Reviewerly/exHarmony huggingface dataset:
SHAKTI-MS: a RISC-V processor for memory safety in C

In this era of IoT devices, security is very often traded off for smaller device footprint and low power consumption. Considering the exponentially growing security threats of IoT and cyber-physical systems, it is important that these devices have built-in features that enhance security. In this paper, we present Shakti-MS, a lightweight RISC-V processor with built-in support for both temporal and spatial memory protection. At run time, Shakti-MS can detect and stymie memory misuse in C and C++ programs, with minimum runtime overheads. The solution uses a novel implementation of fat-pointers to efficiently detect misuse of pointers at runtime. Our proposal is to use stack-based cookies for crafting fat-pointers instead of having object-based identifiers. We store the fat-pointer on the stack, which eliminates the use of shadow memory space, or any table to store the pointer metadata. This reduces the storage overheads by a great extent. The cookie also helps to preserve control flow of the program by ensuring that the return address never gets modified by vulnerabilities like buffer overflows. Shakti-MS introduces new instructions in the microprocessor hardware, and also a modified compiler that automatically inserts these new instructions to enable memory protection. This co-design approach is intended to reduce runtime and area overheads, and also provides an end-to-end solution. The hardware has an area overhead of 700 LUTs on a Xilinx Virtex Ultrascale FPGA and 4100 cells on an open 55nm technology node. The clock frequency of the processor is not affected by the security extensions, while there is a marginal increase in the code size by 11% with an average runtime overhead of 13%.
Gated Spectral Units

Exploiting historical data of users to make future predictions lives at the heart of building effective recommender systems (RS). Recent approaches for sequential recommendations often render past actions of a user into a sequence, seeking to capture the temporal dynamics in the sequence to predict the next item. However, the interests of users evolve over time together due to their mutual influence, and most of existing methods lack the ability to utilize the rich coevolutionary patterns available in underlying data represented by sequential graphs. In order to capture the co-evolving knowledge for sequential recommendations, we start from introducing an efficient spectral convolution operation to discover complex relationships between users and items from the spectral domain of a graph, where the hidden connectivity information of the graph can be revealed. Then, the spectral convolution is generalized into an recurrent method by utilizing gated mechanisms to model sequential graphs. Experimentally, we demonstrate the advantages of modeling co-evolving patterns, and Gated Spectral Units (GSUs) achieve state-of-the-art performance on several benchmark datasets.
End-to-end DNN based text-independent speaker recognition for long and short utterances

Recently several end-to-end speaker verification systems based on deep neural networks (DNNs) have been proposed. These systems have been proven to be competitive for text-dependent tasks as well as for text-independent tasks with short utterances. However, for text-independent tasks with longer utterances, end-to-end systems are still outperformed by standard i-vector + PLDA systems. In this work, we present an end-to-end speaker verification system that is initialized to mimic an i-vector + PLDA baseline. The system is then further trained in an end-to-end manner but regularized so that it does not deviate too far from the initial system. In this way we mitigate overfitting which normally limits the performance of end-to-end systems. The proposed system outperforms the i-vector + PLDA baseline on both long and short duration utterances.
Reinforcement Learning for User Intent Prediction in Customer Service Bots

A customer service bot is now a necessary component of an e-commerce platform. As a core module of the customer service bot, user intent prediction can help predict user questions before they ask. A typical solution is to find top candidate questions that a user will be interested in. Such solution ignores the inter-relationship between questions and often aims to maximize the immediate reward such as clicks, which may not be ideal in practice. Hence, we propose to view the problem as a sequential decision making process to better capture the long-term effects of each recommendation in the list. Intuitively, we formulate the problem as a Markov decision process and consider using reinforcement learning for the problem. With this approach, questions presented to users are both relevant and diverse. Experiments on offline real-world dataset and online system demonstrate the effectiveness of our proposed approach.
Business information architecture for successful project implementation based on sentiment analysis in the tourist sector

In the today’s market, there is a wide range of failed IT projects in specialized small and medium-sized companies because of poor control in the gap between the business and its vision. In other words, acquired goods are not being sold, a scenario which is very common in tourism retail companies. These companies buy a number of travel packages from big companies and due to lack of demand for these packages, they expire, becoming an expense, rather than an investment. To solve this problem, we propose to detect the problems that limit a company by re-engineering the processes, enabling the implementation of a business architecture based on sentimental analysis, allowing small and medium-sized tourism enterprises (SMEs) to make better decisions and analyze the information that most possess, without knowing how to exploit it. In addition, a case study was applied using a real company, comparing data before and after using the proposed model in order to validate feasibility of the applied model.
Improved Method for Interleaving Parameter Estimation in a Non-Cooperative Context

In a non-cooperative context, a sequence acquired from remote sensing through satellites and aircraft can be recognized as an unknown sequence to a receiver who lacks the information of the transmission parameters.Therefore, the transmission parameters have to be estimated to reconstruct the unknown sequence.This paper focuses on the estimation of an interleaving period among transmission parameters and proposes an improved method to blindly estimate the interleaving period.Through computer simulations, we validate the method by analyzing the estimation performance in terms of the detection probability and the false alarm probability in a fading channel.
Nonparametric Estimation of Multivariate Mixtures

A multivariate mixture model is determined by three elements: the number of components, the mixing proportions, and the component distributions. Assuming that the number of components is given and ...
EnsembleGAN

Generating qualitative responses has always been a challenge for human-computer dialogue systems. Existing dialogue systems generally derive from either retrieval-based or generative-based approaches, both of which have their own pros and cons. Despite the natural idea of an ensemble model of the two, existing ensemble methods only focused on leveraging one approach to enhance another, we argue however that they can be further mutually enhanced with a proper training strategy. In this paper, we propose ensembleGAN, an adversarial learning framework for enhancing a retrieval-generation ensemble model in open-domain conversation scenario. It consists of a language-model-like generator, a ranker generator, and one ranker discriminator. Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts. The experimental results on a large short-text conversation data demonstrate the effectiveness of the ensembleGAN by the amelioration on both human and automatic evaluation metrics.
One-Size-Fits-None: A Heuristic for Proactive Value Sensitive Environmental Design

This article advocates for a contrasting Value Sensitive Design (VSD) framework based on principles of environmental justice, which encourages environmental-sensing wearable developers to make their values apparent to users in their designs in order to transform users into researchers rather than passive data collectors; make technological systems more transparent rather than opaque; and connect users to larger networks of individuals, who share common justice-oriented goals.
An Improved Convolutional Neural Network Algorithm and Its Application in Multilabel Image Labeling

In today's society, image resources are everywhere, and the number of available images can be overwhelming. Determining how to rapidly and effectively query, retrieve, and organize image information has become a popular research topic, and automatic image annotation is the key to text-based image retrieval. If the semantic images with annotations are not balanced among the training samples, the low-frequency labeling accuracy can be poor. In this study, a dual-channel convolution neural network (DCCNN) was designed to improve the accuracy of automatic labeling. The model integrates two convolutional neural network (CNN) channels with different structures. One channel is used for training based on the low-frequency samples and increases the proportion of low-frequency samples in the model, and the other is used for training based on all training sets. In the labeling process, the outputs of the two channels are fused to obtain a labeling decision. We verified the proposed model on the Caltech-256, Pascal VOC 2007, and Pascal VOC 2012 standard datasets. On the Pascal VOC 2012 dataset, the proposed DCCNN model achieves an overall labeling accuracy of up to 93.4% after 100 training iterations: 8.9% higher than the CNN and 15% higher than the traditional method. A similar accuracy can be achieved by the CNN only after 2,500 training iterations. On the 50,000-image dataset from Caltech-256 and Pascal VOC 2012, the performance of the DCCNN is relatively stable; it achieves an average labeling accuracy above 93%. In contrast, the CNN reaches an accuracy of only 91% even after extended training. Furthermore, the proposed DCCNN achieves a labeling accuracy for low-frequency words approximately 10% higher than that of the CNN, which further verifies the reliability of the proposed model in this study.
An Augmented Paper Game With Socio-Cognitive Support

This paper presents an augmented reality-based Chinese character composition game (ARC) that employs augmented papers to engage lower primary school students in collaborative Chinese character learning. A design research approach was used to gain a holistic view of designing, enacting, and evaluating the ARC. The participants included four teachers and five classes of students from two primary schools. The school-based research findings suggested that: 1) the ARC was an effective system for improving beginning learners' orthographic knowledge of Chinese characters, particularly for those students with low Chinese language proficiency; 2) the tangible paper interface engaged students in group work and increased the game playfulness; and 3) students' language proficiency had a significant effect on their perceptions of ease of use and usefulness of the augmented paper game. This paper not only substantiated the potential of the augmented paper as a carrier of educational content, but also instantiated how to implement it to promote social and cognitive supports for interaction and collaboration.
Taking MT Evaluation Metrics to Extremes: Beyond Correlation with Human Judgments

Automatic Machine Translation (MT) evaluation is an active field of research, with a handful of new metrics devised every year. Evaluation metrics are generally benchmarked against manual assessment of translation quality, with performance measured in terms of overall correlation with human scores. Much work has been dedicated to the improvement of evaluation metrics to achieve a higher correlation with human judgments. However, little insight has been provided regarding the weaknesses and strengths of existing approaches and their behavior in different settings. In this work we conduct a broad meta-evaluation study of the performance of a wide range of evaluation metrics focusing on three major aspects. First, we analyze the performance of the metrics when faced with different levels of translation quality, proposing a local dependency measure as an alternative to the standard, global correlation coefficient. We show that metric performance varies significantly across different levels of MT quality: Metrics perform poorly when faced with low-quality translations and are not able to capture nuanced quality distinctions. Interestingly, we show that evaluating low-quality translations is also more challenging for humans. Second, we show that metrics are more reliable when evaluating neural MT than the traditional statistical MT systems. Finally, we show that the difference in the evaluation accuracy for different metrics is maintained even if the gold standard scores are based on different criteria.
Lightning Network: A Comparative Review of Transaction Fees and Data Analysis

Blockchain is a revolutionary, immutable database disrupting the finance industry with a potential to provide payments in a secure environment, unhindered by intermediaries. However, scalability and throughput issues plague the technology and prevent it’s mass scale adoption. The paper focusses on Lightning Network, the off-chain, scalable and high throughput payment solution from Bitcoin. A comparison is conducted to highlight the fee incurred for payment transactions through Lightning Network, Raiden, Stellar, Bitcoin and conventional payment systems to assess its viability as a blockchain-based payment system. The paper also provides an analysis of the data of Lightning Network, to give a global overview of its usage and reachability.
A Framework for Self-Training Perceptual Agents in Simulated Photorealistic Environments

The development of high-performance perception for mobile robotic agents is still challenging. Learning appropriate perception models usually requires extensive amounts of labeled training data that ideally follows the same distribution as the data an agent will encounter in its target task. Recent developments in gaming industry led to game engines able to generate photorealistic environments in real-time, which can be used to realistically simulate the sensory input of an agent.We propose a novel framework which allows the definition of different learning scenarios and instantiates these scenarios in a high quality game engine where a perceptual agent can act and learn in. The scenarios are specified in a newly developed scenario description language that allows the parametrization of the virtual environment and the perceptual agent. New scenarios can be sampled from a task-specific object distribution that allows the automatic generation of extensive amounts of different learning environments for the perceptual agent.We will demonstrate the plausibility of the framework by conducting object recognition experiments on a real robotic system which has been trained within our framework.
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification

Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed.One could consider the emotion of each dialogue turn to be independent, but in this paper, we introduce a hierarchical approach to classify emotion, hypothesizing that the current emotional state depends on previous latent emotions.We benchmark several feature-based classifiers using pre-trained word and emotion embeddings, state-of-the-art end-toend neural network models, and Gaussian processes for automatic hyper-parameter search.In our experiments, hierarchical architectures consistently give significant improvements, and our best model achieves a 76.77% F1-score on the test set.
Generalized Zero Shot Learning via Synthesis Pseudo Features

Compared with conventional zero-shot learning (ZSL), generalized ZSL (GZSL) is more challenging because the test instances may come from seen and unseen classes. The most existing GZSL methods learn a visual-semantic mapping function to bridge the knowledge transfer from seen to unseen classes by using semantic information and other labeled training data. However, these methods often suffer from severe performance degradation because they ignore similar structures between different classes. To solve these problems, we propose a GZSL method that transforms GZSL problems to conventional supervised learning ones by synthesizing pseudo features for unseen classes. This technique has two key aspects. The first one is the synthesis strategy; the proposed strategy directly synthesizes the pseudo features of unseen classes contrary to current synthesis-based methods, which synthesize pseudo instances. Our method regards the combination of N features of instances as the pseudo features. These N features belong to N different classes that are similar to unseen ones. This synthesis strategy is in line with the cognitive style of human beings. The second key aspect is that we preserve the similar structures between seen and unseen classes. Inspired by the center loss method, we assign each semantic vector as the center of deep features in the training stage. This way preserves the similar structures between the classes. Such preservation can be beneficial for improving classification accuracy. The experimental results on four benchmark datasets demonstrate that our model outperforms state-of-the-art methods for the GZSL. The source code is available at https://github.com/guizilaile23/SPF-GZSL.
A Fully Convolutional Encoder–Decoder Spatial–Temporal Network for Real-Time Background Subtraction

Background subtraction is described as the task of distinguishing pixels into moving objects and the background in a frame. In this paper, we propose a fully convolutional encoder-decoder spatial-temporal network (FCESNet) to achieve real-time background subtraction. In the proposed many-to-many architecture method encoded features of consecutive frames are fed into a spatial-temporal information transmission (STIT) module to capture the spatial-temporal correlation in the frame sequence, and then a decoder is designed to output the subtraction results of all frames. A “patch-based” training method is designed to increase the practicability and flexibility of the proposed method. The experiments over CDNet2014 have shown that the proposed method could achieve state-of-the-art performance. The proposed method is proved to be able to achieve real-time background subtraction.
The Living Room of the Future

Emergent media services are turning towards the use of audience data to deliver more personalised and immersive experiences. We present the Living Room of The Future (LRoTF), an embodied design fiction built to both showcase future adaptive physically immersive media experiences exploiting the Internet of Things (IoT) and to probe the adoption challenges confronting their uptake in everyday life. Our results show that audiences have a predominantly positive response to the LRoTF but nevertheless entertain significant reservations about adopting adaptive physically immersive media experiences that exploit their personal data. We examine ‘user’ reasoning to elaborate a spectrum of adoption challenges that confront the uptake of adaptive physically immersive media experiences in everyday life. These challenges include data legibility, privacy concerns and potential dystopias, concerns over agency and control, the social need for customisation, value trade-off and lack of trust.
A Data-Driven Design Framework for Customer Service Chatbot

User experience in customer service is critical. It is because customer service is what a customer first requests for a service. The service fails to satisfactory response will cause a crucial damage. Albeit business includes a chatbot for better responsiveness, customization is still necessary to fulfill the satisfaction from customer service. For customization, a designer performs qualitative research such as surveys, self-reports, interviews, and user observation to pull out key characteristics and to build personas based on the characteristics. However, a small sample size and cognitive limitation of a researcher demand more data to model persona better. Therefore, in this study, we introduce a data-driven framework for designing customer service chatbot that utilizes the past customer behavior data from clickstreams and a customer service chatbot. We apply this framework to a cartoon streaming service, Laftel. In result, we generate three types of customer service chatbots for three personas such as explorer, soft user, and hard user. In the future, we will validate our result by conducting a field experiment.
Timed-Elastic Bands for Manipulation Motion Planning

Motion planning is one of the main problems studied in the field of robotics. However, it is still challenging for the state-of-the-art methods to handle multiple conditions that allow better paths to be found. For example, considering joint limits, path smoothness and a mixture of Cartesian and joint-space constraints at the same time pose a significant challenge for many of them. This letter proposes to use timed-elastic bands for representing the manipulation motion planning problem, allowing to apply continuously optimized constraints to the problem during the search for a solution. Due to the nature of our method, it is highly extensible with new constraints or optimization objectives. The proposed approach is compared against state-of-the-art methods in various manipulation scenarios. The results show that it is more consistent and less variant, while performing in a comparable manner to that of the state of the art. This behavior allows the proposed method to set a lower-bound performance guarantee for other methods to build upon.
Randomized Sensor Selection for Nonlinear Systems With Application to Target Localization

Given a nonlinear dynamical system, this letter considers the problem of selecting a subset of the total set of sensors that has provable guarantees on standard metrics related to the nonlinear observability Gramian. The key contribution is a simple randomized algorithm that samples the sensors uniformly without replacement, and yields probabilistic guarantees on the minimum eigenvalue or the inverse of the condition number of the nonlinear observability Gramian relative to that of the complete set of sensors. Numerical studies reveal that the utility of the theoretical results lies in the regime of large total number of sensors wherein the combinatorial nature of the problem presents a significant computational challenge. The results are demonstrated numerically on a problem of moving target localization using an extended Kalman filter in two scenarios: one using range sensors and another with time-difference-of-arrival measurements. A graceful degradation of performance with a decreased number of sensors is observed when compared to the use of all of the sensors for localization. It is also observed that for certain metrics, the proposed approach provides an improvement over a heuristic that selects the sensors in a greedy manner based on the contribution of an additional sensor toward the observability Gramian metric.