Reviewerly/exHarmony huggingface dataset:
Robot Machines and Civil Liability

The legal consideration of a robot machine as a ‘product’ has led to the application of civil liability rules for producers. Nevertheless, some aspects of the relevant European regulation suggest special attention should be devoted to a review in this field in relation to robotics. Types of defect, the meanings of the term ‘producer’, the consumer expectation test and non-pecuniary damages are some of the aspects that could give rise to future debate. The inadequacy of the current Directive 85/374/EEC for regulating damages caused by robots, particularly those with self-learning capability, is highlighted by the document ‘Follow up to the EU Parliament Resolution of 16 February 2017 on Civil Law Rules on Robotics’. Other relevant documents are the Report on “Liability for AI and other emerging digital technologies” prepared by the Expert Group on Liability and New Technologies, the “Report on the safety and liability implications of Artificial Intelligence, the Internet of Things and Robotics” [COM(2020) 64 final, 19.2.2020] and the White Paper “On Artificial Intelligence – A European approach to excellence and trust” [COM(2020) 65 final, 19.2.2020].
A Teaching-Learning-Based Optimization with Modified Learning Phases for Continuous Optimization

The deviation between the modelling of teaching-learning based optimization (TLBO) framework and actual scenario of classroom teaching and learning process is considered as one factor which contributes to the imbalance of algorithm’s exploration and exploitation searches, hence restricting its search performance. In this paper, the TLBO with modified learning phases (TLBO-MLPs) is proposed to achieve better search performance of algorithm through the further refinement of learning framework so that it can reflect the actual teaching and learning processes in classroom more accurately. A modified teacher phase is first introduced in TLBO-MLPs, where each learner is modelled to have different perspectives of mainstream knowledge in classroom to maintain the diversity of population’s knowledge. A modified learner phase consisting of an adaptive peer learning mechanism and a self-learning mechanism are also proposed in TLBO-MLPs. The former mechanism enables each learner to interact with multiple learners in gaining new knowledge for different subjects, while the latter facilitates the update of new knowledge through personal efforts. The overall performances of TLBO-MLPs in solving the CEC 2014 test functions are compared with seven competitors. Extensive simulation results show that TLBO-MLPs has demonstrated the best search performance among all compared methods in solving majority of test functions.
Document Classification by Word Embeddings of BERT

Bidirectional Encoder Representations from Transformers (BERT) is a pre-training model that uses the encoder component of a bidirectional transformer and converts an input sentence or input sentence pair into word enbeddings. The performance of various natural language processing systems has been greatly improved by BERT. However, for a real task, it is necessary to consider how BERT is used based on the type of task. The standerd method for document classification by BERT is to treat the word embedding of special token [CLS] as a feature vector of the document, and to fine-tune the entire model of the classifier, including a pre-training model. However, after normalizing each the feature vector consisting of the mean vector of word embeddings outputted by BERT for the document, and the feature vectors according to the bag-of-words model, we create a vector concatenating them. Our proposed method involves using the concatenated vector as the feature vector of the document.
Local quantum Fisher information and one-way quantum deficit in spin-1/2 XX Heisenberg chain with three-spin interaction

We explore quantum phase transitions in the spin-1/2 [Formula: see text] chain with three-spin interaction in terms of local quantum Fisher information and one-way quantum deficit, together with the demonstration of quantum fluctuations. Analytical results are derived and analyzed in detail.
Optimized tour planning for drone-based urban traffic monitoring

Drones or Unmanned Aerial Vehicles (UAVs) have become a reliable and efficient tool for road traffic monitoring. Compared to loop detectors and bluetooth receivers (with high capital and operational expenditure), drones are a low-cost alternative that offers great flexibility and high quality data.In this work, we derive optimized tour plans that a fleet of drones can follow for rapid traffic monitoring across particular regions of the transportation network. To derive these tours, we first identify monitoring locations over which drones should fly through and then compute minimum travel-time tours based on realistic resource constrains. Evaluation results are presented over a real road network topology to demonstrate the applicability of the proposed approach.
Pengembangan Lembar Kerja Siswa Berbasis STEAM untuk Siswa Sekolah Dasar

The aim of this research is to develop and evaluate STEAM based student worksheet book (LKS). This research used Research Design, development ADDIE’s Model consisting of Analysis, Design, Development, Implementation and Evaluation. This research had been implemented in SDN 05 Aikmel, in which the population of the study was 23 students of the third grade. It had 6 samples where the samples were taken by purpose sampling method with low, medium, and high achievement criteria. The collected data were gained by interview and questionnaire. The gained data were then analyzed by using rating scale, on a scale of 5 which were subsequently converted to qualitative data. The result of the study showed that the average value of the experts was 4, 28 with very good category, while the user response (teachers and students) was 4, 55 with a very good category. The conclusions of this research shows that STEAM based student worksheet book which is developed eligibel and can be used as a supporting learning resources for 2013 curriculum based thematic learning.
Towards a Tool to Translate Brazilian Sign Language (Libras) to Brazilian Portuguese and Improve Communication with Deaf

In Brazil, only 15% of deaf people are fluent in Brazilian Portuguese. Although Brazilian law says that public spaces should provide access in Libras, deaf still face lots of issues accessing customer services. Also, there is no tool able to allow them to talk in Libras via customer service communication channels, such as chats. Thus, this work aims to help deaf to access the customer services of a big computer manufacturer by providing a way for them to communicate in Libras while the call center attendant receives a translation in Brazilian Portuguese. The method to translate Libras to Portuguese includes the use of a CNN. The results in recognizing and classifying signs (captured from video) reached over 90% of the accuracy in the training phase.
Production of Digital Fashion Contents based on Augmented Reality Using CLO 3D and Vuforia

This study defines the pipeline for digital fashion contents production using CLO 3D, a 3D fashion design software that supports virtual clothing visualization as a cutting-edge simulation technology for fashion, and vuforia, a mobile platform augmented reality (AR) development kit for creating AR applications. The proposed production pipeline is organized in a method to produce a virtual clothing model through CLO 3D software through works of patterns, sewing lines, textures, etc., and AR contents based on computer vision techniques using the functions and properties of vuforia development kits in the Unity engine development environment. In addition, we present application method that can be practically utilized from the perspective of practical users, such as fashion designers and directors, by creating a new type of AR digital fashion contents directly as a flow of the defined production pipeline.
Image subset communication for resource-constrained applications in wireless sensor networks

JPEG is the most widely used image compression standard for sensing, medical, and security applications. JPEG provides a high degree of compression but field devices relying on battery power must further economize on data transmissions to prolong deployment duration with particular use cases in wireless sensor networks. Transmitting a subset of image data could potentially enhance the battery life of power-constrained devices and also meet the application requirements to identify the objects within an image. Depending on an application's needs, after the first selected subset is received at the base station, further transmissions of the image data for successive refinements can also be requested. Needs for such progressive refinements exist in applications including tele-medicine, security, and surveillance, where an initial assessment could govern further exploration of only a small region. We propose a scheme for selecting minimum information for a coarser reconstruction by transmitting only the DC coefficients as the first or base layer. This initial layer of information could on request be augmented by transmitting either more data representing an entire image or a selected region of interest. We compare our results with those of other power economization and progressive communications techniques. The proposed scheme offers significant advantages for a range of application scenarios under resource constraints.
The Gradient Complexity of Linear Regression

We investigate the computational complexity of several basic linear algebra primitives, including largest eigenvector computation and linear regression, in the computational model that allows access to the data via a matrix-vector product oracle. We show that for polynomial accuracy, $\Theta(d)$ calls to the oracle are necessary and sufficient even for a randomized algorithm. \n\nOur lower bound is based on a reduction to estimating the least eigenvalue of a random Wishart matrix. This simple distribution enables a concise proof, leveraging a few key properties of the random Wishart ensemble.
Learning a Single Neuron with Gradient Methods

We consider the fundamental problem of learning a single neuron $\bx\mapsto \sigma(\bw^\top\bx)$ in a realizable setting, using standard gradient methods with random initialization, and under general families of input distributions and activations. On the one hand, we show that some assumptions on both the distribution and the activation function are necessary. On the other hand, we prove positive guarantees under mild assumptions, which go significantly beyond those studied in the literature so far. We also point out and study the challenges in further strengthening and generalizing our results.
Data-Driven Model-Based Analysis of the Ethereum Verifier's Dilemma

In proof-of-work based blockchains such as Ethereum, verification of blocks is an integral part of establishing consensus across nodes. However, in Ethereum, miners do not receive a reward for verifying. This implies that miners face the Verifier's Dilemma: use resources for verification, or use them for the more lucrative mining of new blocks? We provide an extensive analysis of the Verifier's Dilemma, using a data-driven model-based approach that combines closed-form expressions, machine learning techniques and discrete-event simulation. We collect data from over 300,000 smart contracts and experimentally obtain their CPU execution times. Gaussian Mixture Models and Random Forest Regression transform the data into distributions and inputs suitable for the simulator. We show that, indeed, it is often economically rational not to verify, in particular for miners with less hashing power. We consider two approaches to mitigate the implications of the Verifier's Dilemma, namely parallelization and active insertion of invalid blocks, both will be shown to be effective.
HybCache: Hybrid Side-Channel-Resilient Caches for Trusted Execution Environments

Modern multi-core processors share cache resources for maximum cache utilization and performance gains. However, this leaves the cache vulnerable to side-channel attacks, where timing differences in shared cache behavior are exploited to infer information on the victim's execution patterns, ultimately leaking private information. The root cause for these attacks is mutually distrusting processes sharing cache entries and accessing them in a deterministic manner. Various defenses against cache side-channel attacks have been proposed. However, they either degrade performance significantly, impose impractical restrictions, or can only defeat certain classes of these attacks. More importantly, they assume that side-channel-resilient caches are required for the entire execution workload and do not allow to selectively enable the mitigation only for the security-critical portion of the workload. We present a generic mechanism for a flexible and soft partitioning of set-associative caches and propose a hybrid cache architecture, called HybCache. HybCache can be configured to selectively apply side-channel-resilient cache behavior only for isolated execution domains, while providing the non-isolated execution with conventional cache behavior, capacity and performance. An isolation domain can include one or more processes, specific portions of code, or a Trusted Execution Environment. We show that, with minimal hardware modifications and kernel support, HybCache can provide side-channel-resilient cache only for isolated execution with a performance overhead of 3.5-5%, while incurring no performance overhead for the remaining execution workload. We provide a simulator-based and hardware implementation of HybCache to evaluate the performance and area overheads, and show how it mitigates typical access-based and contention-based cache attacks.
DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation

Image spam emails are often used to evade text-based spam filters that detect spam emails with their frequently used keywords. In this paper, we propose a new image spam email detection tool called DeepCapture using a convolutional neural network (CNN) model. There have been many efforts to detect image spam emails, but there is a significant performance degrade against entirely new and unseen image spam emails due to overfitting during the training phase. To address this challenging issue, we mainly focus on developing a more robust model to address the overfitting problem. Our key idea is to build a CNN-XGBoost framework consisting of eight layers only with a large number of training samples using data augmentation techniques tailored towards the image spam detection task. To show the feasibility of DeepCapture, we evaluate its performance with publicly available datasets consisting of 6,000 spam and 2,313 non-spam image samples. The experimental results show that DeepCapture is capable of achieving an F1-score of 88%, which has a 6% improvement over the best existing spam detection model CNN-SVM [19] with an F1-score of 82%. Moreover, DeepCapture outperformed existing image spam detection solutions against new and unseen image datasets.
Aircraft Fingerprinting Using Deep Learning

Aircraft periodically broadcast their position, identity and other information using the ADS-B protocol. This allows safe air traffic flow as ground stations and other aircraft can depend on the sent information. However, these messages are not authenticated or encrypted. Therefore, this system is vulnerable to attacks from Software Defined Radios (SDRs) and other transmitters. We propose a deep learning-based approach for fingerprinting of aircraft messages based on physical characteristics. This helps to verify the origin of an observed message.
Lightweight Virtual Payment Channels

Blockchain systems have severe scalability limitations e.g., long confirmation delays. Layer-2 protocols are designed to address such limitations. The most prominent class of such protocols are payment channel networks e.g., the Lightning Network for Bitcoin where pairs of participants create channels that can be concatenated into networks. These allow payments across the network without interaction with the blockchain. A drawback is that all intermediary nodes within a payment path must be online. Virtual Channels, as recently proposed by Dziembowski et al. (CCS’18), allow payments without this limitation. However, these can only be implemented on blockchains with smart contract capability therefore limiting its applicability. Our work proposes the notion of –Lightweight– Virtual Payment Channels, i.e. only requiring timelocks and multisignatures, enabling Virtual Channels on a larger range of blockchain systems of which a prime example is Bitcoin. More concretely, other contributions of this work are (1) to introduce a fully-fledged formalization of our construction, and (2) to present a simulation based proof of security in Canetti’s UC Framework.
Validation of the impact of marketing knowledge management on business performance via digital financial innovation as a mediating factor

Purpose This study aims to examine and validate the impact of marketing knowledge management (MKM) (assets and capabilities) on business performance (BP) via the mediating role of the digital financial innovation in Jordanian commercial banks. Design/methodology/approach Based on a literature review, recourses-based theory, knowledge-based theory and financial innovation theory, an integrated conceptual framework has been developed to guide the study. A quantitative survey approach was used, and the data was collected from 336 managers and employees in all 13 Jordanian commercial banks using online and in hand instruments. Structural equation modeling was used to analyze and verify the study variables. Findings The main findings revealed that the MKM had a significant positive influence on BP. Digital financial innovation acted as a partial mediators in this relationship. Originality/value This paper contributes to theory by filling a gap in the literature regarding the role of MKM assets and capabilities in commercial banks operating in developing countries such as Jordan. It empirically examines and validates the role of digital financial innovation as mediators between MKM and BP.
Visor: Privacy-Preserving Video Analytics as a Cloud Service.

Video-analytics-as-a-service is becoming an important offering for cloud providers. A key concern in such services is privacy of the videos being analyzed. While trusted execution environments (TEEs) are promising options for preventing the direct leakage of private video content, they remain vulnerable to side-channel attacks. We present Visor, a system that provides confidentiality for the user’s video stream as well as the ML models in the presence of a compromised cloud platform and untrusted co-tenants. Visor executes video pipelines in a hybrid TEE that spans both the CPU and GPU. It protects the pipeline against side-channel attacks induced by data-dependent access patterns of video modules, and also addresses leakage in the CPU-GPU communication channel. Visor is up to 1000× faster than naive oblivious solutions, and its overheads relative to a non-oblivious baseline are limited to 2×–6×.
Learning Inter- and Intra-manifolds for Matrix Factorization-based Multi-Aspect Data Clustering

Clustering on the data with multiple aspects, such as multi-view or multi-type relational data, has become popular in recent years due to their wide applicability.The approach using manifold learning with the Non-negative Matrix Factorization (NMF) framework, that learns the accurate low-rank representation of the multi-dimensional data, has shown effectiveness.We propose to include the inter-manifold in the NMF framework, utilizing the distance information of data points of different data types (or views) to learn the diverse manifold for data clustering.Empirical analysis reveals that the proposed method can find partial representations of various interrelated types and select useful features during clustering.Results on several datasets demonstrate that the proposed method outperforms the state-of-the-art multi-aspect data clustering methods in both accuracy and efficiency.
The interplay between artificial intelligence and fog radio access networks

The interplay between artificial intelligence (AI) and fog radio access networks (F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning (RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatio-temporal unknown content popularity to showcase the potential of applying AI to F-RANs.
New Local Region Based Model for the Segmentation of Medical Images

Segmentation of images having inhomogeneous intensities is always challenging. In this paper, we propose a model based on new local data statistics using local means and variances for detection of region of interest in medical images suffered from intensity inhomogeneity. This is done by introducing a new probability density function based on coefficient of variation, which is a best measure for inhomogeneous data. The new energy functional in the proposed model is then expressed in terms of level set function and is minimized for optimal energy. Minimization of the energy will lead to a partial differential equation, which is solved by using well known explicit method. Results of the proposed model are compared with other state of the art models and found that the proposed model outperform other existing models. Comparison is given in both qualitative and quantitative way. Furthermore, the proposed model is tested on different type of medical images like MRI, CT, Mammogram and skin lesion etc.
Decentralized Embedding Framework for Large-Scale Networks

Network embedding aims to learn vector representations of vertices, that preserve both network structures and properties. However, most existing embedding methods fail to scale to large networks. A few frameworks have been proposed by extending existing methods to cope with network embedding on large-scale networks. These frameworks update the global parameters iteratively or compress the network while learning vector representation. Such network embedding schemes inevitably lead to a high cost of either high communication overhead or sub-optimal embedding quality. In this paper, we propose a novel decentralized large-scale network embedding framework called DeLNE. As the name suggests, DeLNE divides a network into smaller partitions and learn vector representation in a distributed fashion, avoiding any unnecessary communication overhead. Our proposed framework uses Variational Graph Convolution Auto-Encoders to embed the structure and properties of each sub-network. Secondly, we propose an embedding aggregation mechanism, that captures the global properties of each node. Thirdly, we propose an alignment function, that reconciles all sub-networks embedding into the same vector space. Due to the parallel nature of DeLNE, it scales well on large clustered environments. Through extensive experimentation on realistic datasets, we show that DeLNE produces high-quality embedding and outperforms existing large-scale network embeddings frameworks, in terms of both efficiency and effectiveness.