Reviewerly/exHarmony huggingface dataset:
A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever.One of the main techniques that would be beneficial in this regard is called the Clustering method.Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible.One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm.The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters.Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time.Besides, the selection of appropriate initial seeds can reduce the cluster's inconsistency.In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm.For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds.Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm.The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively.Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.
The Use of Decision Trees for Analysis of the Epilepsy

Due to the rapid development of artificial intelligence (AI) technologies, the medical system knows a permanent improvement in the doctor-patient relationship that has undergone a long and unproductive transition throughout the ages. Nowadays, the smart systems focused on how to obtain an accurate and detailed medication history for each patient. The purpose of this paper was to develop a method that can produce a disease profile based on the decision trees algorithm, taking into account medical reports made along years for different patients from all over the world. It is well known that in the medical decision making there are many situations where decision must be made rapidly, rigorously and efficiently. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in biomedicine domain.
Bounding the separable rank via polynomial optimization

We investigate questions related to the set SEPd consisting of the linear maps ρ acting on Cd⊗Cd that can be written as a convex combination of rank one matrices of the form xx⁎⊗yy⁎. Such maps are known in quantum information theory as the separable bipartite states, while nonseparable states are called entangled. In particular we introduce bounds for the separable rank ranksep(ρ), defined as the smallest number of rank one states xx⁎⊗yy⁎ entering the decomposition of a separable state ρ. Our approach relies on the moment method and yields a hierarchy of semidefinite-based lower bounds, that converges to a parameter τsep(ρ), a natural convexification of the combinatorial parameter ranksep(ρ). A distinguishing feature is exploiting the positivity constraint ρ−xx⁎⊗yy⁎⪰0 to impose positivity of a polynomial matrix localizing map, the dual notion of the notion of sum-of-squares polynomial matrices. Our approach extends naturally to the multipartite setting and to the real separable rank, and it permits strengthening some known bounds for the completely positive rank. In addition, we indicate how the moment approach also applies to define hierarchies of semidefinite relaxations for the set SEPd and permits to give new proofs, using only tools from moment theory, for convergence results on the DPS hierarchy from Doherty et al. (2002) [16].
Community-aware Social Recommendation: A Unified SCSVD Framework

Recommender system provides personalized suggestions based on users' interests and social connections. However, most existing social recommendation models utilize social relationships in a direct manner, i.e., they only consider the user-user connections, neglecting the clustering nature of social networks. As social information recursively spreads in the social network, the community structure, which contains richer information in contrast to pure user-user relationships, would emerge. To dismiss these limitations, in this paper, we propose a unified recommendation framework named Simultaneous Community detection and Singular Value Decomposition (SCSVD), which utilizes the underlying community structure to regularize user latent preferences. We propose a well-designed iterative optimization algorithm to tackle social recommendation efficiently. In addition, we theoretically analyze the proposed algorithm in terms of convergence, time complexity, and also the unified process of community detection and user embedding learning. Extensive experiments are conducted on three benchmark real-world datasets of product reviews, demonstrating the effectiveness, robustness, and flexibility of SCSVD in both rating prediction and top-N recommendation tasks, compared to fifteen state-of-the-art approaches.
GazeEnViz4D: 4-D Gaze-in-Environment Visualization Pipeline

Eye-tracking data visualization and analysis is often performed in three dimensions (x,y,t). It involves overlaying the gaze point (x,y) on scene-camera images and creating a pipeline to process the gaze-overlaid spatio-temporal data. In this project, we present a pipeline (called GazeEnViz4D) to extract 3D data from raw 2D eye-tracking data, and we have developed a custom ENvironment VIsualiZer (EnViz4D§) to allow researchers to visualize the pipeline-processed data in four dimensions (x,y,z,t) for extensive, interactive analysis. GazeEnViz4D consists of creating a 3D point cloud of the environment, calculating the observer motion, locating the 2D gaze obtained from the eye-tracker in the 3D model, and visualizing the data over time using EnViz4D. EnViz4D allows a researcher to zoom in the environment at any instance, pause or play the 3D data, speed up or slow down, forward or reverse, essentially recreating the data collection episode in four dimensions from an arbitrary modifiable viewpoint.
Dueling Network Architecture for Multi-Agent Deep Deterministic Policy Gradient

Recently, reinforcement learning has made remarkable achievements in the fields of natural science, engineering, medicine and operational research. Reinforcement learning addresses sequence problems and considers long-term returns. This long-term view of reinforcement learning is critical to find the optimal solution of many problems. The existing multi-agent reinforcement learning methods usually update the value function of state action slowly, and the reward value of agents is low. This paper presents a Dueling Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method based on MADDPG, which modifies critic's network structure. The main work is to add two subnetworks behind the critic network of the traditional MADDPG method. This method allows the critic network to update its parameters faster and receive higher rewards. Finally, in order to verify the validity of the network structure, the improved framework is compared with the traditional MADDPG, DQN and DDPG methods in the simulation environment.
A Perfect Match: Deep Learning Towards Enhanced Data Trustworthiness in Crowd-Sensing Systems

The advent of IoT edge devices has enabled the collection of rich datasets, as part of Mobile Crowd Sensing (MCS), which has emerged as a key enabler for a wide gamut of safetycritical applications ranging from traffic control, environmental monitoring to assistive healthcare.Despite the clear advantages that such unprecedented quantity of data brings forth, it is also subject to inherent data trustworthiness challenges due to factors such as malevolent input and faulty sensors.Compounding this issue, there has been a plethora of proposed solutions, based on the use of traditional machine learning algorithms, towards assessing and sifting faulty data without any assumption on the trustworthiness of their source.However, there are still a number of open issues: how to cope with the presence of strong, colluding adversaries while at the same time efficiently managing this high influx of incoming user data.In this work, we meet these challenges by proposing the hybrid use of Deep Learning schemes (i.e., LSTMs) and conventional Machine Learning classifiers (i.e.One-Class Classifiers) for detecting and filtering out false data points.We provide a prototype implementation coupled with a detailed performance evaluation under various (attack) scenarios, employing both real and synthetic datasets.Our results showcase how the proposed solution outperforms various existing resilient aggregation and outlier detection schemes.
A new metaheuristic‐based method for solving the virtual machines migration problem in the green cloud computing

Summary Cloud computing (CC) provides dynamic hiring of server abilities as scalable virtualized services to end‐users. However, data center hosting wastes massive amounts of energy resulting in high operational costs and carbon footprints. Also, virtualization is one of CC's main features, and physical resources are delivered by virtual machine (VM). Therefore, in the present article, a new method is provided to improve the VM energy consumption and execution time in the VM migration problem using a hybrid optimization algorithm. Since this issue is one of the famous NP‐hard problems, a method is proposed in this article works based on genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The hybrid algorithm uses a GA to dominate PSO algorithms' constraints, such as weak convergence and stymie in global optima. The CloudSim simulator is employed to show the efficiency of the method compared to others. Using this method will keep the proficiency and power performance of the data centers at the same level. The results showed that energy consumption in the proposed method is better than the other three methods and has been improved by an average of 23.19%. Also, the results showed that execution time is better than the other three methods and has been improved by an average of 29.01%.
Classification of Overlapping Cells in Microscopic Cervical Images: A Transfer Learning Approach

One of the leading cause of mortality in women, is Cervical cancers. Timely and accurate diagnosis at earlier stage can reduce the mortality rate significantly. Pap smear is the popular screening test used for diagnosis of cervical cancer. Now a days, deep learning and convolution neural network based techniques are more popular due to excellent performance over traditional machine learning techniques. The aim of this study was to investigate the use of Deep learning based transfer learning for classification of overlapping cells in cervical smear. Transfer learning technique with Alexnet framework is implemented to improve the classification accuracy. Multicell cervical cytology Cervix 93 dataset is used which is publicly available. Hyper parameters of the network are fine tuned to make the model suitable for cervical cancer classification problem. To evaluate the performance of the network statistical parameters like Accuracy, Sensitivity, Specificity, Precision, NPV, PPV, F-score and Matthews correlation coefficient (MCC) etc. are calculated. The proposed network reported outstanding performance with 99.86% accuracy and specificity. The overall performance of the proposed framework is promising and can assist the clinicians in diagnosis process.
Improving the non‐compensatory trace‐clustering decision process

Abstract In flexible environments (such as healthcare or customer service), the observed behavior is expected to considerably vary, namely there is no dominant flow path. Such a high variability obstructs the process discovery task since it regularly leads to “spaghetti” process models. Trace clustering is about grouping behaviors, and discovering a distinct model per group, thus delivering more comprehensible results. In previous works, we have proposed a multiple‐criteria non‐compensatory approach to create a similarity metric and finally perform trace clustering. The main problem that we tried to respond to is how to summarize a process event log, when a lot of variability exists, thus facilitating knowledge discovery. The underpinnings of the non‐compensatory approach are first the fact that a sufficient number of criteria must be concordant with the similarity (concordance setting) and second that there should not exist any criterion raising a veto logic, that is, among the criteria that are not concordant, none of them must be conflicting with the similarity (discordance setting). This work challenges improved support for the decision‐maker (DM) and it extends the previous approach by (i) proposing an improved clustering technique based on spectral clustering; (ii) guiding the clustering process by allowing reinforced or counterveto effects and pairwise constraints; (iii) handling outliers through a trimming approach as an integer linear program. All improvements aiming at making elements of the trace‐clustering process more accessible to the DMs and enhancing the understandability of the analysis.
Predictive PER: Balancing Priority and Diversity Towards Stable Deep Reinforcement Learning

Prioritized experience replay (PER) samples important transitions, rather than uniformly, to improve data efficiency of a deep reinforcement learning agent. We claim that such prioritization must be balanced with sample diversity to make the deep Q-network (DQN) stabilized and prevent severe forgetting. Our proposed improvement over PER, called Predictive PER (PPER), takes three countermeasures (TDInit, TDClip, TDPred) for (i) eliminating priority outliers and explosions; (ii) improving the diversity of samples and their distributions, weighted by priorities. Both contribute to stabilizing the learning process, thus forgetting less. The most notable among the three is TDPred, the second DNN introduced for generalizing in-distribution priorities. Ablation and experimental studies with Atari games show that each countermeasure, in its own way, and PPER successfully contribute to enhancing stability hence performance, over PER.
Towards Collaborative and Intelligent Learning Environments Based on Eye Tracking Data and Learning Analytics: A Survey

The current pandemic has significantly impacted educational practices, modifying many aspects of how and when we learn. In particular, remote learning and the use of digital platforms have greatly increased in importance. Online teaching and e-learning provide many benefits for information retention and schedule flexibility in our on-demand world while breaking down barriers caused by geographic location, physical facilities, transportation issues, or physical impediments. However, educators and researchers have noticed that students face a learning and performance decline as a result of this sudden shift to online teaching and e-learning from classrooms around the world. In this paper, we focus on reviewing eye-tracking techniques and systems, data collection and management methods, datasets, and multi-modal learning data analytics for promoting pervasive and proactive learning in educational environments. We then describe and discuss the crucial challenges and open issues of current learning environments and data learning methods. The review and discussion show the potential of transforming traditional ways of teaching and learning in the classroom, and the feasibility of adaptively driving learning processes using eye-tracking, data science, multimodal learning analytics, and artificial intelligence. These findings call for further attention and research on collaborative and intelligent learning systems, plug-and-play devices and software modules, data science, and learning analytics methods for promoting the evolution of face-to-face learning and e-learning environments and enhancing student collaboration, engagement, and success.
Human Pose Estimation Based on Domain of Interest and Part Affinity Field

This paper proposes an effective method to detect the 2D pose estimation of multiple people in an image. Learn human joint points through convolutional network, obtain the attention domain of joint points, classify the attention domain of the joint points, and obtain part affinity field of the information at the same time; combine the attention domain and part of the affinity field information of the joint points, using a greedy algorithm Perform clustering of joint points to finally obtain the human post. Simulation experimental data show that the method in this paper has achieved satisfactory results in performance and accuracy.
When Delta Sync Meets Message-Locked Encryption: a Feature-based Delta Sync Scheme for Encrypted Cloud Storage

As increasingly prevalent, more and more data are stored in the cloud storage, which brings us two major challenges. First, the modified files in the cloud should be quickly synchronized (sync) to ensure data consistency, e.g., delta sync achieves efficient cloud sync by synchronizing only the updated part of the file. Second, the huge data in the cloud needs to be deduplicated and encrypted, e.g., message-locked encryption (MLE) implements data deduplication by encrypting the content between different users. However, when both are combined, few updates in the content can cause large sync traffic amplification for both keys and ciphertext in the MLE-based cloud storage, which significantly degrading the cloud sync efficiency. In this paper, we propose an feature-based encryption sync scheme FeatureSync to improve the performance of synchronizing multiple encrypted files by merging several files before synchronizing. The performance evaluations on a lightweight prototype implementation of FeatureSync show that FeatureSync reduces the cloud sync time by 72.6% and the cloud sync traffic by 78.5% on average, compared with the state-of-the-art sync schemes.
Federated Learning Model with Augmentation and Samples Exchange Mechanism

The use of intelligent solutions often comes down to the use of already trained classifiers, which is caused by one of their biggest drawbacks. It is the accuracy or effectiveness of artificial intelligence methods, which are algorithms called data-hungry. It means that it depends on the number of samples in the database, and the quality of the classifier could be better if their number is high and the samples are different. In this paper, we propose a solution based on the idea of federated learning in an application for intelligent systems. The proposed solution consists not only in the division of the database among workers but also in the quality of the samples and their possible exchange. Exchanging samples for a particular worker means labeling difficult to classify samples. These samples are used to expand the sets using the generative adversarial network. The mathematical model of a proposal is described, then the experimental results are shown and discussed with the comparison to the classic approach.
Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering

Discovering the logical sequence of events is one of the cornerstones in Natural Language Understanding. One approach to learn the sequence of events is to study the order of sentences in a coherent text. Sentence ordering can be applied in various tasks such as retrieval-based Question Answering, document summarization, storytelling, text generation, and dialogue systems. Furthermore, we can learn to model text coherence by learning how to order a set of shuffled sentences. Previous research has relied on RNN, LSTM, and BiLSTM architecture for learning text language models. However, these networks have performed poorly due to the lack of attention mechanisms. We propose an algorithm for sentence ordering in a corpus of short stories. Our proposed method uses a language model based on Universal Transformers (UT) that captures sentences’ dependencies by employing an attention mechanism. Our method improves the previous state-of-the-art in terms of Perfect Match Ratio (PMR) score in the ROCStories dataset, a corpus of nearly 100K short human-made stories. The proposed model includes three components: Sentence Encoder, Language Model, and Sentence Arrangement with Brute Force Search. The first component generates sentence embeddings using SBERT-WK pre-trained model fine-tuned on the ROCStories data. Then a Universal Transformer network generates a sentence-level language model. For decoding, the network generates a candidate sentence as the following sentence of the current sentence. We use cosine similarity as a scoring function to assign scores to the candidate embedding and the embeddings of other sentences in the shuffled set. Then a Brute Force Search is employed to maximize the sum of similarities between pairs of consecutive sentences.
MPFA: An Efficient Multiple Faults-Based Persistent Fault Analysis Method for Low-Cost FIA

In recent studies, a fault analysis method, called persistent fault analysis (PFA), is proposed for cracking block ciphers. Unlike widely used differential fault analysis methods, PFA does not require correct cihpertexts and precise time control of fault injection. The existing PFA methods mainly assume single fault, i.e., the fault injection process induces a single fault in the target cipher devices. However, the existing low-cost fault injection attack (FIA) techniques, such as clock glitch injection and electromagnetic pulse (EMP) injection usually induce multiple faults per injection. Given multiple faults, the existing PFA methods are either not applicable or faced with high computational complexity in practice. In this article, a new PFA method, called MPFA, is proposed for multiple persistent faults, which reduces both the computational complexity and the required ciphertexts. MPFA can be applied to the ciphertexts-only attack scenario, where all fault positions, fault values, and fault quantity are unknown. The experiments show that compared to the existing PFA methods, the MPFA method reduces the required ciphertexts for cracking AES by at least 57.5% and reduces the computational complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$NF^{16}$ </tex-math></inline-formula> times for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$NF$ </tex-math></inline-formula> faults. The proposed MPFA is also evaluated on the block ciphers LED and PRINCE. Moreover, a real EMP FIA is carried out and the key of AES-128 is successfully cracked by the MPFA method, demonstrating its validity and efficiency.
Citation Recommendation for Research Papers via Knowledge Graphs

Citation recommendation for research papers is a valuable task that can help researchers improve the quality of their work by suggesting relevant related work. Current approaches for this task rely primarily on the text of the papers and the citation network. In this paper, we propose to exploit an additional source of information, namely research knowledge graphs (KGs) that interlink research papers based on mentioned scientific concepts. Our experimental results demonstrate that the combination of information from research KGs with existing state-of-the-art approaches is beneficial. Experimental results are presented for the STM-KG (STM: Science, Technology, Medicine), which is an automatically populated knowledge graph based on the scientific concepts extracted from papers of ten domains. The proposed approach outperforms the state of the art with a mean average precision of 20.6% (+0.8) for the top-50 retrieved results.