Miracle-dz/UniFAR-FTUs huggingface dataset:
iSpan: Parallel Identification of Strongly Connected Components with Spanning Trees

Detecting strongly connected components (SCCs) in a directed graph is crucial for understanding the structure of graphs. Most real-world graphs have one large SCC that contains the majority of the vertices, as well as many small SCCs whose sizes are reversely proportional to the frequency of their occurrences. For both types of SCCs, current approaches that rely on depth or breadth first search (DFS and BFS) face the challenges of both strict synchronization requirement and high computation cost. In this paper, we advocate a new paradigm of identifying SCCs with simple spanning trees, since SCC detection requires only the knowledge of connectivity among the vertices. We have developed a prototype called iSpan, which consists of parallel, relaxed synchronization construction of spanning trees for detecting the large and small SCCs, combined with fast trims for small SCCs. We further scale iSpan to distributed memory system by applying different distribution strategies to the data and task parallel jobs. The evaluations show that iSpan is able to significantly outperform current state-of-the-art DFS and BFS-based methods by average 18× and 4×, respectively.
Predicting Autism Using Naive Bayesian Classification Approach

The present era contains large quantity of data in the medicine field. The field of medicine is “data rich” which can be utilized in decision making. Genetics plays a major role in medicine. Large amount of data can be obtained from the human genome. Autism is a neurobehavioral disorder that impairs the ability to communicate and interact. Autism has a strong genetic basis. Mutations in the genes associated with autism can affect the brain development and its functions, starting well before birth. Mutated genes are inherited from the parents to child which becomes a parameter for autism. Herein we propose an approach to rank the set of diseased genes which is present in the autistic individual. From the training data set, the system predicts the autistic behavior of an individual by comparing the similarities between the individual's gene and the diseased gene in the training set. This is implemented using NAIVE BAYESIAN CLASSIFICATION method. For an instance, if an individual's DNA is tested and if the DNA contains the diseased genes which are present in the training set then the autism is predicted. This approach makes the diagnosis of autism easier and at an early stage.
Massive release of the histamine‐degrading enzyme diamine oxidase during severe anaphylaxis in mastocytosis patients

Histaminolytic activity mediated by diamine oxidase (DAO) is present in plasma after induction of severe anaphylaxis in rats, guinea pigs, and rabbits. Heparin released during mast cell degranulation in the gastrointestinal tract might liberate DAO from heparin‐sensitive storage sites. DAO release during anaphylaxis has not been demonstrated in humans.
Three-Stage Stackelberg Game for Defending Against Full-Duplex Active Eavesdropping Attacks in Cooperative Communication

In this paper, we study how to defend against full-duplex active eavesdropping attacks in the cooperative communication system. Unlike passive eavesdropping attackers, a full-duplex active eavesdropper acts as both a jammer and a classical eavesdropper in full-duplex mode, and seeks to maximize eavesdropping. Furthermore, in a real-world network, the relays may be selfish and have their own objectives due to competing/limited resources. This may lead to the failure of cooperative communication if there is no incentive to stimulate relays to participate in forwarding the transmitter's messages. Therefore, in this paper, we propose a three-stage Stackelberg game approach to model the behaviors of attacker, and the competitions among the transmitter, relays and attacker. Through finding the Stackelberg equilibrium of our scheme, the legitimate users can achieve cooperative communication to improve the secrecy capacity and to defend against full-duplex active eavesdropping attacks. Findings from the evaluations demonstrate that with an increasing number of channels, our scheme can significantly reduce the intercept probability; therefore, the security performance is improved. It is shown that our scheme can greatly improve the utilities of both the transmitter and multiple relays over the results of the Nash equilibrium scheme and rand power control scheme, which means that the relays are more willing to participate in the cooperative communication, and the transmitter can achieve better secure transmission in our scheme. We also demonstrate that our scheme outperforms other existing schemes, i.e., power control with smart jamming game in a single-channel model/multiple-channel model.
Fluctuation-Aware and Predictive Workflow Scheduling in Cost-Effective Infrastructure-as-a-Service Clouds

Cloud computing is becoming an increasingly popular platform for the execution of scientific applications such as scientific workflows. In contrast to grids and other traditional high-performance computing systems, clouds provide a customizable infrastructure where scientific workflows can provision desired resources ahead of the execution and set up a required software environment on virtual machines (VMs). Nevertheless, various challenges, especially its quality-of-service prediction and optimal scheduling, are yet to be addressed. Existing studies mainly consider workflow tasks to be executed with VMs having time-invariant, stochastic, or bounded performance and focus on minimizing workflow execution time or execution cost while meeting the quality-of-service requirements. This work considers time-varying performance and aims at minimizing the execution cost of workflow deployed on Infrastructure-as-a-Service clouds while satisfying Service-Level-Agreements with users. We employ time-series-based approaches to capture dynamic performance fluctuations, feed a genetic algorithm with predicted performance of VMs, and generate schedules at run-time. A case study based on real-world third-party IaaS clouds and some well-known scientific workflows show that our proposed approach outperforms traditional approaches, especially those considering time-invariant or bounded performance only.
Can(‘t) Wait to Have a Robot at Home? - Japanese and German Users' Attitudes Toward Service Robots in Smart Homes

In the present study, we compared Japanese and German participants' perceptions of social robots. To do so, participants from both cultural backgrounds evaluated two types of service robots designed for use in the smart home context. One of the prototypes featured a rather technical appearance, whereas the other prototype featured a newly designed social robot head. Against our predictions, Japanese and German participants evaluated both robot types similarly. Japanese participants generally showed a stronger tendency to anthropomorphize nonhuman agents and attributed more mind, particularly on the experience dimension, to both robot types than German participants. Nonetheless, Japanese and German participants attributed similar levels of agency to both robot types. Unexpectedly, Japanese participants perceived the robots even as less humanlike than German participants. As predicted, Japanese participants reported less attitudinal robot acceptance and tended to show less trust toward both robot types than Germans. Japanese and Germans indicated similar levels of robot anxiety, perceived uncanniness, and robot likeability. Japanese and German participants generally perceived the robots rather as machines and tools than as human companions. Especially German participants wanted to use the robots for rather safe, boring, and simple tasks that do not require any direct interaction with them. Japanese and German participants were not yet familiar with robots and showed less positive attitudes toward them than widely believed (e.g., [1]). Implications how to gain a more realistic view on users' perceptions of robots to enable a fruitful and comfortable HRI are discussed.
Design and Implementation of Oil Painting Online Appreciation System Based on Android

This paper takes online display of oil paintings as the main research objective. It studies design and application of oil painting online display system and constructs a set of mobile platform-based oil painting appreciation system to be adapted to response-front layout of mobile terminal device. We apply MVC framework of PHP in WebApp development. Front-end adopts the currently latest HTMLS technology and Bootstrap framework so that system has effective compatibility and platform crossing to provide unified experience for useless devices. The system establishes role authority control-based system and associates content to corresponding roles through detailed role distribution of content so the development of system business logic is more standardized. Finally, various functions and performance indexes in oil painting online display system are tested and the feasibility of this improved scheme is proved.
A Lightweight Signature-Based IDS for IoT Environment

With the advent of large-scale heterogeneous networks comes the problem of unified network control resulting in security lapses that could have otherwise avoided. A mechanism is needed to detect and deflect intruders to safeguard resource constraint edge devices and networks as well. In this paper we demonstrate the use of an optimized pattern recognition algorithm to detect such attacks. Furthermore, we propose an Intrusion Detection System (IDS) methodology and design architecture for Internet of Things that makes the use of this search algorithm to thwart various security breaches. Numerical results are presented from tests conducted with the aid of NSL KDD cup dataset showing the efficacy the IDS
Location-Aware Influence Blocking Maximization in Social Networks

In real social networks, it is often the case that opposite opinions, ideas, products, or innovations are propagating simultaneously. Although the competitive influence problem has been extensively studied, existing works neglect the fact that the location information can play an important role in influence propagation. In this paper, we study the location-aware influence blocking maximization (LIBM) problem, which aims to find a positive seed set to maximize the blocked negative influence for a given query region. In order to overcome low efficiency of the greedy algorithm, we propose two heuristic algorithms LIBM-H and LIBM-C based on the quadtree index and the maximum influence arborescence structure. Experimental results on real-world datasets show that both LIBM-H and LIBM-C are able to achieve a matching blocking effect to the greedy algorithm and often better than other heuristic algorithms, whereas they are several orders of magnitude faster than the greedy algorithm.
Product Title Refinement via Multi-Modal Generative Adversarial Learning

Nowadays, an increasing number of customers are in favor of using E-commerce Apps to browse and purchase products. Since merchants are usually inclined to employ redundant and over-informative product titles to attract customers' attention, it is of great importance to concisely display short product titles on limited screen of cell phones. Previous researchers mainly consider textual information of long product titles and lack of human-like view during training and evaluation procedure. In this paper, we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short product title generation, which innovatively incorporates image information, attribute tags from the product and the textual information from original long titles. MM-GAN treats short titles generation as a reinforcement learning process, where the generated titles are evaluated by the discriminator in a human-like view.
The Sheaths of Methanospirillum Are Made of a New Type of Amyloid Protein

The genera Methanospirillum and Methanosaeta contain species of anaerobic archaea that grow and divide within proteinaceous tubular sheaths that protect them from environmental stressors. The sheaths of Methanosaeta thermophila PT are composed of the 60.9 kDa major sheath protein MspA. In this study we show that sheaths purified from Methanospirillum hungatei JF-1 are regularly striated tubular structures with amyloid-like properties similar to those of M. thermophila PT. Depolymerizing the sheaths from M. hungatei JF-1 allowed us to identify a 40.6 kDa protein (WP_011449234.1) that shares 23% sequence similarity to MspA from M. thermophila PT (ABK14853.1), indicating that they might be distant homologs. The genome of M. hungatei JF-1 encodes six homologs of the identified MspA protein. Several homologs also exist in the related strains Methanospirillum stamsii Pt1 (7 homologs, 28–66% sequence identity), M. lacunae Ki8-1 C (15 homologs, 29–60% sequence identity) and Methanolinea tarda NOBI-1 (2 homologs, 31% sequence identity). The MspA protein discovered here could accordingly represent a more widely found sheath protein than the MspA from M. thermophila PT, which currently has no homologs in the NCBI Reference Sequence database (RefSeq).
Design of Numerical Relay for Over Flux and Over Current Protection in Transformers

A “Numerical Relay” is a type of protective relay which operates when the line parameters exceed a preset value. This paper is used to design and develop a protection system for transmission line which automatically respond to the over flux and over current fault based on the DMT and IDMT in which the current is measured using SI, VI, and EI characteristics. It is used to design and fabricate a multi-functional protective relay using PIC micro controller. In order to design it, first the line parameters need to be measured and the measured values to be compared with the preset values. Over flux and over current relay have maximum sensitivity providing a minimum reaction time to the occurrence of fault. This high sensitivity may lead to operation of relays in normal transient events, such as transformers energizing. Therefore, proper methods must be used to prevent mal-trip of the relays, due to these transient currents. This paper presents an over current and over flux caused by fault from transformer energizing, DMT and IDMT characteristics are implemented for over flux relay. To improve the protection system SI, VI and E1 time current characteristics were implemented. DMT and IDMT application is used so that when current raises above threshold value the relay does not trip immediately and the trip time is calculated for that actual current and still increasing for particular duration the relay signal is given manually and tripping occurs for protection of component.
Policy Regret in Repeated Games

The notion of ``policy regret'' in online learning is supposed to capture the reactions of the adversary to the actions taken by the learner, which more traditional notions such as external regret do not take into account. We revisit this notion of policy regret, and first show that there are online learning settings in which policy regret and external regret are incompatible: any sequence of play which does well with respect to one must do poorly with respect to the other. We then focus on the game theoretic setting, when the adversary is a self-interested agent. In this setting we show that the external regret and policy regret are not in conflict, and in fact that a wide class of algorithms can ensure both as long as the adversary is also using such an algorithm. We also define a new notion of equilibrium which we call a ``policy equilibrium'', and show that no-policy regret algorithms will have play which converges to such an equilibrium. Relating this back to external regret, we show that coarse correlated equilibria (which no-external regret players will converge to) are a strict subset of policy equilibria. So in game-theoretic settings every sequence of play with no external regret also has no policy regret, but the converse is not true.
Design of a Photoconductive Antenna for Pulsed-Terahertz Spectroscopy with Polarization Diversity

In the present paper, a photoconductive antenna is designed to work as both pulse-terahertz emitter and receiver. The proposed two-port antenna benefits from polarization diversity with high sensitivity of polarization detection. The antenna electrodes consist of three arms and two gaps that can be excited by laser illumination. An 800 nm wavelength laser beam with output power of 80 mW, 100 MHz repetition rate, and 100 fs laser pulse duration is considered as optical excitation. Each port stimulates a pulse that its polarization is orthogonal to that of the other one according to their arrangement. Along with GaAs as photoconductive substrate beneath the antenna electrodes, an anti-reflection coating of Taconic RF-35 is used for depreciating unwanted terahertz echoes. The simulated results clarify that the proposed antenna shows polarization diversity and also, proper broadband behavior.
An FPGA-Optimized Architecture of Variational Optical Flow

The variational methods for dense optical flow estimation are widely known and adopted techniques for motion detection, object tracking, 3D reconstruction and autonomous navigation in image processing applications. A non-linear model of variational optical flow estimation is the most accurate but also most complicated and computationally intensive method and its implementation in an FPGA is compromise from both a design complexity and a performance. The article is devoted to the specific FPGA-based solution which has been implemented using Verilog hardware description language. The suggested solution is able to process the non-linear optical flow in real time and might be applied as FPGA -accelerator for optical flow processing.
Mutual Information of Wireless Channels and Block-Jacobi Ergodic Operators

Shannon’s mutual information of a random multiple antenna and multipath time varying channel is studied in the general case where the process constructed from the channel coefficients is an ergodic and stationary process which is assumed to be available at the receiver. From this viewpoint, the channel can also be represented by an ergodic self-adjoint block-Jacobi operator, which is close in many aspects to a block version of a random Schrödinger operator. The mutual information is then related to the so-called density of states of this operator. In this paper, it is shown that under the weakest assumptions on the channel, the mutual information can be expressed in terms of a matrix-valued stochastic process coupled with the channel process. This allows numerical approximations of the mutual information in this general setting. Moreover, assuming further that the channel coefficient process is a Markov process, a representation for the mutual information offset in the large signal to noise ratio regime is obtained in terms of another related Markov process. This generalizes previous results from Levy et al. It is also illustrated how the mutual information expressions that are closely related to those predicted by the random matrix theory can be recovered in the large dimensional regime.
Analyzing SDN-Based Vehicular Network Framework in 5G Services: Fog and Mobile Edge Computing

The development of 5G and Software Defined Networking (SDN) are compassed to be singular comprehensive line to allocate massive communication in the future. Conveyance between massive Internet of Everything (IoE) such as machine-to-machine (M2M), vehicle-to-vehicle (V2V), or vehicle-to-infrastructure (V2I) has pressed reseachers to produce fresh or regenerate predecessor framework to collaborate within previous technology aiming toward high-speed connectifity regarding on prerequisites of [1]. Therefore, the contrivance of two popular frameworks, fog computing (FC) and mobile edge computing (MEC) gave more well-knowledges to experts in order to explore extensively about combining on these frameworks and future 5G-based SDN. On this reseach, we will discover about each parameter whose some strengthness and disadvantages on two main research papers from [2] and [3] having explored deeply at these frameworks. In the end, we will suggest concerning of three main component we have discovered, and put references in detail to our statements and whole support inside of them.
Research on Prediction of Brain Functional Connectivity Based on Dynamic Bayesian Hierarchical Model

Imaging-based brain connectivity research has become a useful tool for investigating mental illness. By predicting individual brain connectivity, important insights into disease progression or response to treatment can be obtained. However, some studies have shown that functional connections(FC) are dynamic and fluctuate over time.Dynamic Functional Connectivity (DFC) can more accurately describe changes in neural activity.Therefore, we propose a prediction method based on Dynamic Bayesian Hierarchical Model (DBHM) that focuses on the variability of connections within a single session to overcome the shortcomings of other predictive models. By predicting functional connectivity, we can obtain the progress of the disease and develop specific treatments for patients.
Evolutionary Binary Classification using Cuckoo Search for Malware Perception in API Call Sequences

Malware threats are continuing to grow in volume and sophistication. Current anti-virus software is ineffective on the new generation malware threats. Ongoing developments in machine learning models pose a promising alternative to act against virus attacks including detection of zeroday virus attacks. Some of the contemporary literature has explained the possibility of implementing machine learning algorithms to virus detection. Majority of these algorithms use ngram characteristics of the dot EXE file code, where n is fixed to constant value. This manuscript proposes an algorithm to machine learning, whose learning and detection is also n-gram characteristics, however, the value of n is dynamic. In particular, the contribution of the manuscript is an Evolutionary Binary Classification that is built using cuckoo search (EBC-CS). Results from the execution of the program indicate a strong discrepancy between malicious software and benign software. The changes identified in classifier performance are evaluated in accordance with variations in malware prevalence. Keywords—API call sequence, malware, IDS, EBC-CS, DNA sequence alignment, Cuckoo Search, Bidirectional Pooled Variance Assesment
Comparative Study of Adaptive Single Sensor Based MPPT for PV Applications with Conventional Algorithms

Photovoltaic (PV) Systems are becoming vital for the existence of mankind nowadays. From bulk power generation to space exploration applications, PV systems finds an important role. To extract power from PV device it has to be operated at its Maximum Power Point(MPP) for which different MPPT algorithms are used. On a relative study of these different MPPT algorithms it is found that most of them suffers from certain problems such as highly oscillatory response and slow tracking time. Most of these algorithms uses both current and voltage sensors, this adds to the issue of reliability of such MPPTs. Here in this study, a Single voltage sensor based Adaptive MPPT is compared with different MPPT algorithms using MATLAB/SIMULINK simulations.