Reviewerly/exHarmony huggingface dataset:
Achieving a High Level of Open Market-Information Symmetry with Decentralised Insurance Marketplaces on Blockchains

The legacy insurance industry is failing to cover risk with capital in a free-market way that is in the interest of the consumer. The status quo is highly discretionary with a small set of agents representing capital who decide about risk coverage in their own interest. Thus, customers and the insurance ecosystem face a wasteful context in the value chain of capital allocation. The advent of blockchain technology such as smart contracts, disruptively changes the status quo towards an improved disintermediated context with rapidly increased speed for bringing insurance products to market. Additionally, blockchain technology also allows for replacing a fraud riddled insurance ecosystem that suffers from qualitative highly bureaucratic decision making. Instead, blockchain technology eliminates inefficient layers of bureaucracy and establishes a novel quantitative organisational governance system based on transparent and traceable rules rooted in mathematics. Black Insurance fills this gap with a distributed blockchain-based insurance platform that overcomes currently existing information asymmetries to reinstate free-market forces for risk coverage with capital in the interest of customers.
Cholesky Factorization Based Online Sequential Extreme Learning Machines with Persistent Regularization and Forgetting Factor

The online sequential extreme learning machine with persistent regularization and forgetting factor (OSELM-PRFF) can avoid potential singularities or ill-posed problems of online sequential regularized extreme learning machines with forgetting factors (FR-OSELM), and is particularly suitable for modelling in non-stationary environments. However, existing algorithms for OSELM-PRFF are time-consuming or unstable in certain paradigms or parameters setups. This paper presents a novel algorithm for OSELM-PRFF, named “Cholesky factorization based” OSELM-PRFF (CF-OSELM-PRFF), which recurrently constructs an equation for extreme learning machine and efficiently solves the equation via Cholesky factorization during every cycle. CF-OSELM-PRFF deals with timeliness of samples by forgetting factor, and the regularization term in its cost function works persistently. CF-OSELM-PRFF can learn data one-by-one or chunk-by-chunk with a fixed or varying chunk size. Detailed performance comparisons between CF-OSELM-PRFF and relevant approaches are carried out on several regression problems. The numerical simulation results show that CF-OSELM-PRFF demonstrates higher computational efficiency than its counterparts, and can yield stable predictions.
Unifying Multi-Hypothesis and Graph-Based Tracking with Approximate Track Automata

Multi-target tracking remains a challenging problem, with various approaches addressing different regimes of scenario difficulties. Multi-Hypothesis Tracking (MHT) is often used for short-timescale tracking, while Graph-Based Track stitching (GBT) is often used as a second-stage processor to associate MHT tracks together over time. However, it is often the case that MHT must aggressively prune hypotheses to remain computationally tractable. Moreover, GBT makes strict assumptions about its input, namely Markov or path independence, and is therefore unable to process non-local information such as intermittent attributes on input data. Therefore, both approaches make drastic trade-offs between data association and state estimation: MHT prunes while GBT assumes path independence. We present an approach to the multi-target tracking problem that occupies a “middle” ground between MHT and GBT, reducing to each as a special case. To do this, we represent the current hypothesis set as all those report strings accepted by some automaton: Depending on input statistics (to what extent assignment probabilities are path independent) and on an approximation-fidelity parameter, the automaton will naturally be either an MHT forest, or a min-cost-flow graph, or some intermediate structure. We introduce the formulation, describe algorithms to construct so-called Track Automata, give an Integer Linear Program (ILP) to extract globally optimal tracks from these automata, illustrate key special cases, including where the problem is solvable in polynomial time, and show results for simulated sensor data. In exchange for some user-specified approximation error and polynomial increase in ILP size, the technique is able to delay pruning and improve track purity by implicitly representing many more hypotheses than an MHT forest can.
Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training

Hard visual attention is a promising approach to reduce the computational burden of modern computer vision methodologies. However, hard attention mechanisms can be difficult and slow to train, which is especially costly for applications like neural architecture search where multiple networks must be trained. We introduce a method to amortise the cost of training by generating an extra supervision signal for a subset of the training data. This supervision is in the form of sequences of ‘good’ locations to attend to for each image. We find that the best method to generate supervision sequences comes from framing hard attention for image classification as a Bayesian optimal experimental design (BOED) problem. From this perspective, the optimal locations to attend to are those which provide the greatest expected reduction in the entropy of the classification distribution. We introduce methodology from the BOED literature to approximate this optimal behaviour and generate ‘near-optimal’ supervision sequences. We then present a hard attention network training objective that makes use of these sequences and show that it allows faster training than prior work. We finally demonstrate the utility of faster hard attention training by incorporating supervision sequences in a neural architecture search, resulting in hard attention architectures which can outperform networks with access to the entire image.
Watermarking-based Self-Recovery and Authentication Framework for Colour Images

In this paper, a method of self-recovery and tamper detection applied to color images is proposed. Designed framework divides the image into non-overlapped blocks, and for each block watermarks are generated, which are applied to tamper detection and recovery. In order to avoid the tampering coincidence problem, generated watermarks by each block are embedded using the LSB method in a different block in an embedding sequence, which is generated by a seed. During the authentication and recovery process, previously embedded watermarks in each block are extracted and can locate modified regions, following recovering the manipulated block via usage its recovery watermark. To obtain a better performance the designed scheme embeds three versions of the watermarks generated for each block. In simulation, the quality metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Image Measure) were used, justifying better performance during the recovery process at different tampering rates via comparing the designed scheme with similar state-of-the-art methods.
Minimal nonorthogonal gate decomposition for qubits with limited control

In quantum control theory, a question of fundamental and practical interest is how an arbitrary unitary transformation can be decomposed into a minimum number of elementary rotations for implementation, subject to various physical constraints. Examples include the singlet-triplet (ST) and exchange-only (EO) qubits in quantum-dot systems, and gate construction in the Solovay--Kitaev algorithm. For two important scenarios, we present complete solutions to the problems of optimal decomposition of single-qubit unitary gates with non-orthogonal rotations. For each unitary gate, the criteria for determining the minimal number of pieces is given, the explicit gate construction procedure, as well as a computer code for practical uses. Our results include an analytic explanation to the four-gate decomposition of EO qubits, previously determined numerically by Divincenzo et al. [Nature (London) 408, 339 (2000)]. Furthermore, compared with the approaches of Ramon sequence and its variant [Phys. Rev. Lett. 118, 216802 (2017)], our method can reduce about 50% of gate time for ST qubits. Finally, our approach can be extended to solve the problem of optimal control of topological qubits, where gate construction is achieved through the braiding operations.
Sequential Text-Term Selection in Vector Space Models

Text mining has recently attracted a great deal of attention with the accumulation of text documents in all fields. In this article, we focus on the use of textual information to explain continuous variables in the framework of linear regressions. To handle the unstructured texts, one common practice is to structuralize the text documents via vector space models. However, using words or phrases as the basic analysis terms in vector space models is in high debate. In addition, vector space models often lead to an extremely large term set and suffer from the curse of dimensionality, which makes term selection important and necessary. Toward this end, we propose a novel term screening method for vector space models under a linear regression setup. We first split the entire term space into different subspaces according to the length of terms and then conduct term screening in a sequential manner. We prove the screening consistency of the method and assess the empirical performance of the proposed method with simulations based on a dataset of online consumer reviews for cellphones. Then, we analyze the associated real data. The results show that the sequential term selection technique can effectively detect the relevant terms by a few steps.
On Markov Chains and Some Matrices and Metrics for Undirected Graphs

Metric tasks often arise as a simplification of complex and practically important problems on graphs. The correspondence between the search algorithms of the usual shortest paths and Markov chains is shown. From this starting point a sequence of matrix descriptions of undirected graphs is established. The sequence ends with the description of the explicit form of the Moore-Penrose pseudo inversed incidence matrix. Such a matrix is a powerful analytical and computational tool for working with edge flows with conditionally minimal Euclidian norms. The metrics of a graph are represented as its characteristics generated by the norms of linear spaces of edge and vertex flows. The Euclidian metric demonstrates the advantages of the practice of solving problems on graphs in comparison with traditional metrics based on the shortest paths or minimal cuts.
Task-Adversarial Co-Generative Nets

In this paper, we propose Task-Adversarial co-Generative Nets (TAGN) for learning from multiple tasks. It aims to address the two fundamental issues of multi-task learning, i.e., domain shift and limited labeled data, in a principled way. To this end, TAGN first learns the task-invariant representations of features to bridge the domain shift among tasks. Based on the task-invariant features, TAGN generates the plausible examples for each task to tackle the data scarcity issue. In TAGN, we leverage multiple game players to gradually improve the quality of the co-generation of features and examples by using an adversarial strategy. It simultaneously learns the marginal distribution of task-invariant features across different tasks and the joint distributions of examples with labels for each task. The theoretical study shows the desired results: at the equilibrium point of the multi-player game, the feature extractor exactly produces the task-invariant features for different tasks, while both the generator and the classifier perfectly replicate the joint distribution for each task. The experimental results on the benchmark data sets demonstrate the effectiveness of the proposed approach.
Chunk-level request-grant-transfer mode for QoE-sensitive video delivery in CDN

Remote Direct Memory Access (RDMA) can be deployed in Content Delivery Networks (CDN) Points of Presence (PoPs) to avoid the high CPU overheads caused by traditional TCP/IP stacks. However, RDMA cannot surmount the drawbacks of the window-based conservative of TCP and is insensitive to Quality of Experience (QoE). Moreover, the requirement of lossless networks hinders the widespread application of RDMA. In this paper, we introduce the parallel multipoint-to-multipoint Request-Grant-Transfer (RGT) mode into RDMA to solve the aforementioned problems. Compared with traditional RGT mode, our scheme supports parallel Dynamic Adaptive Streaming over HTTP (DASH) chunk delivery, thereby improving throughput and reducing initial delays. We differentiate the importance of DASH chunks according to QoE-related properties. In this way, we reduce the response time of specific DASH chunks. We provide an efficient approach to select the optimal number of requests for partially traversing pending requests to reduce the overheads of Request stages. We perform comprehensive experiments to demonstrate that our scheme improves the throughput of CDN PoPs and enhances client QoE.
The Development of a User Interface for Mixed-Initiative Plan Management for Human Spaceflight

Deep space travel presents a diverse set of challenges. Of significant concern is the time lag in communication between the crew and mission control as the vehicle travels farther from Earth. Currently, ground based mission control is tasked with managing all aspects of flight, including the creation and management of crew flight plans. However, as communication becomes delayed, ground control will no longer be able to perform these duties effectively. Thus, we are proposing the design of a mixed-initiative plan (MIP) management system to provide crews with greater autonomy in adapting plans to reflect the current state of the mission. Successful MIP systems contain a formal representation of the work, computational methods to optimize potential solutions, and a user interface for which crewmembers may interact with the schedule. This paper details the creation of the user interface from requirements derivation through preliminary design.
Variable formation control of multiple robots via VRc and formation switching to accommodate large heading changes by leader robot

This article describes a novel multi-robot formation control based on a switching technique that allows follower robots to maintain formation when the leader robot’s direction changes rapidly or unexpectedly. The formation pattern is determined using Virtual Robot’s Center of the multi-robot formation. To avoid collision, the formation of robots reformed in optimal size by estimating the distance between the robot and an obstacle in real time. When the leader robot suddenly changes its direction, waypoints of follower robots are switched and the formation is quickly reconstructed. This prevents follower robots from colliding with each other and reduces their radius of movement and allows them to follow the leader robot at higher speed. The proposed method which is inherently a flexible control of multi-robot formation guarantees collision avoidance and prevents sudden changes in waypoints of the system by gradually changing its size. The validity of the proposed method is demonstrated via simulation and experimental results.
Single‐View Modeling of Layered Origami with Plausible Outer Shape

Abstract Modeling 3D origami pieces using conventional software is laborious due to the geometric constraints imposed by the complicated layered structure. Targeting origami models used in visual content such as CG illustrations and movies, we propose an interactive system that dramatically simplifies the modeling of 3D origami pieces with plausible outer shapes, while omitting accurate inner structures. By focusing on flat origami models with a front‐and‐back symmetry commonly found in traditional artworks, our system realizes easy and quick modeling via single‐view interface; given a reference image of the target origami piece, the user draws polygons of planar faces onto the image, and assigns annotations indicating the types of folding operations. Our system automatically rectifies the manually‐specified polygons, infers the folded structures that should yield the user‐specified polygons with reference to the depth order of layered polygons, and generates a plausible 3D model while accounting for gaps between layers. Our system is versatile enough for modeling pseudo‐origami models that are not realizable by folding a single sheet of paper. Our user study demonstrates that even novice users without the specialized knowledge and experience on origami and 3D modeling can create plausible origami models quickly.
Compact Inverse Categories

We prove a structure theorem for compact inverse categories. The Ehresmann-Schein-Nambooripad theorem gives a structure theorem for inverse monoids: they are inductive groupoids. A particularly nice case due to Clifford is that commutative inverse monoids become semilattices of abelian groups. It has also been categorified by Hoehnke and DeWolf-Pronk to a structure theorem for inverse categories as locally complete inductive groupoids. We show that in the case of compact inverse categories, this takes the particularly nice form of a semilattice of compact groupoids. Moreover, one-object compact inverse categories are exactly commutative inverse monoids. Compact groupoids, in turn, are determined in particularly simple terms of 3-cocycles by Baez-Lauda.
Video Sequence Boundary Labeling with Temporal Coherence

We propose a method for video sequence boundary labeling which maintains the temporal coherence. The method is based on two ideas. We limit the movement of the label boxes only to the horizontal direction, and reserve free space for the movement of the label boxes in the label layout. The proposed method is able to position label boxes in video sequence on a lower number of rows than existing methods, while at the same time, it minimizes the movement of label boxes. We conducted an extensive user experiment where the proposed method was ranked the best for panorama video sequences labeling compared to three existing methods.
JASs: Joint Attention Strategies for Paraphrase Generation

Neural attention based sequence to sequence (seq2seq) network models have achieved remarkable performance on NLP tasks such as image caption generation, paraphrase generation, and machine translation. The underlying framework for these models is usually a deep neural architecture comprising of multi-layer encoder-decoder sub-networks. The performance of the decoding sub-network is greatly affected by how well it extracts the relevant source-side contextual information. Conventional approaches only consider the outputs of the last encoding layer when computing the source contexts via a neural attention mechanism. Due to the nature of information flow across the time-steps within each encoder layer as well flow from layer to layer, there is no guarantee that the necessary information required to build the source context is stored in the final encoding layer. These approaches also do not fully capture the structural composition of natural language. To address these limitations, this paper presents several new strategies to generating the contextual feature vector jointly across all the encoding layers. The proposed strategies consistently outperform the conventional approaches to performing the neural attention computation on the task of paraphrase generation.
Research on the Model of Lyric Emotion Algorithm

Abstract With the rapid development of Internet technology, various social networking platforms, especially mobile social networking platforms, continue to increase, resulting in a large amount of public opinion information. Internet public opinion has a clear emotional orientation, and its emotional orientation is very easy to spread and be infected, and even affect the development of the event. Aiming at the characteristics of lyric information rich and which are easy to change with time, the lyric theme analysis model and the lyric emotion evolution model are proposed. The LDA model is used to extract the topic from the lyric text in a period of time, and the sensational heat value is calculated according to the forwarding amount and the number of comments, and the lyrical theme with the highest heat is obtained. The relative entropy between sub-topics in the adjacent time slice of a specific hot topic is calculated, and the degree of association between the topics in the adjacent time slice is determined, thereby analyzing whether there is a split of the sub-topic and a new topic. Then the evaluation object is extracted, combined with the joint deep neural network model to judge the emotions of each evaluation object in different time, and the emotional evolution of the hot topic is analyzed from multiple dimensions. Finally, an example analysis of the network public opinion information from June to July 2018 is carried out to verify the validity of the above model. The model effectively solves the problems of immature emotion analysis model and low accuracy of emotion classification in the current public opinion analysis.
User-Oriented ICT Cloud Architecture for High-Accuracy GNSS-Based Services

We introduce a new information and communication technology (ICT) cloud-based architecture for Global Navigation Satellite System (GNSS) high-accuracy solutions, offering also a commercial overview of GNSS downstream market to show how the developed innovation is thought to fit in the real context. The designed architecture is featured by dynamic scalability, increased integrity, and greater agility of the ICT system. The novelty of the solution developed is a customized ICT architecture, obtained through unique and privileged access to user communities in the frame of the H2020 project TREASURE, allowing the development of a solution entirely driven by user needs. The economic outlook of GNSS downstream markets evolution highlights how the technology proposed effectively matches the evolving business environment, specifically in regard to the increasing need for flexibility and competitive advantage deriving from services. The simultaneous adoption of the technical and commercial perspective is meant to offer interesting findings to both the scientific community and GNSS industry, creating synergies previously unexplored.
Separated Trust Regions Policy Optimization Method

In this work, we propose a moderate policy update method for reinforcement learning, which encourages the agent to explore more boldly in early episodes but updates the policy more cautious. Based on the maximum entropy framework, we propose a softer objective with more conservative constraints and build the separated trust regions for optimization. To reduce the variance of expected entropy return, a calculated state policy entropy of Gaussian distribution is preferred instead of collecting log probability by sampling. This new method, which we call separated trust region for policy mean and variance (STRMV), can be view as an extension to proximal policy optimization (PPO) but it is gentler for policy update and more lively for exploration. We test our approach on a wide variety of continuous control benchmark tasks in the MuJoCo environment. The experiments demonstrate that STRMV outperforms the previous state of art on-policy methods, not only achieving higher rewards but also improving the sample efficiency.