Reviewerly/exHarmony huggingface dataset:
Identification of issues in the implementation of the ISO/IEC 29110 standard: comparison between the state of the art and the state of practice

The ISO/IEC 29110 standard, which aims to improve the quality of software products through improving the Project Management (PG) and the Software Implementation (SI) processes, provides the basic practices that software development teams should meet. However, even with the benefits provided by the standard, there is still a gap for its implementation. This paper aims to identify, the issues that the VSEs present during the standard’s implementation. To achieve it, a comparison between the state of art and the state of practice was performed. As a result, issues related to work products were identified, as well as a set of tools and techniques used by VSEs.
Generating Predictable and Adaptive Dialog Policies in Single- and Multi-domain Goal-oriented Dialog Systems

Most existing commercial goal-oriented chatbots are diagram-based; i.e. they follow a rigid dialog flow to fill the slot values needed to achieve a user’s goal. Diagram-based chatbots are predictable, thus their adoption in commercial settings; however, their lack of flexibility may cause many users to leave the conversation before achieving their goal. On the other hand, state-of-the-art research chatbots use Reinforcement Learning (RL) to generate flexible dialog policies. However, such chatbots can be unpredictable, may violate the intended business constraints, and require large training datasets to produce a mature policy. We propose a framework that achieves a middle ground between the diagram-based and RL-based chatbots: we constrain the space of possible chatbot responses using a novel structure, the chatbot dependency graph, and use RL to dynamically select the best valid responses. Dependency graphs are directed graphs that conveniently express a chatbot’s logic by defining the dependencies among slots: all valid dialog flows are encapsulated in one dependency graph. Our experiments in both single-domain and multi-domain settings show that our framework quickly adapts to user characteristics and achieves up to 23.77% improved success rate compared to a state-of-the-art RL model.
Strategy of state transfer for open quantum systems by no-knowledge feedback control: Via reverse engineering

It has long been interest to control the transfer of population between specified quantum states and protect the coherence of the system at the same time. In this paper, we investigate a scheme to improve the strategy of state transfer for open quantum systems using no-knowledge measurement-based feedback control and reverse engineering. In order to ensure that the system can process information effectively, we first design the control pulse in advance from the perspective of population and coherence and then verify it through numerical simulations. The research results show that, based on the designed control pulse, we can indeed drive the system from any initial state to the desired target state, and the coherence of the system can be effectively protected during the state transition.
One for “All”: a unified model for fine-grained sentiment analysis under three tasks

Background Fine-grained sentiment analysis is used to interpret consumers’ sentiments, from their written comments, towards specific entities on specific aspects. Previous researchers have introduced three main tasks in this field (ABSA, TABSA, MEABSA), covering all kinds of social media data ( e.g., review specific, questions and answers, and community-based). In this paper, we identify and address two common challenges encountered in these three tasks, including the low-resource problem and the sentiment polarity bias. Methods We propose a unified model called PEA by integrating data augmentation methodology with the pre-trained language model, which is suitable for all the ABSA, TABSA and MEABSA tasks. Two data augmentation methods, which are entity replacement and dual noise injection, are introduced to solve both challenges at the same time. An ensemble method is also introduced to incorporate the results of the basic RNN-based and BERT-based models. Results PEA shows significant improvements on all three fine-grained sentiment analysis tasks when compared with state-of-the-art models. It also achieves comparable results with what the baseline models obtain while using only 20% of their training data, which demonstrates its extraordinary performance under extreme low-resource conditions.
Realizing an Error-Correcting Surface Code with Superconducting Qubits

Quantum error correction is a critical technique for transitioning from noisy intermediate-scale quantum (NISQ) devices to fully fledged quantum computers. The surface code, which has a high threshold error rate, is the leading quantum error correction code for two-dimensional grid architecture. So far, the repeated error correction capability of the surface code has not been realized experimentally. Here, we experimentally implement an error-correcting surface code, the distance-3 surface code which consists of 17 qubits, on the \textit{Zuchongzhi} 2.1 superconducting quantum processor. By executing several consecutive error correction cycles, the logical error can be significantly reduced after applying corrections, achieving the repeated error correction of surface code for the first time. This experiment represents a fully functional instance of an error-correcting surface code, providing a key step on the path towards scalable fault-tolerant quantum computing.
Optimizing Tandem Speaker Verification and Anti-Spoofing Systems

As automatic speaker verification (ASV) systems are vulnerable to spoofing attacks, they are typically used in conjunction with spoofing countermeasure (CM) systems to improve security.For example, the CM can first determine whether the input is human speech, then the ASV can determine whether this speech matches the speaker's identity.The performance of such a tandem system can be measured with a tandem detection cost function (t-DCF).However, ASV and CM systems are usually trained separately, using different metrics and data, which does not optimize their combined performance.In this work, we propose to optimize the tandem system directly by creating a differentiable version of t-DCF and employing techniques from reinforcement learning.The results indicate that these approaches offer better outcomes than finetuning, with our method providing a 20% relative improvement in the t-DCF in the ASVSpoof19 dataset in a constrained setting.
Probabilistic Autoencoder Using Fisher Information

Neural networks play a growing role in many scientific disciplines, including physics. Variational autoencoders (VAEs) are neural networks that are able to represent the essential information of a high dimensional data set in a low dimensional latent space, which have a probabilistic interpretation. In particular, the so-called encoder network, the first part of the VAE, which maps its input onto a position in latent space, additionally provides uncertainty information in terms of variance around this position. In this work, an extension to the autoencoder architecture is introduced, the FisherNet. In this architecture, the latent space uncertainty is not generated using an additional information channel in the encoder but derived from the decoder by means of the Fisher information metric. This architecture has advantages from a theoretical point of view as it provides a direct uncertainty quantification derived from the model and also accounts for uncertainty cross-correlations. We can show experimentally that the FisherNet produces more accurate data reconstructions than a comparable VAE and its learning performance also apparently scales better with the number of latent space dimensions.
Odds Estimating with Opponent Hand Belief for Texas Hold’em Poker Agents

Along with the great success of superhuman AI in succession, the development of Texas Hold’em poker agents is entering a new stage. The efforts to create indefectible AI transfers to develop new AIs which can exploit opponents and explain its own decisions better. Hand odds estimating used to state abstracting, situation evaluating, decision assisting is one of the key foundations for such new agents. But all the current methods implicitly assume a uniform distribution over the cards the opponent could be holding, which makes the win rate over-evaluated. In this paper three hand odds estimating methods considering the opponent hand belief are proposed to cope with this problem. We suggest the expected win rate algorithms with start hand range (EWR-SHR) and expected win rate algorithm with fold rate (EWR-FR) for preflop round and flop/turn/river round respectively. These two algorithms predict the opponent’s hand range based on the opponent model and observed action not fold, their additional computation complexity is O(1). The expected win rate algorithm with opponent hand distribution (EWR-HD) is the third method suitable for all rounds which uses the opponent model and observed action check/call/raise to infer the distribution of the opponent hand cards. Their features are compared, usages are summarized, and the experiment result indicates that all of them can evaluate the game situation more precisely than the current methods.
Predicting Research Trends in Artificial Intelligence with Gradient Boosting Decision Trees and Time-aware Graph Neural Networks

The Science4cast 2021 competition focuses on predicting future edges in an evolving semantic network, where each vertex represents an artificial intelligence concept, and an edge between a pair of vertices denotes that the two concepts have been investigated together in a scientific paper. In this paper, we describe our solution to this competition. We present two distinct approaches: a tree-based gradient boosting approach and a deep learning approach, and demonstrate that both approaches achieve competitive performance. Our final solution, which is based on a blend of the two approaches, achieved the 1st place among all the participating teams. The source code for this paper is available at https://github.com/YichaoLu/Science4cast2021.
Quality Metrics of LSB Image Steganography Technique for Color Space HSI

The aim of the paper is to find the distortion rates of the images by using Steganography. The cover and message images are scaled to 256 by 256 pixels. RGB level images are converted into HSI color space. HSI color space based metric establishes a better relationship with human perception for the processing. For the ‘message hiding’, the LSB Steganography method is utilized. In the image application, the least significant bit (LSB) data embedding is performed on the image. The effect of changing LSBs up to the 7th bit plane on the image has been examined with various quality metrics values like PSNR, SSIM, and MSE. Here we focus on the degradation level of the tested image to evaluate the effect of the RGB level image converted to the HSI color space, the altered effect of the original image on the data embedded image and calculate the image quality criteria.
Degenerate Gaussian factors for probabilistic inference

In this paper, we propose a parametrised factor that enables inference on Gaussian networks where linear dependencies exist among the random variables. Our factor representation is effectively a generalisation of traditional Gaussian parametrisations where the positive-definite constraint of the covariance matrix has been relaxed. For this purpose, we derive various statistical operations and results (such as marginalisation, multiplication and affine transformations of random variables) that extend the capabilities of Gaussian factors to these degenerate settings. By using this principled factor definition, degeneracies can be accommodated accurately and automatically at little additional computational cost. As illustration, we apply our methodology to a representative example involving recursive state estimation of cooperative mobile robots.
Features of the Publishing Industry Transformation

There are three stages in the technological history of the modern book industry as a scalable and mass production of books. The main attention is paid to the history of digital books, the beginning of which is associated not so much with the beginning of digital book production, but with the advent of digital distribution. On the technical side, this meant developing common industry standards for the digital book, building a distribution infrastructure based on the Internet, and gaining broadband access to it for the mass consumer. Equally significant were the cultural shifts that accompanied or resulted from this technical transformation. The digitalization of the book itself did not have a significant impact on the industry before the ubiquity of the Internet, since the sphere of existence of digital books was limited to a small number of qualified users. Everything changed in the first two decades of the 21st century, when the laws of the “attention economy” came into force and the mechanism of competition for the user’s time by content was activated. A digital book, on the one hand, is becoming more accessible; on the other hand, the time spent on reading books is being reduced. The development of computer technologies and the Internet, in addition to removing the barrier to access to content, removes a significant number of barriers to content publication. It is important to note that the publishing filter in the context of open publication also ceases to work in legal terms, and its disappearance led, in particular, to the emergence and rapid growth of fan fiction literature, which is in the “gray” zone of copyright. The spread of the Internet, of course, creates not only problems, but also opportunities for publishers, in particular, by changing communication with readers. The Internet has made possible not only direct communication with the reader and reader communities, but also a much more effective prompt response to readers’ requests, including those supported by compelling economic incentives. The changes that are taking place in the book industry at the third stage of technological transformation have a very heterogeneous effect on publishers in different countries, depending on the degree of market development, Internet penetration, and readers’ digital content habits. The main point is that these changes take place in very different ways in different types of book publishing. Obviously, “book”, whatever its definition one gives, is just a convenient collective format for completely different texts, materialized or embodied in digital form in different ways. At the same time, the functional purpose of the text and the way the reader treats it determine the trajectory along which the industrial niche corresponding to certain types of publications will develop, in which direction it will evolve.
Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation

Assessing the structure and function of the right ventricle (RV) is important in the diagnosis of several cardiac pathologies. However, it remains more challenging to segment the RV than the left ventricle (LV). In this paper, we focus on segmenting the RV in both short (SA) and long-axis (LA) cardiac MR images simultaneously. For this task, we propose a new multi-input/output architecture, hybrid 2D/3D geometric spatial TransformEr Multi-Pass fEature pyRAmid (Tempera). Our feature pyramid extends current designs by allowing not only a multi-scale feature output but multi-scale SA and LA input images as well. Tempera transfers learned features between SA and LA images via layer weight sharing and incorporates a geometric target transformer to map the predicted SA segmentation to LA space. Our model achieves an average Dice score of 0.836 and 0.798 for the SA and LA, respectively, and 26.31 mm and 31.19 mm Hausdorff distances. This opens up the potential for the incorporation of RV segmentation models into clinical workflows.
Design and Research on the Data Model of Special Collections of Scientific and Technological Celebrities Based on Knowledge Organization

Celebrity special collections have important academic, historical, and spiritual values. The resource types include books, letters, manuscripts, pictures, audio, videos, physical objects, etc., which need different data description standards, content management models, and resource preservation methods and possess the characteristics of heterogeneous resources across multiple fields like libraries, archives, museums, and science and technology museums. Compared with other special collection resources, this category of resource has more complex characteristics in terms of type, carrier, theme, and content. This article aims to showcase the academic thoughts and spiritual values of celebrities, and uses abstract data models to construct four core entities—resource, person, event, and space, as well as multiple secondary entities such as time, organization, and project. Facing the characteristics of heterogeneous resources, we created virtual entities, explored integrated and cross-domain model of special collections, introduced knowledge organization concepts and methods, and built link entities such as Person_Person, Person_Event, Event_Space, Event_DocumentX, etc. We hope to form a deep-level and multi-dimensional data association model and make the content mining and display of special collections of scientific and technological celebrities more systematic and scientific.
PRIVACY-PRESERVING AUTHENTICATION SCHEME FOR VANETS IN DIGITAL ECONOMY

As the use of smart vehicles is increasing day by day, issues such as privacy and authentication are also being faced by VANETs. To address these problems, a blockchain technique is used. This paper covers a linkable and decentralised architecture of the Internet of Vehicles system for the contact between smart vehicles by using blockchain for safe access verification among vehicles and roadside units (RSUs). As the concept of distributed storage and cloud computing is introduced with blockchain, the reason behind using distributed technology is to shift the trust from a single third party such as a central authority (CA) towards a decentralised approach. But still, most of the models rely on a single party CA or any other third party, and in most of the cases data is not decentralised, due to the trust issue that may arise under such conditions. Also, when a vehicle broadcasts any message in the network, the authentication process is fully dependent on a single RSU. If an RSU fails, then there is no security about the accurate delivery of messages. Due to that, any malicious user can take part in the network. Centred on the blockchain, this scheme offers a privacy-preserving authentication mechanism between vehicles. A comparison table between different features of VANETs is also made. The future of vehicles is dependent on technology and it will enhance the rise of the digital economy.
DEC-LOS-RRT: Decentralized Path Planning for Multi-robot Systems with Line-of-sight Constrained Communication

Decentralized planning for multi-agent systems, such as fleets of robots in a search-and-rescue operation, is often constrained by limitations on how agents can communicate with each other. One such limitation is the case when agents can communicate with each other only when they are in line-of-sight (LOS). Developing decentralized planning methods that guarantee safety is difficult in this case, as agents that are occluded from each other might not be able to communicate until it’s too late to avoid a safety violation. In this paper, we develop a decentralized planning method that explicitly avoids situations where lack of visibility of other agents would lead to an unsafe situation. Building on top of an existing Rapidly exploring Random Tree (RRT)-based approach, our method guarantees safety at each iteration. Simulation studies show the effectiveness of our method and compare the degradation in performance with respect to a clairvoyant decentralized planning algorithm where agents can communicate despite not being in LOS of each other.
Subway Driver Behavior Detection Method Based On Multi-features Fusion

The recognition of subway driver behavior is an important way for early warning of public safety. The current models of behavior recognition focus on action recognition of target objects in large-scene, which are difficult to apply for the subway driver behavior recognition directly because of space-time constraints. RepC3D model is proposed for recognizing subway driver behaviors in the paper. The model fuse the features of C3D model and RepVGG model. Firstly we preprocess the dataset by cutting the subway driver operation video into short videos, then the preprocessed dataset is adopted as the input of RepC3D model and is downsampled with the multiscale convolution layers of the main network VGG, which is used to extract the effective features of the driver's action behavior. Next, as the feature tranning network,RepC3D model identify and classfy the behaviors of the subway driver from the videos. The experimental result shows that the RepC3D model is btteetter than the C3D model and RepVGG model in terms of recognition accuracy, false detection rate, and missed detection rate, the recognition efficiency is also improved. The dataset is available at https://github.com/wtazyy/Datasets.git.
The congruence speed formula

We solve a few open problems related to a peculiar property of the integer tetration ^{b}a, which is the constancy of its congruence speed for any sufficiently large b = b(a). Assuming radix-10 (the well known decimal numeral system), we provide an explicit formula for the congruence speed V(a) ∈ ℕ_0 of any a ∈ ℕ − {0} that is not a multiple of 10. In particular, for any given n ∈ ℕ, we prove to be true Ripà’s conjecture on the smallest a such that V(a) = n. Moreover, for any a ≠ 1 ∶ a ≢ 0 (mod 10), we show the existence of infinitely many prime numbers, p_j = p_j(V(a)), such that V(p_j) = V(a).
An ML-style Record Calculus with Extensible Records

In this work, we develop a polymorphic record calculus with extensible records.Extensible records are records that can have new fields added to them, or preexisting fields removed from them.We also develop a static type system for this calculus and a sound and complete type inference algorithm.Most ML-style polymorphic record calculi that support extensible records are based on row variables.We present an alternative construction based on the polymorphic record calculus developed by Ohori.Ohori based his polymorphic record calculus on the idea of kind restrictions.This allowed him to express polymorphic operations on records such as field selection and modification.With the addition of extensible types, we were able to extend Ohori's original calculus with other powerful operations on records such as field addition and removal.
Exploring Self-training for Imbalanced Node Classification

Graph Neural Networks (GNNs) have achieved tremendous success in various applications. However, datasets are usually imbalanced in many real-world scenarios. When trained on an imbalanced dataset, the performance of GNNs is distant from satisfactory for nodes of minority classes. Due to the small population, these minority nodes have less engagement in the objective function of training and the message-passing mechanism behind GNNs exacerbates this problem further. Hence, in this paper, we present a novel model-agnostic framework, named SET-GNN, which utilizes self-training to expand the labeled training set through using pseudo-labeled nodes for improving the performance of the semi-supervised learning based on GNNs for imbalanced node classification. Extensive experiments on various real-world datasets demonstrate SET-GNN is able to achieve state-of-the-art performance on solving the imbalanced node classification problem.