Reviewerly/exHarmony huggingface dataset:
Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation

Knowledge-grounded dialogue systems are challenging to build due to the lack of training data and heterogeneous knowledge sources.Existing systems perform poorly on unseen topics due to limited topics covered in the training data.In addition, it is challenging to generalize to the domains that require different types of knowledge sources.To address the above challenges, we present PLUG 1 , a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks.We first retrieve relevant information from heterogeneous knowledge sources (e.g., wiki, dictionary, or knowledge graph); Then the retrieved knowledge is transformed into text and concatenated with dialogue history to feed into the language model for generating responses.PLUG is pre-trained on a largescale knowledge-grounded dialogue corpus.The empirical evaluation on two benchmarks shows that PLUG generalizes well across different knowledge-grounded dialogue tasks.It achieves comparable performance with stateof-the-art methods in the fully-supervised setting and significantly outperforms other approaches in zero-shot and few-shot settings.* Work was done when Yu Li was interning at MSR 1 Pre-trained Language model with a Unified knowledge representation for knowledge-Grounded dialogues.
AirDet: Few-Shot Detection Without Fine-Tuning for Autonomous Exploration

Few-shot object detection has attracted increasing attention and rapidly progressed in recent years. However, the requirement of an exhaustive offline fine-tuning stage in existing methods is time-consuming and significantly hinders their usage in online applications such as autonomous exploration of low-power robots. We find that their major limitation is that the little but valuable information from a few support images is not fully exploited. To solve this problem, we propose a brand new architecture, AirDet, and surprisingly find that, by learning class-agnostic relation with the support images in all modules, including cross-scale object proposal network, shots aggregation module, and localization network, AirDet without fine-tuning achieves comparable or even better results than many fine-tuned methods, reaching up to 30–40% improvements. We also present solid results of onboard tests on real-world exploration data from the DARPA Subterranean Challenge, which strongly validate the feasibility of AirDet in robotics. To the best of our knowledge, AirDet is the first feasible few-shot detection method for autonomous exploration of low-power robots. The code and pre-trained models are released at https://github.com/Jaraxxus-Me/AirDet .
A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation

Early exiting allows instances to exit at different layers according to the estimation of difficulty.Previous works usually adopt heuristic metrics such as the entropy of internal outputs to measure instance difficulty, which suffers from generalization and threshold-tuning.In contrast, learning to exit, or learning to predict instance difficulty is a more appealing way.Though some effort has been devoted to employing such "learn-to-exit" modules, it is still unknown whether and how well the instance difficulty can be learned.As a response, we first conduct experiments on the learnability of instance difficulty, which demonstrates that modern neural models perform poorly on predicting instance difficulty.Based on this observation, we propose a simple-yeteffective Hash-based Early Exiting approach (HASHEE) that replaces the learn-to-exit modules with hash functions to assign each token to a fixed exiting layer.Different from previous methods, HASHEE requires no internal classifiers nor extra parameters, and therefore is more efficient.Experimental results on classification, regression, and generation tasks demonstrate that HASHEE can achieve higher performance with fewer FLOPs and inference time compared with previous state-of-the-art early exiting methods.
WANA: Symbolic Execution of Wasm Bytecode for Extensible Smart Contract Vulnerability Detection

Many popular blockchain platforms support smart contracts for building decentralized applications. However, the vulnerabilities within smart contracts have demonstrated to lead to serious financial loss to their end users. In particular, the smart contracts on EOSIO smart contract platform have resulted in the loss of around 380K EOS tokens, which was around 1.9 million worth of USD at the time of attack. The EOSIO smart contract platform is based on the Wasm VM, which is also the underlying system supporting other smart contract platforms as well as Web application. In this work, we present WANA, an extensible smart contract vulnerability detection tool based on the symbolic execution for Wasm bytecode. WANA proposes a set of algorithms to detect the vulnerabilities in EOSIO smart contracts based on Wasm bytecode analysis. Our experimental analysis shows that WANA can effectively and efficiently detect vulnerabilities in EOSIO smart contracts. Furthermore, our case study also demonstrates that WANA can be extended to effectively detect vulnerabilities in Ethereum smart contracts.
Semantic Feature Extraction for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes using the attribute. In this paper, we put forth a new GZSL technique that improves the GZSL classification performance greatly. Key idea of the proposed approach, henceforth referred to as semantic feature extraction-based GZSL (SE-GZSL), is to use the semantic feature containing only attribute-related information in learning the relationship between the image and the attribute. In doing so, we can remove the interference, if any, caused by the attribute-irrelevant information contained in the image feature. To train a network extracting the semantic feature, we present two novel loss functions, 1) mutual information-based loss to capture all the attribute-related information in the image feature and 2) similarity-based loss to remove unwanted attribute-irrelevant information. From extensive experiments using various datasets, we show that the proposed SE-GZSL technique outperforms conventional GZSL approaches by a large margin.
Semi-Supervised 3D Medical Image Segmentation Based on Dual-Task Consistent Joint Learning and Task-Level Regularization

Semi-supervised learning has attracted wide attention from many researchers since its ability to utilize a few data with labels and relatively more data without labels to learn information. Some existing semi-supervised methods for medical image segmentation enforce the regularization of training by implicitly perturbing data or networks to perform the consistency. Most consistency regularization methods focus on data level or network structure level, and rarely of them focus on the task level. It may not directly lead to an improvement in task accuracy. To overcome the problem, this work proposes a semi-supervised dual-task consistent joint learning framework with task-level regularization for 3D medical image segmentation. Two branches are utilized to simultaneously predict the segmented and signed distance maps, and they can learn useful information from each other by constructing a consistency loss function between the two tasks. The segmentation branch learns rich information from both labeled and unlabeled data to strengthen the constraints on the geometric structure of the target. Experimental results on two benchmark datasets show that the proposed method can achieve better performance compared with other state-of-the-art works. It illustrates our method improves segmentation performance by utilizing unlabeled data and consistent regularization.
Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning

Medical instrument segmentation in 3D ultrasound is essential for image-guided intervention. However, to train a successful deep neural network for instrument segmentation, a large number of labeled images are required, which is expensive and time-consuming to obtain. In this article, we propose a semi-supervised learning (SSL) framework for instrument segmentation in 3D US, which requires much less annotation effort than the existing methods. To achieve the SSL learning, a Dual-UNet is proposed to segment the instrument. The Dual-UNet leverages unlabeled data using a novel hybrid loss function, consisting of uncertainty and contextual constraints. Specifically, the uncertainty constraints leverage the uncertainty estimation of the predictions of the UNet, and therefore improve the unlabeled information for SSL training. In addition, contextual constraints exploit the contextual information of the training images, which are used as the complementary information for voxel-wise uncertainty estimation. Extensive experiments on multiple ex-vivo and in-vivo datasets show that our proposed method achieves Dice score of about 68.6%-69.1% and the inference time of about 1 sec. per volume. These results are better than the state-of-the-art SSL methods and the inference time is comparable to the supervised approaches.
Problems, Common Beliefs and Procedures on the Use of Partial Least Squares Structural Equation Modeling in Business Research

Partial least squares structural equation modeling (commonly referred to as PLS-SEM) was not developed without reason. PLS-SEM was developed as an alternative to covariance-based SEM, allowing researchers to conduct exploratory research. In addition, PLS-SEM is considered capable of providing flexibility related to data characteristics, model complexity, and model specifications. Undoubtedly, PLS-SEM is the most frequently used method in many fields of business research. However, many researchers use PLS-SEM incorrectly and even expect more without understanding the basic structural equation modeling method. For this reason, this article will discuss various types of problems and general beliefs about the use of PLS-SEM in business research. In addition, this article can be used as a reference to make it easier for applied researchers to decide what methods, techniques, and tools will be used to complete their research. In addition, at the end of this article, we will discuss how PLS-SEM can be applied to develop theory in business research through a series of technical introductions taking into account user needs. Subsequently, this article will be equipped with a systematic procedure that discusses the evaluation flow of each PLS-SEM test through illustrations with a notated model using SmartPLS.
Blockchain technology adoption in the supply chain (UTAUT2 with risk) – evidence from Indian supply chains

The aim of the present study is to study blockchain technology adoption intentions by supply chain practitioners using the unified theory of adoption and use of technology (UTAUT2) with risk model as the conceptual framework. UTAUT2 is generic model which can be used to study adoption of technologies. The present study applies this model to study blockchain technology adoption in the supply chain. Data was collected from 360 supply chain practitioners in India. The results of the current study indicate that behavioural intentions for blockchain technology adoption are significantly influenced by performance expectancy, social influence, hedonic motivation and price value considerations. The study proposes some guidelines for adopting blockchain in the supply chain. The present study tries to fill in the gap in the existing literature by listing out the factors that influence blockchain technology adoption in the supply chain.
Multi-swarm cooperative multi-objective bacterial foraging optimisation

This paper proposes a novel multi-objective algorithm which is based on the concept of master-slave swarm, namely multi-swarm cooperative multi-objective bacterial foraging optimisation (MCMBFO). In MCMBFO, the multi-swarm cooperative operation which involves several slave-swarms and a master-swarm is developed to accelerate the bacteria to come closer to the true Pareto front. With regard to slave-swarms, each of them evolves collaboratively with others during the steps of chemotaxis and reproduction, using information communication mechanism and cross-reproducing approach respectively to enhance the convergence rate. At the same time, bacteria in the master-swarm are all non-dominated individuals selected from slave-swarms. They evolve based on non-dominated sorting approach and crowding distance operation, aiming to improve the accuracy and diversity of solutions. The superiority of MCMBFO is confirmed by simulation experiments using several test problems and performance metrics chosen from prior representative studies. Simulation results illustrate that MCMBFO is considerably competitive for most of the cases, especially in terms of converging to the true Pareto front.
A hybrid framework for job scheduling on cloud using firefly and BAT algorithm

Nowadays cloud computing is an emerging field, requires more algorithm and techniques for the various process of cloud computing. Here, we have considered the job scheduling process in cloud computing platform that needs a good algorithm to schedule the jobs requested from various users of cloud computing environment. Here, the request can be from any platform so scheduling is indispensable one when a number of users need the particular jobs. In this research, we have intended to develop a hybrid algorithm for job scheduling in cloud computing environment. Accordingly, multiple criteria will be taken for scheduling various jobs located in various servers. Then, the job scheduling will be done based on a hybrid optimisation algorithm. Additionally, different jobs with different constraints will be considered and the cloud computing environment is simulated with the help of cloudsim tool.
Consolidating Security Notions in Hardware Masking

In this paper, we revisit the security conditions of masked hardware implementations. We describe a new, succinct, information-theoretic condition called d-glitch immunity which is both necessary and sufficient for security in the presence of glitches. We show that this single condition includes, but is not limited to, previous security notions such as those used in higher-order threshold implementations and in abstractions using ideal gates. As opposed to these previously known necessary conditions, our new condition is also sufficient. On the other hand, it excludes avoidable notions such as uniformity. We also treat the notion of (strong) noninterference from an information-theoretic point-of-view in order to unify the different security concepts and pave the way to the verification of composability in the presence of glitches. We conclude the paper by demonstrating how the condition can be used as an efficient and highly generic flaw detection mechanism for a variety of functions and schemes based on different operations.
Multilevel Thresholding based Image Segmentation using Whale Optimization Algorithm

Whale Optimization Algorithm (WOA) was proposed by Seyedali Mirjalili and Andrew Lewis in 2016. WOA is nature-inspired, meta-heuristic (randomization and deterministic) algorithm, which is being used to solve various single objective, multi objective and multi-dimensional optimization problems. To determine threshold value for image segmentation Otsu, kapur, thresholding etc. methods are used. In this paper multilevel threshold values are computed using WOA and these multilevel threshold values are used for image segmentation. Fitness is computed using Otsu thresholding. Minimum fitness score is considered as best optimal value. WOA has capability to explore, exploit the search s pace and avoid local optima. In multilevel thresholding, complex images are segmented into L+1 levels for multiple threshold values L =2, 3 etc. This paper addresses about performance of Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) for various benchmark objective functions such as unimodel, multimodel, fix dimension multimodel based on their convergence curves for different number of iterations400,500 600 and compute multilevel threshold values for various level image segmentation using Whale Optimization Algorithm.
Public Relations in Organizations in Student View: Accumulator of Management Tools or Formation of Partnership and Friendly Relations

The relevance of this article is the need to study the students' understanding of public relations as a means of management. The experience of countries with a developed social sphere shows that the use of public relations in the management of social work institutions can improve the social status of social services, form a positive public opinion and, as a result, ensure the social effectiveness of services provided. The purpose of the research is to analyze students' perceptions of the essence of public relations as a means of managing social services in the external environment. Research methods: as a research method, we used a questionnaire survey, which allows us to qualitatively investigate public relations in the organization through the eyes of students. Results of the research: the article considers the students' views on the means of managing social services in the external environment, and reveals the role of public relations in the management of social services in the external environment in the representation of students. The novelty and originality of the research consists in the analysis of public relations as a means of management in the external environment in relation to the activities of social protection institutions in the representation of students. It is revealed that, in the view of students, public relations accumulate management tools and include: the use of various forms of communication aimed at identifying common views or interests of different groups; they contribute to the formation of partnership and friendly relations between the social service, clients and the General public; ensure the achievement of mutual understanding based on truth and full public awareness and, ultimately, form a positive public opinion and a favorable image for the social service. The essential aspects, specific features, structural and functional features and opportunities of public relations as a means of managing social services in the external environment are highlighted in the students' view. It is determined that in the students' views, the management of the social service does not have a well-thought-out concept of organizing public relations, which leads to ineffective management of the social service in the external environment and is used in social work spontaneously. Practical significance: the data obtained in this work can be used in social psychology, Economics, advertising psychology, management psychology, as well as for further theoretical development of this issue.
Mathematical assessment of blocks acceptance in blockchain using Markov model

Blockchain, introduced in bitcoin system is disrupting the way transactions are done in businesses via charging small transaction fees. It is observed that the acceptance of blocks in the blockchain is based on transaction value. Transmittals through blockchain are favoured due to low transaction cost and grander security. However, attempt of implementing blockchain to revolutionise business processes writhes from problem of waiting time for blocks. In this paper, the transaction-confirmation-time is appraised using queuing theory, where the approval rate of blocks in blockchain is concocted using Markov queue model. Four specific use cases of 1, 3, 6 and 60 confirmations are considered for practical purposes.
A new short-term energy price forecasting method based on wavelet neural network

A wavelet neural network (WNN) is proposed for short-term price forecasting (STPF) in electricity markets. Back propagation algorithm is used for training the wavelet neural network for prediction. Weights in the back propagation algorithm are usually initialised with small random values. If the random initial weights happen to be far from a suitable solution or near a poor local optimum, training may take a long time or get trapped in the local optimum. In this paper, we show that WNN has acceptable prediction properties compared to other forecasting techniques. We investigated proper weight initialisations of WNN, and proved that it attains a superior prediction performance. Finally, we used a two-step correlation analysis algorithm for input selecting. This algorithm selects the best relevant and non-redundant input features for WNN. Our model is examined for MCP prediction of the Spanish market and LMP forecasting in PJM (Pennsylvania, New Jersey and Maryland) market for the year 2002 and 2006 respectively.
Semantic Patches for Adaptation of JavaScript Programs to Evolving Libraries

JavaScript libraries are often updated and sometimes breaking changes are introduced in the process, resulting in the client developers having to adapt their code to the changes. In addition to locating the affected parts of their code, the client developers must apply suitable patches, which is a tedious, error-prone, and entirely manual process. To reduce the manual effort, we present JSFIX. Given a collection of semantic patches, which are formalized descriptions of the breaking changes, the tool detects the locations affected by breaking changes and then transforms those parts of the code to become compatible with the new library version. JSFIX relies on an existing static analysis to approximate the set of affected locations, and an interactive process where the user answers questions about the client code to filter away false positives. An evaluation involving 12 popular JavaScript libraries and 203 clients shows that our notion of semantic patches can accurately express most of the breaking changes that occur in practice, and that JSFIX can successfully adapt most of the clients to the changes. In particular, 31 clients have accepted pull requests made by JSFIX, indicating that the code quality is good enough for practical usage. It takes JSFIX only a few seconds to patch, on average, 3.8 source locations affected by breaking changes in each client, with only 2.7 questions to the user, which suggests that the approach can significantly reduce the manual effort required when adapting JavaScript programs to evolving libraries.
TOP-ALCM: A novel video analysis method for violence detection in crowded scenes

Despite the Video Violence Detection (VVD) plays a critical role in video surveillance, it is not trivial in crowded scenes due to the complexity and diversity of violence. Generally, the most typical features of violence are its drastic, disordered, and chaotic motion in contrast to non-violence. To capture these features for violence analysis in a video clip, we propose a novel Angle-level Co-occurrence Matrix (ALCM). Given a video volume, we treat it as a tensor of rank 3, which consists of a bound of fibers in one plane. ALCM records the co-occurrence of two specific quantized angle levels between fibers with their neighbors, which is the distribution of the co-occurrence of fiber pairs with specific similarities in one plane of the tensor of rank 3. To completely characterize the violence in the volume, we compute three ALCMs for three orthogonal planes to form a TOP-ALCM, respectively. We also propose both conventional and deep-learning-based VVD frameworks, in which the former one leverages the features such as entropy, homogeneity, and energy computed from TOP-ALCM for classification, while the latter one directly uses CNN to classify the TOP-ALCMs. Experimental results analysis demonstrates that the proposed TOP-ALCM outperforms the state-of-the-art methods for VVD.