Reviewerly/exHarmony huggingface dataset:
Ultrasound in augmented reality: a mixed-methods evaluation of head-mounted displays in image-guided interventions

Abstract Purpose Augmented reality (AR) and head-mounted displays (HMD) in medical practice are current research topics. A commonly proposed use case of AR-HMDs is to display data in image-guided interventions. Although technical feasibility has been thoroughly shown, effects of AR-HMDs on interventions are not yet well researched, hampering clinical applicability. Therefore, the goal of this study is to better understand the benefits and limitations of this technology in ultrasound-guided interventions. Methods We used an AR-HMD system (based on the first-generation Microsoft Hololens ) which overlays live ultrasound images spatially correctly at the location of the ultrasound transducer. We chose ultrasound-guided needle placements as a representative task for image-guided interventions. To examine the effects of the AR-HMD, we used mixed methods and conducted two studies in a lab setting: (1) In a randomized crossover study, we asked participants to place needles into a training model and evaluated task duration and accuracy with the AR-HMD as compared to the standard procedure without visual overlay and (2) in a qualitative study, we analyzed the user experience with AR-HMD using think-aloud protocols during ultrasound examinations and semi-structured interviews after the task. Results Participants ( n = 20) placed needles more accurately (mean error of 7.4 mm vs. 4.9 mm, p = 0.022) but not significantly faster (mean task duration of 74.4 s vs. 66.4 s, p = 0.211) with the AR-HMD. All participants in the qualitative study ( n = 6) reported limitations of and unfamiliarity with the AR-HMD, yet all but one also clearly noted benefits and/or that they would like to test the technology in practice. Conclusion We present additional, though still preliminary, evidence that AR-HMDs provide benefits in image-guided procedures. Our data also contribute insights into potential causes underlying the benefits, such as improved spatial perception. Still, more comprehensive studies are needed to ascertain benefits for clinical applications and to clarify mechanisms underlying these benefits.
Privacy-aware and Resource-saving Collaborative Learning for Healthcare in Cloud Computing

Electronic health records (EHR), generated in healthcare, contain extensive digital information, such as diagnoses, medications and complications. Recently, many studies have focused on constructing deep learning (DL) models with EHR data to improve the quality of healthcare services. However, in traditional centralized training, the collection of EHR causes serious privacy issues due to vulnerable transmission channels and untrusted DL service providers. An alternative that can mitigate the above privacy threat is federated learning (FL). It enables multiple healthcare institutions to learn a global predictive model by exchanging locally calculated updates without disclosing the private dataset. Unfortunately, the latest studies have shown that the local updates still expose sensitive information about the original training data. While several privacy-preserving FL protocols have been proposed, few prior works focused on energy consumption issues. Specifically, local training requires extensive computational resources, which is prohibitively expensive for resource-limited institutions. To overcome the above problems, we propose PRCL, a Privacy-aware and Resource-saving Collaborative Learning protocol. To reduce the local computational overhead, we design a novel model splitting method that partitions the neural network into three parts and outsources the computationally large middle part to cloud servers. By using the lightweight data perturbation and packed partially homomorphic encryption, PRCL protects the privacy of the original data and labels, as well as the parameters of the model. Moreover, we analyze the security of the proposed protocol, and demonstrate the superior performance of PRCL in terms of accuracy and efficiency.
Kullback–Leibler Divergence Metric Learning

The Kullback-Leibler divergence (KLD), which is widely used to measure the similarity between two distributions, plays an important role in many applications. In this article, we address the KLD metric-learning task, which aims at learning the best KLD-type metric from the distributions of datasets. Concretely, first, we extend the conventional KLD by introducing a linear mapping and obtain the best KLD to well express the similarity of data distributions by optimizing such a linear mapping. It improves the expressivity of data distribution, which means it makes the distributions in the same class close and those in different classes far away. Then, the KLD metric learning is modeled by a minimization problem on the manifold of all positive-definite matrices. To deal with this optimization task, we develop an intrinsic steepest descent method, which preserves the manifold structure of the metric in the iteration. Finally, we apply the proposed method along with ten popular metric-learning approaches on the tasks of 3-D object classification and document classification. The experimental results illustrate that our proposed method outperforms all other methods.
Measuring the similarity between multipolygons using convex hulls and position graphs

Polygon similarity can play an important role in geographic information retrieval, map matching and updating, and spatial data mining applications. Geographic information science (GIS) represents various spatial objects as polygons, including simple polygons and polygons with holes, as well as multipolygons. Spatial objects of multipolygons possess complex structure which makes it difficult to assess their similarity. This study develops a method based on convex hulls and position graphs to measure the similarity between multipolygons. The proposed method first finds correspondences between subpolygons in the two multipolygons based on a control polygon. Thereafter, the method constructs a position graph to denote the distribution of these subpolygons and applies a turning function to compute the similarity between various graphs. Fourier transformation and moment invariants were combined to characterize the different matching relationships among subpolygons. The experiments involve three different kinds multipolygons to verify the effectiveness and robustness of proposed method. The experiments show that this approach effectively measures similarity between multipolygons. Moreover, the proposed method accounts for the relationships across the entire complex geometrical shape and components of multipolygon during measuring similarity.
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning

Word embeddings, i.e., low-dimensional vector representations such as GloVe and SGNS, encode word "meaning" in the sense that distances between words' vectors correspond to their semantic proximity.This enables transfer learning of semantics for a variety of natural language processing tasks.Word embeddings are typically trained on large public corpora such as Wikipedia or Twitter.We demonstrate that an attacker who can modify the corpus on which the embedding is trained can control the "meaning" of new and existing words by changing their locations in the embedding space.We develop an explicit expression over corpus features that serves as a proxy for distance between words and establish a causative relationship between its values and embedding distances.We then show how to use this relationship for two adversarial objectives: (1) make a word a top-ranked neighbor of another word, and (2) move a word from one semantic cluster to another.An attack on the embedding can affect diverse downstream tasks, demonstrating for the first time the power of data poisoning in transfer learning scenarios.We use this attack to manipulate query expansion in information retrieval systems such as resume search, make certain names more or less visible to named entity recognition models, and cause new words to be translated to a particular target word regardless of the language.Finally, we show how the attacker can generate linguistically likely corpus modifications, thus fooling defenses that attempt to filter implausible sentences from the corpus using a language model.
Lexical coverage and profiling

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Influence of the design variables of photovoltaic plants with two-axis solar tracking on the optimization of the tracking and backtracking trajectory

Photovoltaics is experiencing significant growth because it is a renewable energy resource capable of contributing positively to the global energy system. However, the high variability in solar resources and the inter-collectors shadows negatively influence the energy conversion of a photovoltaic plant, making it necessary to develop technologies that minimize these negative aspects. Considering these issues, this paper analyses the influence of the design variables of a PV plant with two-axis tracking (collector size and distance between collectors) on its energy conversion. For that purpose, the tracking/backtracking strategy considered has two important advantages: it allows eliminating the inter-collector shading and optimizes the collection of global solar irradiance during both tracking and backtracking, by considering not only the optimization of direct component of solar irradiance but also the diffuse and reflected components. Based on this analysis, specific design recommendations are proposed for two-axis tracking PV plants located in Cordoba (Spain). It has been proved that differences in orientation between the collectors running according to this tracking/backtracking strategy and those with pure astronomical tracking increases when the clearness index decreases. Similarly, the results show that the period of time requiring backtracking during sunrise and sunset is longer when GCR decreases. In any case, it has been shown that photovoltaic plants in Cordoba with this new tracking strategy could provide annually, at least, 2% more energy than plants using some existing tracking strategies, even those defined as ideal. Hence, this tracking/backtracking strategy might significantly contribute to the development of photovoltaic plants with two-axis trackers.
The Role of Artificial Intelligence and Cyber Security for Social Media

Social media systems such as Facebook and Twitter are playing a major role in society connecting over a billion people worldwide and enabling them to communicate and share information with each other as well as within a group of individuals. These social media systems can vastly help humanity such as spread information about infectious diseases and discuss solutions to problems faced by humanity including preventing child trafficking and violence against women. However, social media systems can also do harm, such as shared false information, more popularly known as fake news, as well as violating the privacy of individuals. With the proliferation of Artificial Intelligence (AI) systems together with powerful machine learning techniques as well as the cyber-attacks on information systems are changing the way the social media systems are being used by humans. This paper discusses the role of both AI and Cyber Security for social media systems including the benefits of AI as well as protecting social media systems.
Knowledge management capability, environmental dynamism and innovation strategy in Malaysian firms

Purpose The objectives of this study is first to assess the effect of organizational antecedents on knowledge management capability and its effect on innovation strategy through the moderating effect of environmental dynamism. Design/methodology/approach The data were collected from SMEs in central region of Malaysia through a cross-sectional survey of 202 owners and analyzed through structural equation modeling using SmartPLS software. Findings The results show that while formal structure, selection policies, incentives and training and development policies have positive affect on knowledge management capability, there is no effect of innovation culture on knowledge management capability. Knowledge management capability has a positive and significant effect on innovation strategy. Interestingly, the findings show that environmental dynamism strengthens the positive effect of knowledge management capability on innovation strategy. Practical implications The findings of this study emphasize on the potential of collaboration among people for creating effective knowledge sharing in organizations and modalities in order to successfully design a collaborative knowledge-based work environment. Originality/value The model links organizational antecedents with knowledge management capability and the critical role of knowledge management capability on innovation strategy of SMEs considering environmental dynamism.
FLEX: A Content Based Movie Recommender

Recommender systems are an efficient and powerful method for enabling users to filter through large information and product spaces. In this paper, we present a movie recommendation framework (FLEX) following a content based filtering approach. FLEX extends existing approaches like Doc2Vec and tf-idf by using a hybrid of the two methods. We use publicly available features such as movie plots, ratings, countries of production and release year to find similarity between movies and generate a recommendation list. By providing recommendations which are consistent with customer interests, the aim is to make a platform personalised and ensure customer engagement.
Guardian: Evaluating Trust in Online Social Networks with Graph Convolutional Networks

In modern online social networks, each user is typically able to provide a value to indicate how trustworthy their direct friends are. Inferring such a value of social trust between any pair of nodes in online social networks is useful in a wide variety of applications, such as online marketing and recommendation systems. However, it is challenging to accurately and efficiently evaluate social trust between a pair of users in online social networks. Existing works either designed handcrafted rules that rely on specialized domain knowledge, or required a significant amount of computation resources, which affected their scalability.In recent years, graph convolutional neural networks (GCNs) have been shown to be powerful in learning on graph data. Their advantages provide great potential to trust evaluation as social trust can be represented as graph data. In this paper, we propose Guardian, a new end-to-end framework that learns latent factors in social trust with GCNs. Guardian is designed to incorporate social network structures and trust relationships to estimate social trust between any two users. Extensive experimental results demonstrated that Guardian can speedup trust evaluation by up to 2, 827 × with comparable accuracy, as compared to the stateof-the-art in the literature.
Wait for it: identifying “On-Hold” self-admitted technical debt

Abstract Self-admitted technical debt refers to situations where a software developer knows that their current implementation is not optimal and indicates this using a source code comment. In this work, we hypothesize that it is possible to develop automated techniques to understand a subset of these comments in more detail, and to propose tool support that can help developers manage self-admitted technical debt more effectively. Based on a qualitative study of 333 comments indicating self-admitted technical debt, we first identify one particular class of debt amenable to automated management: on-hold self-admitted technical debt (on-hold SATD), i.e., debt which contains a condition to indicate that a developer is waiting for a certain event or an updated functionality having been implemented elsewhere. We then design and evaluate an automated classifier which can identify these on-hold instances with an area under the receiver operating characteristic curve (AUC) of 0.98 as well as detect the specific conditions that developers are waiting for. Our work presents a first step towards automated tool support that is able to indicate when certain instances of self-admitted technical debt are ready to be addressed.
Voiceprint Mimicry Attack Towards Speaker Verification System in Smart Home

The advancement of voice controllable systems (VC-Ses) has dramatically affected our daily lifestyle and catalyzed the smart home's deployment. Currently, most VCSes exploit automatic speaker verification (ASV) to prevent various voice attacks (e.g., replay attack). In this study, we present VMask, a novel and practical voiceprint mimicry attack that could fool ASV in smart home and inject the malicious voice command disguised as a legitimate user. The key observation behind VMask is that the deep learning models utilized by ASV are vulnerable to the subtle perturbations in the voice input space. To generate these subtle perturbations, VMask leverages the idea of adversarial examples. Then by adding the subtle perturbations to the recordings from an arbitrary speaker, VMask can mislead the ASV into classifying the crafted speech samples, which mirror the former speaker for human, as the targeted victim. Moreover, psychoacoustic masking is employed to manipulate the adversarial perturbations under human perception threshold, thus making victim unaware of ongoing attacks. We validate the effectiveness of VMask by performing comprehensive experiments on both grey box (VGGVox) and black box (Microsoft Azure Speaker Verification) ASVs. Additionally, a real-world case study on Apple HomeKit proves the VMask's practicability on smart home platforms.
SkyChain: A Deep Reinforcement Learning-Empowered Dynamic Blockchain Sharding System

To overcome the limitations on the scalability of current blockchain systems, sharding is widely considered as a promising solution that divides the network into multiple disjoint groups processing transactions in parallel to improve throughput while decreasing the overhead of communication, computation, and storage. However, most existing blockchain sharding systems adopt a static sharding policy that cannot efficiently deal with the dynamic environment in the blockchain system, i.e., joining and leaving of nodes, and malicious attack. This paper presents SkyChain, a novel dynamic sharding-based blockchain framework to achieve a good balance between performance and security without compromising scalability under the dynamic environment. We first propose an adaptive ledger protocol to guarantee that the ledgers can merge or split efficiently based on the dynamic sharding policy. Then, to optimize the sharding policy under dynamic environment with high dimensional system states, a deep reinforcement learning-based sharding approach has been proposed, the goals of which include: 1) building a framework to evaluate the blockchain sharding systems from the aspects of performance and security; 2) adjusting the re-sharding interval, shard number and block size to maintain a long-term balance of the system’s performance and security. Experimental results show that SkyChain can effectively improve the performance and security of the sharding system without compromising scalability under the dynamic environment in the blockchain system.
A system for educational and vocational guidance in Morocco: Chatbot E-Orientation

Chatbots are currently used in various online applications, most of the case for shopping or as a personal assistant. These chatbots offer a range of potential benefits, including personalization and 24/7 instant availability. These positive aspects of chatbots can be beneficial in the educational sector. They represent a new type of human-machine interface in natural language. However, chatbots in academia have received only limited attention, for example by providing organizational support for studies or courses and exams. This branch of research is just emerging in the scientific community, therefore, in our article, we set up a chatbot in the field of educational and professional guidance which is based on the theory of John Holland and the RIASEC questionnaire in order to determine the dominant type of personality of undergraduate and graduate students that wants to enter the job market.
Identifying Human Emotions from Facial Expressions with Deep Learning

The identification of facial expressions that reveal human emotions can help computers to better assess the human state of mind, so as to provide a more customized interaction. We explore the recognition of human facial expressions through a deep learning approach using a Convolutional Neural Network (CNN) algorithm. The system uses a labelled data set containing around 32,298 images with multiple facial expressions for training and testing. The pre-training phase involves a face detection subsystem with noise removal, including feature extraction. The generated classification model used for prediction can identify seven emotions of the Facial Action Coding System (FACS). Results of our work in progress demonstrate an accuracy of 79.8% for the recognition of all basic seven human emotions, without the application of optimization techniques.
Neural Dense Non-Rigid Structure from Motion with Latent Space Constraints

We introduce the first dense neural non-rigid structure from motion (N-NRSfM) approach, which can be trained end-to-end in an unsupervised manner from 2D point tracks. Compared to the competing methods, our combination of loss functions is fully-differentiable and can be readily integrated into deep-learning systems. We formulate the deformation model by an auto-decoder and impose subspace constraints on the recovered latent space function in a frequency domain. Thanks to the state recurrence cue, we classify the reconstructed non-rigid surfaces based on their similarity and recover the period of the input sequence. Our N-NRSfM approach achieves competitive accuracy on widely-used benchmark sequences and high visual quality on various real videos. Apart from being a standalone technique, our method enables multiple applications including shape compression, completion and interpolation, among others. Combined with an encoder trained directly on 2D images, we perform scenario-specific monocular 3D shape reconstruction at interactive frame rates. To facilitate the reproducibility of the results and boost the new research direction, we open-source our code and provide trained models for research purposes (http://gvv.mpi-inf.mpg.de/projects/Neural_NRSfM/).
Trustful Resource Management for Service Allocation in Fog-Enabled Intelligent Transportation Systems

A fog-enabled intelligent transportation system is constructed using Vehicle-to-Everything (V2X) communications towards the Internet of Vehicles (IoV). In this system, the road side unit (RSU) and vehicle correspond to the fog server and fog device, respectively. In practice, the RSU offers instant infotainment services (e.g., video streaming and traffic assistance) to vehicles. However, the vehicles are considered untrustworthy and could possibly be exploited to attack the system, thereby compromising the service stability and data integrity. To address this problem, this article proposes truthful service allocation using a Vickrey-Clarke-Groves (VCG) auction mechanism. The RSU leads the proposed auction as a seller who sells their computing resources, and the vehicles participate as buyers who purchase these computing resources for the offered services. Consequently, the RSU generates a transaction whenever a service allocation is assigned. To secure the service transactions, we utilize a distributed blockchain system that implements Hyperledger Fabric framework among RSUs for transaction verification. The simulation results demonstrate that the proposed system provides service stability while ensuring service trustfulness.