Reviewerly/exHarmony huggingface dataset:
A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos

The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content and contextual information that may influence the recommendation accuracy, especially tourist attractions’ visual content due to redundant and noisy geotagged photos; therefore, we propose a tourist attraction recommendation model for Flickr-geotagged photos which fuses spatial, temporal, and visual embeddings (STVE). After spatial clustering and extracting visual embeddings of tourist attractions’ representative images, the spatial and temporal embeddings are modeled with the Word2Vec negative sampling strategy, and the visual embeddings are fused with Matrix Factorization and Bayesian Personalized Ranking. The combination of these two parts comprises our proposed STVE model. The experimental results demonstrate that our STVE model outperforms other baseline models. We also analyzed the parameter sensitivity and component performance to prove the performance superiority of our model.
Design of cyber-physical-social systems with forensic-awareness based on deep learning

Cyber-physical-social systems (CPSSs) contain integrated cyber parts, including computing, communication, and physical parts; it uses computations and communication embedded in and interacting with physical processes to add new capabilities to physical systems. While CPSSs opens new opportunities in various fields, it brings new challenges in security. By inventing anti-forensic tools, attackers may break the investigation system and make the analysis of attack scenarios difficult. In this chapter, we propose a scheme of forensics-awareness to support reliable image forensics investigations. The forensic scheme utilizes a Multichannel Convolutional Neural Network (MCNN) to learn hierarchical representations from the input images. Fake images can be identified using the well-trained MCNN. Most existing works aim at detecting a certain manipulation, which may usually lead to misled results, e.g. results with irrelevant features and/or classifiers. To overcome this limitation, we extract the periodicity property and filtering residual feature from the image blocks. The f-map is then generated by linear blending the periodic spectrum and the residual map, and is fed into the MCNN. The overall framework is capable of detecting different types of image manipulations, including cloning, removing, splicing and smoothing. Experimental results show that this scheme outperforms formerly existing ones.
Temporal Performance Modelling of Serverless Computing Platforms

Analytical performance models have been shown very efficient in analyzing, predicting, and improving the performance of distributed computing systems. However, there is a lack of rigorous analytical models for analyzing the transient behaviour of serverless computing platforms, which is expected to be the dominant computing paradigm in cloud computing. Also, due to its unique characteristics and policies, performance models developed for other systems cannot be directly applied to modelling these systems.
High-performance area-efficient polynomial ring processor for CRYSTALS-Kyber on FPGAs

The quantum-resistant attribute is a new design criterion for cryptography algorithms in the era of quantum supremacy. Lattice-based cryptography is proved to be secure against quantum computing. CRYSTALS-Kyber is a lattice-based promising candidate in the post-quantum cryptography standardization process. This paper proposes a high-performance polynomial ring processor for the CRYSTALS-Kyber algorithm. The processor executes optimized polynomial ring arithmetic, which cuts off over 20%/50% on the times of modular multiplication/addition compared with the straightforward implementations. Besides, the forward and inverse Number Theoretic Transform (NTT) reuse the control logic with the help of an efficient configurable butterfly unit to minimize the area of the finite state machine. Further, the underlying dual-column sequential storage scheme breaks the bottleneck of memory accessing. To evaluate the performance, a fully pipelined architecture is implemented on a low-cost FPGA platform. Benefiting from these optimizations, the Kyber1024processor can perform NTT operation for a 4-dimensional polynomial vector in 17.1 μs, and it achieves speedup by a factor of 2.1 compared with the state-of-the-art implementation.
Core Interest Network for Click-Through Rate Prediction

In modern online advertising systems, the click-through rate (CTR) is an important index to measure the popularity of an item. It refers to the ratio of users who click on a specific advertisement to the number of total users who view it. Predicting the CTR of an item in advance can improve the accuracy of the advertisement recommendation. And it is commonly calculated based on users’ interests. Thus, extracting users’ interests is of great importance in CTR prediction tasks. In the literature, a lot of studies treat the interaction between users and items as sequential data and apply the recurrent neural network (RNN) model to extract users’ interests. However, these solutions cannot handle the case when the sequence length is relatively long, e.g., over 100. This is because of the vanishing gradient problem of RNN, i.e., the model cannot learn a users’ previous behaviors that are too far away from the current moment. To address this problem, we propose a new Core Interest Network (CIN) model to mitigate the problem of a long sequence in the CTR prediction task with sequential data. In brief, we first extract the core interests of users and then use the refined data as the input of subsequent learning tasks. Extensive evaluations on real dataset show that our CIN model can outperform the state-of-the-art solutions in terms of prediction accuracy.
High speed long-term visual object tracking algorithm for real robot systems

Although many visual tracking algorithms have made many achievements in video sequences, they have not been confirmed to work well on the real robot systems with the unpredictable changes and limited computing capabilities. In order to face the complex practical conditions, including huge scale variation, occlusion and long-term task, this paper develops a CF-based long-term tracking algorithm. The main strategies are as follows. A novel confidence score is proposed to judge tracking reliability, and the tracking drift is corrected to keep the target’s long-term appearance. Furthermore, once the target is lost, it can be relocated by the multi-scale search. Our tracker performs favorably against other CF-based trackers with strong engineering applicability. Finally, experiments on the datasets and an UAV are carried out to verify the effectiveness for real robot systems.
Attentional Memory Network with Correlation-based Embedding for time-aware POI recommendation

As considerable amounts of point-of-interest (POI) check-in data have been accumulated, POI recommendation has received much attention recently. It is well recognized that spatial–temporal information plays an important role in the user’s decision-making for visiting a POI. However, in time-aware POI recommendation, exploring temporal patterns on user preferences and incorporating multi-view factors for choosing preferred POIs are challenging issues to be resolved. To this end, we propose a novel Attentional Memory Network with Correlation-based Embedding (AMN-CE) for time-aware POI recommendation. Specifically, we first propose a correlation-based POI embedding method to capture geographical influence and interactive correlation between POIs. Sequentially, we design an attentional memory network, which is able to capture the micro-level relationship between time slot pairs. Furthermore, we propose a temporal-level attention mechanism to distinguish and dynamically adjust the influence strength of different time slots on user preferences at the target time slot. The experimental results on four real-life datasets demonstrate significant improvements of our proposed method compared with state-of-the-art models.
A Bibliometric Review of Arab World Research from 1980-2020

The Arab World, a regional block of 22 countries, has been the epicenter of knowledge and innovation for many centuries. After the gap and neglect of centuries, the region is undergoing reforms that would increase scientific activities. Using the InCites tool of Clarivate Analytics, this study used bibliometric indicators of research impact and productivity to present the dynamics of the Arab World’s research published between 1980 and 2020. The findings disclose that the first two decades of the 21st century witnessed a significant increase in research productivity in the region consistent with the Arab World’s recent initiatives to promote scientific research. Saudi publications attracted the highest number of citations. Almost half of the region’s research has collaborated internationally. The region performed better than the world in several indicators like citation impact, impact factor publications, and international collaborations; however, the region’s performance lags behind the world in academic-industry collaborations and publications in Q1 journals and impact relative to the world indicators. The study recommends adopting the open access model of publishing for increased visibility and impact based on the findings. Increased attention is needed in the region to enhance research linkages between universities and industry.
A Privacy-Aware and Traceable Fine-Grained Data Delivery System in Cloud-Assisted Healthcare IIoT

The emerging of healthcare Industrial Internet of Things (HealthIIoT) cannot only facilitate high-quality care services for patients but also enable efficient telemedicine platform for healthcare practitioners. However, it faces several fundamental security and privacy challenges, such as secure fine-grained data delivery, privacy preserving keyword-based ciphertext retrieval, malicious key delegation, and efficiency of the system. To combat these issues, we propose a privacy-aware and traceable fine-grained system (PTFS) for secure data delivery in cloud-assisted HealthIIoT. Compared to the existing solutions that only implement some of the preceding features, the proposed solution enables secure fine-grained data delivery, privacy-preserving data retrieval, efficient encryption and decryption operations, and trace of malicious key delegation simultaneously. For security analysis, rigorous proofs of the proposed scheme are provided to prove its security. In addition, extensive simulations and experiments are conducted for performance evaluation, which demonstrate the feasibility and effectiveness of PTFS.
Robust Online Tracking via Contrastive Spatio-Temporal Aware Network

Existing tracking-by-detection approaches using deep features have achieved promising results in recent years. However, these methods mainly exploit feature representations learned from individual static frames, thus paying little attention to the temporal smoothness between frames. This easily leads trackers to drift in the presence of large appearance variations and occlusions. To address this issue, we propose a two-stream network to learn discriminative spatio-temporal feature representations to represent the target objects. The proposed network consists of a Spatial ConvNet module and a Temporal ConvNet module. Specifically, the Spatial ConvNet adopts 2D convolutions to encode the target-specific appearance in static frames, while the Temporal ConvNet models the temporal appearance variations using 3D convolutions and learns consistent temporal patterns in a short video clip. Then we propose a proposal refinement module to adjust the predicted bounding box, which can make the target localizing outputs to be more consistent in video sequences. In addition, to improve the model adaptation during online update, we propose a contrastive online hard example mining (OHEM) strategy, which selects hard negative samples and enforces them to be embedded in a more discriminative feature space. Extensive experiments conducted on the OTB, Temple Color and VOT benchmarks demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks

We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction defined as the firing of neurons in specific paths. In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout. In essence, we dropout with higher probability those neurons which contribute more to decision making at training time. This approach penalizes high saliency neurons that are most relevant for model prediction, i.e. those having stronger evidence. By dropping such high-saliency neurons, the network is forced to learn alternative paths in order to maintain loss minimization, resulting in a plasticity-like behavior, a characteristic of human brains too. We demonstrate better generalization ability, an increased utilization of network neurons, and a higher resilience to network compression using several metrics over four image/video recognition benchmarks.
The Serverless Application Analytics Framework

To help better understand factors that impact performance on Function-as-a-Service (FaaS) platforms we have developed the Serverless Application Analytics Framework (SAAF). SAAF provides a reusable framework supporting multiple programming languages that developers can integrate into a function's package for deployment to multiple commercial and open source FaaS platforms. SAAF improves the observability of FaaS function deployments by collecting forty-eight distinct metrics to enable developers to profile CPU and memory utilization, monitor infrastructure state, and observe platform scalability. In this paper, we describe SAAF in detail and introduce supporting tools highlighting important features and how to use them. Our client application, FaaS Runner, provides a tool to orchestrate workloads and automate the process of conducting experiments across FaaS platforms. We provide a case study demonstrating the integration of SAAF into an existing open source image processing pipeline built for AWS Lambda. Using FaaS Runner, we automate experiments and acquire metrics from SAAF to profile each function of the pipeline to evaluate performance implications. Finally, we summarize contributions using our tools to evaluate implications of different programming languages for serverless data processing, and to build performance models to predict runtime for serverless workloads.
A novel approach to build accurate and diverse decision tree forest

Decision tree is one of the best expressive classifiers in data mining. A decision tree is popular due to its simplicity and straightforward visualization capability for all types of datasets. Decision tree forest is an ensemble of decision trees. The prediction accuracy of the decision tree forest is more than a decision tree algorithm. Constant efforts are going on to create accurate and diverse trees in the decision tree forest. In this paper, we propose Tangent Weighted Decision Tree Forest (TWDForest), which is more accurate and diverse than random forest. The strength of this technique is that it uses a more accurate and uniform tangent weighting function to create a weighted decision tree forest. It also improves performance by taking opinions from previous trees to best fit the successor tree and avoids the toggling of the root node. Due to this novel approach, the decision trees from the forest are more accurate and diverse as compared to other decision forest algorithms. Experiments of this novel method are performed on 15 well known, publicly available UCI machine learning repository datasets of various sizes. The results of the TWDForest method demonstrate that the entire forest and decision trees produced in TWDForest have high prediction accuracy of 1-7% more than existing methods. TWDForest also creates more diverse trees than other forest algorithms.
A new method for weighted ensemble clustering and coupled ensemble selection

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Evolving Feedforward Neural Networks Using a Quasi-Opposition-Based Differential Evolution for Data Classification

One of the most challenging problems in conducting machine learning is the learning process of feedforward neural networks (FFNN), which means finding the proper weights for connections and biases. The performance of FFNNs is mainly dependent on the success of the learning process. Gradient descent-based methods such as back-propagation (BP) are among the most widely employed learning algorithms, whereas they are susceptible to be trapped in local optima. Population-based metaheuristic algorithms such as differential evolution (DE) are a reliable alternative to tackle complex problems. In this paper, we propose a quasi-opposition-based differential evolution approach for FFNN learning to improve the performance of FFNNs (QODE-FFNN). Our proposed algorithm benefits from a variant of opposition-based learning (OBL) to enhance the performance of FFNN. Based on OBL concept, the opposite of a candidate solution is generated. Afterward, OBL selects the best between a candidate solution and its opposite based on their objective function values. In this paper, we employed a variant of OBL to improve the performance of FFNN, called quasi-OBL, which generates a random point between the center of search space and its opposite. Also, in our proposed algorithm, connection weights and biases are encoded as a candidate solution, while the objective function is based on a classification error. Experimental results confirm the performance of QODE-FFNN compared to other recent approaches.
Image-based mains signal disaggregation and load recognition

Abstract The mains signal is a complex fusion of various electrical equipment load signals in a building. In the non-intrusive load monitoring recognition, our main aim is to be able to extract as much load features as possible from the complex aggregate mains signal in a simpler way through a computer vision-based approach as opposed to the powers series signal approach. Power series methods, which are one dimensional in nature, suffer from poor aggregate and load signal feature localization necessitating a larger training dataset spanning very long time periods and normally require signal formatting and pre-processing. We use Gramian angular summation fields to transform the power series into a reduced image dataset that contains a rich set of localized signal features. A computer vision approach allows us to capture as much information as possible, and then propose an image-based mains load recognition system with high performance. In this paper for the entire recognition system, we use convolutional neural networks that very well adapted to vision recognition. The load signal image disaggregation is achieved through the powerful stacked denoising autoencoder noise extraction network. To test the proposed system, some simulations and comparisons are carried out and the results show that our easier to handle method can achieve acceptable performance.
Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks

Understanding on-road vehicle behaviour from a temporal sequence of sensor data is gaining in popularity. In this paper, we propose a pipeline for understanding vehicle behaviour from a monocular image sequence or video. A monocular sequence along with scene semantics, optical flow and object labels are used to get spatial information about the object (vehicle) of interest and other objects (semantically contiguous set of locations) in the scene. This spatial information is encoded by a Multi-Relational Graph Convolutional Network (MR-GCN), and a temporal sequence of such encodings is fed to a recurrent network to label vehicle behaviours. The proposed framework can classify a variety of vehicle behaviours to high fidelity on datasets that are diverse and include European, Chinese and Indian on-road scenes. The framework also provides for seamless transfer of models across datasets without entailing re-annotation, retraining and even fine-tuning. We show comparative performance gain over baseline Spatio-temporal classifiers and detail a variety of ablations to showcase the efficacy of the framework.
A GA-Based Approach to Fine-Tuning BERT for Hate Speech Detection

There exists a high level of variance in the finetuning of contextual word embedding models like Bi-directional Encoder Representation for Transformers (BERT). Such variance can be introduced by various factors, such as the BERT encoder layers, the fine-tuning architecture or the hyperparameter settings. Each architectural design choice leads to different results for a specific task. Hence, we are interested in reducing this variance given some settings. This study illustrates the use of a Genetic Algorithm (GA) to search, select and design a (near-) optimal fine-tuned BERT architecture for a hate speech detection task. We propose an appropriate encoding scheme for this task which represents the possible solutions in a way that supports a less time-consuming search for the global optima. Each encoding for a single solution represents both the BERT architecture and the fine-tuning architecture for a robust search. The automatic search provided by the GA, helps to reduce the time and cost involved in manual trial and error design methods. The experiments show that the resulting architectural design and hyperparameter settings are good choices for the hate speech detection task. Our method, although validated only on hate speech detection tasks, can easily be extended and generalized to other text classification tasks.
Blockchain and Environmental Sustainability: Case of IBM's Blockchain Water Management

This discussion paper considers the role that blockchain-based technologies can play in advancing initiatives pertaining to environmental sustainability, using the case study of IBM’s water management blockchain development in California. The paper finds that there is substantial room for incorporating blockchain technology into environmental protection and sustainability projects, including in the mitigation of information asymmetry through transparency so as to foster stronger market-mechanisms in the allocation of resources.
Linking software requirements and conceptual models: A systematic literature review

Identification of stakeholder needs and documentation of software requirements are the critical steps to launch a software project. Natural language requirements serve as an agreement among the project stakeholders and they must be transformed into easy-to-understand conceptual models to avoid communication problems. Although conceptual models are mostly created manually with human involvement from the software team, it is seen in recent times that there is a significant increase in studies that automatically generate conceptual models from software requirements. In this study, a Systematic Literature Review (SLR) based on the search of forty-four primary studies (published between 1996 and 2020), which automatically transform software requirements into conceptual models, is reported. These studies are evaluated regarding their approaches, functionalities, dataset used, evaluation methods, generated model types, and languages supported. Finally, several improvable points in the current approaches are highlighted and suggestions are provided as further works.