Reviewerly/exHarmony huggingface dataset:
Detecting Spam Tweets Using Lightweight Detectors on Real-Time Basis and Update the Models Periodically in Batch Mode

The majority of accessible technique for spam detection on Twitter purpose to recognize and block user who put up spam tweet. Here this document, we suggest a semi-supervised spam detection structure for spam discovery at tweet-stage. Planned structure contains of two essential modules: spam discovery module working in concurrent mode and method update module working in batch mode. The spam detection module consists of 4 frivolous detectors: 1) blacklisted area detector to label tweets include blacklisted URLs; 2) close to-reproduction detector to label tweets which can be close to-duplicates of expectantly relabeled tweets; 3) dependable ham detector in the direction of label tweets which are published by means of trusted customers and that do not incorporate spammy words; and 4) multiclassifier primarily based totally detector labels the closing tweets. The data needful thru the detection detail is up to date in batch mode primarily based on the tweets which can be categorized inside the preceding moment in time windowpane. Experiment on top of a massive-scale records set display that the method adaptively learn styles of recent spam actions and hold appropriate accuracy for spam detection in a tweet torrent.
Surrogate-Assisted Particle Swarm Optimization using Space Reduction and Global Search

Evolutionary algorithms (EAs) are powerful tools for solving complex engineering applications problems, however, the function evaluations of the real-world problems are often time-consuming, which hinders the application of EAs to solve these problems. To address this challenge, surrogate-assisted EAs (SAEAs) have been widely studied over the past decades. Model management can help obtain solutions under limited computational budget to accelerate the convergence of the optimization process. In this paper, we propose a SAEA based on social learning particle swarm optimization (SL-PSO), a space reduction strategy and a global model search strategy are developed to search for the best and most uncertain solutions. In the proposed algorithm, the space reduction strategy is used to search for the optimal solution based on the surrogates and the global model search strategy is used to search for the most uncertain solution. The evolutionary search using the space strategy switches to the global search once on further improvement can be observed, and vice versa. Experimental results comparing the proposed algorithm with other two algorithms on several benchmark problems that the proposed algorithm is able to achieve better or competitive solutions.
Single-chip heterodyne characterization of heralded ring resonator photon pair source

We perform on-chip heterodyne tomography of the quantum state generated by a heralded microring single photon source. The quantum state is generated and characterized without ever being coupled off-chip.
E-TELECOMMUNICATION INFORMATION SYSTEM

The transmission of traffic either in form of voice or data from a source to destination over a transmission media such as fiber cable or digital microwave radio is termed telecommunication.&#x0D; The telecommunication is classified into three categories namely: Access network, core network and transmission system. The access network directly provides network services to the subscribers. The core network does the routing or switching of traffic from the source to destination over the transmission media while the transmission system provides the media through which the traffic is transmitted from source to the destination.&#x0D; In this study, an automated web-based application name an E- Telecommunication Information System (ETIS) will be designed and developed to provide accuracy and consistence of network data, reduce Mean Time To Restore (MTTR) faulty equipment, increase revenues from cell sites and improve customers satisfaction in the telecommunication industry.&#x0D; Qualitative research approach and qualitative research method will be adopted so as to achieve reliable objectives of the system.
Generate Keyphrases by Combining Hidden Context and Word Context

As the network develops, highly generalized indexing information becomes more and more important. Keyphrases are multiple words that can simply summarize the semantics of an article. Keyphrase generation is the basis for many natural language processing tasks. Although there are many tasks for keyword generation, they consider hidden layer information and almost ignore each independent word information in the source text. So we attach word-level information to the model and combine this information with hidden layer information. Experimental results show that our model can effectively improve the accuracy of the generated keyphrases.
A PDDL based Disaster Storyline Generation Approach

The rapid development of computer technology has brought challenges to the management of disaster information. When an emergency occurs, a huge amounts of relevant information will be produced. In order to form a clear event development context from the overloaded Internet data and improve the corresponding efficiency of emergency events, in this paper, we propose a method for disaster storyline construction based on intelligent planning. Specifically, we first extract the semantic information from the News text to build a Planning Domain Definition Language(PDDL) domain model, which contains complete domain knowledge. Then we use a planner to plan the event storylines that meets the presets in the domain model. To our knowledge, this is the first attempt to combine natural language processing(NLP) technology with intelligent planning technology for disaster storyline generation. Different from the previous method, our proposed model is more focused both on the event evolve information and the government emergency measures, which can provide more reference value for relevant emergency response.
Machine learning algorithms for medical image security

Expectations for artificial intelligence are increasing day by day with the evolution of machine learning techniques. In recent years, machine learning has shown an increased ability to build security applications. Exceptionally high accuracy is achieved when machine learning is used for data-driven problems such as classification of malware, intrusion detection, securing health prediction systems, autonomous security audits from system logs, etc. Key challenges in adopting machine learning algorithms for security and safety critical areas include lack of transparency, inadequate vulnerability analysis, etc. Many machine learning algorithms for security and privacy are developed with little or no understanding of the underlying security aspects. Malicious external influences can cause the machine learning algorithm to predict falsely or misclassify, which might create a huge impact on security and life critical applications. The efficiency of machine learning algorithms often relies on huge volumes of data, which involves sensitive information as well. It is a greater challenge not to expose the privacy of sensitive data. This chapter addresses the impact of machine learning algorithms for applications involving medical image security.
The ACTIVAGE Marketplace: Hybrid Logic- and Text-Based Discovery of Active and Healthy Ageing IoT Applications

Currently, Active and Healthy Ageing (AHA) IoT solutions for the global ageing population, remain segmented. The ACTIVAGE project aims to integrate prevalent IoT platforms, enhanced with data analytics, developer, deployment, security and privacy tools to enable large-scale AHA pilots all over Europe. This paper presents the ACTIVAGE Marketplace, a one-stop-shop for developers to provide and for end-users, carers and healthcare professionals to discover AHA IoT Apps. The Marketplace offers standard functionality such as Developers uploading, monetizing and tracking analytics for their Apps, Users discovering, buying and downloading them, as well as Administrators managing and moderating content. In addition, it offers its own, hybrid logic- and text-based method to discover alternatives from the wide variety of Apps, from sensor behavioral monitoring to exergames or pill reminder interventions. For this purpose, the Marketplace ontology defines App semantic properties, such as a hierarchy of App Categories, combined with keyword-based text-similarity search.
Perturbative tomography of small errors in quantum gates

We propose an efficient protocol to fully reconstruct a set of high-fidelity quantum gates. Usually, the efficiency of reconstructing high-fidelity quantum gates is limited by the sampling noise. Our protocol is based on a perturbative approach and has two stages. In the first stage, the initial part of noisy quantum gates is reconstructed by measuring traces of maps, and the trace can be measured by amplifying the noise in a way similar to randomised benchmarking and quantum spectral tomography. In the second stage, by amplifying the non-unital part using the unital part, we can efficiently reconstruct the non-unital part. We show that the number of measurements needed in our protocol scales logarithmically with the error rate of gates.
DeviceAR

We present DeviceAR, a set of techniques to facilitate AR-based interactions with connected devices. These techniques allow HoloLens to discover and establish communication with devices, control them, and seamlessly exchange digital information using holographic interfaces.
Periodic Guidance Learning

Tasks with periodic states are widespread in reality. However, Current reinforcement learning (RL) algorithms generally treat such tasks as non-periodic Markov decision process, which results in low exploration efficiency and misleading advantage estimation with high variance. This paper proposes periodic guidance learning (PGL), in which a pruned advantage estimation with lower variance is implemented. Meanwhile, based on periodic states, past good experiences are utilized for better exploration. Our algorithm is evaluated on periodic tasks in MuJoCo. The experimental results show PGL method improves exploration efficiency and outperforms baselines in various periodic tasks. The results also show that PGL achieves a smooth policy optimization. Further experiments on the agent's periodic behavior reveal the strong correlation between period length and the agents motion mode.
Restricting the Maximum Number of Actions for Decision Support Under Uncertainty

Standard approaches for decision support are computing a maximum expected utility or solving a partially observable Markov decision process. To the best of our knowledge, in both approaches, external restrictions are not accounted for. However, restrictions to actions often exists, for example in the form of limited resources. We demonstrate that restrictions to actions can lead to a combinatorial explosion if performed on a ground level, making ground inference intractable. Therefore, we extend a formalism that solves a lifted maximum expected utility problem to handle restricted actions. To test its relevance, we apply the new formalism to enterprise architecture analysis.
Cooperative Detection Model for Phishing Websites Based on Approach

Aiming at the singularity of detection methods of phishing websites and the lack of real-time performance of traditional detection technologies, this paper proposes a collaborative detection model for phishing websites based on approach. This model better uses approach to improve and optimizes existing phishing. The actual detection efficiency of the website detection algorithm. At the same time, a cloud computing processing solution is introduced to improve the data processing capacity and detection rate. The experimental comparison and verification show that the approach-based phishing website collaborative detection model can focus the trust value of the phishing website in a range Compared with other methods, it has a lower false positive rate, false negative rate, and higher speed, which can detect phishing websites well.
DHE$$^{2}$$: Distributed Hybrid Evolution Engine for Performance Optimizations of Computationally Intensive Applications

A large number of real-world optimization and search problems are too computationally intensive to be solved due to their large state space. Therefore, a mechanism for generating approximate solutions must be adopted. Genetic Algorithms, a subclass of Evolutionary Algorithms, represent one of the widely used methods of finding and approximating useful solutions to hard problems. Due to their population-based logic and iterative behaviour, Evolutionary Algorithms are very well suited for parallelization and distribution. Several distributed models have been proposed to meet the challenges of implementing parallel Evolutionary Algorithms. Among them, the MapReduce paradigm proved to be a proper abstraction of mapping the evolutionary process. In this paper, we propose a generic framework, i.e., DHE $$^{2}$$ (Distributed Hybrid Evolution Engine), that implements distributed Evolutionary Algorithms on top of the MapReduce open-source implementation in Apache Hadoop. Within DHE $$^{2}$$ , we propose and implement two distributed hybrid evolution models, i.e., the MasterSlaveIslands and MicroMacroIslands models, alongside a real-world application that avoids the local optimum for clustering in an efficient and performant way. The experiments for the proposed application are used to demonstrate DHE $$^{2}$$ increased performance.
Attacking Naive Bayes Journal Recommendation Systems

Recommendation systems have been extensively adopted in various applications. However, with the security concern of artificial intelligence, the robustness of such systems against malicious attacks has been studied in recent years. In this paper, we build a journal recommendation system based on the Naive Bayesian algorithm which helps recommend suitable journals for the authors. Since journal recommendation systems may also suffer from various attacks, we explore attack methods on the malicious data. We construct specific malicious data to attack the availability of training data, and such deviations in the training data could lead to poor recommendation accuracy. We also conduct extensive experiments and the results show that the recommendation accuracy could be dramatically reduced under such attacks.
Tree-Based Bagging and Boosting Algorithms for Proactive Invoice Management

This paper explores the use of machine learning for proactive invoice management through addressing the problem of predicting delinquent invoices and investigating the factors that correlate with delinquency. Unpaid or late-paid invoices lead to the writing-off of millions of dollars for large organizations globally. A key component in account receivables management is to proactively alleviate bad debts and accelerate payments, which considering the “time-value of money” has a significant impact on ultimate profitability. To achieve this dual goal, the focus is on tree-based ensemble models and use of various learning schemes on real-world invoice data from a Fortune 500 financial company made of several business units servicing several geographies. Our modeling scheme accounts for variations along several customer characteristics including agreed payment policies, type of business, and geo-locations. Our comparative results of Random Forest and LightGBM show that the LightGBM model gives better AUC and Lift across all Business Units.
A Light Weight Detection Network with Anchor-based Pooling Module

Both latency and accuracy are critical metrics for object detection applications. To pursue a good trade-off between accuracy and computational cost in realistic scenarios, this paper proposes a detection network based on YOLOv3. By modifying the backbone of YOLOv3, especially reucing the number of layers, the proposed detection network yields desirable real-time performance with small accuracy loss. Besides, an anchor-based pool module (APM) is utilized to improve the detection network’s adaptability to different aspect ratios of anchor boxes. The proposed detection network is realized and implemented by an embedded platform of Nvidia Xavier. Experiments show that our network can effectively improve accuracy while meeting the timeliness needs of embedded devices, and APM provides stronger features to the detection network.
Improved Analytic Hierarchy Process Based on Fuzzy Synthesis Theory for User Behavior Evaluation

The credibility assessment of network users’ behavior is indispensable for the study of trusted networks. This paper proposes an evaluation model of users’ behavior. Firstly, users’ uncertain behavior is quantitatively shown based on the fuzzy comprehensive evaluation. Then, use the von Neumann entropy with weight to optimize the collected evidence; the weight value of each behavior attribute is calculated by the improved analytic hierarchy process in the form of clustering and merging; finally, the comprehensive evaluation value of the target layer is obtained by calculation, and whether the users’ behavior is credible or not is decided according to the comprehensive credibility. The simulation experiment shows that the users’ behavior evaluation model proposed in this paper has accurate judgment results for the behavior of network users.
IMPLEMENTING EXPERIENTIAL LEARNING METHOD TO ENHANCE STUDENTS’ SPEAKING SKILL

Foreign language students may face difficulties in developing speaking skills due to the reasons of lacking the opportunity to speak, the inhibition or enxity, and lacking the motivation to study. This study aimed to investigate the implementation of Experiential Learning Method as an effort to solve such problems. Thus, there were two objectives in this study which were to examine the difference of speaking achievement on the use of Experiential Learning Method and to find out the students’ responses toward the method. The sample was taken by using random sampling from the population of the first grade students at SMAN1 Banda Aceh in the 2019/2020 academic year. Two classes were taken as the sample, each for experimental class and control class. The data were analyzed by using t-test through SPSS for speaking test and percentage for questionnaire. The result showed that Experiential Learning Method was more effective than conventional method (teacher centred) to teach speaking. Also, the students had a quite positive response to the use of Experiential Learning Method in their speaking class. It can be concluded that the Experiential Learning Method provides a positive contribution for the development of the students’ speaking performance.
Research on Parallel Task Optimization of High Performance Computing Cluster

The application of cluster system has gone deep into all aspects of production and life. The use of the cluster system's cost-effective parallel computing capabilities to solve complex model calculations and mass data processing issues has become an important branch of high-performance computing research. Spark-based parallel computing platforms can greatly improve computational efficiency. However, if the spark task does not perform reasonable parameter settings, it will still cause a huge waste of resources and resource consumption. In this paper, high-performance computing cluster is used as the experimental hardware environment, and based on the Spark platform, the parameters of the parallel task are adjusted, which greatly reduces the hardware resources and improves the utilization of the cluster. Through experimental comparison, it provides a direct reference for the calculation of high-performance computing clusters.
Poster: Proposal of an Intelligent Remote Tutoring Model

Distance education is today a major pedagogical innovation and makes it possible to solve not only the problem of the massification of baccalaureate holders and the professionalization of training, but also and above all to optimize pedagogical and human resources. This type of teaching is based on digital platforms that contain educational resources. Consequently, for a good follow-up of learners in the conduct of the courses, the existing training platforms integrate a so-called tutoring system. However, although this system allows learners to be monitored by their tutors, it has many shortcomings, including the loss of quality in distance education due to a lack of continuous interactivity of tutoring. This lack of interactivity can come from the learner or the tutor and it is even worse if it has to come from the tutor. This paper aims to provide a constant and intelligent solution in the tutoring system. Our solution consists in guiding the learner as much as possible to carry out his tutoring even when the tutor is not available. Thus we propose a model that will take as input the useful course data from the tutor, and then make the necessary settings to allow the system to guide the learner in his learning process while avoiding any interruption in the tutoring.
A Hypothesis-Optimized RANSAC Algorithm for Track Initiation

Track initiation is still a challenge in extremely dense clutter environment. Since the validity of hypothesis in RANSAC approach can not be guaranteed in heavy clutter situations, a novel track initiation algorithm named hypothesis-optimized random sample consensus (HO-RANSAC) is proposed to address the above problem. The proposed HO-RANSAC uses hypothesis splitting and merging strategy in the hypothesis verification procedure to improve the probability of valid hypothesis, in addition, local optimization is applied to update the hypothesis model. Simulation results demonstrate that the proposed algorithm has superior performance compared with RANSAC, the modified logic-based algorithm, the modified Hough transform and DB-RANSAC.
Analysis of Gesture Recognition to Evaluate Hand Signals

Abstract Human Computer Interaction is an effective tool of intersection between the human intellect and computers leading to improvisation in technology. Gestures help to communicate between two persons very effectively even without saying a word but it is really ideal to note that man’s immense potential and intelligence has motivated him to interact through gestures with his own invention called computer. This paper has been prepared on MATLAB using effectively a simple algorithm and basically deals with the recognition of finger gestures. It is executed by simple steps where initially the image is converted into binary which is followed by cutting the image from the point where the longest finger starts and also an amount from the bottom to get a subtle image and then simply counting the number of white objects (i.e. fingers) and this process is repeatedly done by rotating the image in all the four directions and the result is selected taking into account the outcome of the direction where maximum number of outcomes have occurred provided the background is in a solid colour which can reveal the image of the fingers displayed clearly. The application of this work includes choosing an option from any user interface by merely displaying numbers as per the number of fingers shown. This simple and innovative effort is user friendly and can be effectively used in imparting knowledge especially to differently abled.