Reviewerly/exHarmony huggingface dataset:
Robust Understanding of Robot-Directed Speech Commands Using Sequence to Sequence With Noise Injection

This paper describes a new method that enables a service robot to understand spoken commands in a robust manner using off-the-shelf automatic speech recognition (ASR) systems and an encoder-decoder neural network with noise injection. In numerous instances, the understanding of spoken commands in the area of service robotics is modeled as a mapping of speech signals to a sequence of commands that can be understood and performed by a robot. In a conventional approach, speech signals are recognized, and semantic parsing is applied to infer the command sequence from the utterance. However, if errors occur during the process of speech recognition, a conventional semantic parsing method cannot be appropriately applied because most natural language processing methods do not recognize such errors. We propose the use of encoder-decoder neural networks, e.g., sequence to sequence, with noise injection. The noise is injected into phoneme sequences during the training phase of encoder-decoder neural network-based semantic parsing systems. We demonstrate that the use of neural networks with a noise injection can mitigate the negative effects of speech recognition errors in understanding robot-directed speech commands, i.e., increase the performance of semantic parsing. We implemented the method and evaluated it using the commands given during a general purpose service robot (GPSR) task, such as a task applied in RoboCup@Home, which is a standard service robot competition for the testing of service robots. The results of the experiment show that the proposed method, namely, sequence to sequence with noise injection (Seq2Seq-NI), outperforms the baseline methods. In addition, Seq2Seq-NI enables a robot to understand a spoken command even when the speech recognition by an off-the-shelf ASR system contains recognition errors. Moreover, in this paper we describe an experiment conducted to evaluate the influence of the injected noise and provide a discussion of the results.
Quantum Artificial Intelligence: A “precautionary” U.S. approach?

Quantum Computing (QC) and Quantum Artificial Intelligence (QAI) are two powerful new technologies whose potential impacts are just starting to be appreciated. As important as they are likely to be, their implications are still little known. This article's purpose is an attempt to provide a policy space within which to begin fill that void. It presents a non-technical introduction to the concept of quantum mechanics in general, to the unique characteristics of sub-atomic particles, to quantum applications, and to QC and QAI in particular. The development of fully functioning quantum information technologies is in its early stages, but is being pursued aggressively. One of the most salient of these is Quantum Artificial Intelligence (QAI) – a fusion of QC and AI -- which some see as potentially threatening. There is little in established telecommunications or information policies to inform our approach to its capabilities. The article notes some of the challenges to development of quantum-based information technologies and considers the scope of global competition to the U.S. in this field, especially from China. It reviews the treatment of “Quantum Information Science” in U.S. policy. It describes the current policy environment and the range of new measures which might be available, if needed, to incorporate QAI into an overall information policy regime. In conclusion the article reviews the debate over adoption of the “precautionary principle” for QAI, an approach to policy when it must deal with weakly understood causes of potentially catastrophic or irreversible events. It sets out the arguments pro and con, and in the absence of an immediate resolution proposes adoption of a policy space in which stakeholders can address their salient concerns, without materially impairing technological progress and commercial viability.
Image super-resolution based on half quadratic splitting

Model-based optimization methods have been widely used in varies image restoration solutions and achieved some remarkable results. However, finding out a closed mathematical solution for certain priors remains a great challenge. To resolve this problem, this paper presents an improved model-based algorithm for single image super-resolution. Instead of focusing on specific prior knowledge, we exploit the optimization scheme of general image restoration formula. In our approach, the general format of model-optimization problem is transformed into an alternant renewal process through half quadratic splitting. This transform can also separate the optimization into a modular structure and allows us to optimize the fidelity term and regularization term separately. Of which the regular optimization process can be considered as a denoising process. Then the guided filter is taken as a denoiser to realize this optimization, which uses local linear transform to keep the detail and L2 norm constraint to smooth the noise of input image. Experiments with benchmark datasets and our own infrared images show that our method can surpass several famous model-based and data-based methods in PSNR and SSIM.
Performance Evaluation of Machine Learning Algorithms in Handwritten Digits Recognition

The recognition of handwritten characters has always been a very difficult task because of the many variations of handwritten characters with different writing styles. This type of intelligent systems is applied in various fields: check processing, processing of forms, automatic processing of handwritten answers to an examination, etc. This last application is the subject of this work. We compared in this paper the performances of some machine learning algorithms, used for the classification of complex and multiclass problems. In this work, we exploited four machine learning algorithms (K-Nearest Neighbors, Deep Neural Network, Decision Tree and Support Vector Machine) to predict handwritten digits. The training and testing data were extracted from the MNIST digit database containing pre-processed images. The results obtained using different similarity measures such as accuracy, sensitivity and specificity confirm that the classification obtained by deep neural networks is the most accurate compared to the other classifiers studied in this paper.
A Distributed Bilateral Resource Market Mechanism for Future Telecommunications Networks

In this paper, we propose a distributed market mechanism powered by the blockchain technology addressing network ownership models in multiple telecommunications networks use cases. The proposed model is based on the smart contract technology and enables transparent and trusted bilateral trades, where trust among the network operators does not exist, and there is no impartial third-party entity who is trusted by all of the participants. We use an open source permissioned blockchain framework called Hyperledger Fabric to investigate the performance of the distributed market mechanism in cloud environments. After running experiments on Hyperledger Fabric, we discuss the practical feasibility and possible design choices of our blockchain approach based on latency, throughput and resource consumption analysis.
Research and application of situated teaching design for NC machining course based on virtual simulation technology

Abstract As virtual simulation technology can display three‐dimensional materials as well as their dynamic cutting process and the learners’ motivation is enhanced in the situated learning environment, a virtual‐situated learning system for numerical control (NC) machining course is proposed to explore the learning effectiveness of learners when combining virtual simulation technology and the situation learning theory. This study aims to use the concept of embedding NC machining simulation system and the situated learning into a real situation to improve the learners’ situated thinking ability and innovative literacy. The features of the system include (a) the learning activities and contents that are planned and designed using the key factors of situated learning based on the specific teaching contents and objectives, (b) combining virtual objects with a real environment using virtual simulation technology to provide a project‐based learning situation for learners, and (c) probing into the specific methods of implementing situated teaching design in NC machining course. To assess the acceptance of the proposed system, an independent sample t ‐test was then applied to evaluate the learning achievement of learners. The results show that the research scale is highly reliable and that all assumptions made are valid. In addition, the effect of the situated teaching was investigated through questionnaire survey. The experimental results suggest that the virtual‐situated teaching design has significant positive impact on improving the students' learning efficiency and performance skills and indicates that the participants were attracted to and willing to use the system, which directly enhances their incentive to actively learn and promote learning achievement.
Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques.
A Centroid Auto-Fused Hierarchical Fuzzy c-Means Clustering

Like k-means and Gaussian mixture model (GMM), fuzzy c-means (FCM) with soft partition has also become a popular clustering algorithm and still is extensively studied. However, these algorithms and their variants still suffer from some difficulties such as determination of the optimal number of clusters which is a key factor for clustering quality. A common approach for overcoming this difficulty is to use the trial-and-validation strategy, i.e., traversing every integer from large number like √n to 2 until finding the optimal number corresponding to the peak value of some cluster validity index. But it is scarcely possible to naturally construct an adaptively agglomerative hierarchical cluster structure as using the trial-and-validation strategy. Even if possible, existing different validity indices also lead to different number of clusters. To effectively mitigate the problems while motivated by convex clustering, in this article we present a centroid auto-fused hierarchical fuzzy c-means method (CAF-HFCM) whose optimization procedure can automatically agglomerate to form a cluster hierarchy, more importantly, yielding an optimal number of clusters without resorting to any validity index. Although a recently proposed robust-learning fuzzy c-means (RL-FCM) can also automatically obtain the best number of clusters without the help of any validity index, so-involved three hyperparameters need to adjust expensively, conversely, our CAF-HFCM involves just one hyperparameter which makes the corresponding adjustment relatively easier and more operational. Further, as an additional benefit from our optimization objective, the CAF-HFCM effectively reduces the sensitivity to the initialization of clustering performance. Moreover, our proposed CAF-HFCM method is able to be straightforwardly extended to various variants of FCM. Finally, extensive experiments on both synthetic and real data sets demonstrate the effectiveness and efficiency of CAF-HFCM.
Tight Security Bounds for Double-Block Hash-then-Sum MACs

In this work, we study the security of deterministic MAC constructions with a double-block internal state, captured by the double-block hash-then-sum ($$\mathsf {DbHtS}$$) paradigm. Most $$\mathsf {DbHtS}$$ constructions, including $$\mathsf {PolyMAC}$$, $$\mathsf {SUM\text {-}ECBC}$$, $$\mathsf {PMAC\text {-}Plus}$$, $$\mathsf {3kf9}$$ and $$\mathsf {LightMAC\text {-}Plus}$$, have been proved to be pseudorandom up to $$2^{\frac{2n}{3}}$$ queries when they are instantiated with an n-bit block cipher, while the best known generic attacks require $$2^{\frac{3n}{4}}$$ queries. We close this gap by proving the PRF-security of $$\mathsf {DbHtS}$$ constructions up to $$2^{\frac{3n}{4}}$$ queries (ignoring the maximum message length). The core of the security proof is to refine Mirror theory that systematically estimates the number of solutions to a system of equations and non-equations, and apply it to prove the security of the finalization function. Then we identify security requirements of the internal hash functions to ensure 3n/4-bit security of the resulting constructions when combined with the finalization function. Within this framework, we prove the security of $$\mathsf {DbHtS}$$ whose internal hash function is given as the concatenation of a universal hash function using two independent keys. This class of constructions include $$\mathsf {PolyMAC}$$ and $$\mathsf {SUM\text {-}ECBC}$$. Moreover, we prove the security of $$\mathsf {PMAC\text {-}Plus}$$, $$\mathsf {3kf9}$$ and $$\mathsf {LightMAC\text {-}Plus}$$ up to $$2^{\frac{3n}{4}}$$ queries.
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models

Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments. In much the same way, we would like our learning agents to quickly adapt to new tasks. In this paper, we explore how model-based Reinforcement Learning (RL) can facilitate transfer to new tasks. We develop an algorithm that learns an action-conditional, predictive model of expected future observations, rewards and values from which a policy can be derived by following the gradient of the estimated value along imagined trajectories. We show how robust policy optimization can be achieved in robot manipulation tasks even with approximate models that are learned directly from vision and proprioception. We evaluate the efficacy of our approach in a transfer learning scenario, re-using previously learned models on tasks with different reward structures and visual distractors, and show a significant improvement in learning speed compared to strong off-policy baselines. Videos with results can be found at this https URL
GEval: A Modular and Extensible Evaluation Framework for Graph Embedding Techniques

While RDF data are graph shaped by nature, most traditional Machine Learning (ML) algorithms expect data in a vector form. To transform graph elements to vectors, several graph embedding approaches have been proposed. Comparing these approaches is interesting for 1) developers of new embedding techniques to verify in which cases their proposal outperforms the state-of-art and 2) consumers of these techniques in choosing the best approach according to the task(s) the vectors will be used for. The comparison could be delayed (and made difficult) by the choice of tasks, the design of the evaluation, the selection of models, parameters, and needed datasets. We propose GEval, an evaluation framework to simplify the evaluation and the comparison of graph embedding techniques. The covered tasks range from ML tasks (Classification, Regression, Clustering), semantic tasks (entity relatedness, document similarity) to semantic analogies. However, GEval is designed to be (easily) extensible. In this article, we will describe the design and development of the proposed framework by detailing its overall structure, the already implemented tasks, and how to extend it. In conclusion, to demonstrate its operating approach, we consider the parameter tuning of the KGloVe algorithm as a use case.
Cryptocurrency Token: An Overview

Shirole, Mahesh Darisi, Maneesh Bhirud, SunilWith the advent of blockchain, the trustless transactions between cross-border parties becomes easy. The profusion of eclectic tokens coming into the cryptocurrency world, one primary requirement is to enable robust and secure exchange between different varieties of tokens. With the rise of ICOs into the cryptocurrency market, the funding of research-based projects has become meteoric in nature. The dearth of knowledge about these different varieties of tokens is blocking amateur developers to unlock the true potential of the blockchain technology. There are myriad tokens, which are either fungible or non-fungible tokens that are used to represent different types of assets and research projects. This paper mainly focuses to provide an overview of tokens and comparative analysis for token standards available in the present cryptocurrency world.
Governance of Blockchain and Distributed Ledger Technology Projects

Blockchains are the most well-known example of a distributed ledger technology (DLT). Unlike classic databases, the ledger is not maintained by any central authority. The integrity of the ledger is maintained automatically by an algorithmic consensus process whereby nodes vote and agree upon the authoritative version. In effect, the consensus algorithm operates in the manner of a decision-making process within a governance system.The technological characteristics of blockchain systems are well documented (Narayanan, Bonneau, Felton and Miller, 2016). We propose that one of the reasons why it has so far proved very difficult to seed large-scale commercial DLT (blockchain) projects lies in the arena of project ownership and governance. Unlike classic centralised database systems, DLTs have no one central point of “ownership” of any of the system’s infrastructure or data. In this piece of exploratory research, we propose applying theories of club governance to both the technical design and operational development of a range of DLT (blockchain) systems, including (but not necessarily limited to) cryptocurrencies and enterprise applications to explore how they can explain the development of (or lack of development of) sustainable solutions to real business problems. There are many parallels to the governance arrangements observed historically in the origins of complex distributed telecommunications networks.
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning.

Many planning applications involve complex relationships defined on high-dimensional, continuous variables. For example, robotic manipulation requires planning with kinematic, collision, visibility, and motion constraints involving robot configurations, object poses, and robot trajectories. These constraints typically require specialized procedures to sample satisfying values. We extend PDDL to support a generic, declarative specification for these procedures that treats their implementation as black boxes. We provide domain-independent algorithms that reduce PDDLStream problems to a sequence of finite PDDL problems. We also introduce an algorithm that dynamically balances exploring new candidate plans and exploiting existing ones. This enables the algorithm to greedily search the space of parameter bindings to more quickly solve tightly-constrained problems as well as locally optimize to produce low-cost solutions. We evaluate our algorithms on three simulated robotic planning domains as well as several real-world robotic tasks.
QUANOS

Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial attacks, wherein, a model gets fooled by applying slight perturbations on the input. In this paper, we investigate the use of quantization to potentially resist adversarial attacks. Several recent studies have reported remarkable results in reducing the energy requirement of a DNN through quantization. However, no prior work has considered the relationship between adversarial sensitivity of a DNN and its effect on quantization. We propose QUANOS- a framework that performs layer-specific hybrid quantization based on Adversarial Noise Sensitivity (ANS). We identify a novel noise stability metric (ANS) for DNNs, i.e., the sensitivity of each layer's computation to adversarial noise. ANS allows for a principled way of determining optimal bit-width per layer that incurs adversarial robustness as well as energy-efficiency with minimal loss in accuracy. Essentially, QUANOS assigns layer significance based on its contribution to adversarial perturbation and accordingly scales the precision of the layers. We evaluate the benefits of QUANOS on precision scalable Multiply and Accumulate (MAC) hardware architectures with data gating and subword parallelism capabilities. Our experiments on CIFAR10, CIFAR100 datasets show that QUANOS outperforms homogeneously quantized 8-bit precision baseline in terms of adversarial robustness (3 -- 4% higher) while yielding improved compression (> 5×) and energy savings (> 2×) at iso-accuracy. At iso-compression rate, QUANOS yields significantly higher adversarial robustness (> 10%) than similar sized baseline against strong white-box attacks. We also find that combining QUANOS with state-of-the-art defense methods outperforms the state-of-the-art in robustness (~ 5% -- 16% higher) against very strong attacks.
Affect, biopower, and ‘the fascist inside you’: the (Un-)making of microfascism in schools and classrooms

This essay demonstrates how Deleuze and Guattari’s notion of ‘microfascism’ is of crucial importance to understanding the complexities of contemporary pedagogical efforts to combat populism, right-wing extremism, and fascism. The author discusses how ‘affect’ and ‘biopower’ are entangled in everyday processes of discipline and control, and argues that these concepts are pivotal for appreciating the affective relations and capacities of microfascism. To illustrate how affect and biopower are intimately linked to microfascist practices in schools and classrooms, the author analyzes two examples—one in health education and another in citizenship education. Finally, the author suggests pedagogical strategies that could contribute to unmaking microfascist subjectivities, emphasizing that it is crucial to pay attention to the connections between micro- and macro-level fascisms.