Reviewerly/exHarmony huggingface dataset:
Attention-guided RGBD saliency detection using appearance information

Most of the deep convolutional neural networks (CNNs) based RGBD saliency models either regard the RGB and depth cues as the same status or trust the depth information excessively. However, they ignore that the low-quality depth map is an interference factor and the multi-level deep features that originated from RGB images contain abundant appearance information. Therefore, we propose a novel RGBD saliency model, where the attention-guided bottom-up and top-down modules are powerfully combined by using multi-level deep RGB features, to utilize the deep RGB and depth features in a sufficient and appropriate way. Concretely, a two-stream structure based bottom-up module is first constructed to dig and fuse the RGB and depth information, yielding the fused deep feature. Besides, the module embeds the depth cue based attention maps to guide the indication of salient objects. Then, considering the abundant appearance information, a top-down module is deployed to perform coarse-to-fine saliency inference, where the fused deep feature is progressively integrated with appearance information. Similarly, the attention map is also inserted into this module for locating salient objects. Extensive experiments are performed on five public RGBD datasets and the corresponding experimental results firmly demonstrate the effectiveness and superiority of our model when compared with the state-of-the-art RGBD saliency models.
Topic Modeling for Short Texts via Word Embedding and Document Correlation

Topic modeling is a widely studied foundational and interesting problem in the text mining domains.Conventional topic models based on word co-occurrences infer the hidden semantic structure from a corpus of documents.However, due to the limited length of short text, data sparsity impedes the inference process of conventional topic models and causes unsatisfactory results on short texts.In fact, each short text usually contains a limited number of topics, and understanding semantic content of short text needs to the relevant background knowledge.Inspired by the observed information, we propose a regularized non-negative matrix factorization topic model for short texts, named TRNMF.The proposed model leverages pre-trained distributional vector representation of words to overcome the data sparsity problem of short texts.Meanwhile, the method employs the clustering mechanism under document-totopic distributions during the topic inference by using Gibbs Sampling Dirichlet Multinomial Mixture model.TRNMF integrates successfully both word co-occurrence regularization and sentence similarity regularization into topic modeling for short texts.Through extensive experiments on constructed real-world short text corpus, experimental results show that TRNMF can achieve better results than the state-of-the-art methods in term of topic coherence measure and text classification task.
Ensemble learning based predictive framework for virtual machine resource request prediction

The cloud service providers require a large number of computing resources to provide services on-demand that consume the electricity at large and leave high carbon footprints which must be minimized. A cloud system must optimally use its resources to achieve a low operational cost without degrading the quality of services. In this context, an ensemble learning based workload forecasting method is presented that uses extreme learning machines and their corresponding forecasts are weighted by a voting engine. A metaheuristic algorithm inspired by blackhole theory is used to select the optimal weights. The accuracy of the approach is tested on CPU and memory demand requests of Google cluster trace. The method is also compared with recent existing work in the literature on CPU utilization of Google cluster and PlanetLab traces. The results validate the superiority of the approach over existing methods with an improvement up to 99.20% in root mean squared error.
Determining The Appropiate Cluster Number Using Elbow Method for K-Means Algorithm

Several algorithms are applied in the literature of clustering such as Kmeans, Fuzzy C-Means, and Single Linkage, etc. K-means Algorithm is the most commonly used because of its simplicity.How ever, the best number of clusters in K-means possesses a weakness.This study was carried out in determining cluster numbers by using Elbow Method.Meanwhile, Silhouette Technique was applied for the testing methods in order to measure the quality of the clusters.The results obtained were in the form of two clusters.
Interoperability Challenges in the Cybersecurity Information Sharing Ecosystem

Threat intelligence helps businesses and organisations make the right decisions in their fight against cyber threats, and strategically design their digital defences for an optimised and up-to-date security situation. Combined with advanced security analysis, threat intelligence helps reduce the time between the detection of an attack and its containment. This is achieved by continuously providing information, accompanied by data, on existing and emerging cyber threats and vulnerabilities affecting corporate networks. This paper addresses challenges that organisations are bound to face when they decide to invest in effective and interoperable cybersecurity information sharing and categorises them in a layered model. Based on this, it provides an evaluation of existing sources that share cybersecurity information. The aim of this research is to help organisations improve their cyber threat information exchange capabilities, to enhance their security posture and be more prepared against emerging threats.
PoV: An Efficient Voting-Based Consensus Algorithm for Consortium Blockchains

The blockchain has a great vogue in recent years, and its core consensus algorithms also become the focus of research. At present, most of the research on consensus mechanisms are oriented to the public blockchain and based on existing consensus mechanisms or sophisticated distributed algorithms. Various application scenarios have been developed based on the consortium blockchain, while few researchers pay attention to customize consistency algorithms. Moreover, there is a trade-off between security and performance in designing consensus mechanisms. We propose a novel consensus algorithm called Proof of Vote (PoV), where the distributed nodes controlled by consortium members could reach consensus and come to a decentralized arbitration by voting. PoV separates the voting rights and bookkeeping rights with the essential idea of establishing different security identities for network nodes. Contrary to the third-party intermediary or uncontrollable public awareness, the production and verification of PoV blocks are decided by the voting results among the core consortium members. We theoretically prove that PoV blocks can reach transaction finality by only one confirmation. Compared with the total traffic complexity of BFT-based consensus, PoV has just that of O( ), which is a great improvement when the number of nodes is over 100.
A decomposition-based multi-objective optimization approach for extractive multi-document text summarization

Currently, due to the overflow of textual information on the Internet, automatic text summarization methods are becoming increasingly important in many fields of knowledge. Extractive multi-document text summarization approaches are intended to automatically generate summaries from a document collection, covering the main content and avoiding redundant information. These approaches can be addressed through optimization techniques. In the scientific literature, most of them are single-objective optimization approaches, but recently multi-objective approaches have been developed and they have improved the single-objective existing results. In addition, in the field of multi-objective optimization, decomposition-based approaches are being successfully applied increasingly. For this reason, a Multi-Objective Artificial Bee Colony algorithm based on Decomposition (MOABC/D) is proposed to solve the extractive multi-document text summarization problem. An asynchronous parallel design of MOABC/D algorithm has been implemented in order to take advantage of multi-core architectures. Experiments have been carried out with Document Understanding Conferences (DUC) datasets, and the results have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The obtained results have improved the existing ones in the scientific literature for ROUGE-1, ROUGE-2, and ROUGE-L scores, also reporting a very good speedup.
Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation

To provide more accurate recommendation, it is a trending topic to go beyond modeling user-item interactions and take context features into account. Factorization Machines (FM) with negative sampling is a popular solution for context-aware recommendation. However, it is not robust as sampling may lost important information and usually leads to non-optimal performances in practical. Several recent efforts have enhanced FM with deep learning architectures for modelling high-order feature interactions. While they either focus on rating prediction task only, or typically adopt the negative sampling strategy for optimizing the ranking performance. Due to the dramatic fluctuation of sampling, it is reasonable to argue that these sampling-based FM methods are still suboptimal for context-aware recommendation.
End-to-End Automatic Pronunciation Error Detection Based on Improved Hybrid CTC/Attention Architecture

Advanced automatic pronunciation error detection (APED) algorithms are usually based on state-of-the-art automatic speech recognition (ASR) techniques. With the development of deep learning technology, end-to-end ASR technology has gradually matured and achieved positive practical results, which provides us with a new opportunity to update the APED algorithm. We first constructed an end-to-end ASR system based on the hybrid connectionist temporal classification and attention (CTC/attention) architecture. An adaptive parameter was used to enhance the complementarity of the connectionist temporal classification (CTC) model and the attention-based seq2seq model, further improving the performance of the ASR system. After this, the improved ASR system was used in the APED task of Mandarin, and good results were obtained. This new APED method makes force alignment and segmentation unnecessary, and it does not require multiple complex models, such as an acoustic model or a language model. It is convenient and straightforward, and will be a suitable general solution for L1-independent computer-assisted pronunciation training (CAPT). Furthermore, we find that find that in regards to accuracy metrics, our proposed system based on the improved hybrid CTC/attention architecture is close to the state-of-the-art ASR system based on the deep neural network-deep neural network (DNN-DNN) architecture, and has a stronger effect on the F-measure metrics, which are especially suitable for the requirements of the APED task.
SED-MDD: Towards Sentence Dependent End-To-End Mispronunciation Detection and Diagnosis

A mispronunciation detection and diagnosis (MD&D) system typically consists of multiple stages, such as an acoustic model, a language model and a Viterbi decoder. In order to integrate these stages, we propose SED-MDD, an end-to-end model for sentence dependent mispronunciation detection and diagnosis (MD&D) . Our proposed model takes mel-spectrogram and characters as inputs and outputs the corresponding phone sequence. Our experiments prove that SED-MDD can implicitly learn the phonological rules in both acoustic and linguistic features directly from the phonological annotation and transcription in the training data. To the best of our knowledge, SED-MDD is the first model of its kind and it achieves an accuracy of 86.35% and a correctness of 88.61% on L2-ARCTIC which significantly outperforms the existing end-to-end mispronunciation detection and diagnosis (MD&D) model CNN-RNN-CTC.
Authentication Of Transaction Process In E-marketplace Based On Blockchain ​​technology

&#x0D; The very rapid development of technology is currently causing a revolution digital and the era of technological disruption or known as industry 4.0. with the presence of this 4.0 industry occurred computer growth and record-keeping automation all fields. This allows every human job to be replaced by robots, causing changes in the trading system that was done directly can now be done online or also known as Marketplace. With trade conducted through the internet, the transaction process is carried out on a traditional or directly, is now turning to the digital transaction process through Internet. The transaction process carried out digitally requires accuracy, security, and good trust between seller and buyer. Management Customer Relations or better known as Customer Relationship Management (CRM) is one model that can be used in maintaining the relationship between the customer and the company. Blockchain technology is technology that is able to maintain the transaction process, to maintain management efficiency identity, tracking system implementation, identifying product authenticity, and synchronization recording data to all parties, is expected to increase trust and the relationship between the customer and the seller.
Anonymous Certificate-Based Broadcast Encryption With Personalized Messages

Broadcast encryption with personalized messages (BEPM) is a newly proposed cryptographic primitive, which supports a broadcaster to deliver the common encrypted broadcast message to a group of authorized users. It also supports a broadcaster to deliver the personalized messages to the individual authorized users simultaneously. However, none of the existing BEPM schemes consider the anonymity of authorized users, which is a crucial security requirement for many BEPM application scenarios. Besides, the previous studies on BEPM only achieve security against chosen-plaintext attack adversaries. Certificate-based cryptosystem gives a viable way to construct more secure and efficient BEPM schemes. We propose the new concept of anonymous certificate-based broadcast encryption with personalized messages (ANON-CBBE-PM). Subsequently, we illustrate the formal definition and the security model of ANON-CBBE-PM. Furthermore, we present an ANON-CBBE-PM scheme, which solves key escrow problem in identity-based cryptosystem and certificate management problem in traditional public key cryptosystem. Compared with the existing BEPM schemes, the proposed scheme not only protects the privacy of authorized users, but also has advantage in performance. To be more specific, the lengths of the user secret key and the system public parameters, as well as the cost for decryption are constant. For those BEPM applications with a multitude of users whose computing ability is limited, the proposed scheme is more practical. In addition, our scheme simultaneously achieves anonymity and confidentiality under adaptive chosen-ciphertext attack in the standard model.
Effect of Distance Metrics in Determining K-Value in K-Means Clustering Using Elbow and Silhouette Method

Clustering is one of the main task in datamining.It is useful to group and cluster the data.There are a few ways to cluster the data such as partitional-based, hierarchical-based and density based.Partitional-based clustering is a way to cluster data with non-overlapping subsets.One of the most popular partitional-based clustering algorithm is K-means.K-means is an algorithm to cluster data in to K cluster and based their distance to its centroid.Due to the pational, a few factors that must be determined before using K-means is the value of K. Determining the value of K is a big problem because there is no universal way to find the value of K. Two popular ways to determine the value of K is using elbow and silhouette method.This method is graph based.But before using this method another factor is important to determine and that is the metrics distance that will be used.This paper will show the effect of three distance metric Manhattan, Euclidian and Minkowski in finding the value of K using elbow and silhouette method.Based on this study the choice of distance matrix used has little impact in determining the value of K in K-means using elbow and silhouette.Manhattan distance has the most variant in the elbow and silhouette graph.Elbow method is difficult to use and sometimes it is unable to define the value of K in K-means based on its graph.
A Novel Action Recognition Framework Based on Deep-Learning and Genetic Algorithms

Recognition of human actions in partially cluttered environments is an important research field of computer vision and human-computer interaction.This field has recently garnered attention from a large number of academic researchers in various fields of application.This study proposes a novel deep-learningbased architecture for the recognition and prediction of human actions based on a hybrid model.The main contribution of this study is to propose a new hybrid architecture, integrating four wide-ranging pre-trained network models in an optimized manner, using a metaheuristic algorithm.This architecture consists of four main stages: namely, the creation of the data set, the design of deep neural network (DNN) architecture, training and optimization of the proposed DNN architecture, and evaluation of the trained DNN.By adapting the aforementioned architecture, reliable features are obtained for the training procedure.In order to validate the superiority of the proposed architecture over other state-of-the-art studies, a performance evaluation between these architectures is presented using benchmark datasets.The results reveal that the proposed architecture outperforms previously developed architectures in terms of predicting human actions.
Scalable Spin-Glass Optical Simulator

Many developments in science and engineering depend on tackling complex optimizations on large scales. The challenge motivates an intense search for specific computing hardware that takes advantage of quantum features, nonlinear dynamics, or photonics. A paradigmatic optimization problem is to find low-energy states in classical spin systems with fully random interactions. To date, no alternative computing platform can address such spin-glass problems on a large scale. Here, we propose and realize an optical scalable spin-glass simulator based on spatial light modulation and multiple light scattering. By tailoring optical transmission through a disordered medium, we optically accelerate the computation of the ground state of large spin networks with all-to-all random couplings. Scaling of the operation time with the problem size demonstrates an optical advantage over conventional computing. Our results highlight optical vector-matrix multiplication as a tool for spin-glass problems and provide a general route toward large-scale computing that exploits speed, parallelism, and coherence of light.
Fabry-Perot Lasers as Enablers for Parallel Reservoir Computing

We introduce the use of Fabry-Perot (FP) lasers as potential neuromorphic computing machines with parallel processing capabilities. With the use of optical injection between a master FP laser and a slave FP laser under feedback we demonstrate the potential for scaling up the processing power at longitudinal mode granularity and perform real-time processing for signal equalization in 25 Gbaud intensity modulation direct detection optical communication systems. We demonstrate the improvement of classification performance as the number of nodes increases and the capability of simultaneous processing of arbitrary data streams. Extensive numerical simulations show that up to 8 longitudinal modes in typical Fabry-Perot lasers can be leveraged so as to enhance classification performance.
ICT for Development in Rural India: A Longitudinal Study of Women’s Health Outcomes

With a view toward improving the success of information and communication technology (ICT) initiatives in less developed countries in general and India in particular, this work seeks to uncover reasons for success and failure of ICT for development (ICT4D
Quality-Aware Neural Adaptive Video Streaming With Lifelong Imitation Learning

Existing Adaptive Bitrate (ABR) algorithms pick future video chunks' bitrates via fixed rules or offline trained models to ensure good quality of experience (QoE) for Internet video. Nevertheless, data analysis demonstrates that a good ABR algorithm is required to continually and fast update for adapting itself to time-varying network conditions. Therefore, we propose Comyco, a video quality-aware learning-based ABR approach that enormously improves recent schemes by i) picking the chunk with higher perceptual video qualities rather than video bitrates; ii) training the policy via imitating expert trajectories given by the expert strategy; iii) employing the lifelong learning method to continually train the model w.r.t the fresh trace collected by the users. To achieve this, we develop a complete quality-aware lifelong imitation learning-based ABR system, construct quality-based neural network architecture, collect a quality-driven video dataset, and estimate QoE metrics with video quality features. Using trace-driven and real-world experiments, we demonstrate Comyco reaches 1700-fold improvements in the number of samples required and 16-fold speedup in the training time compared with the prior work. Meanwhile, Comyco outperforms existing methods, with the improvements on average QoE of 7.5%-16.79%. Moreover, experimental results on continual training also illustrate that lifelong learning helps Comyco further improve the average QoE of 1.07%-9.81% in comparison to the offline trained model.
Knowledge Enhanced Event Causality Identification with Mention Masking Generalizations

Identifying causal relations of events is a crucial language understanding task. Despite many efforts for this task, existing methods lack the ability to adopt background knowledge, and they typically generalize poorly to new, previously unseen data. In this paper, we present a new method for event causality identification, aiming to address limitations of previous methods. On the one hand, our model can leverage external knowledge for reasoning, which can greatly enrich the representation of events; On the other hand, our model can mine event-agnostic, context-specific patterns, via a mechanism called event mention masking generalization, which can greatly enhance the ability of our model to handle new, previously unseen cases. In experiments, we evaluate our model on three benchmark datasets and show our model outperforms previous methods by a significant margin. Moreover, we perform 1) cross-topic adaptation, 2) exploiting unseen predicates, and 3) cross-task adaptation to evaluate the generalization ability of our model. Experimental results show that our model demonstrates a definite advantage over previous methods.
Noisy Quantum Metrology Enhanced by Continuous Nondemolition Measurement

We show that continuous quantum nondemolition (QND) measurement of an atomic ensemble is able to improve the precision of frequency estimation even in the presence of independent dephasing acting on each atom. We numerically simulate the dynamics of an ensemble with up to N=150 atoms initially prepared in a (classical) spin coherent state, and we show that, thanks to the spin squeezing dynamically generated by the measurement, the information obtainable from the continuous photocurrent scales superclassically with respect to the number of atoms N. We provide evidence that such superclassical scaling holds for different values of dephasing and monitoring efficiency. We moreover calculate the extra information obtainable via a final strong measurement on the conditional states generated during the dynamics and show that the corresponding ultimate limit is nearly achieved via a projective measurement of the spin-squeezed collective spin operator. We also briefly discuss the difference between our protocol and standard estimation schemes, where the state preparation time is neglected.