Reviewerly/exHarmony huggingface dataset:
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

Multitask Reinforcement Learning is a promising way to obtain models with better performance, generalisation, data efficiency, and robustness. Most existing work is limited to compatible settings, where the state and action space dimensions are the same across tasks. Graph Neural Networks (GNN) are one way to address incompatible environments, because they can process graphs of arbitrary size. They also allow practitioners to inject biases encoded in the structure of the input graph. Existing work in graph-based continuous control uses the physical morphology of the agent to construct the input graph, i.e., encoding limb features as node labels and using edges to connect the nodes if their corresponded limbs are physically connected. In this work, we present a series of ablations on existing methods that show that morphological information encoded in the graph does not improve their performance. Motivated by the hypothesis that any benefits GNNs extract from the graph structure are outweighed by difficulties they create for message passing, we also propose Amorpheus, a transformer-based approach. Further results show that, while Amorpheus ignores the morphological information that GNNs encode, it nonetheless substantially outperforms GNN-based methods.
Machine learning of high dimensional data on a noisy quantum processor

We present a quantum kernel method for high-dimensional data analysis using Google's universal quantum processor, Sycamore. This method is successfully applied to the cosmological benchmark of supernova classification using real spectral features with no dimensionality reduction and without vanishing kernel elements. Instead of using a synthetic dataset of low dimension or pre-processing the data with a classical machine learning algorithm to reduce the data dimension, this experiment demonstrates that machine learning with real, high dimensional data is possible using a quantum processor; but it requires careful attention to shot statistics and mean kernel element size when constructing a circuit ansatz. Our experiment utilizes 17 qubits to classify 67 dimensional data - significantly higher dimensionality than the largest prior quantum kernel experiments - resulting in classification accuracy that is competitive with noiseless simulation and comparable classical techniques.
Interference Signal Recognition Based on Multi-Modal Deep Learning

With the development of communication countermeasure technology, the future communication systems will face multi-source and multi-pattern interference deliberately released by the enemy, and the electromagnetic environment is becoming more complex. Effective communication anti-jamming technology needs to identify the interference signal of the enemy. Therefore, an interference signal recognition method based on multi-modal deep learning is proposed in this paper. Recently, multi-modal deep learning has become more and more important in machine learning. However, it has few applications in the field of communication. At present, most of the CNN-based signal recognition methods that have been studied are only based on the unimodal feature of the signal. We exploit a signal contour stellar images-I/Q waveform multimodal fusion (CWMF) method to achieve interference signal recognition based on AlexNet networks and deep complex-valued neural network. After feature extraction from multimodal data using AlexNet networks and deep complex-valued neural network, To merge multimodal features, we use the feature fusion method. In addition, we use decision-based fusion to merge multimodal models to obtain better distinguishable features and realize a better recognition performance.
Virtual Currencies and Anti-Money Laundering – The Shortcomings of the 5th AML Directive (EU) and How to Address Them

Virtual currencies pose a serious threat to be used for money laundering, weakening the European Union’s financial system. Directive (EU) 2018/843 (the fifth anti-money laundering Directive) intends to mitigate these risks by introducing a definition of virtual currencies within Union law. Some service providers connected to virtual currencies are made subject to anti-money laundering law. Member States are required to transpose this Directive into national law by January 2020. Consultations on national level are currently ongoing.This article analyses how the Directive applies to current forms of cryptocurrencies, their adjacent services and intermediaries. It highlights the Directive’s imprecise wording as well as its limited scope. If Member States transpose it verbatim, they will create legal uncertainty and loopholes for relevant entities. Therefore, this article seeks to contribute to the national consultations of Member States by providing concrete legislative recommendations on how to fix the Directive’s shortcomings.
Secure Information Access Strategy for a Virtual Data Centre

With the arrival of on-demand computing, data centre requirements are extensive, with fluid boundaries. Loaded Internet applications, service-oriented architectures, virtualization and security provisioning are the major operations of a data centre. Security is an absolute necessity of any network architecture, and the virtual IT data centre is no exception. At the boundary, security is focused on securing the terminals of the data centre from external threats and providing a secure gateway to the Internet. The paradigm shift towards a new computing environment makes communications more complicated for Infrastructure Providers (InP). This complexity includes the security of the data centre’s components to protect data from malicious attacks or from being compromised. Threats/attacks are inevitable if the data are generated from a public network, such as Wi-Fi in an Airport, Railway station and other public places. Since these places create enormous amounts of data from anonymous and naive users, it is essential to store the information in a data centre. In this article, we propose an efficient, secure, and privacy-preservation information access algorithm to access data centres in public wifi networks. This algorithm is based on the primitive root approach for sending and receiving credentials through the anonymous authentication of the users and ensuring protected data access from the data centre. Security and Performance Analysis and its evaluation prove that our approach is successful with respect to security, privacy preservation and computational complexity.
Stablecoins 2.0: Economic Foundations and Risk-based Models

Stablecoins are one of the most widely capitalized type of cryptocurrency. However, their risks vary significantly according to their design and are often poorly understood. We seek to provide a sound foundation for stablecoin theory, with a risk-based functional characterization of the economic structure of stablecoins. First, we match existing economic models to the disparate set of custodial systems. Next, we characterize the unique risks that emerge in non-custodial stablecoins and develop a model framework that unifies existing models from economics and computer science. We further discuss how this modeling framework is applicable to a wide array of cryptoeconomic systems, including cross-chain protocols, collateralized lending, and decentralized exchanges. These unique risks yield unanswered research questions that will form the crux of research in decentralized finance going forward.
Tensorflow and Keras Programming for Deep Learning

Tensorflow is an end-to-end open-source platform for machine learning containing a comprehensive, flexible ecosystem of tools, libraries, and community resources ( https://www.tensorflow.org/ ). It provides multiple levels of abstractions to choose the right one. The high-level Keras API can be used to build and train models by easily getting started with Tensorflow. Keras allows employing Tensorflow without losing its flexibility and capability. In the following, two applications (i.e., temporal and spatial deep learning) are presented to illustrate how to use Keras with python.
Virtual reality authoring tools for teachers to create novel and immersive learning scenarios

This paper aims to encourage teachers from all areas, with a special focus on the area of language teaching, to explore the possibilities of creating virtual learning environments using technologies such as Virtual Reality (VR). Through the current work, the authors intend to share with other teachers their experience with the design and implementation of a 360º German-language app created by using Tour Creator. The latter is a freely available authoring tool developed by Google and allows stakeholders with no specific computer skills to easily create their own VR tours. This way, teachers could provide their students with learning resources that are usually difficult to implement in conventional teaching environments. For the purposes of the current project, the authors designed a VR-based app called Cádiz 360º that permits German language learners by means of numerous panoramic views to explore the Spanish city of Cádiz and to interact with its different places and sights, while practicing, at the same time, their listening and reading skills.
Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities

With tremendous rise in deep learning adoption comes questions about the trustworthiness of the deep neural networks that power a variety of applications. In this work, we introduce the concept of trust matrix, a novel trust quantification strategy that leverages the recently introduced question-answer trust metric by Wong et al. to provide deeper, more detailed insights into where trust breaks down for a given deep neural network given a set of questions. More specifically, a trust matrix defines the expected question-answer trust for a given actor-oracle answer scenario, allowing one to quickly spot areas of low trust that needs to be addressed in order to improve the trustworthiness of a deep neural network. We further extend the concept of trust densities with the notion of conditional trust densities.
AI-aided detection of malignant lesions in mammography screening – evaluation of a program in clinical practice

Evaluation of the degree of concordance between an artificial intelligence (AI) program and radiologists in assessing malignant lesions in screening mammograms.The study population consisted of all consecutive cases of screening-detected histopathologically confirmed breast cancer in females who had undergone mammography at the NU Hospital Group (Region Västra Götaland, Sweden) in 2018 to 2019. Data were retrospectively collected from the AI program (lesion risk score in percent and overall malignancy risk score ranging from 1 to 10) and from medical records (independent assessments by two radiologists). Ethical approval was obtained.Altogether, 120 females with screening-detected histopathologically confirmed breast cancer were included in this study. The AI program assigned the highest overall malignancy risk score 10 to 86% of the mammograms. Five cases (4%) were assigned an overall malignancy risk score ≤5. Lack of consensus between the two radiologists involved in the initial assessment was associated with lower overall malignancy risk scores (p = 0,002).The AI program detected a majority of the cancerous lesions in the mammograms. The investigated version of the program has, however, limited use as an aid for radiologists, due to the pre-calibrated risk distribution and its tendency to miss the same lesions as the radiologists. A potential future use for the program, aimed at reducing radiologists' workload, might be to preselect and exclude low-risk mammograms. Although, depending on cut-off score, a small percentage of the malignant lesions can be missed using this procedure, which thus requires a thorough risk-benefit analysis.This study conducts an independent evaluation of an AI program's detection capacity under screening-like conditions which has not previously been done for this program.
Neural Machine Translation 2020, by Philipp Koehn, Cambridge, Cambridge University Press, ISBN 978-1-108-49732-9, pages 393.

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A Novel Reinforcement-Learning-Based Approach to Workflow Scheduling Upon Infrastructure-as-a-Service Clouds

Recently, the cloud computing paradigm has become increasingly popular in large-scale and complex workflow applications. The workflow scheduling problem, which refers to finding the most suitable resource for each task of the workflow to meet user defined quality of service, attracts considerable research attention. Multi-objective optimization algorithms in workflow scheduling have many limitations (e.g., the encoding schemes in most existing heuristic-based scheduling algorithms require prior experts' knowledge), and thus, they can be ineffective when scheduling workflows upon dynamic cloud infrastructures with real time. A novel reinforcement-learning-based algorithm to multi-workflow scheduling over IaaS is proposed. It aims at optimizing make-span and dwell time and is to achieve a unique set of correlated equilibrium solution. The proposed algorithm is evaluated for famous workflow templates and real-world industrial IaaS by simulation and compared to the current state-of-the-art heuristic algorithms. The result shows that the algorithm outperforms compared algorithm.
A Unified Framework for Convolution-based Graph Neural Networks

Graph Convolutional Networks (GCNs) have attracted a lot of research interest in the machine learning community in recent years. Although many variants have been proposed, we still lack a systematic view of different GCN models and deep understanding of the relations among them. In this paper, we take a step forward to establish a unified framework for convolution-based graph neural networks, by formulating the basic graph convolution operation as an optimization problem in the graph Fourier space. Under this framework, a variety of popular GCN models, including the vanilla-GCNs, attention-based GCNs and topology-based GCNs, can be interpreted as a same optimization problem but with different carefully designed regularizers. This novel perspective enables a better understanding of the similarities and differences among many widely used GCNs, and may inspire new approaches for designing better models. As a showcase, we also present a novel regularization technique under the proposed framework to tackle the oversmoothing problem in graph convolution. The effectiveness of the newly designed model is validated empirically.
Learning a Latent Search Space for Routing Problems using Variational Autoencoders

Methods for automatically learning to solve routing problems are rapidly improving in performance. While most of these methods excel at generating solutions quickly, they are unable to effectively utilize longer run times because they lack a sophisticated search component. We present a learning-based optimization approach that allows a guided search in the distribution of high-quality solutions for a problem instance. More precisely, our method uses a conditional variational autoencoder that learns to map points in a continuous (latent) search space to high-quality, instance-specific routing problem solutions. The learned space can then be searched by any unconstrained continuous optimization method. We show that even using a standard differential evolution search strategy our approach is able to outperform existing purely machine learning based approaches.
Use of Information Technologies in Educational Purposes – Case from Serbia

Secondary education in developing countries is largely based on obsolete teaching methods and technologies. The aim of this paper is to analyze the use and preferences of mobile technologies for the needs of education among the high school population, with special emphasis on social media. The paper presents researches conducted in 2016 and 2020 in one high school, based on the analysis of questionnaires on the practice and preferences of using mobile technologies and social media for educational purposes. Results obtained show that the advantages of mobile learning in high school practice are still not sufficiently utilized, since students rarely use the Internet on mobile phones or tablets to acquire new knowledge or information in the educational process. It is noticeable that the trends in the use of information technology in domestic high schools are at approximately the same level as in other countries, as well as that there is room for improving the application of modern information technology in education process. The results of the research are important for future studies, since this was conducted on a sample of high school of economic students, and the possibilities of applying this method of work on different educational profiles arises.
Indian and English Language to Sign Language Translator- an Automated Portable Two Way Communicator for Bridging Normal and Deprived Ones

Human thought are conveyed through visual manual modality. Sign languages are the kind of language that is expressed via the manual sign stream together with non -manual components. Whenever communities of deaf folks exist, sign languages have developed as handy suggests that of communication and that they kind the core of native deaf cultures. Living with a incapacity isn't simple, however the majority United Nations agency are into disability usually develop alternative heightened senses and skills, associated area unit able to live an nearly traditional life and have a heightened sense of hearing and bit, whereas the deaf and mute area unit able to browse lips and communicate mistreatment language. However most of the final population don't understand language and area unit unable to speak effectively with the deaf. Therefore the deaf realize it tough to converse with folks on daily to day basis, this issue are often solved through a smartphone application. By the application deaf and dumb folks will simply learn several languages mistreatment their convenient language severally, user will either learn in online or perhaps offline modes, additionally makes user to interactively participate and additionally provides higher level of comfort, enhances higher understanding, provides easy surroundings. The applying development is freed from value and it needs humanoid studio installation. Mobile application is absolutely adopted, can address most of the challenges facing those with hearing disorder and speech impairment.
Automated Symbol Rate Estimation Over Frequency-Selective Fading Channel by Using Deep Neural Network

The adaptive communication system is going to play a major role for fifth-generation (5G) and beyond wireless communication where the physical layer signal parameters need to be changed at the transmitters as per system requirement and the receiver needs to estimate them to recover the signal. In this paper, we have proposed an efficient and robust automated symbol rate estimation model for single carrier system over frequency-selective fading environment by using deep neural network (DNN) approach. The proposed scheme estimates symbol rate without having any prior knowledge of the signal bandwidth which was the main assumption for existing statistical methods. In the proposed scheme, no additional knowledge such as channel state information (CSI) and synchronization parameters are required to estimate the symbol rate. The proposed model outperforms the existing statistical models in terms of the performance. The performance of the symbol rate estimator is depicted by the normalized mean square error (NMSE).
Artificial Intelligence for Blockchain I

Artificial Intelligence (AI) and Blockchain are two trending technologies in the current era. Blockchain stands to revolutionize communication and transaction tracing, building a secure connection among strangers and enabling increased origin. AI, on the other hand, performs incredibly complex tasks and applies a vigorous amount of computing power to learn these tasks to near perfection. So how can these two contrasting technologies be merged to create even more opportunity? Although both the technologies have different fundamentals and infrastructure, they have a lot to offer each other. And although these technologies have different developers, researchers are discussing and exploring their combination. Every innovation has its level of unpredictability; however, both can profit from one another and help each other. Blockchain can likewise make AI increasingly coherent and fathomable, and we can follow and decide each progression settled on dynamically by AI. Blockchain can record all information and factors in its recording required in dynamic AI. In addition, AI can increase blockchain capabilities to be more remarkable than people or even ordinary processing. Taking a gander at how blockchains are running presently on regular PCs requires a great deal of handling power even to play out some essential undertakings. So, here comes the need for change. Through AI, we can save a lot of the power required for computing the ‘nonce.’ An estimated trillion amount of energy is necessary for computing. Decentralized AI can become a revolutionary change added to the blockchain; it is an approach to solve complex learning, planning, and decision-making problems. It is distressingly parallel, thus able to deed extensive scale calculation and a spatial distribution of computing resources. These assets allow it to solve problems that require the processing of extensive data sets. This is what is needed in the blockchain. In the blockchain, there are a large number of chains and lots of computational power is required.
Intrinsic Dynamic Shape Prior for Dense Non-Rigid Structure from Motion

While dense non-rigid structure from motion (NRSfM) has been extensively studied from the perspective of the reconstructability problem over the recent years, almost no attempts have been undertaken to bring it into the practical realm. The reasons for the slow dissemination are the severe ill-posedness, high sensitivity to motion and deformation cues, and the difficulty to obtain reliable point tracks in the vast majority of practical scenarios.To fill this gap, we propose a new framework that first extracts prior knowledge from an input image sequence with NRSfM. Our Dynamic Shape Prior Reconstruction (DSPR) approach then uses the obtained 3D reconstructions as a dynamic shape prior for sequential surface recovery in scenarios with recurrence. DSPR can be combined with existing dense NRSfM techniques while its energy functional is optimised with multi-start gradient descent at real-time frame rates for new incoming point tracks. The proposed versatile framework with a new core NRSfM approach outperforms several other methods in the ability to handle inaccurate and noisy point tracks, provided we have access to a representative (in terms of the deformation variety) image sequence. Comprehensive experiments highlight convergence properties and the accuracy of DSPR under different disturbing effects. We also perform a joint study of tracking and reconstruction and show applications to shape compression and heart reconstruction under occlusions. We achieve state-of-the-art metrics (accuracy and compression ratios) in different scenarios.