stream of behaviour before it becomes or not action. The aim is to cope with environmental changes without the need to predict them precisely at design-time. To this end, we introduce the Influence-Reaction Model into the agent behaviour management. To facilitate its application, we implement the resulting architecture as a Java library called MECA. We experiment it with an agricultural robot moving through a field.
Comparing the Effectiveness of Multiple Quantitative Trading Strategies

Investment in stock has drawn worldwide attention from individuals and investment companies. There are many common practices of trading strategies. An easy but effective one is the buy and hold strategy strongly advocated by Warren Buffett, where investigators would buy one or a group of stocks and let time make money. A common strategy for individual investors was by reading the stock chart, based on their personal judgement, which often was not quite different from random guesses due to the lack of information and experience. Another recent strategy is to use machine-learning techniques to predict the stock market. I am interested in investigating which of the above strategies were more effective in the current stock market. To gain an up-to-date view, I applied these strategies on 4 different popular stocks and observed their performance for 100 randomly chosen time frame in 2017 and 2018. By evaluating the return and risk of each strategy, my results provide guidance for quantitative trading for general investors. Specifically, if the stock market is stably increasing, the optimal strategy is to use the buy-and-hold strategy. If the stock market is comparatively stable, using a good machine learning strategy is expected to help. Overall, individual investors should devote more efforts in selecting a promising stock or portfolio than focusing too closely on daily price changes.
Study on the effect of regulation of Cordyceps militaris polypeptide on the immune function of mice based on a transcription factor regulatory network.

BACKGROUND The pathogenesis of the abnormality of the immune system is still not clear at present. Chemosynthetic drugs, human or animal immune products and microbiological drugs are used as the main drugs in clinics currently, but these drugs have different side effects. So researchers turned to safer natural products in order to find immunomodulatory active substances from natural products and their extracts. METHODS Immunosuppressed mice were induced by cyclophosphamide and administered with Cordyceps militaris polypeptide (CMP) for the study on the effect of CMP on the immune function of mice and its mechanism. Based on the 1748 differential gene sets selected in our previous work, the transcription factors and their corresponding target genes were screened by integrating the TRED (Transcriptional Regulatory Element Database), a transcriptional factor-target gene regulatory network was constructed, then the role of transcription factors in the regulatory network was elucidated by statistically analyzing the key nodes, and finally, the correlation of network genes with diseases was analyzed by using the DAVID database. RESULTS The results of animal experiments showed that CMP could increase the immune organ indexes, the number of white blood cells, the degree of delayed allergy and the content of hemolysin in the serum of mice. CMP was found to be involved in the regulation of immune function in mice through genes Kdr, Spp1, Ptgs2, Rel, and Smad3, and transcription factors Ets1, E2f2 and E2f1. E2F2 and E2F1 are members of the E2F family, so we speculated that the E2F family might play an important role, and its main regulatory pathways were the PI3K-Akt signaling pathway and TNF signaling pathway. CONCLUSION CMP can improve the immunity of mice. CMP can regulate the immune function of mice through multiple genes and transcription factors, and may also play a role in immune-related diseases, such as cancer.