Reviewerly/exHarmony huggingface dataset:
Learning Dense Wide Baseline Stereo Matching for People

Existing methods for stereo work on narrow baseline image pairs giving limited performance between wide baseline views. This paper proposes a framework to learn and estimate dense stereo for people from wide baseline image pairs. A synthetic people stereo patch dataset (S2P2) is introduced to learn wide baseline dense stereo matching for people. The proposed framework not only learns human specific features from synthetic data but also exploits pooling layer and data augmentation to adapt to real data. The network learns from the human specific stereo patches from the proposed dataset for wide-baseline stereo estimation. In addition to patch match learning, a stereo constraint is introduced in the framework to solve wide baseline stereo reconstruction of humans. Quantitative and qualitative performance evaluation against state-of-the-art methods of proposed method demonstrates improved wide baseline stereo reconstruction on challenging datasets. We show that it is possible to learn stereo matching from synthetic people dataset and improve performance on real datasets for stereo reconstruction of people from narrow and wide baseline stereo data.
A unified framework of identity-based sequential aggregate signatures from 2-level HIBE schemes

Identity-based sequential aggregate signature (IBSAS for short) schemes, introduced by Boldyreva et al. [CCS 2007], allow a large quantity of signers to generate one signature sequentially, in which these messages as well as their order can be attested by employing their identities. In such a scheme, storage space and bandwidth overhead can be reduced. To our best knowledge, though many concrete IBSAS schemes have been constructed in literature, none of them is constructed under a standard computational hardness assumption and unforgeable under the standard model. The problem of how to construct such schemes is still open. Latterly, Gentry et al. [PKC 2018] proposed a unified construction of SAS (i.e., abbreviated form of sequential aggregate signature) schemes by employing trapdoor permutation and ideal ciphers. Motivated by the above problem and hints, here we study how to construct IBSAS schemes in a new unified perspective. By employing 2-level HIBE (i.e., abbreviated form of hierarchical identity-based encryption) schemes, we present unified construction of IBSAS schemes and give a rigorous proof of their unforgeability. The unified construction is then instantiated to get a concrete IBSAS scheme, which has existential unforgeability under the standard model using a standard computational hardness assumption (i.e., the CDH assumption). An extra fruit is that it can be used to construct an existentially unforgeable IBSAS scheme using the Learning with Errors problem, which is constructed under a lattice hardness assumption for the first time. In the end, we show a detailed performance comparison among our schemes and previous ones.
Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation

Domain Adaptation (DA), the process of effectively adapting task models learned on one domain, the source, to other related but distinct domains, the targets, with no or minimal retraining, is typically accomplished using the process of source-to-target manifold alignment. However, this process often leads to unsatisfactory adaptation performance, in part because it ignores the task-specific structure of the data. In this paper, we improve the performance of DA by introducing a discriminative discrepancy measure which takes advantage of auxiliary information available in the source and the target domains to better align the source and target distributions. Specifically, we leverage the cohesive clustering structure within individual data manifolds, associated with different tasks, to improve the alignment. This structure is explicit in the source, where the task labels are available, but is implicit in the target, making the problem challenging. We address the challenge by devising a deep DA framework, which combines a new task-driven domain alignment discriminator with domain regularizers that en-courage the shared features as task-specific and domain invariant, and prompt the task model to be data structure preserving, guiding its decision boundaries through the low density data regions. We validate our framework on standard benchmarks, including Digits (MNIST, USPS, SVHN, MNIST-M), PACS, and VisDA. Our results show that our proposal model consistently outperforms the state-of-the-art in unsupervised domain adaptation.
Blockchain Platform for Internet of Things

Blockchain is a public ledger without a central authority. Immutable and inalterable data blocks prove reliability and security which are the deficiencies of the current Internet of things (IoT) devices. IoT devices are hardware constrained devices which have specific functions but create large network to gather different data. Because of the security issues of the IoT devices, industrial use is limited. Generally, secure data transaction and storage are the main concerns over IoT devices and in order to solve the problem various solutions are being tested. However, decentralized and secure structure like the blockchain could be a solution. In this study, challenges of blockchain implementation on hardware constraint devices are discussed. Studies regarding blockchain implementation methods and blockchain implemented projects are mentioned. Experiments on frameworks used by blockchain are conducted and with the test results of the experiments, feasibility of implementing the blockchain on IoT devices are discussed.
Remote Configuration of Integrated Circuit Features and Firmware Management via Smart Contract

The cost to develop a new integrated circuit (IC), its fabrication, debug and volume production has been escalating with scaling of transistor feature size. According to an IBS report, the cost of developing a System on Chip (SoC) at 14nm may be as high as $300 million [1]. The economics of semiconductor IC development favors high volume production, while high volume cannot be attained without developing an IC that serves a large number of applications. Some of these applications are in low margin Internet of Things (IoT) devices, where an SoC cannot command a high price. Consequently, without the ability to customize IC features after production, the price of an IC will be determined by its lowest priced application. This motivates the manufacturers to develop capabilities for post-production IC customization. The commodity microprocessor business offers an example of post-production customization, where a manufacturer can tailor cache size, number of cores and frequency of operation for a target market segment after a chip has been manufactured. Today, such customization is limited to one-time programming (OTP) for predetermined IC bins. In this paper, we explore how an IC can be programmed repeatedly and securely using a blockchain-based smart contract. This will enable users to upgrade IC features, or rent upgraded IC features for a fixed period after it has been purchased. The availability of such a system could, for example, allow a buyer to upgrade her processor from i3 to i5 after it has been purchased to scale to her computing needs in exchange of a payment made to the manufacturer. IC feature configuration is implemented by firmware updates from the manufacturer. The smart contract takes the feature configuration request from the IC as input and outputs the source of corresponding firmware. To support remote and authorized update by manufacturer, we propose an on-die hardware module that communicates with the smart contract and enforces its functionalities. Availability of this module also facilitates secure firmware update. The blockchain makes the update protocol secure and prevents users from obtaining unauthorized update.
A Live Storytelling Virtual Reality System with Programmable Cartoon-Style Emotion Embodiment

Virtual reality (VR) is a promising new medium for immersive storytelling. While previous research works on VR narrative have tried to engage audiences through nice scenes and interactivity, the emerging live streaming shows the role of a presenter, especially the conveyance of emotion, for promoting audience involvement and enjoyment. In this paper, to lower the requirement of emotion embodiment, we borrow experience from cartoon animation and comics, and propose a novel cartoon-style hybrid emotion embodiment model to increase a storyteller's presence during live performance, which contains an avatar with six basic emotions and auxiliary multimodal display to enhance emotion expressing. We further design and implement a system to teleoperate the embodiment model in VR for live storytelling. In particular, 1) we design a novel visual programming tool that allows users to customize emotional effects based on the emotion embodiment model; 2) we design a novel face tracking module to map presenters' emotional states to the avatar in VR. Our lightweight web-based implementation also makes the application very easy to use. We conduct two preliminary qualitative studies to explore the potential of the hybrid model and the storytelling system, including interviews with three experts and a workshop study with local secondary school students. Results show the potential of the VR storytelling system for education.
Granular neural networks: The development of granular input spaces and parameters spaces through a hierarchical allocation of information granularity

The issue of granular output optimization of neural networks with fixed connections within a given input space is explored. The numeric output optimization is a highly nonlinear problem if nonlinear activation functions are used; the granular output optimization becomes an even more challenging task. We solve the problem by developing an optimal hierarchical allocation of information granularity, proposing a new objective function which considers both specificity and evidence, and engaging here efficient techniques of evolutionary optimization. In contrast to the existing techniques, the hierarchical one builds a three-level hierarchy to allocate information granularity to the input space and the architecture (parameters) of the network. Granulating both the input features and the architecture at the same time return a different result with the single factor granulation. The constructed granular neural network emphasizes the abstract nature of data and the granular nature of nonlinear mapping of the architecture. Experimental studies completed for synthetic data and publicly available data sets are used to realize the algorithm.
Face inpainting network for large missing regions based on weighted facial similarity

Recently, deep learning has made great achievements in the field of image inpainting, especially filling the large missing regions based on generative adversarial net (GAN). However, GAN is the model to capture the data distribution rather than image content. Therefore, for the corrupted face image with large holes, it may generate a new image which is greatly different from the original one. To solve above problem, we present a face inpainting network based on weighted face similarity (WFS-Net) to generate a better restoration. Firstly, according to the structural similarity index (SSIM), a weighted similar face set (WSFS) can be built to provide more reference information for the recovery of missing regions. And then, WFS-Net is designed to fill the damaged face image by exploring the relationship between the missing region and the available information, which include the known parts of the damaged image and its WSFS. Furthermore, a new loss function is presented in pixel level and texture level. The experimental results show that our proposed approach outperforms other state-of-the-arts.
Baseline Analysis of a Conventional and Virtual Reality Lifelog Retrieval System

Continuous media capture via a wearable devices is currently one of the most popular methods to establish a comprehensive record of the entirety of an individual’s life experience, referred to in the research community as a lifelog. These vast multimodal corpora include visual and other sensor data and are enriched by content analysis, to generate as extensive a record of an individual’s life experience. However, interfacing with such datasets remains an active area of research, and despite the advent of new technology and a plethora of competing mediums for processing digital information, there has been little focus on newly emerging platforms such as virtual reality. In this work, we suggest that the increase in immersion and spatial dimensions provided by virtual reality could provide significant benefits to users when compared to more conventional access methodologies. Hence, we motivate virtual reality as a viable method of exploring multimedia archives (specifically lifelogs) by performing a baseline comparative analysis using a novel application prototype built for the HTC Vive and a conventional prototype built for a standard personal computer.
A Collision-Free 3D Path Planning Strategy for Mobile Robots

This work presents a collision-free 3D path planning strategy for the non-holonomic mobile robot control. The non-holonomic mobile robot travels through an unknown 3D environment with obstacles and reaches a given destination safely with no collisions. In addition, our approach enables to find the optimal path to the target efficiently based on the avoiding plane selected. The performance of the presented strategy is demonstrated via computer simulations and good results are obtained.
Named-Entity Recognition in Sports Field Based on a Character-Level Graph Convolutional Network

Traditional methods for identifying naming ignore the correlation between named entities and lose hierarchical structural information between the named entities in a given text. Although traditional named-entity methods are effective for conventional datasets that have simple structures, they are not as effective for sports texts. This paper proposes a Chinese sports text named-entity recognition method based on a character graph convolutional neural network (Char GCN) with a self-attention mechanism model. In this method, each Chinese character in the sports text is regarded as a node. The edge between the nodes is constructed using a similar character position and the character feature of the named-entity in the sports text. The internal structural information of the entity is extracted using a character map convolutional neural network. The hierarchical semantic information of the sports text is captured by the self-attention model to enhance the relationship between the named entities and capture the relevance and dependency between the characters. The conditional random fields classification function can accurately identify the named entities in the Chinese sports text. The results conducted on four datasets demonstrate that the proposed method improves the F-Score values significantly to 92.51%, 91.91%, 93.98%, and 95.01%, respectively, in comparison to the traditional naming methods.
Operationally meaningful representations of physical systems in neural networks

To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems. The representations learnt by most current machine learning techniques reflect statistical structure present in the training data; however, these methods do not allow us to specify explicit and operationally meaningful requirements on the representation. Here, we present a neural network architecture based on the notion that agents dealing with different aspects of a physical system should be able to communicate relevant information as efficiently as possible to one another. This produces representations that separate different parameters which are useful for making statements about the physical system in different experimental settings. We present examples involving both classical and quantum physics. For instance, our architecture finds a compact representation of an arbitrary two-qubit system that separates local parameters from parameters describing quantum correlations. We further show that this method can be combined with reinforcement learning to enable representation learning within interactive scenarios where agents need to explore experimental settings to identify relevant variables.
Speech Recognition Using Convolutional Neural Networks on Small Training Sets

Abstract The algorithm of separate words automatic recognition based on convolutional neural networks is developed and presented in this paper. Distinctive feature of this algorithm is the training on sets consisting of only hundreds or thousands of samples. Therefore, important problem is the selection of optimal architecture for neural network, which was firstly proposed and tested. After that, four different cases for recognition were researched: speaker-dependent recognition without noise, speaker-independent recognition without noise, speaker-dependent recognition with noise, speaker-independent recognition without noise. Finally, we analyse the experiment results that showed good results for all cases of interest.
MBRP: Model-Based Requirements Prioritization Using PageRank Algorithm

Requirements prioritization plays an important role in driving project success during software development. Literature reveals that existing requirements prioritization approaches ignore vital factors such as interdependency between requirements. Existing requirements prioritization approaches are also generally time-consuming and involve substantial manual effort. Besides, these approaches show substantial limitations in terms of the number of requirements under consideration. There is some evidence suggesting that models could have a useful role in the analysis of requirements interdependency and their visualization, contributing towards the improvement of the overall requirements prioritization process. However, to date, just a handful of studies are focused on model-based strategies for requirements prioritization, considering only conflict-free functional requirements. This paper uses a meta-model-based approach to help the requirements analyst to model the requirements, stakeholders, and inter-dependencies between requirements. The model instance is then processed by our modified PageRank algorithm to prioritize the given requirements. An experiment was conducted, comparing our modified PageRank algorithm's efficiency and accuracy with five existing requirements prioritization methods. Besides, we also compared our results with a baseline prioritized list of 104 requirements prepared by 28 graduate students. Our results show that our modified PageRank algorithm was able to prioritize the requirements more effectively and efficiently than the other prioritization methods.
Identification of the Agile Mindset and Its Comparison to the Competencies of Selected Agile Roles

In this paper we present the results of the identification and evaluation of the elements of an agile mindset as well as its comparison to the competence models for the roles of Scrum Master, Product Owner and agile analyst. We have identified 70 unique agile mindset elements from literature and 5 interviews with experts. Based on an opinion survey among 52 agile practitioners we evaluated the importance of 26 selected elements of the agile mindset to the effectiveness of an agile team. The competence models contain 29 competencies of a Scrum Master, 16 competencies of a Product Owner and 40 competencies of an agile analyst, divided into behavioral, technical and contextual ones. We discuss which agile mindset elements are important to each agile role. This paper is an extended version of the paper titled “On the Agile Mindset of an Effective Team – An Industrial Opinion Survey” presented at the 3rd International Conference on Lean and Agile Software Development LASD 2019 [28].
Convolutional Neural Networks: Alternate Drivers' Visual Perception

The number of U.S. states considering legislation related to autonomous vehicles has increased in the past few years, and a truck without a cab or driver was recently test-driven in Sweden. These self-driving vehicles would not have emerged without the advent of artificial-intelligence technologies and their success with everyday problems. A crucial task in autonomous driving is providing a substitute for human visual perception of the routes and surroundings so that the vehicle can operate by itself. This task primarily involves image classification-that is, automatically assigning a category label, such as a pedestrian, lane marking, or pothole, to a photo captured by a vehicle's camera sensors, which is the focus of this article.
A Novel Method for Person Re-Identification: Conditional Translated Network Based on GANs

The main challenge of person re-identification (re-id) lies in the strikingly discrepancy between different camera views, including illumination, background and human pose.Existing person re-id methods rely mostly on implicit solutions, such as seeking robust features or designing discriminative distance metrics.Compared to these methods, human solutions are more straightforward.That is, imagine the appearance of the target person under different camera views before matching target person.The key idea is that human can intuitively implement viewpoint transfer, noting the association of the target person under different camera views but the machine failed.In this paper, we attempt to imitate such human behavior that transfer person image to certain camera views before matching.In practice, we propose a conditional transfer network (cTransNet) that conditionally implement viewpoint transfer, which transfers image to the viewpoint with the biggest domain gap through a variant of Generative Adversarial Networks (GANs).After that, we obtain hybrid person representation by fusing the feature of original image with the transferred image then perform similarity ranking according to cosine distance.Compared with former methods, we propose a human-like approach and obtains consistent improvement of the rank-1 precision over the baseline in Market-1501, DukeMTMC-ReID and MSMT17 dataset by 3%,4%,4%, respectively.
Bipartite Split-Merge Evolutionary Clustering

We propose a split-merge framework for evolutionary clustering. The proposed clustering technique, entitled Split-Merge Evolutionary Clustering is supposed to be more robust to concept drift scenarios by providing the flexibility to consider at each step a portion of the data and derive clusters from it to be used subsequently to update the existing clustering solution. The proposed framework is built around the idea to model two clustering solutions as a bipartite graph, which guides the update of the existing clustering solution by merging some clusters with ones from the newly constructed clustering while others are transformed by splitting their elements among several new clusters. We have evaluated and compared the discussed evolutionary clustering technique with two other state of the art algorithms: a bipartite correlation clustering (PivotBiCluster) and an incremental evolving clustering (Dynamic split-and-merge).