Reviewerly/exHarmony huggingface dataset:
CBIR system using Capsule Networks and 3D CNN for Alzheimer's disease diagnosis

Alzheimer's disease (AD) is an irreversible disorder of the brain related to loss of memory, commonly seen in the elderly and aging population. Implementation of revolutionary computer aided diagnosis techniques with Content Based Image Retrieval (CBIR) has created new potentials in Magnetic resonance imaging (MRI) in relevant image retrieval and training for detection of progression of AD in early stages. This paper proposed a CBIR system using 3D Capsule Network, 3D-Convolutional Neural Network and pre-trained 3D-autoencoder technology for early detection of Alzheimer's. A 3D-Capsule Networks (CapsNets) is capable of fast learning, even for small datasets and can effectively handle robust image rotations and transitions. It was observed that, an ensemble method using 3D-CapsNets and convolution neural network (CNN) with 3D-autoencoder, increased the detection performances comparing to Deep-CNN alone. CBIR using the proposed model was found to be up to 98.42% accurate in AD classification. Moreover, we argue that CapsNet seems to be a promising new technique for image classification, and further experiments using more robust computation resources and refined CapsNet architectures may produce better outcomes.
Convolutional Relational Machine for Group Activity Recognition

We present an end-to-end deep Convolutional Neural Network called Convolutional Relational Machine (CRM) for recognizing group activities that utilizes the information in spatial relations between individual persons in image or video. It learns to produce an intermediate spatial representation (activity map) based on individual and group activities. A multi-stage refinement component is responsible for decreasing the incorrect predictions in the activity map. Finally, an aggregation component uses the refined information to recognize group activities. Experimental results demonstrate the constructive contribution of the information extracted and represented in the form of the activity map. CRM shows advantages over state-of-the-art models on Volleyball and Collective Activity datasets.
Attack Directories, Not Caches: Side Channel Attacks in a Non-Inclusive World

Although clouds have strong virtual memory isolation guarantees, cache attacks stemming from shared caches have proved to be a large security problem. However, despite the past effectiveness of cache attacks, their viability has recently been called into question on modern systems, due to trends in cache hierarchy design moving away from inclusive cache hierarchies. In this paper, we reverse engineer the structure of the directory in a sliced, non-inclusive cache hierarchy, and prove that the directory can be used to bootstrap conflict-based cache attacks on the last-level cache. We design the first cross-core Prime+Probe attack on non-inclusive caches. This attack works with minimal assumptions: the adversary does not need to share any virtual memory with the victim, nor run on the same processor core. We also show the first high-bandwidth Evict+Reload attack on the same hardware. We demonstrate both attacks by extracting key bits during RSA operations in GnuPG on a state-of-the-art non-inclusive Intel Skylake-X server.
Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy

Colorectal cancer is the third most common cancer worldwide, and early therapeutic treatment of precancerous tissue during colonoscopy is crucial for better prognosis and can be curative. Navigation within the colon and comprehensive inspection of the endoluminal tissue are key to successful colonoscopy but can vary with the skill and experience of the endoscopist. Computer-assisted interventions in colonoscopy can provide better support tools for mapping the colon to ensure complete examination and for automatically detecting abnormal tissue regions.We train the conditional generative adversarial network pix2pix, to transform monocular endoscopic images to depth, which can be a building block in a navigational pipeline or be used to measure the size of polyps during colonoscopy. To overcome the lack of labelled training data in endoscopy, we propose to use simulation environments and to additionally train the generator and discriminator of the model on unlabelled real video frames in order to adapt to real colonoscopy environments.We report promising results on synthetic, phantom and real datasets and show that generative models outperform discriminative models when predicting depth from colonoscopy images, in terms of both accuracy and robustness towards changes in domains.Training the discriminator and generator of the model on real images, we show that our model performs implicit domain adaptation, which is a key step towards bridging the gap between synthetic and real data. Importantly, we demonstrate the feasibility of training a single model to predict depth from both synthetic and real images without the need for explicit, unsupervised transformer networks mapping between the domains of synthetic and real data.
Cost-sensitive KNN classification

KNN (K Nearest Neighbors) classification is one of top-10 data mining algorithms. It is significant to extend KNN classifiers sensitive to costs for imbalanced data classification applications. This paper designs two efficient cost-sensitive KNN classification models, referred to Direct-CS-KNN classifier and Distance-CS-KNN classifier. The two CS-KNN classifiers are further improved with extant strategies, such as smoothing, minimum-cost k-value selection, feature selection and ensemble selection. We evaluate our methods with real data sets, to show that our CS-KNN classifiers can significantly reduce misclassification cost.
Mobile virtual reality for musical genre learning in primary education

Mobile virtual reality (VR) is increasingly becoming popular and accessible to everyone that holds a smartphone. In particular, digital didactics can take advantage of natural interaction and immersion in virtual environments, starting from primary education. This paper investigates the problem of enhancing music learning in primary education through the use of mobile VR. To this end, technical and methodological frameworks were developed, and were tested with two classes in the last year of a primary school (10 years old children). The classes were involved in an evaluation study on music genre identification and learning with a multi-platform mobile application called VR4EDU. Students were immersed in music performances of different genres (e.g., classical, country, jazz, and swing), navigating inside several musical rooms. The evaluation of the didactic protocol shows a statistically significant improvement in learning genre characterization (i.e., typical instruments and their spatial arrangements on stage) compared to traditional lessons with printed materials and passive listening. These results show that the use of mobile VR technologies in synergy with traditional teaching methodologies can improve the music learning experience in primary education, in terms of active listening, attention, and time. The inclusion of pupils with certified special needs strengthened our results.
Robust Facial Landmark Detection via Occlusion-Adaptive Deep Networks

In this paper, we present a simple and effective framework called Occlusion-adaptive Deep Networks (ODN) with the purpose of solving the occlusion problem for facial landmark detection. In this model, the occlusion probability of each position in high-level features are inferred by a distillation module that can be learnt automatically in the process of estimating the relationship between facial appearance and facial shape. The occlusion probability serves as the adaptive weight on high-level features to reduce the impact of occlusion and obtain clean feature representation. Nevertheless, the clean feature representation cannot represent the holistic face due to the missing semantic features. To obtain exhaustive and complete feature representation, it is vital that we leverage a low-rank learning module to recover lost features. Considering that facial geometric characteristics are conducive to the low-rank module to recover lost features, we propose a geometry-aware module to excavate geometric relationships between different facial components. Depending on the synergistic effect of three modules, the proposed network achieves better performance in comparison to state-of-the-art methods on challenging benchmark datasets.
DenseU-Net-Based Semantic Segmentation of Small Objects in Urban Remote Sensing Images

Class imbalance is a serious problem that plagues the semantic segmentation task in urban remote sensing images.Since large object classes dominate the segmentation task, small object classes are usually suppressed, so the solutions based on optimizing the overall accuracy are often unsatisfactory.In the light of the class imbalance of the semantic segmentation in urban remote sensing images, we developed the concept of the Down-sampling Block (DownBlock) for obtaining context information and the Up-sampling Block (UpBlock) for restoring the original resolution.We proposed an end-to-end deep convolutional neural network (DenseU-Net) architecture for pixel-wise urban remote sensing image segmentation.The main idea of the DenseU-Net is to connect convolutional neural network features through cascade operations and use its symmetrical structure to fuse the detail features in shallow layers and the abstract semantic features in deep layers.A focal loss function weighted by the median frequency balancing (MFB_Focal loss ) is proposed; the accuracy of the small object classes and the overall accuracy are improved effectively with our approach.Our experiments were based on the 2016 ISPRS Vaihingen 2D semantic labeling dataset and demonstrated the following outcomes.In the case where boundary pixels were considered (GT), MFB_Focal loss achieved a good overall segmentation performance using the same U-Net model, and the F1score of the small object class ''car'' was improved by 9.28% compared with the cross-entropy loss function.Using the same MFB_Focal loss loss function, the overall accuracy of the DenseU-Net was better than that of U-Net, where the F1-score of the ''car'' class was 6.71% higher.Finally, without any post-processing, the DenseU-Net+MFB_Focal loss achieved the overall accuracy of 85.63%, and the F1-score of the ''car'' class was 83.23%, which is superior to HSN+OI+WBP both numerically and visually.
Blockchain-Assisted Public-Key Encryption with Keyword Search Against Keyword Guessing Attacks for Cloud Storage

Cloud storage enables users to outsource data to storage servers and retrieve target data efficiently. Some of the outsourced data are very sensitive and should be prevented for any leakage. Generally, if users conventionally encrypt the data, searching is impeded. Public-key encryption with keyword search (PEKS) resolves this tension. Whereas, it is vulnerable to keyword guessing attacks (KGA), since keywords are low-entropy. In this paper, we present a secure PEKS scheme called SEPSE against KGA, where users encrypt keywords with the aid of dedicated key servers via a threshold and oblivious way. SEPSE supports key renewal to periodically replace an existing key with a new one on each key server to thwart the key compromise. Furthermore, SEPSE can efficiently resist online KGA, where each keyword request made by a user is integrated into a transaction on a public blockchain (e.g., Ethereum), which allows key servers to learn the number of keyword requests made by the user without requiring a synchronization between them for per-user rate limiting. Security analysis and performance evaluation demonstrate that SEPSE provides a stronger security guarantee compared with existing schemes, at the expense of acceptable computational costs.
Exploration information systems – A proposal for the future use of GIS in mineral exploration targeting

The advent of modern data collection and storage technologies has brought about a huge increase in data volumes with both traditional and machine learning tools struggling to effectively handle, manage and analyse the very large data quantities that are now available. The mineral exploration industry is by no means immune to this big data issue. Exploration decision-making has become much more complex in the wake of big data, in particular with respect to questions about how to best manage and use the data to obtain information, generate knowledge and gain insight. One of the ways in which the mineral exploration industry works with big data is by using a geographic information system (GIS). For example, GIS platforms are often used for integration, interrogation and interpretation of diverse geoscience and mineral exploration data with the goal of refining and prioritising known and identifying new targets. Here we (i) briefly discuss the importance of carefully translating conceptual ore deposit models into effective exploration targeting maps, (ii) propose and describe what we term exploration information systems (EIS): a new idea for an information system designed to better integrate the conceptual mineral deposit model (i.e., the critical and constituent processes of the targeted mineral system) with data available to support exploration targeting, and (iii) discuss how best to categorise mineral systems in an EIS as scale-dependent subsystems to form mineral deposits. Our vision for the future use of EIS in exploration targeting is one whereby the mappable ingredients of a targeted mineral system are translated and combined into a set of weighted evidence (or proxy) maps automatically, resulting in an auto-generated mineral prospectivity map and a series of ranked exploration targets. We do not envisage the EIS replacing human input and ingenuity; rather we envisage the EIS as an additional tool in the exploration toolbox and as an intelligence amplifying system in which humans are making use of machines to achieve the best possible results.
Hybrid teaching–learning-based optimization and neural network algorithm for engineering design optimization problems

Neural network algorithm (NNA) is one of the newest meta-heuristic algorithms, which is inspired by biological nervous systems and artificial neural networks. Benefiting from the unique structure of artificial neural networks, NNA has good global search ability. However, slow convergence is its drawback that restricts its practical application. Teaching–learning-based optimization (TLBO) is an algorithm without any effort for fine tuning initial parameters, which has fast convergence speed while it is easy to fall into local optimum in solving complex global optimization problems. Considering the features of NNA and TLBO, an effective hybrid method based on TLBO and NNA, named TLNNA, is proposed for solving engineering optimization problems. The performance of TLNNA for 30 well-known unconstrained benchmark functions and 4 challenging engineering optimization problems is examined and the optimization results are compared with other competitive meta-heuristic algorithms. Such comparisons suggest that TLNNA has not only good global search ability of NNA but also fast convergence speed of TLBO and is more successful for most test problems in terms of solution quality and computational efficiency.
SCOPS: Self-Supervised Co-Part Segmentation

Parts provide a good intermediate representation of objects that is robust with respect to camera, pose and appearance variations. Existing work on part segmentation is dominated by supervised approaches that rely on large amounts of manual annotations and also can not generalize to unseen object categories. We propose a self-supervised deep learning approach for part segmentation, where we devise several loss functions that aids in predicting part segments that are geometrically concentrated, robust to object variations and are also semantically consistent across different object instances. Extensive experiments on different types of image collections demonstrate that our approach can produce part segments that adhere to object boundaries and also more semantically consistent across object instances compared to existing self-supervised techniques.
Lower bounds for finding stationary points I

We prove lower bounds on the complexity of finding $$\epsilon $$ -stationary points (points x such that $$\Vert \nabla f(x)\Vert \le \epsilon $$ ) of smooth, high-dimensional, and potentially non-convex functions f. We consider oracle-based complexity measures, where an algorithm is given access to the value and all derivatives of f at a query point x. We show that for any (potentially randomized) algorithm $$\mathsf {A}$$ , there exists a function f with Lipschitz pth order derivatives such that $$\mathsf {A}$$ requires at least $$\epsilon ^{-(p+1)/p}$$ queries to find an $$\epsilon $$ -stationary point. Our lower bounds are sharp to within constants, and they show that gradient descent, cubic-regularized Newton's method, and generalized pth order regularization are worst-case optimal within their natural function classes.
Opposition-based moth-flame optimization improved by differential evolution for feature selection

This paper provides an alternative method for creating an optimal subset from features which in turn represent the whole features through improving the moth-flame optimization (MFO) efficiency in searching for such optimal subset. The improvement is performed by combining the opposition-based learning technique and the differential evolution approach with the MFO. The opposition-based learning is used to generate an optimal initial population to improve the convergence of the MFO; meanwhile, the differential evolution is applied to improve the exploitation ability of the MFO. Therefore, the proposed method noted as OMFODE has the ability to avoid getting stuck in a local optimal value, unlike the traditional MFO algorithm and increase the fast convergence. The performance evaluation of our approach will be through a group of experimental results. In the first one, the proposed method has been tested over several CEC2005 benchmark functions. The second experimental series aims to assess the quality of the proposed method to improve the classification of ten UCI datasets by performing feature selection on such datasets. Another experiment is testing our method for classifying a real dataset, which represents some types of the galaxy images. The experimental results illustrated that the proposed algorithm is superior to the state-of-the-art meta-heuristic algorithms in terms of the performance measures.
Comparison between Multinomial and Bernoulli Naïve Bayes for Text Classification

Document/Text Classification has become an important area in the field of Machine Learning. On account of its wide applications in business, ham/spam filtering, health, e-commerce, social media sentiment, product sentiment among customers etc., various approaches have been devised to accurately predict the category or to classify any of the new text/document under consideration. Nowadays, news articles in the newspaper present various kinds of sentiments or inclination of the news article towards a negative or positive sentiment and hence, the content of the news can actively be used to judge the impact on the reader. The paper aims to predict that whether the sentiment of the news article is positive or negative using the two popular approaches of Naïve Bayes Text Categorization i.e. Multivariate Bernoulli Naïve Bayes Classification and Multinomial Naïve Bayes Classification. Also, the research aims to identify that which approach between the given two approaches perform better for the given dataset.
A constrained multi-swarm particle swarm optimization without velocity for constrained optimization problems

The original particle swarm optimization (PSO) is not able to tackle constrained optimization problems (COPs) due to the absence of constraint handling techniques. Furthermore, most existing PSO variants can only perform well in certain types of optimization problem and tend to suffer with premature convergence due to the limited search operator and directional information used to guide the search process. An improved PSO variant known as the constrained multi-swarm particle swarm optimization without velocity (CMPSOWV) is proposed in this paper to overcome the aforementioned drawbacks. Particularly, a constraint handling technique is first incorporated into CMPSOWV to guide population searching towards the feasible regions of search space before optimizing the objective function within the feasible regions. Two evolution phases known as the current swarm evolution and memory swarm evolution are also introduced to offer the multiple search operators for each CMPSOWV particle, aiming to improve the robustness of algorithm in solving different types of COPs. Finally, two diversity maintenance schemes of multi-swarm technique and probabilistic mutation operator are incorporated to prevent the premature convergence of CMPSOWV. The overall optimization performances of CMPSOWV in solving the CEC 2006 and CEC 2017 benchmark functions and real-world engineering design problems are compared with selected constrained optimization algorithms. Extensive simulation results report that the proposed CMPSOWV has demonstrated the best search accuracy among all compared methods in solving majority of problems.
An Initial Model of Trust in Chatbots for Customer Service—Findings from a Questionnaire Study

Abstract Chatbots are predicted to play a key role in customer service. Users’ trust in such chatbots is critical for their uptake. However, there is a lack of knowledge concerning users’ trust in chatbots. To bridge this knowledge gap, we present a questionnaire study (N = 154) that investigated factors of relevance for trust in customer service chatbots. The study included two parts: an explanatory investigation of the relative importance of factors known to predict trust from the general literature on interactive systems and an exploratory identification of other factors of particular relevance for trust in chatbots. The participants were recruited as part of their dialogue with one of four chatbots for customer service. Based on the findings, we propose an initial model of trust in chatbots for customer service, including chatbot-related factors (perceived expertise and responsiveness), environment-related factors (risk and brand perceptions) and user-related factors (propensity to trust technology). RESEARCH HIGHLIGHTS We extend the current knowledge base on natural language interfaces by investigating factors affecting users’ trust in chatbots for customer service. Chatbot-related factors, specifically perceived expertise and responsiveness, are found particularly important to users’ trust in such chatbots, but also environment-related factors such as brand perception and user-related factors such as propensity to trust technology. On the basis of the findings, we propose an initial model of users’ trust chatbots for customer service.
BlockchainDB

In this paper we present BlockchainDB , which leverages blockchains as a storage layer and introduces a database layer on top that extends blockchains by classical data management techniques (e.g., sharding) as well as a standardized query interface to facilitate the adoption of blockchains for data sharing use cases. We show that by introducing the additional database layer, we are able to improve the performance and scalability when using blockchains for data sharing and also massively decrease the complexity for organizations intending to use blockchains for data sharing.