Reviewerly/exHarmony huggingface dataset:
Filtering spam messages and mails using fuzzy C means algorithm

Advancement in computer technology has changed the world in many different ways. Communication is just a click away with the power of internet. For effective, low cost and fast communication between people email plays a very important role and thus there is a great need of email services in daily life of users. From all the transactions to business or general communication these are done through the help of emails. But often the communication is effected by the attacks on the email system which include spam mails. Spamming is the use of messaging or electronic messaging system that send huge amount of data. Spam often fills the internet with multiple copies of a message and are sent to different recipients repeatedly without their request and urges to open them. In this paper we analyze different machine learning techniques with feature selection and without feature selection algorithms and their performance to detect the best classifier for spam mail classification. First, we apply each classifier without selecting any features in order to experiment on the dataset and examine the outcome. Next, to select the desired features we apply best first feature selection algorithm and apply various algorithms for classification. We found that the accuracy has improved when we applied feature selection process in the experimentation.
Expected margin–based pattern selection for support vector machines

Support Vector Machines (SVMs) are amongst the most powerful classification algorithms in machine learning and data mining. However, SVMs are limited by high training complexity when training with large datasets. Pattern selection methods have been proposed to reduce the training complexity by selecting a smaller subset of important patterns among all training patterns. In this paper, we propose a new pattern selection method called Expected Margin–based Pattern Selection (EMPS), which selects patterns based on an estimated margin for SVM classifiers. With the estimated margin, EMPS selects patterns that are likely to become support vectors located on the margin boundary and inside the margin region; however, other patterns including noise support vectors are discarded. The experimental results involving 15 benchmark datasets and one real–world semiconductor manufacturing dataset showed that EMPS exhibits excellent performance and stability.
Attack trees in Isabelle extended with probabilities for quantum cryptography

In this paper, we present a proof calculus for Attack Trees and how its application to Quantum Cryptography is made possible by extending the framework to probabilistic reasoning on attacks. Attack trees are a well established and useful model for the construction of attacks on systems since they allow a stepwise exploration of high level attacks in application scenarios. Using the expressiveness of Higher Order Logic in Isabelle, we succeed in developing a generic theory of attack trees with a state-based semantics based on Kripke structures and CTL. The resulting framework allows mechanically supported logic analysis of the meta-theory of the proof calculus of attack trees and at the same time the developed proof theory enables application to case studies. A central correctness and completeness result proved in Isabelle establishes a connection between the notion of attack tree validity and CTL. Furthermore in this paper, we illustrate the application of Attack Trees to security protocols on the example of the Quantum Key Distribution (QKD) algorithm. The application motivates the extension of the Attack Tree proof calculus by probabilities. We therefore introduce probabilities to quantify finite event sequences and show how this extension can be used to extend CTL to its probabilistic version PCTL. We show on the example of QKD how probabilistic reasoning with PCTL enables proof of quantitative security properties.
Comparative Study for Recent Technologies in Arabic Language Parsing

Parsing the natural language is an interesting issue for many of natural language processing (NLP) applications. Parsing a complex language such as Arabic represents a challenging task for many researchers. Arabic is a complex language due to the rich morphology and the complicated structure of its sentences, since it contains difficult linguistic features. This comparative study discusses the existing and the developed Arabic parsing systems, we describe here current technologies which are employed for parsing Arabic text. A comparison between several parsing systems is proposed in this paper as well, by stating the key features and limitations for each of them. Finally, solutions to address the main limitations of these Arabic parsing systems have been suggested.
Understanding the Different Levels of Challenges in Global Software Development

Global software development (GSD) faces several inherent challenges due to temporal, organizational, socio-cultural and geographical distances. Since GSD operates at different functional levels that include country, company and team levels, there is a need to understand and categorize GSD challenges at these levels. This paper aims to re-investigate GSD challenges and categorize them at the country, company and team level. It will help academia to look into GSD issues with respect to operational levels and allow software companies to improve their processes and management at these levels.
Outlier Detection in Non-stationary Data Streams

Continuous outlier detection in data streams is an important topic in data mining and has applications in various domains such as fraud detection, weather analysis, and intrusion detection. The non-stationary characteristic of real-world data streams brings the challenge of updating the outlier detection model in a timely and accurate manner. In this paper, we propose a framework for outlier detection in non-stationary data streams (O-NSD) which detects changes in the underlying data distribution to trigger a model update. We propose an improved distance function between sliding windows which offers a monotonicity property; we develop two accurate change detection algorithms, one of which is parameter-free; and we further propose new evaluation measures that quantify the timeliness of the detected changes. Our extensive experiments with real-world and synthetic datasets show that our change detection algorithms outperform the state-of-the-art solution. In addition, we demonstrate our O-NSD framework with two popular unsupervised outlier classifiers. Empirical results show that our framework offers higher accuracy and requires a much lower running time, compared to retrain-based and incremental update approaches.
Controllability of <i>k</i>-Valued Fuzzy Cognitive Maps

Fuzzy cognitive maps (FCMs) as a kind of knowledge-based tools are widely applied to model complex dynamical systems using causal relations. Besides the representation and reasoning of systems behaviors, how to control the given systems into a desirable target by causal objects established by FCMs is also an open problem. Although, so far, there are some existing works about the applications of FCMs on the control-related problems, it is still a lack of the theoretical analysis in this domain. In this paper, the controllability of k-valued FCMs is studied. To improve the universality of models, a temporal extension of generalized FCMs is implemented. By means of semitensor product, the algebraic representation of k-valued FCMs with controls is established and a generalized formula of control-depending network transition matrices is achieved. A necessary and sufficient condition is proved to determine the control-depending fixed points of k-valued FCMs with temporalization. By utilizing three kinds of controls, the controllability of the discrete FCMs is discussed, respectively. The reachability condition of a specific target state from a given initial state at time s is studied, and the reachable set along with the corresponding reachable probability are also provided by analytic formula. Results provide a way to make FCMs evolving into the designed states by controls, which can further conduct the behaviors of the modeled systems in reality. Examples are shown to demonstrate the effectiveness and feasibility of the proposed scheme.
Anti-Withholding Reward System to Secure Blockchain Mining Pools

In decentralized blockchain systems such as Bitcoin, the miners are rewarded for generating new blocks and processing the transactions. In order to reduce the reward variance, the miners join mining pools where the miners aggregate their computational resources and divide the reward according to their contributions, measured by shares. The miner-based attacks such as Block Withholding (BWH) and Fork After Withholding (FAW) yield unfair reward advantages to the attackers at the expense of the other honest miners by having the attackers withhold blocks while still submitting the shares and pretending to contribute to the victim pool. This paper introduces Anti-Withholding Reward System (AWRS) to prevent FAW and BWH attacks. Implemented at the pool manager (reducing the adoption overhead and supporting backward-compatibility), AWRS deprives the incentive for FAW and BWH attacks by providing greater rewards to the block submissions. AWRS completely disincentivizes the FAW attack and reduces the optimal attacker's behavior to honest mining, making the withholding-based threats irrelevant for rational miners.
The Information System for Critical Use Access Process Dependability Modeling

Objective reasons for the introduction of multi-server architecture for the creation of information systems for critical use (ISCU) have led to complications in ensuring their confidentiality, as a component of information security, and in assessment of their reliability. The paper proposes to comprehensively consider these characteristics of the ISCU within the framework of the integral qualitative characteristic of dependability. In order to assess the reliability of the procedure for access to ISCU, authors proposed a Markov model of the dependence of the functional capacity of ISCU on the influence of negative factors. This model describes a set of ISCU states and allows to get an analytical expressions to calculate the probabilities for the presence of ISCU in the corresponding states at selected moments of time depending on the probability of occurrence of negative factors and configurations of the access separation subsystem that controls the authorization process. For practical application of the model the appropriate methodology is developed. This methodology allowed, in particular, to confirm the adequacy of the model using standard methods of the planning experiment theory, including Fisher's criterion.
Biomedical event trigger detection with convolutional highway neural network and extreme learning machine

Detecting biomedical events in text plays a critical role in building natural language processing applications, such as in medical search, disease prevention, and pharmacovigilance. Since an event trigger can signify the occurrence of the event, the detection of biomedical event triggers is a critical step in biomedical event extraction. Current methods usually extract rich features and then feed these features to a classifier. To enhance both automatic feature selection and classification, this paper presented an end-to-end convolutional highway neural network and extreme learning machine (CHNN–ELM) framework to detect biomedical event triggers. This structure has two stages. In the first stage, CHNN is used to efficiently select higher level semantic features based on four different dimensions: embedding, convolutional layer, pooling layer, and highway layer. In the second stage, the proposed model leverages ELM, which has great scalability and generalization performance, to identify various types of biomedical event triggers. Extensive experiments are conducted on the Multi-Level Event Extraction (MLEE) dataset. To the best of our knowledge, this paper is the first to introduce ELM into this task. The results demonstrated that with better feature selection and classification, our approach outperforms several current state-of-the-art methods.
On-Device Deep Learning Inference for Efficient Activity Data Collection

Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and "quality" of annotations; therefore, it is inevitable to rely on users and to keep them motivated to provide activity labels. While mobile and embedded devices are increasingly using deep learning models to infer user context, we propose to exploit on-device deep learning inference using a long short-term memory (LSTM)-based method to alleviate the labeling effort and ground truth data collection in activity recognition systems using smartphone sensors. The novel idea behind this is that estimated activities are used as feedback for motivating users to collect accurate activity labels. To enable us to perform evaluations, we conduct the experiments with two conditional methods. We compare the proposed method showing estimated activities using on-device deep learning inference with the traditional method showing sentences without estimated activities through smartphone notifications. By evaluating with the dataset gathered, the results show our proposed method has improvements in both data quality (i.e., the performance of a classification model) and data quantity (i.e., the number of data collected) that reflect our method could improve activity data collection, which can enhance human activity recognition systems. We discuss the results, limitations, challenges, and implications for on-device deep learning inference that support activity data collection. Also, we publish the preliminary dataset collected to the research community for activity recognition.
Two-Stream Action Recognition in Ice Hockey using Player Pose Sequences and Optical Flows

Current action recognition algorithms in ice hockey do not fully exploit the temporal cues available in video. To solve this challenge, we introduce a two-stream network utilizing player pose sequences and optical flow features for recognizing hockey actions. Player pose sequences are compact representations consisting of frame by frame human and stick joint locations and angles between joints. The optical flow features are obtained by a state-of-the-art optical flow algorithm. The player pose sequences are processed by a two-layered Long short-term memory (LSTM) network. The LSTM output is fused with optical flow features processed by a convolutional neural network (CNN). Experimental results demonstrate the efficacy of the method by achieving 90.48% test accuracy on the HARPET (Hockey Action Recognition Pose Estimation, Temporal) dataset thus surpassing current benchmark by 5%. The network performs better than the current benchmark in segregating similar classes like passing and shooting. It achieves a 90% reduction in parameters and 80% reduction in floating point operations per second (FLOPs) than the benchmark on the HARPET dataset, thus furthering the effectiveness of the network.
A Power Efficient Neural Network Implementation on Heterogeneous FPGA and GPU Devices

Deep neural networks (DNNs) have seen tremendous industrial successes in various applications, including image recognition, machine translation, audio processing, etc. However, they require massive amounts of computations and take a lot of time to process. This quickly becomes a problem in mobile and handheld devices where real-time multimedia applications such as face detection, disaster management, and CCTV require lightweight, fast, and effective computing solutions. The objective of this project is to utilize specialized devices such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) in a heterogeneous computing environment to accelerate the deep learning computations with the constraints of power efficiency. We investigate an efficient DNN implementation and make use of FPGA for fully-connected layer and GPU for floating-point operations. This requires the deep neural network architecture to be implemented in a model parallelism system where the DNN model is broken down and processed in a distributed fashion. The proposed heterogeneous framework idea is implemented using an Nvidia TX2 GPU and a Xilinx Artix-7 FPGA. Experimental results indicate that the proposed framework can achieve faster computation and much lower power consumption.
Many Could be Better Than All: A Novel Instance-Oriented Algorithm for Multi-modal Multi-label Problem

With the emergence of diverse data collection techniques, objects in real applications can be represented as multi-modal features. What's more, objects may have multiple semantic meanings. Multi-modal and Multi-label [1] (MMML) problem becomes a universal phenomenon. The quality of data collected from different channels are inconsistent and some of them may not benefit for prediction. In real life, not all the modalities are needed for prediction. As a result, we propose a novel instance-oriented Multi-modal Classifier Chains (MCC) algorithm for MMML problem, which can make convince prediction with partial modalities. MCC extracts different modalities for different instances in the testing phase. Extensive experiments are performed on one real-world herbs dataset and two public datasets to validate our proposed algorithm, which reveals that it may be better to extract many instead of all of the modalities at hand.
Accountable identity-based encryption with distributed private key generators

Distributed private key generators (PKGs) in identity-based encryption (IBE) is a viable approach to mitigate the inherent key escrow problem, where the user's private key is generated by multiple PKGs, and hence, there is no single PKG can impersonate the user. Nevertheless, these PKGs can still collude to generate a user's private key and auction it without the risk of being caught. In the traditional IBE setting, accountable IBE can identify the creator of a pirated private key between the user and the PKG. Unfortunately, the similar problem in IBE with distributed PKGs remains an open research problem. To fill this gap, we concentrate on adding accountability to IBE with distributed PKGs. Specifically, we propose the formal definition of A-IBE with distributed PKGs (A-dIBE) and the corresponding security models. Subsequently, we present a concrete construction with the corresponding security proof. This cryptographic primitive enjoys the advantages of both the IBE with distributed PKGs and A-IBE. Specifically, it distributes the power to multiple PKGs, while preserving the traceability that could give a convincing judgment to identify the suspect between the user and the PKGs. Furthermore, our construction could be easily extended to achieve IND-ID-CCA security and the revocation of the PKGs is efficient.
Fast Person Search Pipeline

In this work, we study the person search problem, which incorporates both person detection and person re-identification. The high time consumption of person search system obstructs the development of this field in practice. For the purpose of real-time person search, we analyze the bottleneck of the person search task and therefore develop a more efficient method called the Fast Person Search Pipeline (FPSP), consisting of a multi-scale person detector as well as a part-based person re-identification descriptor. Extensive experiments show that our proposed method FPSP can accomplish the person search task approximately FIVE times faster than the state-of-the-art methods while achieving a competitive accuracy.
Automatic Badminton Action Recognition Using CNN with Adaptive Feature Extraction on Sensor Data

With the fast development of sensor technology, a sensor chip can be easily inserted into the badminton racket so that the data of the activity of the badminton player can be recorded continuously. It has become a very challenging problem whether we can use these sensor-based data to effectively recognize the badminton actions. Although there have been some researches on the general human action recognition such as sitting, walking and running, there is few result or public dataset concerning badminton action recognition. In order to investigate the problem of badminton action recognition, we implement a specialized sensor chip being inserted into the badminton racket to collect the data of ten major badminton actions. On such a dataset, a deep convolutional neural network (CNN) can get a much better result of badminton action recognition than the conventional methods, but its recognition accuracy is not good enough for practical applications. The key difficulty is that the attributes recorded as time series in the sensor data have different measures and magnitudes so that the conventional feature normalization cannot work effectively in this complicated situation. In order to overcome this difficulty, we propose a specific block of adaptive feature extraction to enhance the performance of CNN for badminton action recognition. It is demonstrated by the experimental results on the sensor dataset that our proposed CNN with the adaptive feature extraction block can get 98.65% action recognition accuracy and outperforms the competitive methods remarkably.
Predicting the Suitability of IT Students' Skills for the Recruitment in Saudi Labor Market

In a technical world, knowledge of information technology (IT) skills is an important aspect, especially in Saudi Arabia, with the development in the technical labor market. This paper proposed a solution to examining the demand for recruitment in the Saudi market, using data extracted from online job portals. By utilizes machine learning algorithms to develop a web-based application that helps in predicting the suitability of IT students' skills for the recruitment in Saudi labor market. In addition, this solution offers general recommendations of the highest needed IT skills in Saudi Arabia in order to overcome the unemployment problem.
Comparison of NoSQL Datastores for Large Scale Data Stream Log Analytics

With the advent of cyber-physical systems, industrial internet of things (IIoT) and industrial analytics numerous application scenarios have emerged where business and mission-critical decisions depend upon large scale analysis of data in form of sensor streams. However, large volumes of sensor stream data generated at high frequency pose substantial challenges for existing scalable data analysis techniques requiring the use of high-performance distributed datastores. This work covers in-depth performance comparison of three principal categories of distributed state-of the-art NoSQL datastores by evaluating their applicability and efficiency for large-scale analysis of sensor logs from real-world hydraulic power systems. One central datastore is selected from each of the three principal categories of NoSQL datastores: MongoDB from the document store, Cassandra from the column store and Redis from the distributed main memory key-value store to be included in the performance evaluation. Understanding the differences and behavior of this type of systems are crucial for optimizing application performance. Key insights from this work can serve as a basis for an improved understanding of the applicability of NoSQL datastores in systems for large scale data stream analysis. This will be important for supporting data analytics in IIoT applications as found in monitoring and control of Power plants, Smart Cities, Transportation systems, Environmental and Health monitoring, etc.