Reviewerly/exHarmony huggingface dataset:
Gliders2d: Source Code Base for RoboCup 2D Soccer Simulation League

We describe Gliders2d, a base code release for Gliders, a soccer simulation team which won the RoboCup Soccer 2D Simulation League in 2016. We trace six evolutionary steps, each of which is encapsulated in a sequential change of the released code, from v1.1 to v1.6, starting from agent2d-3.1.1 (set as the baseline v1.0). These changes improve performance by adjusting the agents’ stamina management, their pressing behaviour and the action-selection mechanism, as well as their positional choice in both attack and defense, and enabling riskier passes. The resultant behaviour, which is sufficiently generic to be applicable to physical robot teams, increases the players’ mobility and achieves a better control of the field. The last presented version, Gliders2d-v1.6, approaches the strength of Gliders2013, and outperforms agent2d-3.1.1 by four goals per game on average. The sequential improvements demonstrate how the methodology of human-based evolutionary computation can markedly boost the overall performance with even a small number of controlled steps.
Object Recognition for the Visually Impaired

Today images and videos are everywhere. In fact, the sheer quantity of images on social media and networking sites is unfathomable. Every device is now fitted with a camera. This opens up huge possibilities. Object Recognition is a process of detecting an object and identifying it using various image algorithms. The main purpose of this paper is to recognize objects in real time and allot the objects to the classes that are previously defined. The algorithms that we utilized are more computationally efficient. Previously, object detection was done using RFID and IR technologies which required dedicated hardware. But with the advent of image processing and neural networks, we require almost no new hardware. Almost everything has camera these days from pens to mobile phones. This has given rise to a new field called computer vision i.e. using pictures and videos to detect, segregate and track objects or events so that we can “understand” a real world scenario.
A New Approach to Process the Unknown Words in Financial Public Opinion

The processing of unknown words is an important issue that impedes the performance of many natural language systems. In terms of the processing of financial public opinion, due to a wide variety of terminology in the financial sector and the suddenness of financial public opinion events as well as the proliferation of network vocabulary, a variety of unknown new words emerge while the traditional text classification method is basically incapable of processing these unrecognized characters. In this paper, an approach equipped with a new directional substitution model for processing the unknown words is proposed. Based on the semantic similarity of the context in word2ve algorithm, the model trains synonym substitution list and substitutes the unknown words in the original texts with synonyms. Also, the TFIDF (term frequency-inverse document frequency) -weighted Naive Bayes classifier is used to carry out the text classification experiments on the traditional datasets and the synthetic datasets including the unknown words. The experimental results show that the model has better classification effect than the traditional methods and can accurately identify the categories of the financial public opinion texts involving the unknown words by transforming the meaningless unknown words into the words containing meaning.
Perceptually Optimized Quality Adaptation of Viewport-Dependent Omnidirectional Video Streaming

Viewport-Dependent Streaming (VDS) is a preferred way in practice to deliver the omnidirectional videos, of which a High-Quality (HQ) scale is applied for the content in current viewport but a Low-Quality (LQ) scale elsewhere. Quality adaptation or refinement happens after users stabilize their fixations to a new viewport. In this article, we formulate this as a perceptually optimized quality adaptation problem to maximize the Quality of Experience (QoE) for the refinement from a LQ scale to another HQ level within a specific duration under the given network constraint. With our developed perceptual model considering the adaptation quality for VDS of omnidirectional videos, we first provide baseline solutions numerically, demonstrating the noticeable subjective improvements of model-driven solution against the heuristic selection based approach. We also propose a novel viewport prediction algorithm based on the Hidden Markov Model (HMM), and experimental results show that it significantly outperforms the relevant methods with better prediction accuracy. We then improve the adaptation strategy with proposed viewport prediction-based data prefetching, leading to better visual perception than the baseline system at the same bandwidth constraint. Generally, prefetching the content of predicted next viewport one second ahead of its playback time, would lead to more than 8% Bjontegaard Delta Rate (BD-Rate) gain.
Fork Rate-Based Analysis of the Longest Chain Growth Time Interval of a PoW Blockchain

Nakamoto's consensus protocol, which is well known for its resistance to sybil attacks by using PoW (Proof of Work), enables us to build public blockchains, such as Bitcoin. In this protocol, miners seek to extend the longest chain by solving blockhash-based cryptographic puzzles and the required time is probabilistically determined. Therefore, the distribution of the time interval affects security, performance and applications which utilize the block height information. Some researchers assumed that the time follows an exponential distribution but this assumption requires that the blockchain network is fully synchronized. To overcome this unreal scenario, the bounded delay model, in which there is an upper bound for block propagation delay on the network, was proposed. However, it is difficult to calculate the upper bound without observing delay and bandwidth on real-world network links. To solve this problem, we proposed another method to analyze the distribution of the longest chain growth time interval by using the observed fork rate. We derived a closed-form lower bound for the CDF (Cumulative Distribution Function) of the time to update the global block height. We also obtained the Pearson distance which can be used as the metric to judge whether the network is approximately synchronous or not. Finally, we conducted network simulations for comparing our lower bound with the lower bound that is based on the bounded delay model. In numerical examples, we show how the block size affects these lower bounds.
Visual–Textual Hybrid Sequence Matching for Joint Reasoning

Reasoning is one of the central topics in artificial intelligence. As an important reasoning paradigm, entailment recognition has attracted much research interest, which judges if a hypothesis can be inferred from given premises. However, existing research mainly focuses on text-based analysis, that is, recognizing textual entailment (RTE), which limits its depth and width. Actually, the knowledge and inference process of human are across different sensory organs like language and vision, with unique perspectives to represent complementary reasoning cues. It is significant to extend existing entailment recognition research to cross-media scenarios, that is, recognizing cross-media entailment (RCE). Therefore, this article focuses on one representative RCE task: visual-textual reasoning, and proposes the visual-textual hybrid sequence matching (VHSM) approach. VHSM can reason from image-text premises to text hypotheses, whose contributions are: 1) visual-textual hybrid multicontext inference is proposed to address RCE via matching with hybrid context embeddings, along with adaptive gated aggregation to obtain the final prediction results. It can fully exploit complementary visual-textual cue interaction during joint reasoning; 2) memory attention-based context embedding is proposed to sequentially encode hybrid context embeddings, with the memory attention networks to compare neighboring time-steps. This can capture the important memory dimensions by coefficient assignment, which fully exploits the visual-textual context correlation; and 3) cross-task and visual-textual transfer strategy is further proposed to enrich correlation training information for boosting reasoning accuracy, which transfers knowledge not only from cross-media retrieval task to RCE but also between corresponding text and image premises. The experimental results of recognizing visual-textual entailment task on the SNLI dataset verify the effectiveness of VHSM.
Minimization of digital logic gates and ultra-low power AES encryption core in 180CMOS technology

This work concentrate on the design of low power substitution box architecture with enhanced galois field based transform towards multiplier in advanced cryptographic encryption core. Basic components such as AND, XOR, XNOR are being used for the designing process. Initially tree based XNOR gates are picked up and then the AND gates are replaced by NAND gated with less number of transistor in it. Here, logical method is carried out, which is actually considered as better method for re-sizing of transistor. This will further decrease the overall delay and power consumption. The less delay can be obtained by 4-input XOR gate architecture. The implementation results with an 180 nm CMOS standard library show that the proposed AES core can reduce the area and power consumption significantly.
Multi-criteria Decision Making Model for Vulnerabilities Assessment in Cloud Computing regarding Common Vulnerability Scoring System

Vulnerability is associated with the probability of resistance of actions of a threat. A vulnerability exists when a force of threat exceeds the capacity of resistance. Virtualization and its exclusive architecture have numerous features and advantages over non-conventional virtual machines. However, this new uniqueness creates new vulnerabilities and attacks on a cloud system. Assessing the security of software services on Cloud is complex because the security depends on the vulnerability of infrastructure, platform and software services. In 2017, over 14,000 new vulnerabilities were disclosed, so, a key question for administrators is which vulnerabilities to prioritize. The Common Vulnerability Scoring System (CVSS) is often used to decide which vulnerabilities pose the greatest risk. CVSS V3 creates a metric for each vulnerability and establishes a very broad definition of vulnerabilities, therefore, multi-criteria decision making (MCDM) is necessary to making a choice of the best alternative from among a finite set of decision alternatives in terms of multiple criteria. We propose a model for the evaluation and prioritization of vulnerabilities in cloud architectures based on the Common Vulnerability Scoring System and multi-criteria decision making.
A novel mission planning method for UAVs’ course of action

Military course of action for unmanned aerial vehicles (UAVs) has the characteristics with antagonism of both sides, uncertainty of execution results, diversity of war ways and urgency of war time. According to this, a novel mission planning method for UAVs’ course of action is proposed in this paper, which is based on a two-segment nested scheme generation strategy. The method is mainly divided into two parts: task decomposition stage and resource scheduling stage. Compared with other methods, this method can generate multiple schemes automatically of the whole operational process, and the generated schemes have great advantages in diversity and task completion time. And the correlation coefficient can be adjusted to realize the tendency of different optimization indexes. The effectiveness of this method is verified by the modelling and simulation of UAV cooperative operation.
Can Citizen Science using social media inform sanitation planning?

Improving the sanitation situation in a community that relies on on-site sanitation requires information about the quantity and quality of faecal sludge (FS) produced. Existing tools for data collection about FS characteristics are either imprecise or very costly. As information and communication technologies are becoming more available in low-income countries, Citizen Science is becoming an option for the collection of sanitation data. This study used an online survey distributed via social media to predict FS characteristics (Total Solids (TS), Total Suspended Solids (TSS), Volatile Suspended Solids (VSS), Chemical Oxygen Demand (COD) and Sludge Volume Index (SVI)) at 40 households in Blantyre, Malawi. Furthermore, the study investigated whether and how an online survey could yield cost-effective and representative information about the sanitation situation at the household level. Facebook and WhatsApp both introduced a bias towards higher income households as low-income households still lack access to smartphones and internet. WhatsApp (€1.01/reply) was significantly cheaper than Facebook (€3.00/reply) and conventional data generation with household interviewers (€1.47-2.20/reply). Monetary incentives generated more replies than pure social messaging. TSS, VSS and COD were significantly correlated to TS. The slope of the regression line of COD to TS (0.97) was similar to one reported for Uganda (0.88), suggesting that the ratio of COD to TS might be independent of the location. Statistically significant median differences of TS concentrations were found for the survey parameters "system type" and "solid waste", making these parameters candidates for predictors of FS characteristics. The sample size was too small for the other survey parameters to detect any differences statistically. Overall, using social media in a Citizen Science approach appears to be a promising new tool for FS characterization. In the current form, it can probably not replace other data collection tools, but rather complement them as a cost-efficient method to gain an initial assessment. Besides sanitation planning, social media and the Citizen Science approach could be employed for data collection in various fields of development cooperation.
Efficient deformable 3D face model tracking with limited hardware resources

Face fitting methods align deformable models to faces on images using the information given by the image pixels. However, most algorithms are designed to be used in desktop personal computers (PC), or hardware with significant computational power. These approaches are therefore too demanding for devices with limited computational power, like the increasingly used ARM-based devices. Besides the hardware limitations, the particularities of each operating system include additional challenges to the implementation of real-time face tracking solutions. To fill the lack of methods designed for platforms with a limited computational power we present an efficient way to fit 3D human face models to monocular images. This approach estimates the head pose and gesture in a 3D environment based on a full perspective projection, using parametric non-linear optimisation. We compare the performance of this method running it on similar ARM-based devices with different operating systems (Linux, Android, and iOS). In all cases, we have measured both accuracy and performance. The efficiency of the method makes it possible to run it in real-time ($\backsim $30fps) on devices with limited computational power like smartphones and embedded systems. These kind of efficient methods are a vital component for human behaviour analysis applications, like driver monitoring systems and human-machine interfaces for disabled people among others.
Deep heterogeneous network embedding based on Siamese Neural Networks

Heterogeneous network embedding aims at mapping a heterogeneous network into a low-dimensional latent space. There exist diverse relations among different types of objects in heterogeneous networks. However, most existing heterogeneous network embedding methods focus on exploring network structures instead of relations among different objects, so some redundant and fuzzy relations are inevitably captured. To address the problem, we propose a Relation-Oriented Deep Embedding (RODE) framework for heterogeneous networks that explores different relations among nodes. The captured relations are modeled through node similarity and dissimilarity. Based on the similarity and dissimilarity, a multi-task Siamese Neural Network is formulated to perform network embedding and optimize embedding representations. Extensive experiments are conducted on four heterogeneous networks. Experimental results demonstrate our method outperforms state-of-the-art embedding algorithms on several network mining tasks, such as link prediction, node classification and node clustering.
Learning reliable-spatial and spatial-variation regularization correlation filters for visual tracking

Single-object tracking is a significant and challenging computer vision problem. Recently, discriminative correlation filters (DCF) have shown excellent performance. But there is a theoretical defects that the boundary effect, caused by the periodic assumption of training samples, greatly limit the tracking performance. Spatially regularized DCF (SRDCF) introduces a spatial regularization to penalize the filter coefficients depending on their spatial location, which improves the tracking performance a lot. However, this simple regularization strategy implements unequal penalties for the target area filter coefficients, which makes the filter learn a distorted object appearance model. In this paper, a novel spatial regularization strategy is proposed, utilizing a reliability map to approximate the target area and to keep the penalty coefficients of relevant region consistent. Besides, we introduce a spatial variation regularization component that the second-order difference of the filter, which smooths changes of filter coefficients to prevent the filter over-fitting current frame. Furthermore, an efficient optimization algorithm called alternating direction method of multipliers (ADMM) is developed. Comprehensive experiments are performed on three benchmark datasets: OTB-2013, OTB-2015 and TempleColor-128, and our algorithm achieves a more favorable performance than several state-of-the-art methods. Compared with SRDCF, our approach obtains an absolute gain of 6.6% and 5.1% in mean distance precision on OTB-2013 and OTB-2015, respectively. Our approach runs in real-time on a CPU.
CENNA: Cost-Effective Neural Network Accelerator

Convolutional neural networks (CNNs) are widely adopted in various applications. State-of-the-art CNN models deliver excellent classification performance, but they require a large amount of computation and data exchange because they typically employ many processing layers. Among these processing layers, convolution layers, which carry out many multiplications and additions, account for a major portion of computation and memory access. Therefore, reducing the amount of computation and memory access is the key for high-performance CNNs. In this study, we propose a cost-effective neural network accelerator, named CENNA, whose hardware cost is reduced by employing a cost-centric matrix multiplication that employs both Strassen’s multiplication and a naïve multiplication. Furthermore, the convolution method using the proposed matrix multiplication can minimize data movement by reusing both the feature map and the convolution kernel without any additional control logic. In terms of throughput, power consumption, and silicon area, the efficiency of CENNA is up to 88 times higher than that of conventional designs for the CNN inference.
A Fair Anonymous Auction Scheme Utilizing Trusted Hardware and Blockchain

In this paper, we propose an anonymous English auction scheme that utilizes trusted hardware and blockchain for the enhancement of the privacy of the bidders and the correctness of the auction. We use group signature to provide anonymity to the bidders. The hardware-based trusted execution environment (TEE) is utilized to ensure that the group manager de-anonymizes a group member only once for identifying the winner. To incorporate this idea, the blockchain transactions are signed using a group signature scheme instead of an ordinary signature scheme.
Aligning in the dark: Variable and shifting (user-) settings in developing point-of-care diagnostics for tuberculosis and HIV

To be effective, healthcare technologies should be attuned to particular contexts of use. This article examines how such attuning is articulated in global innovation practices for tuberculosis and HIV diagnostics, and to what effect. It examines the development of point-of-care (POC) diagnostics - promised to be designed for users outside laboratories or in resource constrained settings - to study what developers and implementers do to align diagnostic technologies to the POC. Fieldwork among global health actors involved in diagnostic development, including manufacturers, donors, industry consultants, international organizations, policymakers, regulators and researchers, is combined with fieldwork among users of diagnostics in India, including decision-makers, NGOs, program officers, laboratory technicians and nurses. The article adds to STS's theory of alignment and user interaction, where the setting and user to which developers and implementers of global health diagnostics align are multiple, varied, emerging and keep shifting. The characteristics of a local user setting include multiple engaged and imagined user settings, but also the settings of developers, of global intermediaries, competitors and diseases. As such, alignment is happening across multiple dimensions and scales and has an important temporal dimension. The results reveal how alignment happens to some extent in the dark, characterized by uncertainty about the elements that should align. Standardizing elements, politics and scarce resources cause frictions in the temporalities of aligning and over what constitutes a well-aligned diagnostic.
Adaptive parameter estimation of GMM and its application in clustering

Parameter estimation of Gaussian mixture model (GMM) has begun to gain attention in the field of science and engineering, and it has gradually become one of the most popular research topics. In particular, the rapid development of theoretical progress on globally optimal convergence promotes the widespread application of Gaussian mixtures in data clustering. So this paper introduces a novel parameter estimation algorithm called TDAVBEM, which combines the Tsallis entropy and a deterministic annealing (DA) algorithm on the basis of the variational bayesian expected maximum (VBEM) to simultaneously implement the parameter estimation and select the optimal components of GMM. We experimentally certified the effectiveness and robustness of our proposed algorithm passes through comparing it with several parameter evaluation methods and its application in data clustering.
Faithful Face Image Completion for HMD Occlusion Removal

Head-mounted-displays (HMDs) provide immersive experiences of virtual content. While being flexible, HMDs could be a hindrance for Virtual Reality (VR) applications such as VR teleconference where facial components and expressions of the user are partially occluded thus cannot be seen by others. We present an automatic face image completion solution that treats the occluded region as a hole and completes the hole with the help of an occlusion-free reference image of the same person. Given the occluded input image and an occlusion-free reference image, our method first computes head pose features from estimated facial landmarks. The head pose features, as well as images, are then fed into a generative adversarial network (GAN) to synthesize the output image. Our method can generate faithful results from various input cases and outperforms other face completion methods. It provides a light-weighted solution to HMD occlusion removal and has the potential to benefit VR applications.