Reviewerly/exHarmony huggingface dataset:
Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges

The Internet of things model enables a world in which all of our everyday devices can be integrated and communicate with each other and their surroundings to gather and share data and simplify task implementation. Such an Internet of things environment would require seamless authentication, data protection, stability, attack resistance, ease of deployment, and self-maintenance, among other things. Blockchain, a technology that was born with the cryptocurrency Bitcoin, may fulfill Internet of things requirements. However, due to the characteristics of both Internet of things devices and Blockchain technology, integrating Blockchain and the Internet of things can cause several challenges. Despite a large number of papers that have been published in the field of Blockchain and the Internet of things, the problems of this combination remain unclear and scattered. Accordingly, this paper aims to provide a comprehensive survey of the challenges related to Blockchain–Internet of things integration by evaluating the related peer-reviewed literature. The paper also discusses some of the recommendations for reducing the effects of these challenges. Moreover, the paper discusses some of the unsolved concerns that must be addressed before the next generation of integrated Blockchain–Internet of things applications can be deployed. Lastly, future trends in the context of Blockchain–Internet of things integration are discussed.
Quantum embedding theories to simulate condensed systems on quantum computers

Quantum computers hold promise to improve the efficiency of quantum simulations of materials and to enable the investigation of systems and properties that are more complex than tractable at present on classical architectures. Here, we discuss computational frameworks to carry out electronic structure calculations of solids on noisy intermediate-scale quantum computers using embedding theories, and we give examples for a specific class of materials, that is, solid materials hosting spin defects. These are promising systems to build future quantum technologies, such as quantum computers, quantum sensors and quantum communication devices. Although quantum simulations on quantum architectures are in their infancy, promising results for realistic systems appear to be within reach. Quantum embedding theory promises the simulation of realistic materials in quantum computers. In this Perspective, challenges and opportunities of applying different embedding frameworks to calculate solid materials properties are discussed, with a focus on electronic structures of spin defects.
Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation

Watch-time prediction remains to be a key factor in reinforcing user engagement via video recommendations. It has become increasingly important given the ever-growing popularity of online videos. However, prediction of watch time not only depends on the match between the user and the video but is often mislead by the duration of the video itself. With the goal of improving watch time, recommendation is always biased towards videos with long duration. Models trained on this imbalanced data face the risk of bias amplification, which misguides platforms to over-recommend videos with long duration but overlook the underlying user interests. This paper presents the first work to study duration bias in watch-time prediction for video recommendation. We employ a causal graph illuminating that duration is a confounding factor that concurrently affects video exposure and watch-time prediction---the first effect on video causes the bias issue and should be eliminated, while the second effect on watch time originates from video intrinsic characteristics and should be preserved. To remove the undesired bias but leverage the natural effect, we propose a Duration-Deconfounded Quantile-based (D2Q) watch-time prediction framework, which allows for scalability to perform on industry production systems. Through extensive offline evaluation and live experiments, we showcase the effectiveness of this duration-deconfounding framework by significantly outperforming the state-of-the-art baselines. We have fully launched our approach on Kuaishou App, which has substantially improved real-time video consumption due to more accurate watch-time predictions.
TransDeepLab: Convolution-Free Transformer-Based DeepLab v3+ for Medical Image Segmentation

Convolutional neural networks (CNNs) have been the de facto standard in a diverse set of computer vision tasks for many years. Especially, deep neural networks based on seminal architectures such as U-shaped model with skip-connections or atrous convolution with pyramid pooling have been tailored to a wide range of medical image analysis tasks. The main advantage of such architectures is that they are prone to detaining versatile local features. However, as a general consensus, CNNs fail to capture long-range dependencies and spatial correlations due to the intrinsic property of confined receptive field size of convolution operations. Alternatively, Transformer, profiting from global information modeling that stems from the self-attention mechanism, has recently attained remarkable performance in natural language processing and computer vision. Nevertheless, previous studies prove that both local and global features are critical for a deep model in dense prediction, such as segmenting complicated structures with disparate shapes and configurations. This paper proposes TransDeepLab, a novel DeepLab-like pure Transformer for medical image segmentation. Specifically, we exploit hierarchical Swin-Transformer with shifted windows to extend the DeepLabv3 and model the Atrous Spatial Pyramid Pooling (ASPP) module. A thorough search of the relevant literature yielded that we are the first to model the seminal DeepLab model with a pure Transformer-based model. Extensive experiments on various medical image segmentation tasks verify that our approach performs superior or on par with most contemporary works on an amalgamation of Vision Transformer and CNN-based methods, along with a significant reduction of model complexity. The codes and trained models are publicly available at github .
What Artificial Neural Networks Can Tell Us about Human Language Acquisition

Rapid progress in machine learning for natural language processing has the potential to transform debates about how humans learn language. However, the learning environments and biases of current artificial learners and humans diverge in ways that weaken the impact of the evidence gleaned from learning simulations. Today's most effective neural language models are trained on roughly one thousand times the amount of linguistic data available to a typical child. To increase the relevance of learnability results from computational models, we need to train model learners without significant advantages over humans. If an appropriate model successfully acquires some target linguistic knowledge, it can provide a proof of concept that the target is learnable in a hypothesised human learning scenario. Plausible model learners will enable us to carry out experimental manipulations to make causal inferences about variables in the learning environment, and to rigorously test poverty-of-the-stimulus-style claims arguing for innate linguistic knowledge in humans on the basis of speculations about learnability. Comparable experiments will never be possible with human subjects due to practical and ethical considerations, making model learners an indispensable resource. So far, attempts to deprive current models of unfair advantages obtain sub-human results for key grammatical behaviours such as acceptability judgements. But before we can justifiably conclude that language learning requires more prior domain-specific knowledge than current models possess, we must first explore non-linguistic inputs in the form of multimodal stimuli and multi-agent interaction as ways to make our learners more efficient at learning from limited linguistic input.
Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the features related to individual users and their corresponding contexts. To overcome this issue, we propose a deep learning model based on an attention mechanism in this study. The suggested technique employs an attention mechanism that focuses on the pattern’s friendship, which is responsible for concentrating on the relevant features related to individual users. To compute context-aware similarities among diverse users, our model employs six features of each user as inputs, including user ID, hour, month, day, minute, and second of visiting time, which explore the influences of both spatial and temporal features for the users. In addition, we incorporate geographical information into our attention mechanism by creating an eccentricity score. Specifically, we map the trajectory of each user to a shape, such as a circle, triangle, or rectangle, each of which has a different eccentricity value. This attention-based mechanism is evaluated on two widely used datasets, and experimental outcomes prove a noteworthy improvement of our model over the state-of-the-art strategies for POI recommendation.
Learning a Set of Interrelated Tasks by Using a Succession of Motor Policies for a Socially Guided Intrinsically Motivated Learner

We aim at a robot capable to learn sequences of actions to achieve a field of complex tasks. In this paper, we are considering the learning of a set of interrelated complex tasks hierarchically organized. To learn this high-dimensional mapping between a continuous high-dimensional space of tasks and an infinite dimensional space of unbounded sequences of actions, we introduce a new framework called "procedures", which enables the autonomous discovery of how to combine previously learned skills in order to learn increasingly complex combinations of motor policies. We propose an active learning algorithmic architecture, capable of organizing its learning process in order to achieve a field of complex tasks by learning sequences of primitive motor policies. Based on heuristics of active imitation learning, goal-babbling and strategic learning using intrinsic motivation, our algorithmic architecture leverages our procedures framework to actively decide during its learning process which outcome to focus on and which exploration strategy to apply. We show on a simulated environment that our new architecture is capable of tackling the learning of complex motor policies by adapting the complexity of its policies to the task at hand. We also show that our "procedures" enable the learning agent to discover the task hierarchy and exploit his experience of previously learned skills to learn new complex tasks.
Covert Security with Public Verifiability: Faster, Leaner, and Simpler

The notion of covert security for secure two-party computation serves as a compromise between the traditional semi-honest and malicious security definitions. Roughly, covert security ensures that cheating behavior is detected by the honest party with reasonable probability (say, 1/2). It provides more realistic guarantees than semi-honest security with significantly less overhead than is required by malicious security. The rationale for covert security is that it dissuades cheating by parties that care about their reputation and do not want to risk being caught. But a much stronger disincentive is obtained if the honest party can generate a publicly verifiable certificate when cheating is detected. While the corresponding notion of publicly verifiable covert (PVC) security has been explored, existing PVC protocols are complex and less efficient than the best covert protocols, and have impractically large certificates. We propose a novel PVC protocol that significantly improves on prior work. Our protocol uses only “off-the-shelf” primitives (in particular, it avoids signed oblivious transfer) and, for deterrence factor 1/2, has only 20–40% overhead compared to state-of-the-art semi-honest protocols. Our protocol also has, for the first time, constant-size certificates of cheating (e.g., 354 bytes long at the 128-bit security level). As our protocol offers strong security guarantees with low overhead, we suggest that it is the best choice for many practical applications of secure two-party computation.
StreetWise

This paper explores the use of conversational speech question and answer systems in the challenging context of public spaces in slums. A major part of this work is a comparison of the source and speed of the given responses; that is, either machine-powered and instant or human-powered and delayed. We examine these dimensions via a two-stage, multi-sited deployment. We report on a pilot deployment that helped refine the system, and a second deployment involving the installation of nine of each type of system within a large Mumbai slum for a 40-day period, resulting in over 12,000 queries. We present the findings from a detailed analysis and comparison of the two question-answer corpora; discuss how these insights might help improve machine-powered smart speakers; and, highlight the potential benefits of multi-sited public speech installations within slum environments.
Deep Semantic Mapping for Heterogeneous Multimedia Transfer Learning Using Co-Occurrence Data

Transfer learning, which focuses on finding a favorable representation for instances of different domains based on auxiliary data, can mitigate the divergence between domains through knowledge transfer. Recently, increasing efforts on transfer learning have employed d eep n eural n etworks (DNN) to learn more robust and higher level feature representations to better tackle cross-media disparities. However, only a few articles consider the correction and semantic matching between multi-layer heterogeneous domain networks. In this article, we propose a d eep semantic mapping model for h eterogeneous multimedia t ransfer l earning (DHTL) using co-occurrence data. More specifically, we integrate the DNN with c anonical c orrelation a nalysis (CCA) to derive a deep correlation subspace as the joint semantic representation for associating data across different domains. In the proposed DHTL, a multi-layer correlation matching network across domains is constructed, in which the CCA is combined to bridge each pair of domain-specific hidden layers. To train the network, a joint objective function is defined and the optimization processes are presented. When the deep semantic representation is achieved, the shared features of the source domain are transferred for task learning in the target domain. Extensive experiments for three multimedia recognition applications demonstrate that the proposed DHTL can effectively find deep semantic representations for heterogeneous domains, and it is superior to the several existing state-of-the-art methods for deep transfer learning.
Infinite deflectometry enabling 2π-steradian measurement range

We present a novel deflectometry implementation termed Infinite Deflectometry. The technique provides a full aperture surface reconstruction sag map of freeform surfaces, including previously challenging to measure optics such as highly convex surfaces. The method relies on the creation of a virtual source enclosure around the tested optic, which creates a virtual 2π-steradian measurement range. To demonstrate the performance, a fast f/1.26 convex optical surface was measured with a commercial interferometer and with the Infinite Deflectometry system. After removing Zernike terms 1 through 37, the metrology tests resulted in absolute RMS surface values of 18.48 nm and 16.26 nm, respectively. Additionally, a freeform Alvarez lens was measured with the new technique and measured 22.34 휇m of surface sag RMS after piston, tip/tilt, and defocus had been removed. The result deviated by 488 nm RMS from a profilometer measurement while standard interferometry failed to measure the Alvarez lens due to its non-nulled wavefront dynamic range limitation.
Context Definition and Query Language: Conceptual Specification, Implementation, and Evaluation

As IoT grows at a staggering pace, the need for contextual intelligence is a fundamental and critical factor for IoT intelligence, efficiency, effectiveness, performance, and sustainability. As the standardisation efforts for IoT are fast progressing, efforts in standardising context management platforms led by the European Telecommunications Standards Institute (ETSI) are gaining more attention from both academic and industrial research organizations. These standardisation endeavours will enable intelligent interactions between 'things', where things could be devices, software components, web-services, or sensing/actuating systems. Therefore, having a generic platform to describe and query context is crucial for the future of IoT applications. In this paper, we propose Context Definition and Query Language (CDQL), an advanced approach that enables things to exchange, reuse and share context between each other. CDQL consists of two main parts, namely: context definition model, which is designed to describe situations and high-level context; and Context Query Language (CQL), which is a powerful and flexible query language to express contextual information requirements without considering details of the underlying data structures. An important feature of the proposed query language is its ability to query entities in IoT environments based on their situation in a fully dynamic manner where users can define situations and context entities as part of the query. We exemplify the usage of CDQL on three different smart city use cases to highlight how CDQL can be utilised to deliver contextual information to IoT applications. Performance evaluation has demonstrated scalability and efficiency of CDQL in handling a fairly large number of concurrent context queries.
Exploring the Monero Peer-to-Peer Network

As of September 2019, Monero is the most capitalized privacy-preserving cryptocurrency, and is ranked tenth among all cryptocurrencies. Monero’s on-chain data privacy guarantees, i.e., how mixins are selected in each transaction, have been extensively studied. However, despite Monero’s prominence, the network of peers running Monero clients has not been analyzed. Such analysis is of prime importance, since potential vulnerabilities in the peer-to-peer network may lead to attacks on the blockchain’s safety (e.g., by isolating a set of nodes) and on users’ privacy (e.g., tracing transactions flow in the network). This paper provides the first step study on understanding Monero’s peer-to-peer (P2P) network. In particular, we deconstruct Monero’s P2P protocol based on its source code, and develop a toolset to explore Monero’s network, which allows us to infer its topology, size, node distribution, and node connectivity. During our experiments, we collected 510 GB of raw data, from which we extracted 21,678 IP addresses of Monero nodes distributed in 970 autonomous systems. We show that Monero’s network is highly centralized—13.2% of the nodes collectively maintain 82.86% of the network connections. We have identified approximately 2,758 active nodes per day, which is 68.7% higher than the number reported by the MoneroHash mining pool. We also identified all concurrent outgoing connections maintained by Monero nodes with very high probability (on average 97.98% for nodes with less than 250 outgoing connections, and 93.79% for nodes with more connections).
Neural-Network Lexical Translation for Cross-lingual IR from Text and Speech

We propose a neural network model to estimate word translation probabilities for Cross-Lingual Information Retrieval (CLIR). The model estimates better probabilities for word translations than automatic word alignments alone, and generalizes to unseen source-target word pairs. We further improve the lexical neural translation model (and subsequently CLIR), by incorporating source word context, and by encoding the character sequences of input source words to generate translations of out-of-vocabulary words. To be effective, neural network models typically need training on large amounts of data labeled directly on the final task, in this case relevance to queries. In contrast, our approach only requires parallel data to train the translation model, and uses an unsupervised model to compute CLIR relevance scores.
SGD Converges to Global Minimum in Deep Learning via Star-convex Path.

Stochastic gradient descent (SGD) has been found to be surprisingly effective in training a variety of deep neural networks. However, there is still a lack of understanding on how and why SGD can train these complex networks towards a global minimum. In this study, we establish the convergence of SGD to a global minimum for nonconvex optimization problems that are commonly encountered in neural network training. Our argument exploits the following two important properties: 1) the training loss can achieve zero value (approximately), which has been widely observed in deep learning; 2) SGD follows a star-convex path, which is verified by various experiments in this paper.  In such a context, our analysis shows that SGD, although has long been considered as a randomized algorithm, converges in an intrinsically deterministic manner to a global minimum.
Optimizing Hearthstone agents using an evolutionary algorithm

Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hidden information, uncertainty and planning, among other aspects. This paper proposes the use of evolutionary algorithms (EAs) to develop agents who play a card game, Hearthstone, by optimizing a data-driven decision-making mechanism that takes into account all the elements currently in play. Agents feature self-learning by means of a competitive coevolutionary training approach, whereby no external sparring element defined by the user is required for the optimization process. One of the agents developed through the proposed approach was runner-up (best 6%) in an international Hearthstone Artificial Intelligence (AI) competition. Our proposal performed remarkably well, even when it faced state-of-the-art techniques that attempted to take into account future game states, such as Monte-Carlo Tree search. This outcome shows how evolutionary computation could represent a considerable advantage in developing AIs for collectible card games such as Hearthstone.
Improving Convolutional Neural Network (CNN) Architecture (miniVGGNet) with Batch Normalization and Learning Rate Decay Factor for Image Classification

The image classification is a classical problem of image processing, computer vision, and machine learning.This paper presents an analysis of the performance using Convolutional Neural Network (CNN) for image classifying using deep learning.MiniVGGNet is CNN architecture used in this paper to train a network for image classification, and CIFAR-10 is selected dataset used for this purpose.The performance of the network was improved by hyper parameter tuning techniques using batch normalization and learning rate decay factor.This paper compares the performance of the trained network by adding batch normalization layer and adjusting the value of learning rate decay factor for the network architecture.Based on the experimental results, adding batch normalization layer allow the networks to improve classification accuracy from 80% to 82%.Applying learning rate decay factor will improve classification accuracy to 83% and reduce the effects of overfitting in learning plot.Performance analysis shows that applying hyper parameter tuning can improve the performance of the network and increasing the ability of the model to generalize.