Reviewerly/exHarmony huggingface dataset:
Anytime Game-Theoretic Planning with Active Reasoning About Humans’ Latent States for Human-Centered Robots

A human-centered robot needs to reason about the cognitive limitation and potential irrationality of its human partner to achieve seamless interactions. This paper proposes an anytime game-theoretic planner that integrates iterative reasoning models, a partially observable Markov decision process, and chance-constrained Monte-Carlo belief tree search for robot behavioral planning. Our planner enables a robot to safely and actively reason about its human partner’s latent cognitive states (bounded intelligence and irrationality) in real-time to maximize its utility better. We validate our approach in an autonomous driving domain where our behavioral planner and a low-level motion controller hierarchically control an autonomous car to negotiate traffic merges. Simulations and user studies are conducted to show our planner’s effectiveness.
The College Students’ Oral English Education Strategy Using Human-Computer Interaction Simulation System From the Perspective of Educational Psychology

The role of the human-computer interaction (HCI) system in college students' oral English learning is discussed to analyze the current situation of college students' oral English based on the HCI simulation system. The purpose is to study the oral education of college students. First, the theories of educational psychology, the HCI system, and the current situation of college students' oral English learning are elaborated. Meanwhile, in oral English teaching, teachers use support vector machines and multimodal fusion intention perception methods in set theory to realize the interactive teaching between students and machines; then, the HCI simulation of oral English is explained. The current situation of college students' oral English learning is analyzed by a questionnaire from the perspective of educational psychology. Finally, the HCI system in college students' oral English learning is explored based on the learning level detection. The results show that 12% of college students are unqualified in oral English; 25% of them think their oral English level is medium; most of college students' English learning anxiety is related to English progress anxiety; 18% of the students believe that they will study oral English for life; 32% of the students think that they have more opportunities to learn English at ordinary times; and most of the students learn English through English movies and songs outside of class. What attracts college students to learn oral English through the HCI system is that learning is not limited by time and space. Most students believe that their English level is good and hope that learning anxiety can be reduced through HCI systems. The strategies of college students' oral English education with an HCI simulation system are evaluated based on the perspective of educational psychology, providing a research basis for oral English education in other regions and even the whole country to facilitate the better development of oral English education.
UC-Check: Characterizing Micro-operation Caches in x86 Processors and Implications in Security and Performance

The modern x86 processor (e.g., Intel, AMD) translates CISC-style x86 instructions to RISC-style micro operations (uops) as RISC pipelines are more efficient than CISC pipelines. However, this x86 decoding process requires complex hardware logic (i.e., x86 decoder) to identify variable-length x86 instructions, which incurs high translation overhead. To avoid this overhead, the x86 processors adopt a micro-operation cache (uop cache) to bypass the expensive x86 decoder by caching the decoded uops.
Blockchain Service Operations - A Structured Approach to Operate a Blockchain Solution

Participating in or operating a permissioned blockchain is a complex challenge and poses a significant hurdle for many organizations. This paper creates a structure for the operation of a permissioned blockchain with seven blockchain operations categories. An onboarding process of joining a blockchain exemplifies the use of the blockchain operations categories. The article illustrates the IT management structures necessary to operate a blockchain, the on-chain and off-chain interplay for a secure blockchain solution. Our results support the efficient management of permissioned blockchains. The approach is grounded in a research project that uses a scenario-driven approach to design blockchain infrastructures for supply chains. This project uses on Hyperledger Fabric as the blockchain framework.
A Model of Socially Sustained Self-Tracking for Food and Diet

Studies of personal informatics systems primarily examine people's use or non-use, but people often leverage other technology towards their long-term behavior change processes such as social platforms. We explore how tracking technologies and social platforms together help people build healthy eating behaviors by interviewing 18 people who use Chinese food journaling apps. We contribute a Model of Socially Sustained Self-Tracking in personal informatics, building on the past model of Personal Informatics and the learning components of Social Cognitive Theory. The model illustrates how people get advice from social platforms on when and how to track, transfer data to and apply knowledge from social platforms, evolve to use social platforms after tracking, and occasionally resume using tracking tools. Observational learning and enactive learning are central to these processes, with social technologies helping people to gain deeper and more reliable domain knowledge. We discuss how lapsing and abandoning of tracking can be viewed as evolving to social platforms, offering recommendations for how technology can better facilitate this evolution.
RAS

Capacity reservation is a common offering in public clouds and on-premise infrastructure. However, no prior work provides capacity reservation with SLO guarantees that takes into account random and correlated hardware failures, datacenter maintenance, and heterogeneous hardware. In this paper, we describe how Facebook's region-scale Resource Allowance System (RAS) addresses these issues and provides guaranteed capacity. RAS uses a capacity abstraction called reservation to represent a set of servers dynamically assigned to a logical cluster. We take a two-level approach to scale resource allocation to all datacenters in a region, where a mixed-integer-programming solver continuously optimizes server-to-reservation assignments off the critical path, and a traditional container allocator does real-time placement of containers on servers in a reservation. As a relatively new component of Facebook's 10-year old cluster manager Twine, RAS has been running in production for almost two years, continuously optimizing the allocation of millions of servers to thousands of reservations. We describe the design of RAS and share our experience of deploying it at scale.
Accurate CNN-based pupil segmentation with an ellipse fit error regularization term

Semantic segmentation of images by Fully Convolutional Neural Networks (FCN) has gained increased attention in recent years as FCNs greatly outperform traditional segmentation algorithms. In this paper we propose using Ellipse Fit Error as a shape prior regularization term that can be added to a pixel-wise loss function, e.g., binary cross entropy, to train a CNN for pupil segmentation. We evaluate the performance of the proposed method by training a lightweight UNet architecture, and use three widely used real-world datasets for pupil center estimation, i.e., ExCuSe, ElSe, and Labeled Pupils in the Wild (LPW), containing a total of ∼230.000 images for performance evaluation. Experimental results show that the proposed method gives the best-known pupil detection rates for all datasets.
Triangle-Reward Reinforcement Learning

Image captioning aims to generate a sentence consisting of sequential linguistic words, to describe visual units (i.e., objects, relationships, and attributes) in a given image. Most of existing methods rely on the prevalent supervised learning with cross-entropy (XE) function to transfer visual units into a sequence of linguistic words. However, we argue that the XE objective is not sensitive to visual-linguistic alignment, which cannot discriminately penalize the semantic inconsistency and shrink the context gap. To solve these problems, we propose the Triangle-Reward Reinforcement Learning (TRRL) method. TRRL uses the scene graph (G)---objects as nodes and relationships as edges---to represent images, generated sentences, and ground truth sentences individually, and mutually align them during the training process. Specifically, TRRL formulates the image captioning into cooperative agents, where the first agent aims to extract visual scene graph (Gimg) from image (I) and the second agent translates this graph into sentence (S). To discriminately penalize the visual-linguistic inconsistency, TRRL proposes the novel triangle-reward function: 1) the generated sentence and its corresponding ground truth are decomposed into the linguistic scene graph (Gsen) and ground-truth scene graph (Ggt), respectively; 2) Gimg, Gsen, and Ggt are paired to calculate the semantic similarity scores which are proportionally assigned to reward each agent. Meanwhile, to make the training objective sensitive to context changes, we propose the node-level and triplet-level scoring methods to jointly measure the visual-linguistic graph correlations. Extensive experiments on the MSCOCO dataset demonstrate the superiority of TRRL. Additional ablation studies further validate its effectiveness.
Human Activity Recognition Using Convolutional Neural Networks

Using smartphone sensors to recognize human activity may be advantageous due to the abundant volume of data that can be obtained. In this paper, we propose a sensor data based deep learning approach for recognizing human activity. Our proposed recognition method uses linear accelerometer (LAcc), gyroscope (Gyr), and magnetometer (Mag) sensors to perceive eight transportation and locomotion activities. The eight activities include: Still, Walk, Run, Bike, Bus, Car, Train, and Subway. In this study, the Sussex-Huawei Locomotion (SHL) Dataset of three participants are used to recognize the physical activities of the users. Fast Fourier Transform (FFT) spectrograms generated from the three axes of the LAcc, Gyr, and Mag sensor data are used as input data for our proposed Convolutional Neural Network (CNN) model. Experimental results on the task of human activity recognition demonstrated the effectiveness of our proposed user-independent approach over that of competitive baselines.
Two-stream 2D/3D Residual Networks for Learning Robot Manipulations from Human Demonstration Videos

Learning manipulation skills from observing human demonstration videos is a promising aspect for intelligent robotic systems. Recent advances in video to command provide an end-to-end approach to translate a video into robot plans. However, the general video captioning methods focus more on the understanding of the full frame, while they lack the consideration of the spatio-temporal features in videos. In this paper, we proposed the two-stream 2D/3D residual networks for robots to learn manipulation tasks from human demonstration videos. We integrate spatial features with 2D residual network and temporal features with 3D residual network as inputs for RNN layers. An encoder-decoder architecture is then used to encode the spatio-temporal features and sequentially generate the command words. Experimental results on an extended manipulation dataset show that our approach outperforms the state-of-the-art methods. Real-world experiments results on a Baxter robotic arm indicate that our method could produce more accurate commands from video demonstrations.
Fast Few-Shot Classification by Few-Iteration Meta-Learning

Autonomous agents interacting with the real world need to learn new concepts efficiently and reliably. This requires learning in a low-data regime, which is a highly challenging problem. We address this task by introducing a fast optimization-based meta-learning method for few-shot classification. It consists of an embedding network, providing a general representation of the image, and a base learner module. The latter learns a linear classifier during the inference through an unrolled optimization procedure. We design an inner learning objective composed of (i) a robust classification loss on the support set and (ii) an entropy loss, allowing transductive learning from unlabeled query samples. By employing an efficient initialization module and a Steepest Descent based optimization algorithm, our base learner predicts a powerful classifier within only a few iterations. Further, our strategy enables important aspects of the base learner objective to be learned during meta-training. To the best of our knowledge, this work is the first to integrate both induction and transduction into the base learner in an optimization-based meta-learning framework. We perform a comprehensive experimental analysis, demonstrating the speed and effectiveness of our approach on four few-shot classification datasets. The Code is available at https://github.com/4rdhendu/FIML.
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation

Few-shot semantic segmentation addresses the learning task in which only few images with ground truth pixel-level labels are available for the novel classes of interest. One is typically required to collect a large mount of data (i.e., base classes) with such ground truth information, followed by meta-learning strategies to address the above learning task. When only image-level semantic labels can be observed during both training and testing, it is considered as an even more challenging task of weakly supervised few-shot semantic segmentation. To address this problem, we propose a novel meta-learning framework, which predicts pseudo pixel-level segmentation masks from a limited amount of data and their semantic labels. More importantly, our learning scheme further exploits the produced pixel-level information for query image inputs with segmentation guarantees. Thus, our proposed learning model can be viewed as a pixel-level meta-learner. Through extensive experiments on benchmark datasets, we show that our model achieves satisfactory performances under fully supervised settings, yet performs favorably against state-of-the-art methods under weakly supervised settings.
CBFL: A Communication-Efficient Federated Learning Framework From Data Redundancy Perspective

Federated learning (FL) is an emerging machinelearning framework, which enables multiple mobile users to collaboratively train a global model without uploading their local sensitive data. Due to limited network bandwidth, communication efficiency has become a significant bottleneck for the implementation of FL. Existing works attempt to improve this situation by reducing the total bits transferred for each client update via data compression. However, these research works are only from the perspective of update parameters to reduce the transmission of redundant parameters. They do not explore the intrinsic reasons for redundant parameters. In this article, we propose a coreset-based FL (CBFL) framework. Instead of training model on full datasets with a regular network model, CBFL uses a much smaller well-matched evolutionary network model on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coreset</i> . CBFL indirectly reduces the total transmission bits for each client while achieving a similar accuracy as training with full datasets. CBFL includes a novel distributed coreset construction and adaptive model evolution algorithms. The network model is adaptively adjusted during this process, which dynamically removes the least important connections from the current model. Experimental results with various datasets and models show that CBFL is able to find an optimized evolutionary model that has about 10% of the total number of connections in the original regular model while only about 2% degradation in model accuracy.
Classification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps

Ultrasound (US) imaging is widely utilized as a diagnostic screening method, and deep learning has recently drawn attention for the analysis of US images for the pathological status of tissues. While low image quality and poor reproducibility are the common obstacles in US analysis, the small size of the dataset is a new limitation for deep learning due to lack of generalization. In this work, a convolutional neural network (CNN) using multiple feature maps, such as entropy and phase images, as well as a B-mode image, was proposed to classify breast US images. Although B-mode images contain both anatomical and textual information, traditional CNNs experience difficulties in abstracting features automatically, especially with small datasets. For the proposed CNN framework, two distinct feature maps were obtained from a B-mode image and utilized as new inputs for training the CNN. These feature maps can also be made from the evaluation data and applied to the CNN separately for the final classification decision. The experimental results with 780 breast US images in three categories of benign, malignant, and normal, showed that the proposed CNN framework using multiple feature maps exhibited better performances than the traditional CNN with B-mode only for most deep network models.
An Analysis of the Persuasive Technology Design Features that Support Behavioural Change

Persuasive Technologies (PTs) are designed to change attitudes or behaviours of the users through persuasion and social influence. Recent studies have espoused the need for integrating design features in PTs to motivate users to perform desired action. However, the efficacy of these design features has not been empirically tested. This study seeks to assess whether the planned integration of design features into PTs lead to behaviour change. The study adopted the Persuasive Systems Design (PSD) model and empirically tested the design features among 150 Ghanaian Junior High School (JHS) students who were exposed to an interactive mathematic software program. All four constructs of the model; primary task support, dialogue support, system credibility support, and social support were all found to significantly influence behavioural change. However, social support had the most impact whilst dialogue support had the least impact on actual behavior change. Future studies can assess the applicability of the model with different user groups and in other jurisdictions.
A Novel Cryptocurrency Prediction Method Using Optimum CNN

In recent years, cryptocurrency has become gradually more significant in economic regions worldwide. In cryptocurrencies, records are stored using a cryptographic algorithm. The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media. Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels. Therefore, this work focuses on extracting Tweets and gathering data from different sources to classify them into positive, negative, and neutral categories, and further examining the correlations between cryptocurrency movements and Tweet sentiments. This paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction; this method is used to predict the prices of four cryptocurrencies, namely, Litecoin, Monero, Bitcoin, and Ethereum. The results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%. The method is validated by comparison with existing methods using visualization tools.
Post-Quantum Adaptor Signature for Privacy-Preserving Off-Chain Payments

Adaptor signatures (AS) are an extension of digital signatures that enable the encoding of a cryptographic hard problem (e.g., discrete logarithm) within the signature itself. An AS scheme ensures that (i) the signature can be created only by the user knowing the solution to the cryptographic problem; (ii) the signature reveals the solution itself; (iii) the signature can be verified with the standard verification algorithm. These properties have made AS a salient building block for many blockchain applications, in particular, off-chain payment systems such as payment-channel networks, payment-channel hubs, atomic swaps or discrete log contracts. Current AS constructions, however, are not secure against adversaries with access to a quantum computer. In this work, we present $$\mathsf {IAS}$$ , a construction for adaptor signatures that relies on standard cryptographic assumptions for isogenies, and builds upon the isogeny-based signature scheme CSI-FiSh. We formally prove the security of $$\mathsf {IAS}$$ against a quantum adversary. We have implemented $$\mathsf {IAS}$$ and our evaluation shows that $$\mathsf {IAS}$$ can be incorporated into current blockchains while requiring $$\sim $$ 1500 bytes of storage size on-chain and $$\sim $$ 140 ms for digital signature verification. We also show how $$\mathsf {IAS}$$ can be seamlessly leveraged to build post-quantum off-chain payment applications without harming their security and privacy.
AdaSim

In the literature, various link-based similarity measures such as Adamic/Adar (in short Ada), SimRank, and random walk with restart (RWR) have been proposed. Contrary to SimRank and RWR, Ada is a non-recursive measure, which exploits the local graph structure in similarity computation. Motivated by Ada's promising results in various graph-related tasks, along with the fact that SimRank is a recursive generalization of the co -citation measure, in this paper, we propose AdaSim, a recursive similarity measure based on the Ada philosophy. Our AdaSim provides identical accuracy to that of Ada on the first iteration and it is applicable to both directed and undirected graphs. To accelerate our iterative form, we also propose a matrix form that is dramatically faster while providing the exact AdaSim scores. We conduct extensive experiments with five real-world datasets to evaluate both the effectiveness and efficiency of our AdaSim in comparison with those of existing similarity measures and graph embedding methods in the task of similarity computation of nodes. Our experimental results show that 1) AdaSim significantly improves the effectiveness of Ada and outperforms other competitors, 2) its efficiency is comparable to that of SimRank* while being better than the others, 3) AdaSim is not sensitive to the parameter tuning, and 4) similarity measures are better than embedding methods to compute similarity of nodes.
Teaching English to Special Educational Need Students through an Online Tool

Abstract Language learning through online tools has become customary, especially during COVID‐19 pandemic, but this type of education does not always consider the requirements for students with special educational needs (SEN) despite teachers struggle to include disabled students in online teaching. This article presents the educational potential of using Universal Design for Learning (UDL) and Web Content Accessibility Guidelines (WCAG) to help SEN students learn English. First, a theoretical framework that covers the basis of the En‐Abilities project and the UDL pedagogical principles that sustain the platform organisation and contents is provided. Then, practical examples of some of the activities and the results gathered after its practical implementation are shown. In the conclusions, achievements are summarised and new paths to explore in the field of foreign languages learning for students with SEN are foreseen.