Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Distributed Ledger Technologies for Smart Contract Development

Development of Distributed Ledger Technology (DLT)-based applications requires an appropriate platform that meets the application requirements. However, due to the abundance of such platforms such as Ethereum, NEM, IOTA, and OpenChain, and the differences among them in terms of scalability, throughput, and features, it is not easy to select a platform for a given use-case. Selection of the right DLT platform is pivotal for the performance of applications and thus-forth directly affects consumer satisfaction. Therefore, the aforementioned factors must be taken into account to decide on a particular platform. To fill this gap, in this paper, we conduct a comparative analysis of different DLT platforms. The choice of platform is based on their popularity and current market share as well as the evolving trends and approaches. In essence, we choose Ethereum, EOS, Hyperledger Sawtooth and NEO. We compare these platforms from both development and performance perspectives. The comparison revealed that Sawtooth provides a huge customization capability that affects the performance and EOS maintains a stable throughput under varying network scales and loads.
An investigation on the user interaction modes of conversational recommender systems for the music domain

Conversational Recommender Systems (CoRSs) implement a paradigm that allows users to interact in natural language with the system for defining their preferences and discovering items that best fit their needs. CoRSs can be straightforwardly implemented as chatbots that, nowadays, are becoming more and more popular for several applications, such as customer care, health care, and medical diagnoses. Chatbots implement an interaction based on natural language, buttons, or both. The implementation of a chatbot is a challenging task since it requires knowledge about natural language processing and human–computer interaction. A CoRS might be particularly useful in the music domain since music is generally enjoyed in contexts when a standard interface cannot be exploited (driving, doing homeworks, running). However, there is no work in the literature that analytically compares different interaction modes for a conversational music recommender system. In this paper, we focus on the design and implementation of a CoRS for the music domain. Our CoRS consists of different components. The system implements content-based recommendation, critiquing and adaptive strategies, as well as explanation facilities. The main innovative contribution is that the user can interact through different interaction modes: natural language, buttons, and mixed. Due to the lack of available datasets for testing CoRSs, we carried out an in vivo experimental evaluation with the goal of investigating the impact of the different interaction modes on the recommendation accuracy and on the cost of interaction for the final user. The experiment involved 110 people, and 54 completed the whole process. The analysis of the results shows that the best interaction mode is based on a mixed strategy that combines buttons and natural language. In addition, the results allow to clearly understand which are the steps in the dialog that are particularly strenuous for the user.
Partition-balanced families of codes and asymptotic enumeration in coding theory

We introduce the class of partition-balanced families of codes, and show how to exploit their combinatorial invariants to obtain upper and lower bounds on the number of codes that have a prescribed property. In particular, we derive precise asymptotic estimates on the density functions of several classes of codes that are extremal with respect to minimum distance, covering radius, and maximality. The techniques developed in this paper apply to various distance functions, including the Hamming and the rank metric distances. Applications of our results show that, unlike the Fqm-linear MRD codes, the Fq-linear MRD codes are not dense in the family of codes of the same dimension. More precisely, we show that the density of Fq-linear MRD codes in Fqn×m in the set of all matrix codes of the same dimension is asymptotically at most 1/2, both as q→+∞ and as m→+∞. We also prove that MDS and Fqm-linear MRD codes are dense in the family of maximal codes. Although there does not exist a direct analogue of the redundancy bound for the covering radius of Fq-linear rank metric codes, we show that a similar bound is satisfied by a uniformly random matrix code with high probability. In particular, we prove that codes meeting this bound are dense. Finally, we compute the average weight distribution of linear codes in the rank metric, and other parameters that generalize the total weight of a linear code.
Unsupervised Deep Contrast Enhancement With Power Constraint for OLED Displays

Various power-constrained contrast enhance-ment (PCCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the pow-er demands of the display while preserving the image qual-ity. In this paper, we propose a new deep learning-based PCCE scheme that constrains the power consumption of the OLED displays while enhancing the contrast of the displayed image. In the proposed method, the power con-sumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is pre-served as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN). Furthermore, our CNN can learn the PCCE technique without a reference image by unsupervised learning. Ex-perimental results show that the proposed method is supe-rior to conventional ones in terms of image quality assess-ment metrics such as a visual saliency-induced index (VSI) and a measure of enhancement (EME).1.
PenQuest: a gamified attacker/defender meta model for cyber security assessment and education

Abstract Attacks on IT systems are a rising threat against the confidentiality, integrity, and availability of critical information and infrastructures. At the same time, the complex interplay of attack techniques and possible countermeasures makes it difficult to appropriately plan, implement, and evaluate an organization’s defense. More often than not, the worlds of technical threats and organizational controls remain disjunct. In this article, we introduce PenQuest, a meta model designed to present a complete view on information system attacks and their mitigation while providing a tool for both semantic data enrichment and security education. PenQuest simulates time-enabled attacker/defender behavior as part of a dynamic, imperfect information multi-player game that derives significant parts of its ruleset from established information security sources such as STIX, CAPEC, CVE/CWE and NIST SP 800-53. Attack patterns, vulnerabilities, and mitigating controls are mapped to counterpart strategies and concrete actions through practical, data-centric mechanisms. The gamified model considers and defines a wide range of actors, assets, and actions, thereby enabling the assessment of cyber risks while giving technical experts the opportunity to explore specific attack scenarios in the context of an abstracted IT infrastructure. We implemented PenQuest as a physical serious game prototype and successfully tested it in a higher education environment. Additional expert interviews helped evaluate the model’s applicability to information security scenarios.
Seeing and Hearing Egocentric Actions: How Much Can We Learn?

Our interaction with the world is an inherently multimodal experience.However, the understanding of humanto-object interactions has historically been addressed focusing on a single modality.In particular, a limited number of works have considered to integrate the visual and audio modalities for this purpose.In this work, we propose a multimodal approach for egocentric action recognition in a kitchen environment that relies on audio and visual information.Our model combines a sparse temporal sampling strategy with a late fusion of audio, spatial, and temporal streams.Experimental results on the EPIC-Kitchens dataset show that multimodal integration leads to better performance than unimodal approaches.In particular, we achieved a 5.18% improvement over the state of the art on verb classification.
Automated detection and quantification of breast cancer brain metastases in an animal model using democratized machine learning tools

Abstract Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may not be immediately available in the traditional wet-lab environment. Here, we propose a simple and accessible workflow to automate detection and quantification of brain epithelial metastases on digitized histological slides. We leverage 100 Hematoxylin &amp; Eosin (H&amp;E)-stained whole slide images (WSIs) from 25 Balb/c mice with various level of brain metastatic tumor burden. A supervised training of the Trainable Weka Segmentation (TWS) from Fiji was achieved from annotated WSIs. Upon comparison with manually drawn regions, it is apparent that the algorithm learned to identify and segment cancer cell-specific nuclei and normal brain tissue. Our approach resulted in a robust and highly concordant correlation between automated metastases quantification of brain metastases and manual human assessment (R 2 = 0.8783; P &lt; 0.0001). This simple approach is amenable to other similar analyses, including that of human tissues. Widespread adoption of these tools aims to democratize ML and improve precision in traditionally qualitative tasks in histopathology-based research.
Customisable wearables

This paper explores the design space of wearable technology from the customisation point of view. We identify emerging customisation practices and customisable attributes from previous work in research and in a dataset of 129 commercial wearable devices. We distinguish between attributes on functional customisation, interaction techniques, location on the body, and appearance. While people have a variety of conventional clothing and accessories options to fulfil their personal preferences and lifestyle, this is a lot less for wearable devices. The commercial wearables still cluster around few use cases, especially fitness, and wrist-worn form factors.
Native three-body interaction in superconducting circuits

This paper shows how a superconducting circuit can implement three qubits interacting via a direct three-body coupling. As the coupling is direct, the timescale of the interaction is extremely fast, on the order of a nanosecond. This coupling can be used to implement a controlled operation relevant for quantum computing, where the control is a quantum degree of freedom, and the operation time is so short that noise becomes much less relevant. The approach is general and could be used to implement other direct multi-qubit interactions in superconducting circuits.
Linear-Size Constant-Query IOPs for Delegating Computation

We study the problem of delegating computations via interactive proofs that can be probabilistically checked. Known as interactive oracle proofs (IOPs), these proofs extend probabilistically checkable proofs (PCPs) to multi-round protocols, and have received much attention due to their application to constructing cryptographic proofs (such as succinct non-interactive arguments). The relevant complexity measures for IOPs in this context are prover and verifier time, and query complexity. We construct highly efficient IOPs for a rich class of nondeterministic algebraic computations, which includes succinct versions of arithmetic circuit satisfiability and rank-one constraint system (R1CS) satisfiability. For a time-T computation, we obtain prover arithmetic complexity $$O(T \log T)$$ and verifier complexity polylog(T). These IOPs are the first to simultaneously achieve the state of the art in prover complexity, due to [14], and in verifier complexity, due to [7]. We also improve upon the query complexity of both schemes. The efficiency of our prover is a result of our highly optimized proof length; in particular, ours is the first construction that simultaneously achieves linear-size proofs and polylogarithmic-time verification, regardless of query complexity.
Defeating hidden audio channel attacks on voice assistants via audio-induced surface vibrations

Voice access technologies are widely adopted in mobile devices and voice assistant systems as a convenient way of user interaction. Recent studies have demonstrated a potentially serious vulnerability of the existing voice interfaces on these systems to "hidden voice commands". This attack uses synthetically rendered adversarial sounds embedded within a voice command to trick the speech recognition process into executing malicious commands, without being noticed by legitimate users.
A tour-guide robot: Moving towards interaction with humans

The aim of this research project is to develop a smart social robot showing sufficient intelligence to work as a tour-guide in different environments. In doing so, both a software and a hardware architecture are proposed, the different modules of which, such as a laser, cameras, platform, face, and voice, among others, control the different components of the robot. Those components are in turn used by other modules designed for navigation and interaction. A sensor fusion for the purposes of localization is implemented by means of an Extended Kalman Filter, which is one of the navigation module components, together with the proposed fuzzy controllers needed for path following. A fuzzy emotion system that controls the face and the voice modules also forms part of this architecture for assisting interaction. Finally, all the modules are controlled with a customized programming language that is a mixture of C, Pascal, and JavaScript. The modules are optimized for immediate execution to achieve realistic human–machine interaction.
A Method Combining CNN and ELM for Feature Extraction and Classification of SAR Image

Due to the large number of Sigmoid activation function derivation in the traditional convolution neural network (CNN), it is difficult to solve the question of the low efficiency of extracting the feature of Synthetic Aperture Radar (SAR) images. The Sigmoid activation function in the CNN is improved to be a rectified linear unit (ReLU) activation function, and the classifier is modified by the Extreme Learning Machine (ELM). Finally, in this CNN model, the improved CNN works as the feature extractor and ELM performs as a recognizer. A SAR image recognition algorithm based on the CNN-ELM algorithm is proposed by combining the CNN and the ELM algorithm. The experiment is conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database which contains 10 kinds of target images. The experiment result shows that the algorithm can realize the sparsity of the network, alleviate the overfitting problem, and speed up the convergence speed of the network. It is worth mentioning that the running time of this experiment is very short. Compared with other experiment on the same database, it indicates that this experiment has generated a higher recognition rate. The accuracy of the SAR image recognition is 100%.
Artificial Intelligence in Journalism: A Boon or Bane?

With the advancements in information and communication technologies (ICTs), news media industries have undergone sea changes. Moreover, the emergence of artificial intelligence (AI) and machine learning has redefined the implications of technologies in numerous fields. The field of journalism is no exception to it. The rise of the robot reporter and automated journalism has made inroads in the area of news and journalism [1]. As of today, digital media has intensified the dissemination of news manifold. The process from production to consumption of news stories has slowly witnessed the newer technological adoption. The production, distribution, and consumption in journalism have been influenced by the use of newer technologies. The current chapter intends to assess the implications of AI in journalism worldwide.
Toward Efficient Encrypted Image Retrieval in Cloud Environment

Outsourcing image search services to public clouds is an ever-increasing trend. However, directly outsourcing image datasets to untrusted clouds introduces privacy concerns. Several secure image retrieval schemes have been proposed recently. However, most of them require participation from image owners when building secure indexes, which wastes many computational resources of the image owners. Several schemes are proposed to solve this problem, but they suffer from low search accuracy on large datasets. In this paper, we propose the first secure image retrieval scheme that simultaneously solves these two problems. To obtain higher search accuracy, we extract image features via fine-tuned convolutional neural networks. Then, the image features are encrypted by using the secure k-Nearest Neighbor algorithm. To improve search speed and reduce the cost of image owners, we let cloud servers locally build a secure hierarchical index graph by using the encrypted image features. Besides, the secure index can be built and updated in parallel. We provide security analysis for the proposed scheme. Performance evaluations on the CIFAR-10 dataset show that the proposed scheme is practical. Moreover, compared with a recent scheme, our scheme can save more index construction time and cost of image owners when building secure indexes.
Assessing the Impact of Contextual Embeddings for Portuguese Named Entity Recognition

Modern approaches to Named Entity Recognition (NER) use neural networks (NN) to automatically extract features from text and seamlessly integrate them with sequence taggers in an end-to-end fashion. Word embeddings, which are a side product of pre-trained neural language models (LMs), are key ingredients to boost the performance of NER systems. More recently, contextual word embeddings, which adapt according to the context where the word appears, have proved to be an invaluable resource to improve NER systems. In this work, we assess how different combinations of (shallow) word embeddings and contextual embeddings impact NER for the Portuguese Language. We show a comparative study of 16 different combinations of shallow and contextual embeddings and explore how textual diversity and the size of training corpora used in LMs impact our NER results. We evaluate NER performance using the HAREM corpus. Our best NER system outperforms the state-of-the-art in Portuguese NER by 5.99 in absolute percentage points.
Joint Channel Reliability and Correlation Filters Learning for Visual Tracking

Multi-channel discriminative correlation filter (DCF) tracking methods have exhibited superior performance on several benchmarks. However, existing methods usually treat each channel of the features equally, whereas they pay less attention to the contribution of different channels. Different channels exhibit variant properties in the tracking process. A DCF learned with equally important channels is likely to be contaminated by the unreliable ones, which results in model degradation. To address this problem, we propose a new formulation for jointly learning the channel reliability and the correlation filters. The formulation is generic, and it can be combined with existing techniques in the DCF framework to further improve the performance. Our method can adaptively increase the impact of reliable channels and down-weight the corrupted ones. To solve the joint learning problem, we propose an optimization strategy that alternates between the correlation filters and the channel weights. Further, we prove the upper bound of the objective function and solve the channel weights efficiently. The joint learning strategy makes the correlation filters more discriminative and the channel weights more accurate. To verify the joint formulation, we propose a tracker based on the proposed formulation and the techniques used in the ECO tracker. We conduct extensive experiments to evaluate the proposed tracker on three benchmarks. The experimental results show that our formulation is effective and efficient, and that it performs favorably against other state-of-the-art trackers.
Multidomain Multimodal Fusion for Human Action Recognition Using Inertial Sensors

One of the major reasons for misclassification of multiplex actions during action recognition is the unavailability of complementary features that provide the semantic information about the actions. In different domains these features are present with different scales and intensities. In existing literature, features are extracted independently in different domains, but the benefits from fusing these multidomain features are not realized. To address this challenge and to extract complete set of complementary information, in this paper, we propose a novel multidomain multimodal fusion framework that extracts complementary and distinct features from different domains of the input modality. We transform input inertial data into signal images, and then make the input modality multidomain and multimodal by transforming spatial domain information into frequency and time-spectrum domain using Discrete Fourier Transform (DFT) and Gabor wavelet transform (GWT) respectively. Features in different domains are extracted by Convolutional Neural networks (CNNs) and then fused by Canonical Correlation based Fusion (CCF) for improving the accuracy of human action recognition. Experimental results on three inertial datasets show the superiority of the proposed method when compared to the state-of-the-art.
A detailed analysis of trust models in cloud environment

Cloud Computing has been considered as a future technology of internet. This is all because of sharing of IT resources, feature of scalability, flexibility and higher levels of automation. With this the rapid growth, Cloud Computing has bought concerns of security trust. Various trust issues of Cloud have been addressed by a combination of frameworks, standards and related technologies. Sometimes, consumers avoid a specific technology whenever it shows no ability to cope with their security demands. This type of loss can occur in computing platforms such as Cloud platforms and mobile platforms.
Clustering by transmission learning from data density to label manifold with statistical diffusion

Owing to the tremendous diversity and complexity of data in today’s world, some new insights for clustering on data are often desired by developing an alternative to the existing clustering approaches. In this paper, based on the new concepts of the Bayesian transmission system and its transmission learning, a label manifold-based transmission learning machine for clustering (LMTLMC) is accordingly developed. As the first attempt to explain the clustering behavior in a lifelike way, LMTLMC is well justified by revealing the natural parallel between its gradient-based optimization process and the statistical diffusion in statistical physics through the modified Fick’s diffusion law for clustering. Practically, LMTLMC is distinctive in its easy implementation in terms of its global analytical solution, its easy parameter settings and its stable and efficient clustering results. Extensive experiments on synthetic datasets and real datasets demonstrate the promising performance and superiority of LMTLMC for clustering tasks, in contrast to the existing clustering algorithms.