Reviewerly/exHarmony huggingface dataset:
CAPKM++2.0: An upgraded version of the collaborative annealing power <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3512" altimg="si57.svg"><mml:mi>k</mml:mi></mml:math>-means++ clustering algorithm

The collaborative annealing power k-means++ (CAPKM++) clustering algorithm has been recently proposed based on multiple modules by minimizing annealed power-mean functions. This paper presents an upgraded version of CAPKM++ called CAPKM++2.0. Different from CAPKM++ where the anchor points of surrogate functions for majorizing the power-mean functions are re-initialized and minimized repeatedly after annealing, CAPKM++2.0 re-initializes the weights of the majorization function during annealing. In addition, unlike CAPKM++ that minimizes the majorization function of the power-mean sum, CAPKM++2.0 adds an inner loop to minimize the power-mean sum iteratively and locally at every annealing step. Ablation study results are discussed to justify the adoption of the power-mean and the collaboration of multiple modules. Experimental results on sixteen benchmark datasets are elaborated to demonstrate the superior clustering performance of the upgraded algorithm compared with its predecessor and six other mainstream algorithms in terms of cluster validity indices and algorithmic complexities.
Formula-Driven Supervised Learning in Computer Vision: A Literature Survey

Current computer vision research uses huge datasets with millions of images to pre-train vision models. This results in escalation of time and capital, ethical issues, moral issues, privacy issues, copyright issues, fairness issues, and others. To address these issues, several alternative learning schemes have been developed. One such scheme is formula-based supervised learning (FDSL). It is a form of supervised learning, which involves the use of mathematically generated images for the pre-training of deep models. Promising results have been obtained for computer-vision-related applications. In this comprehensive survey paper, a gentle introduction to FDSL is presented. The supporting theory, databases, experimentation and ensuing results are discussed. The research outcomes, issues and scope are also discussed. Finally, some of the most promising future directions for FDSL research are discussed. As FDSL is an important learning technique, this survey represents a useful resource for interested researchers working on solving various problem in computer vision and related areas of application.
Interactive Disambiguation for Behavior Tree Execution

In recent years, robots are used in an increasing variety of tasks, especially by small- and medium sized enterprises. These tasks are usually fast-changing, they have a collaborative scenario and happen in unpredictable environments with possible ambiguities. It is important to have methods capable of generating robot programs easily, that are made as general as possible by handling uncertainties. We present a system that integrates a method to learn Behavior Trees (BTs) from demonstration for pick and place tasks, with a framework that uses verbal interaction to ask follow-up clarification questions to resolve ambiguities. During the execution of a task, the system asks for user input when there is need to disambiguate an object in the scene, i.e. when the targets of the task are objects of a same type that are present in multiple instances. The integrated system is demonstrated on different scenarios of a pick and place task, with increasing level of ambiguities. The code used for this paper is made publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/matiov/disambiguate-BT-execution.
CrystalBLEU: Precisely and Efficiently Measuring the Similarity of Code

Recent years have brought a surge of work on predicting pieces of source code, e.g., for code completion, code migration, program repair, or translating natural language into code. All this work faces the challenge of evaluating the quality of a prediction w.r.t. some oracle, typically in the form of a reference solution. A common evaluation metric is the BLEU score, an n-gram-based metric originally proposed for evaluating natural language translation, but adopted in software engineering because it can be easily computed on any programming language and enables automated evaluation at scale. However, a key difference between natural and programming languages is that in the latter, completely unrelated pieces of code may have many common n-grams simply because of the syntactic verbosity and coding conventions of programming languages. We observe that these trivially shared n-grams hamper the ability of the metric to distinguish between truly similar code examples and code examples that are merely written in the same language. This paper presents CrystalBLEU, an evaluation metric based on BLEU, that allows for precisely and efficiently measuring the similarity of code. Our metric preserves the desirable properties of BLEU, such as being language-agnostic, able to handle incomplete or partially incorrect code, and efficient, while reducing the noise caused by trivially shared n-grams. We evaluate CrystalBLEU on two datasets from prior work and on a new, labeled dataset of semantically equivalent programs. Our results show that CrystalBLEU can distinguish similar from dissimilar code examples 1.9–4.5 times more effectively, when compared to the original BLEU score and a previously proposed variant of BLEU for code.
PredART: Towards Automatic Oracle Prediction of Object Placements in Augmented Reality Testing

While the emerging Augmented Reality (AR) technique allows a lot of new application opportunities, from education and communication to gaming, current augmented apps often have complaints about their usability and/or user experience due to placement errors of virtual objects. Therefore, identifying noticeable placement errors is an important goal in the testing of AR apps. However, placement errors can only be perceived by human beings and may need to be confirmed by multiple users, making automatic testing very challenging. In this paper, we propose PredART, a novel approach to predict human ratings of virtual object placements that can be used as test oracles in automated AR testing. PredART is based on automatic screenshot sampling, crowd sourcing, and a hybrid neural network for image regression. The evaluation on a test set of 480 screenshots shows that our approach can achieve an accuracy of 85.0% and a mean absolute error, mean squared error, and root mean squared error of 0.047, 0.008, and 0.091, respectively.
Automatic Comment Generation via Multi-Pass Deliberation

Deliberation is a common and natural behavior in human daily life. For example, when writing papers or articles, we usually first write drafts, and then iteratively polish them until satisfied. In light of such a human cognitive process, we propose DECOM, which is a multi-pass deliberation framework for automatic comment generation. DECOM consists of multiple Deliberation Models and one Evaluation Model. Given a code snippet, we first extract keywords from the code and retrieve a similar code fragment from a pre-defined corpus. Then, we treat the comment of the retrieved code as the initial draft and input it with the code and keywords into DECOM to start the iterative deliberation process. At each deliberation, the deliberation model polishes the draft and generates a new comment. The evaluation model measures the quality of the newly generated comment to determine whether to end the iterative process or not. When the iterative process is terminated, the best-generated comment will be selected as the target comment. Our approach is evaluated on two real-world datasets in Java (87K) and Python (108K), and experiment results show that our approach outperforms the state-of-the-art baselines. A human evaluation study also confirms the comments generated by DECOM tend to be more readable, informative, and useful.
Toward Improving the Robustness of Deep Learning Models via Model Transformation

Deep learning (DL) techniques have attracted much attention in recent years, and have been applied to many application scenarios, including those that are safety-critical. Improving the universal robustness of DL models is vital and many approaches have been proposed in the last decades aiming at such a purpose. Among existing approaches, adversarial training is the most representative. It advocates a post model tuning process via incorporating adversarial samples. Although successful, they still suffer from the challenge of generalizability issues in the face of various attacks with unsatisfactory effectiveness. Targeting this problem, in this paper we propose a novel model training framework, which aims at improving the universal robustness of DL models via model transformation incorporated with a data augmentation strategy in a delta debugging fashion. We have implemented our approach in a tool, called Dare, and conducted an extensive evaluation on 9 DL models. The results show that our approach significantly outperforms existing adversarial training techniques. Specifically, Dare has achieved the highest Empirical Robustness in 29 of 45 testing scenarios under various attacks, while the number drops to 5 of 45 for the best baseline approach.
InvCon: A Dynamic Invariant Detector for Ethereum Smart Contracts

Smart contracts are self-executing computer programs deployed on blockchain to enable trustworthy exchange of value without the need of a central authority. With the absence of documentation and specifications, routine tasks such as program understanding, maintenance, verification, and validation, remain challenging for smart contracts. In this paper, we propose a dynamic invariant detection tool, InvCon, for Ethereum smart contracts to mitigate this issue. The detected invariants can be used to not only support the reverse engineering of contract specifications, but also enable standard-compliance checking for contract implementations. InvCon provides a Web-based interface and a demonstration video of it is available at: https://youtu.be/Y1QBHjDSMYk.
Fully Bayesian Libby-Novick Beta Mixture Model with Feature Selection

In this work, we propose a novel clustering algorithm called Libby-Novick Beta mixture model with feature selection which is developed on a new double bounded distribution. Thanks to its additional shape parameters, this new distribution offers much more flexibility compared to conventional distributions in its family or widely used ones such as Gaussian distribution. We learn our proposed model by fully Bayesian inference, estimate model’s parameter with Markov Chain Monte Carlo technique, and apply Gibbs sampling within Metropolis-Hastings for Monte Carlo simulation. Moreover, we integrated feature selection approach simultaneously within the framework to choose the most informative features for our model. To demonstrate the power and capability of this new unsupervised model, we evaluated it on real medical applications and analyzed pathological images of white blood cells and lung and colon cancer images. The outcomes of our experiment indicates the robustness of our model compared to conventional alternatives.
Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model

A smart contract, as an important part of blockchain technology, has attracted considerable interest from both industry and academia. It provides the basis for the realization of a variety of practical blockchain applications and plays a crucial role in the blockchain ecosystem. While it also holds a large number of digital assets, the frequent occurrence of smart contract vulnerabilities have caused huge economic losses and destroyed the blockchain-based credit system. Currently, the security and reliability of smart contracts have become a new focus of research, and there are a number of smart contract vulnerability detection methods, such as traditional detection tools based on static or dynamic analysis. However, most of them often rely on expert rules, and therefore have poor scalability and high false negative and false positive rates. Recent deep learning methods alleviate this issue, but without considering the semantic information and context of source code. To this end, we propose a hybrid attention mechanism (HAM) model to detect security vulnerabilities in smart contracts. We extract code fragments from the source code, which focus on key points of vulnerability. We conduct extensive experiments on two public smart contract datasets (a total of 24,957 contracts). Empirical results show remarkable accuracy improvement over the state-of-the art methods on five kinds of vulnerabilities, where the detection accuracy could achieve 93.36%, 80.85%, 82.56%, 85.62%, and 82.19% for reentrancy, arithmetic vulnerability, unchecked return value, timestamp dependency, and tx.origin, respectively.
An Integrated Fusion Engine for Early Threat Detection Demonstrated in Public-Space Trials

Counter terrorism is a huge challenge for public spaces. Therefore, it is essential to support early detection of threats, such as weapons or explosives. An integrated fusion engine was developed for the management of a plurality of sensors to detect threats without disrupting the flow of commuters. The system improves security of soft targets (such as airports, undergrounds and railway stations) by providing security operators with real-time information of the threat combined with image and position data of each person passing the monitored area. This paper describes the results of the fusion engine in a public-space trial in a metro station in Rome. The system consists of 2D-video tracking, person re-identification, 3D-video tracking, and command and control (C&C) formulating two co-existing data pipelines: one for visualization on smart glasses and another for hand-over to another sensor. Over multiple days, 586 commuters participated in the trial. The results of the trial show overall accuracy scores of 97.4% and 97.6% for the visualization and hand-over pipelines, respectively, and each component reached high accuracy values (2D Video = 98.0%, Re-identification = 100.0%, 3D Video = 99.7% and C&C = 99.5%).
SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm

IPv6 geolocation is necessary for many location-based Internet services. However, the accuracy of the current IPv6 geolocation methods including machine-learning-based or deep-learning-based location algorithms are unsatisfactory for users. Strong geographic correlation is observed for measurement path features close to the target IP, so previous methods focused more on stable paths in the vicinity of the probe. Based on this, this paper proposes a new IPv6 geolocation algorithm, SubvectorS_Geo, which is mainly divided into three steps: firstly, it filters geographically relevant routing feature codes layer by layer to approximate the fine-grained trusted region of the target; secondly, it extracts delay vectors into the trusted region; thirdly, it evaluates the vector similarity to determine the final target geolocation information. The final experiments show that the median error distance range is 7.025 km to 9.709 km on three real datasets (Shanghai, New York State, and Tokyo). Compared with the advanced method, the median distance error distance is reduced by at least 6.8% and the average error distance is reduced by at least 9.2%.
Design of a Computational Heuristic to Solve the Nonlinear Li閚ard Differential Model

In this study, the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks (ANNs) along with the hybridization procedures of global and local search approaches.The global search genetic algorithm (GA) and local search sequential quadratic programming scheme (SQPS) are implemented to solve the nonlinear Liénard model.An objective function using the differential model and boundary conditions is designed and optimized by the hybrid computing strength of the GA-SQPS.The motivation of the ANN procedures along with GA-SQPS comes to present reliable, feasible and precise frameworks to tackle stiff and highly nonlinear differential models.The designed procedures of ANNs along with GA-SQPS are applied for three highly nonlinear differential models.The achieved numerical outcomes on multiple trials using the designed procedures are compared to authenticate the correctness, viability and efficacy.Moreover, statistical performances based on different measures are also provided to check the reliability of the ANN along with GA-SQPS.
Experimental study on the influence of virtual tourism spatial situation on the tourists’ temperature comfort in the context of metaverse

The Metaverse is a new application of the internet and social form which integrates a myriad of new technologies. It can not only create a parallel space that is closely connected to the real world while highly independent, but also bring the immersive experience of virtual scenarios without delay. The virtual tourism space situations that integrate realistic visual, audio, and temperature sensations can restore the real tourism environment to the greatest extent, and improve tourists' perception and satisfaction with the experience. The purpose of this experimental lab study is to examine the effects of the virtual tourism audio-visual conditions and environmental temperature on tourists' thermal sensation and temperature comfort. VR equipment and microclimate simulation technology was applied in a 3 × 2 × 2 experimental design (n = 180), simulating the virtual tourism scenarios. Electrocardiogram devices were also employed to assess participants' physiological indicators. Study results suggest that: (1) Virtual tourism spatial situations (environmental temperature and audio-visual conditions) significantly affect participants' thermal sensation and part of the physiological indicators; (2) Virtual tourism spatial situations (environmental temperature and audio-visual conditions) significantly affect participants' temperature comfort; and (3) Physiological responses (indicators) mediate the effect from tourism spatial situations to temperature comfort. The study mainly contributes to the literature about virtual tourism experience and spatial situations under the concept of the Metaverse, as well as provides theoretical and managerial implications for the development of "immersive" virtual tourism scenarios.
Combining Model-Agnostic Meta-Learning and Transfer Learning for Regression

For cases in which a machine learning model needs to be adapted to a new task, various approaches have been developed, including model-agnostic meta-learning (MAML) and transfer learning. In this paper, we investigate how the differences in the data distributions between the old tasks and the new target task impact performance in regression problems. By performing experiments, we discover that these differences greatly affect the relative performance of different adaptation methods. Based on this observation, we develop ensemble schemes combining multiple adaptation methods that can handle a wide range of data distribution differences between the old and new tasks, thus offering more stable performance for a wide range of tasks. For evaluation, we consider three regression problems of sinusoidal fitting, virtual reality motion prediction, and temperature forecasting. The evaluation results demonstrate that the proposed ensemble schemes achieve the best performance among the considered methods in most cases.
Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems

The information obtained from external sources within the cloud and the resulting computations are not always reliable. This is attributed to the absence of tangible regulations and information management on the part of the information owners. Although numerous techniques for safeguarding and securing external information have been developed, security hazards in the cloud are still problematic. This could potentially pose a significant challenge to the effective adoption and utilization of cloud technology. In terms of performance, many of the existing solutions are affected by high computation costs, particularly in terms of auditing. In order to reduce the auditing expenses, this paper proposes a well-organised, lightweight system for safeguarding information through enhanced integrity checking. The proposed technique implements a cryptographic hash function with low-cost mathematic operations. In addition, this paper explores the role of a semi-trusted server with regard to smart device users. This facilitates the formal management of information prior to distribution through the IoT-cloud system. Essentially, this facilitates the validation of the information stored and exchanged in this environment. The results obtained show that the proposed system is lightweight and offers features such as a safeguarding capability, key management, privacy, decreased costs, sufficient security for smart device users, one-time key provision, and high degree of accuracy. In addition, the proposed method exhibits lower computation complexity and storage expenses compared with those of other techniques such as bilinear map-based systems.
Knowledge Adaptive Multi-Way Matching Network for Biomedical Named Entity Recognition via Machine Reading Comprehension

Rapid and effective utilization of biomedical literature is paramount to combat diseases like COVID19. Biomedical named entity recognition (BioNER) is a fundamental task in text mining that can help physicians accelerate knowledge discovery to curb the spread of the COVID-19 epidemic. Recent approaches have shown that casting entity extraction as the machine reading comprehension task can significantly improve model performance. However, two major drawbacks impede higher success in identifying entities (1) ignoring the use of domain knowledge to capture the context beyond sentences and (2) lacking the ability to deeper understand the intent of questions. In this paper, to remedy this, we introduce and explore external domain knowledge which cannot be implicitly learned in text sequence. Previous works have focused more on text sequence and explored little of the domain knowledge. To better incorporate domain knowledge, a multi-way matching reader mechanism is devised to model representations of interaction between sequence, question and knowledge retrieved from Unified Medical Language System (UMLS). Benefiting from these, our model can better understand the intent of questions in complex contexts. Experimental results indicate that incorporating domain knowledge can help to obtain competitive results across 10 BioNER datasets, achieving absolute improvement of up to 2.02% in the f1 score.
Model-Free Undetectable Attacks on Linear Systems Using LWE-Based Encryption

We show that the homomorphic property, a desired property in encrypted control, can lead to failure in the cyber defense of a dynamical control system from undetectable attacks, even though individual signal sequences remain unknown to the attacker. We consider an encryption method based on the Learning with Errors (LWE) problem and demonstrate how model-free undetectable attacks on linear systems over integers can be computed from sampled inputs and outputs that are encrypted. Previous work has shown that computing such attacks is possible on nonencrypted systems. Applying this earlier work to our scenario, with minor modifications, typically amplifies the error in encrypted messages unless a short vector problem is solved. Given that an attacker obtains a short vector, we derive the probability that the attack is detected and show how it explicitly depends on the encryption parameters. Finally, we simulate an attack obtained by our method on an encrypted linear system over integers and conduct an analysis of the probability that the attack will be detected.