Reviewerly/exHarmony huggingface dataset:
What is the Best Action for Children to "Wake Up" and "Put to Sleep" a Conversational Agent?

Popular conversational assistants like Amazon Alexa, Google Assistant, and Apple Siri are activated by pronouncing their wake-up word ("Alexa", "OK Google", and "Hey Siri", respectively), and they stop listening when they recognize a pause. We explore whether this paradigm is appropriate for children and whether there are alternative, more effective ways for children to "wake-up" and "put to sleep" a conversational system. We consider seven possibilities: wake word, free-form speech, physical button, digital button, mouse, gaze, and buzzer. We propose a methodological framework of analysis based on the Multi-Attribute Value Theory (MAVT). As part of the decision-making process, we ran a study involving 42 children aged 9 to 10 who experienced the seven approaches. Our results suggest that the physical button is the most appropriate solution for this target group, which opens up new directions in the design of interaction affordances of conversational agents for children.
Breaking the Encryption Scheme of the Moscow Internet Voting System

In September 2019, voters for the election at the Parliament of the city of Moscow were allowed to use an Internet voting system. The source code of it had been made available for public testing. In this paper we show two successful attacks on the encryption scheme implemented in the voting system. Both attacks were sent to the developers of the system, and both issues had been fixed after that. The encryption used in this system is a variant of ElGamal over finite fields. In the first attack we show that the used key sizes are too small. We explain how to retrieve the private keys from the public keys in a matter of minutes with easily available resources. When this issue had been fixed and the new system had become available for testing, we discovered that the new implementation was not semantically secure. We demonstrate how this newly found security vulnerability can be used for counting the number of votes cast for a candidate.
Hybrid decentralized PBFT Blockchain Framework for OpenStack message queue

Abstract Cloud computing based on OpenStack is widely used as a distributed computing platform. OpenStack has progressed at a rapid pace, incorporating a variety of service modules; it is supported by many companies, has a community of active developers, and a diverse user base. OpenStack uses message queue to coordinate and exchange operation and status information between services. OpenStack supports various message queue services including RabbitMQ, Qpid, and ZeroMQ, whereas its distribution architecture uses RabbitMQ. As an OpenStack’s message queue service, RabbitMQ runs on a controller node as a centralized service. In case of the centralized service, increased usage may cause slowed response times and security vulnerability. This paper proposes a Hybrid decentralized Practical byzantine fault tolerance Blockchain Framework with two-step verification for OpenStack message queue service. When compared to existing OpenStack message queue service, OpenStack with the proposed framework demonstrates identical reliability a faster response time by approximately 46.75% with a two-step verification process and decentralization approach. Additionally, a reduction in the security vulnerability in the OpenStack message queue information with saving the message queue information into each node by blockchain-based decentralized data duplication approach.
High-Performance Deep-Learning Coprocessor Integrated into x86 SoC with Server-Class CPUs Industrial Product

Demand for high performance deep learning (DL) inference in software applications is growing rapidly. DL workloads run on myriad platforms, including general purpose processors (CPU), system-on-chip (SoC) with accelerators, graphics processing units (GPU), and neural processing unit (NPU) addin cards. DL software engineers typically must choose between relatively slow general hardware (e.g., CPUs, SoCs) or relatively expensive, large, power-hungry hardware (e.g., GPUs, NPUs). This paper describes Centaur Technology's Ncore, the industry's first high-performance DL coprocessor technology integrated into an x86 SoC with server-class CPUs. Ncore's 4096 byte-wide SIMD architecture supports INT8, UINT8, INT16, and BF16 datatypes, with 20 tera-operations-per-second compute capability. Ncore shares the SoC ring bus for low-latency communication and work sharing with eight 64-bit x86 cores, offering flexible support for new and evolving models. The x86 SoC platform can further scale out performance via multiple sockets, systems, or third-party PCIe accelerators. Ncore's software stack automatically converts quantized models for Ncore consumption and leverages existing DL frameworks. In MLPerf's Inference v0.5 closed division benchmarks, Ncore achieves 1218 IPS throughput and 1.05ms latency on ResNet-50v1.5 and achieves lowest latency of all Mobilenet-V1 submissions (329μs). Ncore yields 23x speedup over other x86 vendor percore throughput, while freeing its own x86 cores for other work. Ncore is the only integrated solution among the memory intensive neural machine translation (NMT) submissions.
Text classification based recurrent neural network

Recurrent neural networks (RNNs) have shown outstanding performance for natural language processing tasks, influenced by the repeated multiplication of the recurrent weight matrices, the problem of gradient vanishing and explosion problem will be encountered when training RNN. Independently recurrent neural network (IndRNN) makes neurons independent and constrains recursive weights to effectively solve gradient problems. We combine IndRNN with long short-term memory (LSTM) and attention model, and test it for text classification, the results show that our models can effectively adapt to text classification task.
Extrinsic camera calibration for non-overlapping cameras with Gray code projection

Multi-camera setups require the transformations between the involved cameras to be known. The intrinsic camera calibration can be done relatively easily in advance in lab conditions before positioning the cameras. The extrinsic calibration on the other hand can only be obtained when the cameras are mounted in their final position. This is usually done using checkerboards or similar markers, which require some degree of overlap of the field of view. In this work, we propose a method where Gray code is projected on a plane (floor or wall) using a standard projector. The extrinsic calibration is found using a bundle adjustment method that optimizes the pose of the plane and the cameras with respect to the projector. No overlap is required in the field of view of the cameras; it is enough that in each camera part of the Gray code pattern is visible. A sensitivity analysis is performed on simulated images. The method has a low sensitivity to sensor noise and to errors in the intrinsic calibration of the projector. Using real-world experiments, we show that the method is accurate for both overlapping and non-overlapping cameras, with median rotation errors of 0.36° and below. The accuracy of the proposed method is comparable to the state of the art, but this method provides a more practical procedure: no checkerboards must be held in place and only one projector pose is needed.
Déjà View: Spatio-Temporal Compute Reuse for‘ Energy-Efficient 360° VR Video Streaming

The emergence of virtual reality (VR) and augmented reality (AR) has revolutionized our lives by enabling a 360° artificial sensory stimulation across diverse domains, including, but not limited to, sports, media, healthcare, and gaming. Unlike the conventional planar video processing, where memory access is the main bottleneck, in 360° VR videos the compute is the primary bottleneck and contributes to more than 50% energy consumption in battery-operated VR headsets. Thus, improving the computational efficiency of the video processing pipeline in a VR is critical. While prior efforts have attempted to address this problem through acceleration using a GPU or FPGA, none of them has analyzed the 360° VR pipeline to examine if there is any scope to optimize the computation with known techniques such as memoization.Thus, in this paper, we analyze the VR computation pipeline and observe that there is significant scope to skip computations by leveraging the temporal and spatial locality in head orientation and eye correlations, respectively, resulting in computation reduction and energy efficiency. The proposed Déjà View design takes advantage of temporal reuse by memoizing head orientation and spatial reuse by establishing a relationship between left and right eye projection, and can be implemented either on a GPU or an FPGA. We propose both software modifications for existing compute pipeline and microarchitectural additions for further enhancement. We evaluate our design by implementing the software enhancements on an NVIDIA Jetson TX2 GPU board and our microarchitectural additions on a Xilinx Zynq-7000 FPGA model using five video workloads. Experimental results show that Déjà View can provide 34% computation reduction and 17% energy saving, compared to the state-of-the-art design.
Quantitative Comparison of Time‐Dependent Treemaps

Abstract Rectangular treemaps are often the method of choice to visualize large hierarchical datasets. Nowadays such datasets are available over time, hence there is a need for (a) treemaps that can handle time‐dependent data, and (b) corresponding quality criteria that cover both a treemap's visual quality and its stability over time. In recent years a wide variety of (stable) treemapping algorithms has been proposed, with various advantages and limitations. We aim to provide insights to researchers and practitioners to allow them to make an informed choice when selecting a treemapping algorithm for specific applications and data. To this end, we perform an extensive quantitative evaluation of rectangular treemaps for time‐dependent data. As part of this evaluation we propose a novel classification scheme for time‐dependent datasets. Specifically, we observe that the performance of treemapping algorithms depends on the characteristics of the datasets used. We identify four potential representative features that characterize time‐dependent hierarchical datasets and classify all datasets used in our experiments accordingly. We experimentally test the validity of this classification on more than 2000 datasets, and analyze the relative performance of 14 state‐of‐the‐art rectangular treemapping algorithms across varying features. Finally, we visually summarize our results with respect to both visual quality and stability to aid users in making an informed choice among treemapping algorithms. All datasets, metrics, and algorithms are openly available to facilitate reuse and further comparative studies.
Betting on DOTA 2’s Battle Pass: Gamblification and productivity in play

The transformation of games with the advent of platformized distribution systems continues to produce new and agile forms of consumption and exploitation. Valve Corporation’s DOTA 2 is a key example of a gaming space that is constantly atomized and rebuilt with the aim of optimizing player participation. This participatory form is ever-more gamblified and framed by systems designed to habituate players to a new form of consumption. This article explores how DOTA 2 transforms every year with the advent of a yearly Battle Pass, brimming with gambling systems aimed at eliciting specific forms of user participation. We catalog and schematize these systems with the aim of shedding light on the inner workings of DOTA 2 during this season. The purpose of our work is to move the discussion beyond a regulatory focus on symptomatic loot boxes and toward a deeper understanding of the rhetorical systems hiding beneath game systems.
Covert hate speech

Abstract Previous research on extremist discourse has revealed that racism is linguistically shaped by its socio-cultural context. For instance, a comparison between Greek Cypriot and Greek online data indicated that the two communities use different linguistic means and strategies to express their aversion to the Other, and that Greek comments are more overtly insulting than Greek Cypriot comments ( Baider and Constantinou 2017a ; Assimakopoulos and Baider 2019 ). The present study focuses on how irony is used to disseminate hate speech, albeit covertly. Our dataset comprises online Greek and Greek Cypriot comments posted on social media and collected during the same period of time (2015- 2016) within an EU project. We use concepts such as verisimilitude and overt untruthfulness to deconstruct ironic racist comments. We conclude that irony in both datasets fulfils three socio-pragmatic functions: it serves to insult or humiliate members of groups targeted for their ethnic identity; it creates or reinforces negative feelings against such groups; it promotes beliefs that could be used to legitimate their mistreatment. Regarding socio-cultural differences, it emerges that the use of the Greek Cypriot vernacular and the appeal to indigenous in-group social stereotypes influence the way irony shapes racist comments and reinforces in-group membership.
Resource management in the federated cloud environment using Cournot and Bertrand competitions

Cloud federation helps cloud providers to scale up by renting resources from other providers when the workload increased. Moreover, cloud providers with idle or underutilized resources can sell their resources to others and earn revenue inside a cloud federation. One of the most critical problems in the federated cloud environment is the management of resources and cloud providers. The game theory seems to be an excellent way to model cloud federation. This paper introduces a new model for resource management between cloud providers in a centralized federated cloud environment, based on the well known Cournot and Bertrand games. To resolve the problem of heterogeneous resources, a physical resource unit which has specific computational features and can be shared between cloud providers is introduced in this paper. Besides, by introducing a new revenue-sharing approach between providers, this paper increased the collaboration of different providers. This model is implemented by the federated Cloudsim tool, and experiments show that the Cournot model outperforms others in terms of overall benefit and responsiveness. In other words, Cournot model can respond to an acceptable number of requests while making more profit. Besides, the reviews show that the proposed model works better than other methods in terms of time.
Toward Multi-Modal Conditioned Fashion Image Translation

Having the capability to synthesize photo-realistic fashion product images conditioned on multiple attributes or modalities would bring many new exciting applications. In this work, we propose an end-to-end network architecture that built upon a new generative adversarial network for automatically synthesizing photo-realistic images of fashion products under multiple conditions. Given an input pose image that consists of a 2D skeleton pose and a sentence description of products, our model synthesizes a fashion image preserving the same pose and wearing the fashion products described as the text. Specifically, the generator <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G$</tex-math></inline-formula> tries to generate realistic-looking fashion images based on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\langle \mathsf {pose}, \mathsf {text} \rangle$</tex-math></inline-formula> pair condition to fool the discriminator. An attention network is added for enhancing the generator, which predicts a probability map indicating which part of the image needs to be attended for translation. In contrast, the discriminator <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$D$</tex-math></inline-formula> distinguishes real images from the translated ones based on the input pose image and text description. The discriminator is divided into two multi-scale sub-discriminators for improving image distinguishing task. Quantitative and qualitative analysis demonstrates that our method is capable of synthesizing realistic images that retain the poses of given images while matching the semantics of provided sentence descriptions.
Escaping dependency hell: finding build dependency errors with the unified dependency graph

Modern software projects rely on build systems and build scripts to assemble executable artifacts correctly and efficiently. However, developing build scripts is error-prone. Dependency-related errors in build scripts, mainly including missing dependencies and redundant dependencies, are common in various kinds of software projects. These errors lead to build failures, incorrect build results or poor performance in incremental or parallel builds. To detect such errors, various techniques are proposed and suffer from low efficiency and high false positive problems, due to the deficiency of the underlying dependency graphs. In this work, we design a new dependency graph, the unified dependency graph (UDG), which leverages both static and dynamic information to uniformly encode the declared and actual dependencies between build targets and files. The construction of UDG facilitates the efficient and precise detection of dependency errors via simple graph traversals. We implement the proposed approach as a tool, VeriBuild, and evaluate it on forty-two well-maintained open-source projects. The experimental results show that, without losing precision, VeriBuild incurs 58.2% less overhead than the state-of-the-art approach. By the time of writing, 398 detected dependency issues have been confirmed by the developers.
Efficient Novelty Search Through Deep Reinforcement Learning

Novelty search, which was inspired by the nature that evolves creatures with diversity, has shown great potential in solving reinforcement learning (RL) tasks with sparse and deceptive rewards.However, most of the existing novelty search methods evolve the populations through hybrization and mutation, which is inefficient in diverging populations.In this paper, we propose a method which incorporates deep RL with novelty search to improve the efficiency of diverging the populations for novelty search.We first propose a strategy that improves the novelty of individuals generated by genetic algorithm using reinforcement learning.Based on this strategy, we propose a framework that incorporates deep RL with novelty search, and then derive an algorithm to improve the search efficiency of the novelty search for continuous control tasks.Our experimental results show that our method can improve the search efficiency of novelty search and can also provide a competitive performance compared to some of the existing novelty search methods.The implementation of our method is available at: https://github.com/shilx001/NoveltySearch_Improvement.
A Novel Coding Architecture for LiDAR Point Cloud Sequence

In this letter, we propose a novel coding architecture for LiDAR point cloud sequences based on clustering and prediction neural networks. LiDAR point clouds are structured, which provides an opportunity to convert the 3D data to a 2D array, represented as range images. Thus, we cast the LiDAR point clouds compression as a range images coding problem. Inspired by the high efficiency video coding (HEVC) algorithm, we design a novel coding architecture for the point cloud sequence. The scans are divided into two categories: intra-frames and inter-frames. For intra-frames, a cluster-based intra-prediction technique is utilized to remove the spatial redundancy. For inter-frames, we design a prediction network model using convolutional LSTM cells, which is capable of predicting future inter-frames according to the encoded intra-frames. Thus, the temporal redundancy can be removed. Experiments on the KITTI data set show that the proposed method achieves an impressive compression ratio, with 4.10% at millimeter precision. Compared with octree, Google Draco and MPEG TMC13 methods, our scheme also yields better performance in compression ratio.
Thermal Image Super-Resolution Challenge - PBVS 2020

This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with state- of-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, mid-resolution, and high-resolution thermal images by ×2, ×3 and ×4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the ×2 super-resolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered high- resolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.
A Reinforcement Learning-Based Framework for Solving Physical Design Routing Problem in the Absence of Large Test Sets

Advances in Electronic Design Automation(EDA) methods have made the designers and programmers to search for new ways to solve the complex problems seen in today's Very Large Scale Integration circuits. Machine learning (ML), especially supervised learning, has been used to predict design rule violations. However, supervised learning requires large amount of labeled data. With the competitive nature of EDA based companies, there is limited access to benchmarks and labeled data. In this work, we propose a data-independent reinforcement learning (RL) based routing model called Alpha-PD-Router, which learns to route a circuit and correct short violations. The Alpha-PD-Router is based on a two-player collaborative game model that has been trained on a small circuit and successfully resolves 75 violations in 99 cases of 2 pins net arrangements in the testing phase. The proposed model has the potential to be used as a framework to develop RL based routing techniques untethered by the scarce availability of large routing data samples or designer expertise.
Routing space exploration for scalable routing in the quantum Internet

The entangled network structure of the quantum Internet formulates a high complexity routing space that is hard to explore. Scalable routing is a routing method that can determine an optimal routing at particular subnetwork conditions in the quantum Internet to perform a high-performance and low-complexity routing in the entangled structure. Here, we define a method for routing space exploration and scalable routing in the quantum Internet. We prove that scalable routing allows a compact and efficient routing in the entangled networks of the quantum Internet.