Reviewerly/exHarmony huggingface dataset:
Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques

In this modern world, we are accustomed to a constant stream of data. Major social media sites like Twitter, Facebook, or Quora face a huge dilemma as a lot of these sites fall victim to spam accounts. These accounts are made to trap unsuspecting genuine users by making them click on malicious links or keep posting redundant posts by using bots. This can greatly impact the experiences that users have on these sites. A lot of time and research has gone into effective ways to detect these forms of spam. Performing sentiment analysis on these posts can help us in solving this problem effectively. The main purpose of this proposed work is to develop a system that can determine whether a tweet is "spam" or "ham" and evaluate the emotion of the tweet. The extracted features after preprocessing the tweets are classified using various classifiers, namely, decision tree, logistic regression, multinomial naïve Bayes, support vector machine, random forest, and Bernoulli naïve Bayes for spam detection. The stochastic gradient descent, support vector machine, logistic regression, random forest, naïve Bayes, and deep learning methods, namely, simple recurrent neural network (RNN) model, long short-term memory (LSTM) model, bidirectional long short-term memory (BiLSTM) model, and 1D convolutional neural network (CNN) model are used for sentiment analysis. The performance of each classifier is analyzed. The classification results showed that the features extracted from the tweets can be satisfactorily used to identify if a certain tweet is spam or not and create a learning model that will associate tweets with a particular sentiment.
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning

Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance. Most existing approaches only tackle the heterogeneity challenge by restricting the local model update in client, ignoring the performance drop caused by direct global model aggregation. Instead, we propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG), which relieves the issue of direct model aggregation. Concretely, FedFTG explores the input space of local models through a generator, and uses it to transfer the knowledge from local models to the global model. Besides, we propose a hard sample mining scheme to achieve effective knowledge distillation throughout the training. In addition, we develop customized label sampling and class-level ensemble to derive maximum utilization of knowledge, which implicitly mitigates the distribution discrepancy across clients. Extensive experiments show that our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
A state-of-the-art survey on solving non-IID data in Federated Learning

Federated Learning (FL) proposed in recent years has received significant attention from researchers in that it can enable multiple clients to cooperatively train global models without revealing private data. This training mode protects users’ privacy without violating the supervision, and aggregates scattered data to exert great potential. However, the data samples on each participating device of FL are usually not independent and identically distributed (IID), which leads to serious statistical heterogeneity challenges for FL. In this article, we analyze and establish the definition of non-IID data problems, and put forward a series of challenges that this problem may bring to FL. We classify existing methods to solve this problem from the researcher’s entry point and subsequent sub-methods, aiming to provide a comprehensive study for solving the problem of non-IID data in FL. Our research shows that non-IID data will not only reduce the performance of the FL model, but also damage the active participation of users in the FL process. Compared with methods based on data-side sharing, enhancement, and selection, it is more common for researchers to improve federated learning algorithms from models, algorithms, and frameworks to solve non-IID problems. To the best of our knowledge, although many efforts have been made to address the problem of non-IID data, there are currently few authoritative systematic reviews in this field and are not up-to-date. In this article, we will fill the gaps in FL and provide researchers with the state-of-the-art research results to solve non-IID problems in FL and promote the further implementation of FL.
Genetic Algorithms

Genetic algorithm is one of the first evolutionary algorithms, which paces the way for contemporary evolutionary computation. This chapter introduces the basic principle of the genetic algorithm and its fundamental genetic operators: crossover, mutation, and selection.
S-shaped versus V-shaped transfer functions for binary Manta ray foraging optimization in feature selection problem

Feature selection (FS) is considered as one of the core concepts in the areas of machine learning and data mining which immensely impacts the performance of classification model. Through FS, irrelevant or partially relevant features can be eliminated which in turn helps in enhancing the performance of the model. Over the years, researchers have applied different meta-heuristic optimization techniques for the purpose of FS as these overcome the limitations of traditional optimization approaches. Going by the trend, we introduce a new FS approach based on a recently proposed meta-heuristic algorithm called Manta ray foraging optimization (MRFO) which is developed following the food foraging nature of the Manta rays, one of the largest known marine creatures. As MRFO is apposite for continuous search space problems, we have adapted a binary version of MRFO to fit it into the problem of FS by applying eight different transfer functions belonging to two different families: S-shaped and V-shaped. We have evaluated the eight binary versions of MRFO on 18 standard UCI datasets. Of these, the best one is considered for comparison with 16 recently proposed meta-heuristic FS approaches. The results show that MRFO outperforms the state-of-the-art methods in terms of both classification accuracy and number of features selected. The source code of this work is available in https://github.com/Rangerix/MetaheuristicOptimization .
Generative multi-view and multi-feature learning for classification

Multi-view based classification has attracted much attention in recent years. In general, an object can be represented with various views or modalities, and the exploitation of correlation across different views would contribute to improving the classification performance. However, each view can also be described with multiple features and this types of data is called multi-view and multi-feature data. Different from many existing multi-view methods which only model multiple views but ignore intrinsic information among the various features in each view, a generative bayesian model is proposed in this paper to not only jointly take the features and views into account, but also learn a discriminant representation across distinctive categories. A latent variable corresponding to each feature in each view is assumed and the raw feature is a projection of the latent variable from a more discriminant space. Particularly, the extracted variables in each view belonging to the same class are encouraged to follow the same gaussian distribution and those belonging to different classes are conducted to follow different distributions, greatly exploiting the label information. To optimize the presented approach, the proposed method is transformed into a class-conditional model and an effective algorithm is designed to alternatively estimate the parameters and variables. The experimental results on the extensive synthetic and four real-world datasets illustrate the effectiveness and superiority of our method compared with the state-of-the-art.
Traffic Light Recognition With High Dynamic Range Imaging and Deep Learning

Traffic light recognition (TLR) detects the traffic light from an image and then estimates the state of the light signal. TLR is important for autonomous vehicles because running against a red light could cause a deadly car accident. For a practical TLR system, computation time, varying illumination conditions, and false positives are three key challenges. In this paper, a novel real-time method is proposed to recognize a traffic light with high dynamic imaging and deep learning. In our approach, traffic light candidates are robustly detected from low exposure/dark frames and accurately classified using a deep neural network in consecutive high exposure/bright frames. This dual-channel mechanism can make full use of undistorted color and shape information in dark frames as well as the rich context in bright frames. In the dark channel, a non-parametric multi-color saliency model is proposed to simultaneously extract lights with different colors. A multiclass classifier with convolutional neural network (CNN) model is then adopted to reduce the number of false positives in the bright channel. The performance is further boosted by incorporating temporal trajectory tracking. In order to speed up the algorithm, a prior detection mask is generated to limit the potential search regions. Intensive experiments on a large dual-channel dataset show that the proposed approach outperforms the state-of-the-art real-time deep learning object detector, which could cause more false positives because it uses bright images only. The algorithm has been integrated into our autonomous vehicle and can work robustly on real roads.
KB4Rec: A Data Set for Linking Knowledge Bases with Recommender Systems

To develop a knowledge-aware recommender system, a key issue is how to obtain rich and structured knowledge base (KB) information for recommender system (RS) items. Existing data sets or methods either use side information from original RSs (containing very few kinds of useful information) or utilize a private KB. In this paper, we present KB4Rec v1.0, a data set linking KB information for RSs. It has linked three widely used RS data sets with two popular KBs, namely Freebase and YAGO. Based on our linked data set, we first preform qualitative analysis experiments, and then we discuss the effect of two important factors (i.e., popularity and recency) on whether a RS item can be linked to a KB entity. Finally, we compare several knowledge-aware recommendation algorithms on our linked data set.
Cavity Attenuators for Superconducting Qubits

Dephasing induced by residual thermal photons in the readout resonator is a leading factor limiting the coherence times of qubits in the circuit QED architecture. This residual thermal population, of the order of $10^{-1}$--$10^{-3}$, is suspected to arise from noise impinging on the resonator from its input and output ports. To address this problem, we designed and tested a new type of band-pass microwave attenuator that consists of a dissipative cavity well thermalized to the mixing chamber stage of a dilution refrigerator. By adding such a cavity attenuator inline with a 3D superconducting cavity housing a transmon qubit, we have reproducibly measured increased qubit coherence times. At base temperature, through Hahn echo experiment, we measured $T_{2\mathrm{e}}/2T_1 = 1.0\,({+0.0}/{-0.1})$ for two qubits over multiple cooldowns. Through noise-induced dephasing measurement, we obtained an upper bound $2\times 10^{-4}$ on the residual photon population in the fundamental mode of the readout cavity, which to our knowledge is the lowest value reported so far. These results validate an effective method for protecting qubits against photon noise, which can be developed into a standard technology for quantum circuit experiments.
Fast neighbor search by using revised k-d tree

We present two new neighbor query algorithms, including range query (RNN) and nearest neighbor (NN) query, based on revised k-d tree by using two techniques. The first technique is proposed for decreasing unnecessary distance computations by checking whether the cell of a node is inside or outside the specified neighborhood of query point, and the other is used to reduce redundant visiting nodes by saving the indices of descendant points. We also implement the proposed algorithms in Matlab and C. The Matlab version is to improve original RNN and NN which are based on k-d tree, C version is to improve k-Nearest neighbor query (kNN) which is based on buffer k-d tree. Theoretical and experimental analysis have shown that the proposed algorithms significantly improve the original RNN, NN and kNN in low dimension, respectively. The tradeoff is that the additional space cost of the revised k-d tree is approximately O(αnlog (n)).
Automatically identifying code features for software defect prediction: Using AST N-grams

Context: Identifying defects in code early is important. A wide range of static code metrics have been evaluated as potential defect indicators. Most of these metrics offer only high level insights and focus on particular pre-selected features of the code. None of the currently used metrics clearly performs best in defect prediction. Objective: We use Abstract Syntax Tree (AST) n-grams to identify features of defective Java code that improve defect prediction performance. Method: Our approach is bottom-up and does not rely on pre-selecting any specific features of code. We use non-parametric testing to determine relationships between AST n-grams and faults in both open source and commercial systems. We build defect prediction models using three machine learning techniques. Results: We show that AST n-grams are very significantly related to faults in some systems, with very large effect sizes. The occurrence of some frequently occurring AST n-grams in a method can mean that the method is up to three times more likely to contain a fault. AST n-grams can have a large effect on the performance of defect prediction models. Conclusions: We suggest that AST n-grams offer developers a promising approach to identifying potentially defective code.
REDPC: A residual error-based density peak clustering algorithm

The density peak clustering (DPC) algorithm was designed to identify arbitrary-shaped clusters by finding density peaks in the underlying dataset. Due to its aptitudes of relatively low computational complexity and a small number of control parameters in use, DPC soon became widely adopted. However, because DPC takes the entire data space into consideration during the computation of local density, which is then used to generate a decision graph for the identification of cluster centroids, DPC may face difficulty in differentiating overlapping clusters and in dealing with low-density data points. In this paper, we propose a residual error-based density peak clustering algorithm named REDPC to better handle datasets comprising various data distribution patterns. Specifically, REDPC adopts the residual error computation to measure the local density within a neighbourhood region. As such, comparing to DPC, our REDPC algorithm provides a better decision graph for the identification of cluster centroids and better handles the low-density data points. Experimental results on both synthetic and real-world datasets show that REDPC performs better than DPC and other algorithms.
Emotion-Aware Multimedia Systems Security

The interactive robot is expected to support emotion analysis and utilize the deep learning and machine learning to provide users with continuous emotional care. However, it is a great challenge to securely acquire sufficient data for emotion analysis such that the privacy of emotional data is adequately protected. To address the security issue, this paper proposes a security policy based on identity authentication and access control to ensure the security certificate through an interactive robot or edge devices while the access control of private data stored in the edge cloud is adequately protected. Specifically, this paper adopts a polynomial-based access control policy and designs a secure and effective access control scheme. At the same time, this paper puts forward an identity authentication mechanism in view of edge cloud systems, which can reduce the computational overhead and authentication delay in a collaborative authentication of multiple edge clouds. The effectiveness of the proposed access control policy and identity authentication mechanism is verified by an actual testbed platform.
Quantum Circuit Design of a T-count Optimized Integer Multiplier

Quantum circuits of many qubits are extremely difficult to realize; thus, the number of qubits is an important metric in a quantum circuit design. Further, scalable and reliable quantum circuits are based on fault tolerant implementations of quantum gates such as Clifford+T gates. An efficient quantum circuit saves quantum hardware resources by reducing the number of T gates without substantially increasing the number of qubits. This work presents a T-count optimized quantum circuit for integer multiplication with only 4 · n + 1 qubits and no garbage outputs. The proposed quantum multiplier design reduces the T-count by using a novel quantum conditional adder circuit. Also, where one operand to the conditional adder is zero, the conditional adder is replaced with a Toffoli gate array to further save T gates. Average T-count savings of 46:12, 47:55, 62:71 and 26.30 percent are achieved compared to the recent works by Kotiyal et al., Babu, Lin et al., and Jayashree et al., respectively.
Liking, sharing, commenting and reacting on Facebook: User behaviors’ impact on sentiment intensity

The form of communication on Facebook is not only limited to posting and commenting, but also includes sharing, liking and reacting. This study looks into how a Facebook diabetes community uses like, comment, share and reaction in expressing themselves online and how these distinctions can be used to improve sentiment classification from text extracted from the said group. An intensity formula using those behaviors was proposed and experimentations conducted using Weka. The findings reveal a model encompassing user behaviors is able to determine sentiment more accurately compared to one without, with a 94.6 percentage of accuracy. Additional analyses reveal behaviors such as liking, commenting and sharing to contribute more to the sentiment classification compared to reacting. This further cement the need to include such behavioral aspects into sentiment polarity calculation, as it would help algorithms achieve better predictability when classifying sentiment.
Comprehensive Review of the Development of the Harmony Search Algorithm and its Applications

This paper presents a comprehensive overview of the development of the harmony search (HS) algorithm and its applications. HS is a well-known human-based meta-heuristic algorithm that mimics the process of creating a new harmony in music. This algorithm can be applied to different fields of research, owing to its ability to balance between exploitation (i.e., searching around the known best) and exploration (i.e., roaming the entire search space). Thus, numerous studies have been conducted to utilize HS in real-world optimization problems, and many variants and hybrid algorithms of HS have been developed to cope with different problems. In this paper, HS and its variants are reviewed from various aspects. First, we describe the HS algorithm and present how its parameters affect algorithm performance. Second, we describe HS classifications based on the well-known HS variants and hybrid algorithms, along with their applications. Finally, a discussion conducted on the strengths and weaknesses of the HS algorithm and the possibilities for its improvement. Focusing on related work from diverse fields (such as optimization, engineering, computer science, biology, and medicine), this paper can foster interests on the application of HS for multidisciplinary audiences.
Deep low-rank subspace ensemble for multi-view clustering

Multi-view clustering aims to incorporate complementary information from different data views for more effective clustering. However, it is difficult to obtain the true categories of data based on complex distribution and diversified latent attributes of multi-view data. In this paper, we propose a new multi-view clustering method that integrates deep matrix factorization, low-rank subspace learning, and multiple subspace ensemble in a unified framework, which we term as the Deep Low-Rank Subspace Ensemble (DLRSE) method. DLRSE first employs deep matrix factorization to capture diverse and hierarchical structures in data for robust multi-view multi-layer data representations. Then, low-rank subspace representations are learned from the extracted factors to further reveal their correlations, from which more explicit clustering structure can be obtained. We further develop a subspace ensemble learning method with structured sparsity regularization to aggregate different subspaces into a consensus subspace, which can incorporate the intrinsic clustering structure across multiple views better. Extensive experiments on several datasets demonstrate that the proposed method can effectively explore the diversified clustering structure inherent in data and exploit multi-view complementary information, and achieve considerable improvement in performance over existing methods.
An Efficient and Scalable Framework for Processing Remotely Sensed Big Data in Cloud Computing Environments

The large amount of data produced by satellites and airborne remote sensing instruments has posed important challenges to efficient and scalable processing of remotely sensed data in the context of various applications. In this paper, we propose a new big data framework for processing massive amounts of remote sensing images on cloud computing platforms. In addition to taking advantage of the parallel processing abilities of cloud computing to cope with large-scale remote sensing data, this framework incorporates task scheduling strategy to further exploit the parallelism during the distributed processing stage. Using a computation- and data-intensive pan-sharpening method as a study case, the proposed approach starts by profiling a remote sensing application and characterizing it into a directed acyclic graph (DAG). With the obtained DAG representing the application, we further develop an optimization framework that incorporates the distributed computing mechanism and task scheduling strategy to minimize the total execution time. By determining an optimized solution of task partitioning and task assignments, high utilization of cloud computing resources and accordingly a significant speedup can be achieved for remote sensing data processing. Experimental results demonstrate that the proposed framework achieves promising results in terms of execution time as compared with the traditional (serial) processing approach. Our results also show that the proposed approach is scalable with regard to the increasing scale of remote sensing data.