Reviewerly/exHarmony huggingface dataset:
Java Code Clone Detection by Exploiting Semantic and Syntax Information From Intermediate Code-Based Graph

Code clone detection plays a critical role in the field of software engineering. To achieve this goal, developers are required to have rich development experience for finding the “functional” clone code. However, this is unfriendly to novice developers. Although many approaches were proposed to automatically detect code clones, the results are not satisfactory. A major reason is that it is difficult to extract syntax and semantic information from the source code. To resolve this problem, in this article, we develop a novel graph representation approach based on intermediate code to detect the functional code clones. This graph representation is built based on intermediate code compiled from the source code. By using it, we can easily utilize graph embedding techniques to extract syntactic and semantic features from abstract syntax tree, control flow graph, and DFG generated from intermediate code. After that, we use the Softmax classifier to detect functional code clone pairs. We evaluate the performance of the proposed graph representation approach based on intermediate code for the code clone detection task on the BigCloneBench dataset. In order to improve performance, the embedded representation of intermediate code is initialized based on pretrained vectors learned from the collected LLVM IR dataset in advance. The experimental results show that our proposed intermediate code-based graph approach performs better than existing functional code clone detection approaches. Especially for the type-4 code clone detection, our approach outperforms the baseline approaches by an average of 33.49% in the term of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> 1 score.
Video Understanding-Based Random Hand Gesture Authentication

Existing hand gesture authentication methods require the probe gesture types to be consistent with the registered ones, which reduces the user-friendliness and efficiency of authentication. In this paper, a video understanding based random hand gesture authentication method is introduced to eliminate this limitation, in which users only need to improvise a random hand gesture in front of an RGB camera without memory and hesitation in both the enrollment and verification stage. The random hand gesture is a promising biometric trait containing both physiological and behavioral characteristics. To fully unleash the potential of random hand gesture authentication, we design a simple but effective behavior representation (modality), temporal difference map, for better behavioral characteristic understanding, and present an efficient model called 3D Temporal Difference Symbiotic Neural Network (3DTDS-Net) that can separately extract physiological and behavioral features as well as automatically assign fusion weights for the two features to complement each other’s strengths based on the magnitude of behavioral features in an end-to-end fashion. We also adapt and reimplement 17 SOTA neural networks for authentication from other tasks, such as action classification and gait recognition, to make convincing comparisons. Extensive experiments on the SCUT-DHGA dataset demonstrate the effectiveness of temporal difference maps and the superiority of 3DTDS-Net. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SCUT-BIP-Lab/3DTDS-Net</uri> .
A Decision Making Approach Using Linear Diophantine Fuzzy Sets with Dombi Operations

A Linear Diophantine fuzzy set with the addition of references parameters paves way for an advanced approach to handle uncertainties in multicriteria decision making. In this paper, the linear Diophantine fuzzy sets (LDFSs) have been employed to develop a multi criteria decision making method with the help of Dombi mean aggregation operators. In addition, the weighted dice similarity measures with their properties are studied for LDFSs. An illustration is provided to validate the proposed decision making approach.
Inflectional and Derivational Hybrid Stemmer for Sentiment Analysis: A Case Study with Marathi Tweets

Sentiment Analysis (SA) is more complex in Marathi (Indian Language) than in European languages due to the inflectional words and phrases and comparatively limited resources. SA needs several preprocessing steps, viz., cleaning, stemming, and stopword removal, which reduces the feature set's dimensionality. Stemming, a crucial preprocessing step, converts the inflected words to a root or stem without morphological analysis. The first-ever work presents the inflectional and derivational hybrid stemmer for Marathi Tweets, using a rule-based approach and a dictionary look-up. The 10000 sentences (103789 words) are extracted from the OSCAR ( https://oscar-corpus.com/ ) Marathi corpus to design and develop the stemmer. It achieved an average accuracy of 89.37% for ten random samples of 1000 words each. The performance is investigated using Paice's parameters, i.e., under-stemming and over-stemming errors, Index Compression Factor (ICF), and Mean number of words per signature (MWC). The benchmark dataset of 4,245 Marathi political tweets is constructed to validate the system, and the Marathi lexicons (adjective-adverb) are built from SentiWordNet, and Hindi SentiWordNet are used to classify the tweets as positive or negative. The various experiments are performed to measure the stemmer effect on lexicon-based sentence level SA for Marathi tweets. The outcomes evidence improved accuracy using stemmer from 67.52% to 74.49% with F-measure of 0.75. However, some adverse effects of stemming are observed in the result.
Hierarchical Primitive Composition: Simultaneous Activation of Skills With Inconsistent Action Dimensions in Multiple Hierarchies

Deep reinforcement learning has shown its effectiveness in various applications, providing a promising direction for solving tasks with high complexity. However, naively applying classical RL for learning a complex long-horizon task with a single control policy is inefficient. Thus, policy modularization tackles this problem by learning a set of modules that are mapped to primitives and properly orchestrating them. In this study, we further expand the discussion by incorporating simultaneous activation of the skills and structuring them into multiple hierarchies in a recursive fashion. Moreover, we sought to devise an algorithm that can properly orchestrate the skills with different action spaces via multiplicative Gaussian distributions, which highly increases the reusability. By exploiting the modularity, interpretability can also be achieved by observing the modules that are used in the new task if each of the skills is known. We demonstrate how the proposed scheme can be employed in practice by solving a pick and place task with a 6 DoF manipulator, and examine the effects of each property from ablation studies.
Quaternion Screened Poisson Equation for Low-Light Image Enhancement

Image enhancement is a technique to enhance the illumination of dark images while keeping the reality and the naturalness of the enhanced images at the same time. For color images, most methods tackle different color channels in a separate way, which overlooks the connection between the color channels. Therefore, in this paper, we consider a quaternion-based model to reserve the color connectivity, which integrates the color information of a pixel by a quaternion number. Moreover, we propose a regularizer based on the gamma-correction function and incorporate it into a screened Poisson equation&#x00A0;for the image enhancement task. The uniqueness and existence of the solution of the proposed model are analyzed. The numerical results also prove the superiority of our scheme for color image enhancement.
Jacobian Computation for Cumulative B-Splines on SE(3) and Application to Continuous-Time Object Tracking

In this paper we propose a method that estimates the SE(3) continuous trajectories (orientation and translation) of the dynamic rigid objects present in a scene, from multiple RGB-D views.Specifically, we fit the object trajectories to cumulative B-Splines curves, which allow us to interpolate, at any intermediate time stamp, not only their poses but also their linear and angular velocities and accelerations.Additionally, we derive in this work the analytical SE(3) Jacobians needed by the optimization, being applicable to any other approach that uses this type of curves.To the best of our knowledge this is the first work that proposes 6-DoF continuous-time object tracking, which we endorse with significant computational cost reduction thanks to our analytical derivations.We evaluate our proposal in synthetic data and in a public benchmark, showing competitive results in localization and significant improvements in velocity estimation in comparison to discrete-time approaches.
OSIBR: Ontology Focused Semantic Intelligence Approach for Book Recommendation

With the dawn of digital books on the web, finding reliable book suggestions has become a complicated question, and several significant machine learning algorithms were developed to address such requests. Also, there exists a problem of the prevailing recommendation system not presenting a well fit to the semantically designed nature of the world wide web. Moreover, due to the vast digitization of books, a plethora of the old book discovery strategic approaches is intended for physical selection only. Therefore, research interest is increasing in the innovative approach to search, browse, and use the recommendation engine. In this paper, an ontology focused semantic intelligence approach for the book recommendation is proposed using Jaccard similarity, NPMI, and Kullback Leibler divergence with the optimization done by the Krill Herd algorithm. The proposed system OSIBR reaches an overall Precision and Accuracy of 94.16% and 95.55% respectively and with the highest Normalized Discounted Cumulative Gain (nDCG) of 0.96.
Interpretable Local Frequency Binary Pattern (<i>LFrBP</i>) Based Joint Continual Learning Network for Heterogeneous Face Recognition

Heterogeneous Face Recognition (HFR) is a challenging task due to the significant intra-class variation between the query and gallery images. The reason behind this vast intra-class variation is the varying image capturing sensors and the varying image representation techniques. Visual, Infrared, thermal images are the output of different sensors and viewed sketches, and composite sketches are the output of different image representation techniques. Conventional deep learning models are trying to solve the problem. Still, progress is impeded due to small HFR data samples, task-specific models (one model trained for face sketch-photo matching can&#x2019;t perform well for NIR-VIS face matching), joint learning of two different HFR scenarios are not possible by one single deep network, and models are not interpretable. In this paper, to solve these major problems, we presented a novel interpretable Local Frequency Binary Pattern (LFrBP) based continual learning shallow network for HFR. The model is divided into two parts. A modality-invariant CNN model using the LFrBP feature, fine-tuned with CNN, is presented in the first part. The second part is based on continual learning to jointly learn the two HFR scenarios (face sketch-photo and NIR-VIS face matching) using a single network. Recognition results on different challenging HFR databases depict the superiority of the proposed model over other state-of-the-art deep learning-based methods.
Performance Analysis and Optimization on Scheduling Stochastic Cloud Service Requests: A Survey

Performance analysis and optimization is a critical task for the successful development of cloud computing systems and services. Unfortunately, performance analysis and optimization remains complicated and challenging due to several unique characteristics in cloud computing such as stochastic service requests, request sequencing strategies, and request distribution methods. In this paper, we present a comprehensive survey on the performance analysis and optimization for stochastic cloud service requests. By analyzing the main entities and activities in the common routines of performance analysis, we first propose a generic performance analysis framework, which contains five fundamental characteristics: Request, Sequencing, Queue, Distribution and Services. Practical factors of each characteristic are analyzed. We discuss the effects of each characteristic of the framework on optimization objectives including cost, profit, response time, and energy consumption. We then systematically review and compare 13 representative queuing models using the proposed framework. Based on the practical factors of the five characteristics and along with the current research efforts, we also identify several research opportunities and challenges.
New Roles and Competencies in Technical Communication Induced by Semantics and Analytics

Semantic approaches have become a logical extension of the widely-accepted content creation and provision technologies represented in technical communication mostly by component content management systems (CCMS) and content delivery portals (CDP). CCMS are typically used by technical authors trained in the areas of linguistics, structured and standardized topic-based writing and metadata-based content processing. Delivery scenarios and corresponding new technologies widen the demands of organizations towards the role of technical writers as information engineers with competencies in user-centered and use case-driven creation of content. Information engineers cover a wide range of topics being already the core of academic programs and professional education. More recently, increasing demands for business process integration and process management led to new technological and methodological approaches. Semantic technologies based on ontology modelling have been investigated and introduced for example for CMS processes to improve the handling of product and media variants and the corresponding content planning. Other developments address metadata governance in integrated environments of multiple information management systems. On CDP side, there is a need to improve search support by semantics for example through facetted search, content correlations or AI technologies. Furthermore, in both fields of CMS and CDP, analytics gains significant importance for tracking and improving the corresponding processes of content creation, content search and information access. Therefore, we claim that a clearer definition of a new role of information architects and their competencies will be helpful for differentiating educational and working areas from the field of information engineers in TC.
Learning Finite State Models from Recurrent Neural Networks

Explaining and verifying the behavior of recurrent neural networks (RNNs) is an important step towards achieving confidence in machine learning. The extraction of finite state models, like deterministic automata, has been shown to be a promising concept for analyzing RNNs. In this paper, we apply a black-box approach based on active automata learning combined with model-guided conformance testing to learn finite state machines (FSMs) from RNNs. The technique efficiently infers a formal model of an RNN classifier’s input-output behavior, regardless of its inner structure. In several experiments, we compare this approach to other state-of-the-art FSM extraction methods. By detecting imprecise generalizations in RNNs that other techniques miss, model-guided conformance testing learns FSMs that more accurately model the RNNs under examination. We demonstrate this by identifying counterexamples with this testing approach that falsifies wrong hypothesis models learned by other techniques. This entails that testing guided by learned automata can be a useful method for finding adversarial inputs, that is, inputs incorrectly classified due to improper generalization.
Blockchain-Based Network Slice Broker to Facilitate Factory-As-a-Service

The novel concept of factory-as-a-service (FaaS) allows the agility of adapting the manufacturing process by identifying the industry’s supply chain and user requirements. To cater to FaaS, flexibility in networking and cloud services is a must. 5G network slice broker (NSB) is a third-party mediator that caters to networking resource demand from clients to the service providers. Thus, this article introduces a secure blockchain-based NSB to facilitate FaaS. The proposed secure NSB (SNSB) provides secure, cognitive, and distributed network services for resource allocation and security service level agreement (SSLA) formation with coordination of slice managers and SSLA managers. In SNSB, we introduce a federated slice selection algorithm with Stackelberg game model and reinforcement learning algorithm to compute the real time and the optimal unit price and demand level. We provide an extensive implementation and performance evaluation of SNSB using the slice manager and a custom SSLA manager.
Surrogate-Assisted Evolutionary <i>Q</i>-Learning for Black-Box Dynamic Time-Linkage Optimization Problems

Dynamic time-linkage optimization problems (DTPs) are special dynamic optimization problems (DOPs) with the time-linkage property. The environment of DTPs changes not only over time but also depends on the previous applied solutions. DTPs are hardly solved by existing dynamic evolutionary algorithms because they ignore the time-linkage property. In fact, they can be viewed as multiple decision-making problems and solved by reinforcement learning (RL). However, only some discrete DTPs are solved by RL-based evolutionary optimization algorithms with the assumption of observable objective functions. In this work, we propose a dynamic evolutionary optimization algorithm using surrogate-assisted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning for continuous black-box DTPs. To observe the states of black-box DTPs, the state extraction and prediction methods are applied after the search process at each time step. Based on the learned information, a surrogate-assisted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning is introduced to evaluate and select candidate solutions in the continuous decision space in a long-term consideration. We evaluate the components of our proposed algorithm on various benchmark problems to study their behaviors. Results of comparative experiments indicate that the proposed algorithm outperforms other compared algorithms and performs robustly on DTPs with up to 30 decision variables and different dynamic changes.
Knowledge Graph Completion Method Based on Fusing Association Information

Knowledge graph is a carrier of knowledge. The knowledge graph completion task is to predict the links between entities to make the knowledge graph more complete. Currently, the completion method based on knowledge representation learning ignores the association information between each relation in the multiple-step relation paths and the direct relations, and the association information between the head and tail entity types and direct relations. In this study, we extract and utilize these associated information, and propos AiTransE model for knowledge graph completion, which uses the frequency of each relation in the multiple-step relation paths between head and tail entities to calculate the degree of association with the direct relations, and uses head and tail entity types and direct relation types for matching to obtain the degree of association between them, Finally, the two association degrees are linearly weighted and merged and then introduced into the objective function, so that the model can give different degrees of attention to different triples, and improve model knowledge representation learning performance. The experiment with link prediction was carried out on the Tibet Animal Husbandry Dataset and the WN18 dataset, and compared with the TransE, TransH, TransR, and other models. The experimental results show that this model has a significant improvement over other models in the indicators Hits@10 and Mean Rank.
Keyed-Fully Homomorphic Encryption Without Indistinguishability Obfuscation

(Fully) homomorphic encryption ((F)HE) allows users to publicly evaluate circuits on encrypted data. Although public homomorphic evaluation property has various applications, (F)HE cannot achieve security against chosen ciphertext attacks (CCA2) due to its nature. To achieve both the CCA2 security and homomorphic evaluation property, Emura et al. (PKC 2013) introduced keyed-homomorphic public key encryption (KH-PKE) and formalized its security denoted by $$\mathsf {KH}\text {-}\mathsf {CCA}$$ security. KH-PKE has a homomorphic evaluation key that enables users to perform homomorphic operations. Intuitively, KH-PKE achieves the CCA2 security unless adversaries have a homomorphic evaluation key. Although Lai et al. (PKC 2016) proposed the first keyed-fully homomorphic encryption (keyed-FHE) scheme, its security relies on the indistinguishability obfuscation ( $$\mathsf {iO}$$ ), and this scheme satisfies a weak variant of $$\mathsf {KH}\text {-}\mathsf {CCA}$$ security. Here, we propose a generic construction of a $$\mathsf {KH}\text {-}\mathsf {CCA}$$ secure keyed-FHE scheme from an FHE scheme secure against non-adaptive chosen ciphertext attack (CCA1) and a strong dual-system simulation-sound non-interactive zero-knowledge (strong DSS-NIZK) argument system by using the Naor-Yung paradigm. We show that there are a strong DSS-NIZK and an IND-CCA1 secure FHE scheme that are suitable for our generic construction. This shows that there exists a keyed-FHE scheme from simpler primitives than iO.
Characteristic Study on Parallel Reservoir Computation Based on VCSEL Dynamics With Optoelectronic Feedback

We proposed a new reservoir computation (RC) structure using polarization dynamics of vertical cavity surface emitting lasers (VCSELs) with optoelectronic feedback. Santa-Fe time-series prediction and nonlinear channel equalization were used to evaluate its parallel processing capability. We numerically discussed the effects of feedback strength, injection strength, frequency detuning, injection current and scaling factors on the RC performance of the system. The results show that, the operation range of negative feedback is larger than that of positive feedback; under appropriate feedback and lager injection strengths, good RC performance can be achieved for a wider injection current; with continuous variation of feedback strength from negative to positive values, the optimized location for frequency detuning moves from positive to negative values. We also evaluated the processing rate of proposed RC structure. The related study is helpful for the design and optimization of VCSEL-based RC under optoelectronic feedback.