Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of YouTube Video Comments Using Deep Neural Networks

Over the years, social networks have become an important vehicle for communication. Many users on YouTube use comments to express opinions or critique a subject. The amount of comments, for famous videos and channels, is huge, which poses the challenge of analysing user opinions efficiently. This article proposes a sentiment analysis model of YouTube video comments, using a deep neural network. We employed an embedding layer to represent input text as a tensor, then we used a pair of convolutional layers to extract features and a fully connected layer to make the classification. The output of the neural network is the sentiment classification among negative, positive or neutral. Two videos were chosen and their comments were classified by our model, by an alternative statistical model and by humans. The human classification was considered to be 100% accurate. The results showed that our model achieves better accuracy than the statistical model, and the classification accuracy is in the range 60%–84%.
Architecture-Based Reliability-Sensitive Criticality Measure for Fault-Tolerance Cloud Applications

The widespread adoption of service computing allows software to be developed by outsourcing open cloud services (i.e., SOAP-based or RESTful Web APIs) through mashup or service composition techniques. Fault tolerance for the purpose of assuring the stable execution for cloud-based software (or CBS) application has attracted great attention in coping with a loosely coupled CBS operating under dynamic and uncertain running environments. It is too expensive to rent massively redundant cloud services for CBS fault tolerance application. To reduce budget but guarantee the effectiveness of CBS fault tolerance, identifying critical components within a CBS composite system is of significant importance. We integrate CBS composite system architecture analysis and reliability sensitivity analysis approaches and propose an Architecture-based Reliability-sensitive Criticality Measure (or ARCMeas) method in this paper. We verify ARCMeas application through a cost-effective fault tolerance CBS by presenting a particle swarm optimization (PSO)-based cost-effective fault tolerance strategy determination (or PSO-CFTD) algorithm. Experimental results suggest the effectiveness of the approach.
Chatbot Testing Using AI Planning

Chatbots, i.e., systems that can interact with humans in a more appropriate way using natural language, have been of increasing importance. This is due the fact of the availability of computational means for natural language interaction between computers and humans that are becoming closer to the interaction between humans alone. Consequently, there are more and more chatbots available that are intended to support humans organizing tasks or making decisions. In this paper, we focus on how to verify the communication capabilities provided by chatbots. In particular, we introduce an automated approach for generating communication sequences and carrying them out. The approach is based on AI planning where each action can be assumed to be a certain question that is given to the chatbot. The answer of the chatbot should make the action post-condition true, in order to proceed with the plan. In cases of deviations between the actual chatbot behavior and the expected one, re-planning is required. Besides the approach, we discuss its application to the domain of tourism and outline a case study.
Histogram distance metric learning for facial expression recognition

Facial expression recognition is an interesting and challenging problem in computer vision. So far, much research has been performed in this area; however, facial expression recognition in uncontrolled conditions has remained an unresolved problem. The widely-used feature descriptors in computer vision are often histogram data. In this paper, a new metric learning method is presented for histogram data classification. In this method, chi-squared distance is appropriately modified for metric learning. Then, a convex cost function is proposed to use in metric learning optimization. Moreover, the proposed algorithm is redefined as Local Metric Learning for facial expression recognition problem. In this definition, the proposed metric learning method is applied locally on facial sub-regions. Experimental results on four histogram datasets (dslr, webcam, amazon, and caltech) as well as controlled and uncontrolled facial expression recognition datasets (CK+, SFEW, and RAF-DB) show that the proposed method has superior performance compared to the state-of-art methods.
A Deep Reinforcement Learning Framework for UAV Navigation in Indoor Environments

This paper presents a framework for UAV navigation in indoor environments using a deep reinforcement learning based approach. The implementation models the problem as two seperate problems, a Markov Decision Process (MDP), and a Partially Observable Markov Decision Processes (POMDP), separating the search problem into high-level planning and low-level action under uncertainty. We apply deep learning techniques to this layered problem to produce policies for the framework that allow a UAV to plan, act, and react. The approach is simulated and visualised using Gazebo and is evaluated using policies trained using deep-learning. Using recent deep-learning techniques as the basis of the framework, our results indicate that it is capable of providin smooth navigation for a simulated UAV agent exploring an indoor environment with uncertainty in its position. Once extended to real-world operation, this framework could enable UAVs to be applied in an increasing number of applications, from underground mining and oil refinery surveys and inspections, to search and rescue missions and biosecurity surveys.
An adaptive image inpainting method based on the modified mumford-shah model and multiscale parameter estimation

Image inpainting is a process of filling missing and damaged parts of image. By using the Mumford-Shah image model, the image inpainting can be formulated as a constrained optimization problem. The Mumford-Shah model is a famous and effective model to solve the image inpainting problem. In this paper, we propose an adaptive image inpainting method based on multiscale parameter estimation for the modified Mumford-Shah model. In the experiments, we will handle the comparison with other similar inpainting methods to prove that the combination of classic model such the modified Mumford-Shah model and the multiscale parameter estimation is an effective method to solve the inpainting problem.
Image Denoising Using A Generative Adversarial Network

Animation studios render 3D scenes using a technique called path tracing which enables them to create high quality photorealistic frames. Path tracing involves shooting 1000’s of rays into a pixel randomly (Monte Carlo) which will then hit the objects in the scene and, based on the reflective property of the object, these rays reflect or refract or get absorbed. The colors returned by these rays are averaged to determine the color of the pixel. This process is repeated for all the pixels. Due to the computational complexity it might take 8–16 hours to render a single frame. We implemented a neural network-based solution to reduce the time it takes to render a frame to less than a second using a generative adversarial network (GAN), once the network is trained. The main idea behind this proposed method is to render the image using a much smaller number of samples per pixel than is normal for path tracing (e.g., 1, 4, or 8 samples instead of, say, 32,000 samples) and then pass the noisy, incompletely rendered image to our network, which is capable of generating a high-quality photorealistic image.
An edge preserving high-order PDE for multiframe image super-resolution

The multiframe image super-resolution (SR) has appeal great care due to its relevant practical applications. Since the SR methods are always considered as ill-posed problems, great interest is therefore investigated to reconstruct an image with less artefacts. In this paper, we propose a high-order partial differential equation (PDE) that preserve singularities and edge continuities while smoothing. This PDE is based on anisotropic diffusion behavior by adding some constraints to the Weickert coherence enhancing diffusion tensor in order to control the diffusion process near edges and avoids tiny corners destruction. In fact, this PDE combine between the diffusion process of Perona–Malik in the flat regions and the anisotropic diffusion of the Weickert model near strong edges. Moreover, the introduction of the high-order derivatives reduces unwanted artifacts such as staircasing. The given experimental results show the effectiveness of the proposed PDE compared to some competitive methods in both visually and quantitatively.
Building a dynamic searchable encrypted medical database for multi-client

E-medical record is an emerging health information exchange model based on cloud computing. As cloud computing allows companies and individuals to outsource their data and computation, the medical data is always stored at a third party such as cloud, which brings a variety of risks, such as data leakage to the untrusted cloud server, unauthorized access or modification operations. To assure the confidentiality of the data, the data owner needs to encrypt the sensitive data before uploading to the third party. Yet, issues like encrypted data search, flexible access and control on sensitive data have also remained the most significant challenges. In this paper, we investigate a novel searchable encrypted e-medical framework for multi-client which provides both confidentiality and searchability. Different from previous privacy protecting works in secure data outsourcing, we focus on providing a fine-grained access control encrypted data search scheme including clients and data. Our scheme also enables secure data update of the encrypted database by leveraging a secure dynamic searchable encryption. Furthermore, we implement the proposed scheme based on some existed cryptography library, and conduct several experiments on a selected dataset to evaluate its performance. The results demonstrate that our scheme provides a balance between security and efficiency.
Estimating deception in consumer reviews based on extreme terms: Comparison analysis of open vs. closed hotel reservation platforms

We examine how open and closed review posting policies play differentiating roles in creating social media bias. As a supplementary method to existing ones detecting fake reviews, we develop a trust measure estimating how genuine the review is, based on the frequent usage of strongly positive or negative words. Using the hotel industry as our application context, we empirically demonstrate that our trust measure serves as a correction factor that reduces social media bias. Interestingly, we observe particular hotel service features revealing strong upward manipulation to promote the businesses (for example, positive overall recommendation, interesting surroundings, and personal travel). By contrast, we identify some other features that reveal the presence of strong downward manipulation (for example, negative overall recommendation, disappointing room amenities, and poor atmosphere). From a practical perspective, this research can help both managers and consumers make better informed decisions by reducing the impact attributable to social media manipulation.
FSSE: Forward secure searchable encryption with keyed-block chains

Nowadays, people widely use Searchable Symmetric Encryption (SSE) to design the equality query and range query operation for encrypted database systems. A recent attack points out forward privacy is the necessary security goal which, however, has not been achieved by most of SSE schemes. Motivated by this security goal, we propose a forward secure SSE scheme whose index structure consists of keyed-block chains. The proposed scheme is efficient since the keyed-block chain can be implemented by symmetric encryption primitive and the scheme also supports both add and delete operations in one instance. We further employ the one-way permutation function to reduce the storage on the client side. Experiments show that our scheme is 4 × , 300 × and 300 × faster than the state-of-the-art forward private SSE scheme (CCS 2016) in search, update and token generation, respectively.
Encodji

To this day, a variety of information has been obtained from human eye movements, which holds an imense potential to understand and classify cognitive processes and states - e.g., through scanpath classification. In this work, we explore the task of scanpath classification through a combination of unsupervised feature learning and convolutional neural networks. As an amusement factor, we use an Emoji space representation as feature space. This representation is achieved by training generative adversarial networks (GANs) for unpaired scanpath-to-Emoji translation with a cyclic loss. The resulting Emojis are then used to train a convolutional neural network for stimulus prediciton, showing an accuracy improvement of more than five percentual points compared to the same network trained using solely the scanpath data. As a side effect, we also obtain novel unique Emojis representing each unique scanpath. Our goal is to demonstrate the applicability and potential of unsupervised feature learning to scanpath classification in a humorous and entertaining way.
Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search

Decision Trees (DTs) like LambdaMART have been one of the most effective types of learning-to-rank algorithms in the past decade. They typically work well with hand-crafted dense features (e.g., BM25 scores). Recently, Neural Networks (NNs) have shown impressive results in leveraging sparse and complex features (e.g., query and document keywords) directly when a large amount of training data is available. While there is a large body of work on how to use NNs for semantic matching between queries and documents, relatively less work has been conducted to compare NNs with DTs for general learning-to-rank tasks, where dense features are also available and DTs can achieve state-of-the-art performance. In this paper, we study how to combine DTs and NNs to effectively bring the benefits from both sides in the learning-to-rank setting. Specifically, we focus our study on personal search where clicks are used as the primary labels with unbiased learning-to-rank algorithms and a significantly large amount of training data is easily available. Our combination methods are based on ensemble learning. We design 12 variants and compare them based on two aspects, ranking effectiveness and ease-of-deployment, using two of the largest personal search services: Gmail search and Google Drive search. We show that direct application of existing ensemble methods can not achieve both aspects. We thus design a novel method that uses NNs to compensate DTs via boosting. We show that such a method is not only easier to deploy, but also gives comparable or better ranking accuracy.
Are interactive web-tools for environmental scenario visualization worth the effort? An experimental study on the Swiss electricity supply scenarios 2035

Interactive web-tools are regarded as powerful methods to present large scenario ensembles from environmental modelling in an understandable and engaging way. Yet, there is little empirical evidence whether they are more effective than traditional approaches such as story-and-simulation. We conducted a between-groups experiment, where two groups of participants (total N = 313) differed in the scenario information received: (1) an interactive web-tool for exploring a large database of Swiss electricity scenarios 2035 and their environmental, health, and economic impacts; (2) a website presenting four of these scenarios with storylines. Results indicate that our interactive web-tool did not lead to benefits in self-reported understanding and engagement because there was no difference between the conditions. In fact, participants using the interactive web-tool performed worse in an understanding quiz than those using the four scenarios. These unexpected findings call for more empirical research whether interactive tools for scenario visualization meet the needs of intended users.
Multi-view information fusion in mammograms: A comprehensive overview

In the framework of computer-aided diagnosis of breast cancer, many systems were designed for the detection, the classification and/or the content-based mammogram retrieval (CBMR); in order to serve as a second source of decision for the radiologists. Nevertheless, to improve the final decision-making, the concept of multi-view information fusion (MVIF) was recently introduced. Indeed, this concept has been successfully applied in the context of breast cancer, since screening mammography provides two views for each breast: MedioLateral-Oblique (MLO) and CranioCaudal (CC) views. As these two views are complementary, MVIF methods widely proved their effectiveness. In this paper, we review the main methods that have been proposed for MVIF in the context of the detection (abnormality vs. non abnormality), the classification (normal vs. benign vs. malignant) and the content-based retrieval of mammograms. In fact, we classified detection based on MVIF methods into two main sub-classes, including ipsilateral analysis and bilateral analysis. Besides, classification based on MVIF methods were regrouped into two sub-classes, namely classification of breast masses based on MVIF and classification of breast microcalcifications based on MVIF. Lastly, CBMR based on MVIF methods were also classified into two sub-classes: early fusion-based MVIF-CBMR and late fusion-based MVIF-CBMR.
Bio-inspired optimization algorithms for real underwater image restoration

Underwater image restoration algorithms usually take into account the physical model of the acquisition medium to obtain a restored image with good quality. This way, they compensate for the specific degradations introduced by the physical medium. These models often contain several parameters, which represent the type and strength of the medium degradations (e.g., absorption, scattering, and many others). Naturally, the quality of the restored image will depend on the correct estimation of these model parameters. In this work, we propose a restoration algorithm that estimates the model parameters using bio-inspired optimization metaheuristics, whose cost function (objective function) is a No-Reference Image Quality metric (NR-IQA). In this case, Opposition-based Particle Swarm Optimization (OPSO), Repulsive–Attractive Particle Swarm Optimization (RAPSO), Artificial Bee-Colony Algorithm (ABC), Opposition-based Artificial Bee Colony (OABC), and Differential Evolution (DE) metaheuristics have been tested. Since most quality metrics are not designed for underwater scenarios, a study was carried out to choose the best metric for this type of scenario, which is used as the cost function during the optimization process. To do that, an underwater image dataset was built, containing a set of images with underwater degradations and their corresponding subjective quality scores. The subjective quality scores were obtained by performing a subjective quality assessment experiment with voluntary participants that rated the quality of the test images. The proposed study has found out that NIQE metric presents the highest SRCC value, being therefore chosen as the cost function for the optimization algorithms that estimate the physical parameters of Barros's inverse model. The performed experiments have demonstrated the suitability of our approach for underwater image restoring, showing that among the tested metaheuristics methods the PSO and ABC are the better, which provided restored images with a good visual quality.
Computer aided detection in automated 3-D breast ultrasound images: a survey

Nowadays, breast cancer is the leading cause of cancer death for women all over the world. Since the reason of breast cancer is unknown, early detection of the disease plays an important role in cancer control, saving lives and reducing costs. Among different modalities, automated 3-D breast ultrasound (3-D ABUS) is a new and effective imaging modality which has attracted a lot of interest as an adjunct to mammography for women with dense breasts. However, reading ABUS images is time consuming for radiologists and subtle abnormalities may be overlooked. Hence, computer aided detection (CADe) systems can be utilized as a second interpreter to assist radiologists to increase their screening speed and sensitivity. In this paper, a general architecture representing different CADe systems for ABUS images is introduced and the approaches for implementation of each block are categorized and reviewed. In addition, the limitations of these systems are discussed and their performance in terms of sensitivity and number of false positives per volume are compared.
Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations

Deep Convolutional Neural Networks (CNNs) now match human accuracy in many image prediction tasks, resulting in a growing adoption in e-commerce, radiology, and other domains. Naturally, explaining CNN predictions is a key concern for many users. Since the internal workings of CNNs are unintuitive for most users, occlusion-based explanations (OBE) are popular for understanding which parts of an image matter most for a prediction. One occludes a region of the image using a patch and moves it around to produce a heat map of changes to the prediction probability. Alas, this approach is computationally expensive due to the large number of re-inference requests produced, which wastes time and raises resource costs. We tackle this issue by casting the OBE task as a new instance of the classical incremental view maintenance problem. We create a novel and comprehensive algebraic framework for incremental CNN inference combining materialized views with multi-query optimization to reduce computational costs. We then present two novel approximate inference optimizations that exploit the semantics of CNNs and the OBE task to further reduce runtimes. We prototype our ideas in Python to create a tool we call Krypton that supports both CPUs and GPUs. Experiments with real data and CNNs show that Krypton reduces runtimes by up to 5X (resp. 35X) to produce exact (resp. high-quality approximate) results without raising resource requirements.
Combine clustering and frequent itemsets mining to enhance biomedical text summarization

Text summarization has become an important research area, especially in the biomedical domain, where information overload is a major problem. In this paper, we propose a novel biomedical text summarization system that combines two popular data mining techniques: clustering and frequent itemset mining. Biomedical paper is expressed as a set of biomedical concepts using the UMLS metathesaurus. The K-means algorithm is used to cluster similar sentences. Then, the Apriori algorithm is applied to discover the frequent itemsets among the clustered sentences. Finally, the salient sentences from each cluster are selected to build the summary using the discovered frequent itemsets. For the evaluation step, we selected randomly 100 biomedical papers from the BioMed Central database full-text, and we evaluated the performances of our system by comparing the resulting summaries with the abstracts of these papers using the ROUGE metrics in term of recall, precision, and F-measure. We also compared the obtained summaries with those achieved by five well-known summarizers: TextRank, TextTeaser, SweSum, ItemSet Based Summarizer, Microsoft AutoSummarize, and two baselines: summarization using only the frequent itemsets mining (FRQ-CL), and summarization using only the clustering (CL-FRQ). The results demonstrate that this combination can successfully enhance the summarization performances, and the proposed system outperforms other tested summarizers.