Reviewerly/exHarmony huggingface dataset:
Enhancing Multimodal Clustering Framework with Deep Learning to Reveal Image Spam Authorship

This paper introduces a multimodal framework for clustering spam images received in unsolicited emails. Spam images in the same cluster have similar visual and textual contents and could be generated by a common spam source. To perform the clustering task, we first extract three main categories of features: 1) Visual features, extracted by pretrained convolutional neural networks (CNNs); 2) Layout features, the location of illustrations in the spam images; 3) Text features extracted by optical character recognition (OCR) algorithm. We then use a two-stage hierarchical clustering framework to form clusters based on the pair-wise similarity matrices of the extracted features. We evaluate the performance of the proposed approach on a 2,100 spam image dataset collected from three months of emails. The experimental results show that the proposed method achieved satisfactory clustering outcomes in terms of an external entropy-based metric, the V-measure.
Quantum Petri Nets

The development of quantum software applications requires models capable to describe quantum features (superposition, entanglement, reversibility etc.) as well as the software algorithm requirements. The newly proposed Quantum Petri Nets (QPNs) have the structure of the classical Petri Nets (PNs), but their tokens represent superposed quantum states fulfilling the Hilbert space features. Instead of the integer numerical values of the PN marking, QPNs use quantum marking describing an extreme huge space. New conditions for the transition enabling and new mappings for the transition executions are conceived. A PN based language is used for the structure analysis. Replacing the classical tokens with the probability distribution of the quantum vectors, the QPN simulation is possible as well as the construction of a reachability graph.
Quantum regression theorem for multi-time correlators: A detailed analysis in the Heisenberg picture

The quantum regression theorem is one of the central results in open quantum systems and is extensively used for computing multi-point correlation functions. Traditionally it is derived for two-time correlators in the Markovian limit employing the Schr\"odinger picture. In this paper we make use of the Heisenberg picture to derive the quantum regression theorems for multi-time correlation functions, which in the special limit reduce to the well-known two-time regression theorem. For the multi-time correlation function we find that the regression theorem takes the same form as it takes for the two-time correlation function with a mild restriction that one of the times should be greater than all other time variables. Interestingly, the Heisenberg picture also allows us to derive an analog of regression theorem for out-of-time-ordered correlators. We further extend our study for the case of non-Markovian dynamics and report the modifications to the standard quantum regression theorem. We illustrate all of the above results using the paradigmatic dissipative spin-boson model.
Adaptive Navigation Design Model for Education Learning System

Adaptive user interface (AUI) is an evolving research area that offers personalized user experiences based on user activities. It can be implemented in a variety of ways and one of them is the implementation of Adaptive Navigation technique. Even though several approaches were proposed, there are still limitations and issues in supporting navigation such as complex navigation structure can decrease user&#x0027;s satisfaction. Minimal navigation also can be the potential for users to get lost. The purpose of this study is to determine and analyze the element of adaptive navigation in terms of user model and its baseline which are required to improve existing adaptive navigation model in order to enhance AUI. Results of the study would include the suitable design adaptive navigation model which how they affect the user experience despite their increasingly common use. This is to understand user&#x0027;s preferences, abilities, needs, interests, and knowledge by providing navigation that are most relevant to the user in an effort to shape the user&#x0027;s cognitive load. The results are expected to lead towards comprehensive adaptive navigation model-based, which will be more competent, concise and satisfaction to user.
An Integrated Silicon Photonic Chip for Continuous-Variable Quantum Random Numbers Generator Based on Vacuum Fluctuation

Abstract The deployment of quantum random number generator in fiber optics is strongly limited in real world applications due to the size of the components and their cost. Here we present a quantum random number generator based on vacuum fluctuation using an optical homodyne detection in silicon photonic chip achieving minimum entropy/bit of 8.5 for 10 bits sample. The produced random numbers have passed all NIST randomness tests.
BoostNSift: A Query Boosting and Code Sifting Technique for Method Level Bug Localization

Locating bugs is an important, but effort-intensive and time-consuming task, when dealing with large-scale systems. To address this, Information Retrieval (IR) techniques are increasingly being used to suggest potential buggy source code locations, for given bug reports. While IR techniques are very scalable, in practice their effectiveness in accurately localizing bugs in a software system remains low. Results of empirical studies suggest that the effectiveness of bug localization techniques can be augmented by the configuration of queries used to locate buggy code. However, in most IR-based bug localization techniques, presented by researchers, the impact of the queries’ configurations is not fully considered. In a similar vein, techniques consider all code elements as equally suspicious of being buggy while localizing bugs, but this is not always the case either.In this paper, we present a new method-level, information-retrieval-based bug localization technique called "BoostNSift". BoostNSift exploits the important information in queries by ‘boost’ing that information, and then ‘sift’s the identified code elements, based on a novel technique that emphasizes the code elements’ specific relatedness to a bug report over its generic relatedness to all bug reports. To evaluate the performance of BoostNSift, we employed a state-of-the-art empirical design that has been commonly used for evaluating file level IR-based bug localization techniques: 6851 bugs are selected from commonly used Eclipse, AspectJ, SWT, and ZXing benchmarks and made openly available for method-level analyses. The performance of BoostNSift is compared with the openly-available state-of-the-art IR-based BugLocator, BLUiR, and BLIA techniques. Experiments show that BoostNSift improves on BLUiR by up to 324%, on BugLocator by up to 297%, and on BLIA up to 120%, in terms of Mean Reciprocal Rank (MRR). Similar improvements are observed in terms of Mean Average Precision (MAP) and Top-N evaluation measures.
User-Centered Design of an Augmented Reality Tool for Smart Operator in Production Environment

In the context of Industry 4.0, Operator 4.0 paradigm represents a key factor when dealing with the integration of new digital technologies into smart factories that are suited for workers with different skills, capabilities, and preferences. In this regard, to encourage the introduction of these new digital solutions and achieve high user acceptance, it is fundamental to consider human factors and put the worker at the center of the development process through the adoption of structured design strategies such as user-centered design (UCD) approaches. In this perspective, the paper proposes a novel Augmented Reality (AR) tool for supporting operators at the workplace, in real-time, while performing inspection activities on built products. The proposed tool has been developed according to a user-centered design approach by involving end-users in the various design and testing stages. Preliminary tests have been conducted with representative users on a real case study to assess the usability of the proposed solution. The outcomes are very encouraging and lead us towards further investigations for effective and valid implementation of this AR tool in an industrial scenario.
Ethereum-Based Approach for Agricultural Marketing

Agriculture has been the major source of livelihood for majority of people in India, but agriculture is unable to satisfy the economic needs of people, especially for small-scale farmers, which is causing change in social structure. The purpose of this study is to investigate whether Ethereum blockchain will increase productivity and aid in establishing a sustainable decentralization in the agricultural ecosystem. We built a D-App on top of Ethereum blockchain which simulated basic agricultural market operations, i.e. marketing crops, cooperative creation and management, establishing link between product and from the crop it is made and marketing the same. Using Ethereum, we can quickly build a real life usable application and that would increase the productivity relatively, but from a macro perspective, Ethereum will not guarantee decentralization and, in the long run, it will become hard to coordinate in large scale and will be costly. This study hopes to prove that using digital technology we can create a sustainable decentralized system, we need to create a customized decentralized system for agriculture based on the needs of the farmers by continuously taking feedback and integrating with previous systems.
Towards a Taxonomy of Inline Code Comment Smells

Code comments play a vital role in source code comprehension and software maintainability. It is common for developers to write comments for explaining a code snippet. However, low-quality comments can have a detrimental effect on software quality or be ineffective for code understanding. This study aims to create a taxonomy of inline code comment smells and determine how commonly each smell type occurs in software projects. We conducted a multivocal literature review for defining the initial taxonomy of inline comment smells. Afterward, we manually labeled 899 inline comments from three open-source Java projects. We created a taxonomy of 11 inline code comment smell types and found out that the smells exist in practice with varying degrees.
A Survey of Long-Tail Item Recommendation Methods

Recommender systems represent a critical field of AI technology applications. The core function of a recommender system is to recommend items of interest to users, but if it is only user history-based (purchasing or browsing data), it can only recommend similar products to a user, which makes the user feel fatigued (creating so-called “Information Cocoons”). Besides, transaction data (purchasing or browsing data) in various fields usually follow Pareto distributions. Accordingly, 20% of products are purchased or viewed a greater number of times (short-head items), while the remaining 80% of products are purchased or viewed less frequently (long-tail items). Using the traditional recommendation method, considering only the accuracy of recommendations, the coverage rate is relatively low, and most of the recommended items are short-head items. The long-tail item recommendation method not only considers the recommendation of short-head items but also considers recommending more long-tail items to users, thus improving the coverage and diversity of the recommendation results. Long-tail item recommendation research has become a frontier issue in recommendation systems in recent years. While the current research paper is still scarce, there have been related research achievements in top-level conferences in the field of computers, such as VLDB and IJCAI. Due to the fact that there is no review literature in this field, to allow readers to better understand the research status of the long-tail item recommendation method, this paper summarizes the progress of the research on long-tail item recommendation methods (from clustering-based, which began in 2008, to deep learning-based methods, which began in 2020) and the future directions associated with this research.
Variable motion model for lidar motion distortion correction

Cameras capture every pixel in transient duration for one frame image, while lidars capture every point in relevant long duration for one frame point cloud. When lidars stay static, point cloud will not cause motion distortion, when lidars move, point cloud will have motion distortion, this issue becomes worse with the increase of lidars’ linear velocity and angular velocity. As an inertial space movements’ sensor, IMU (inertial measurement unit) captures motions by a high frequency, which can be utilized to correct lidars motion distortion. In this paper, a variable motion model to correct lidars motion distortion is proposed. Comparing to the constant motion model, the proposed model has better performance on handling distortion caused by varying motion. Experiments results show that the variable motion model has a beneficial influence on distortion correction.
A New Implementation of <scp>ontostep</scp>: Flexible Generation of Ontology and Knowledge Graphs of <scp>express</scp>-Driven Data

Abstract ontostep is a method to translate the STandard for the Exchange of Product Model Data (STEP) schema and its instances to an ontology and knowledge graphs represented in the web ontology language (owl). ontostep models can be integrated with any owl models to enrich their semantics. However, the current implementation has several limitations, mainly in (1) supporting the latest International Organization for Standardization (ISO) 10303 schemas and (2) generating various representation types depending on the purpose of use. We present an improved implementation of ontostep to overcome these limitations. In this article, we demonstrate that the new implementation can successfully translate STEP schemas and instances in a faster and more flexible way, thus furthering the adoption of the full capabilities of ISO 10303. By encoding STEP entities in owl, we facilitate integration with other standards through knowledge graphs.
Production specific language characteristics to improve NLP applications on the shop floor

A variety of assistance functions have been developed based on the rising data availability on the shop floor and increasing capabilities of artificial intelligence applications. An often-mentioned risk is the low data quality, especially in manual text entries for e.g. deviations or defects. This paper aims to evaluate production specific language characteristics to adjust natural language processing applications. To achieve this goal three industry data sets are analyzed, and the findings are used to improve a recommendation engine for previously solved problems.
Automatic Control System of Sluice Based on PLC, MCGS and MODBUS Communication

In order to realize the integrated function of sluice management and control, S7-1200 PLC is used as the master control unit and S7-200 smart is used as the slave control unit. The load, opening degree, water level and other data of the sluice are collected through the special instrument, and the data is transmitted to PLC for storage and processing through MODBUS-RTU protocol. MCGS human-computer interface is designed, and data exchange with PLC through Modbus-TCP protocol is implemented, and the efficiency and intelligence of the sluice operation are greatly improved.
Modelling Usage M-Learning using Mamdani Fuzzy Logic System in along Covid-19 Pandemic at ITS - Indonesia

Fuzzy logic system (FLS) of Mamdani is a method that has the ability to reason similar to human abilities. In this paper is conduct the modelling of usage m-learning. The model systems is able to measure qualitative categories in modeling the usage of mobile-learning during the Covid-19 pandemic. Fuzzy logic system model for “perceptions of student behavior in usage m-learning”, with 4 variables, i.e. (i) Teacher Readiness-TR, (ii) Student Readiness - SR, (iii) Subjective Norms - NS, and (iv) Intention Behavioral - IB. The four variables are indicators that stated in the question instrument. The fourth variables is input modelling system. Each instrument with a grading answered, i.e.: strongly disagree (SA), disagree (D), neutral (N), agree (A), and strongly agree (SA). The model is structured into two subsystems. Output of sub-system 1 is TR, SR, NS and IB variables, and output of sub-system 2 is “Behavior of Usage m-learning (UB)”. Model system is design in 3 scenarios, to choose the best one. The difference of each scenarios is in the interval variations and number of membership functions of fuzzy logic system. The SLF model was tested on 546 respondents. The fuzzy model in 3 scenarios shows the Mean of Average Percentage error (MAPE) value in the range of 5 - 50%, while the test results using SEM (Structural Equation Modelling) software show the MAPE value is 12%.
Localized Incomplete Multiple Kernel <i>k</i>-Means With Matrix-Induced Regularization

Localized incomplete multiple kernel k -means (LI-MKKM) is recently put forward to boost the clustering accuracy via optimally utilizing a quantity of prespecified incomplete base kernel matrices. Despite achieving significant achievement in a variety of applications, we find out that LI-MKKM does not sufficiently consider the diversity and the complementary of the base kernels. This could make the imputation of incomplete kernels less effective, and vice versa degrades on the subsequent clustering. To tackle these problems, an improved LI-MKKM, called LI-MKKM with matrix-induced regularization (LI-MKKM-MR), is proposed by incorporating a matrix-induced regularization term to handle the correlation among base kernels. The incorporated regularization term is beneficial to decrease the probability of simultaneously selecting two similar kernels and increase the probability of selecting two kernels with moderate differences. After that, we establish a three-step iterative algorithm to solve the corresponding optimization objective and analyze its convergence. Moreover, we theoretically show that the local kernel alignment is a special case of its global one with normalizing each base kernel matrices. Based on the above observation, the generalization error bound of the proposed algorithm is derived to theoretically justify its effectiveness. Finally, extensive experiments on several public datasets have been conducted to evaluate the clustering performance of the LI-MKKM-MR. As indicated, the experimental results have demonstrated that our algorithm consistently outperforms the state-of-the-art ones, verifying the superior performance of the proposed algorithm.
Distributed Scalable Association Rule Mining over Covid-19 Data

The worldwide Covid-19 widespread in 2020 has turned into a phenomenon that has shaken human life significantly. It is widely recognized that taking faster measurements is crucial for monitoring and preventing the further spread of COVID-19. The advent of distributive computing frameworks provides one efficient solution for the issue. One method uses non-clinical techniques, such as data mining tools and other artificial intelligence technologies. Spark is a widely used framework and accepted by the big data community. This research used a cross-country Covid-19 dataset to assess the performance of the Apriori and FP-growth through different components of Spark (different numbers of cores and transactions). This involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. This research aims to understand the difference between FP-growth and Apriori and find the ideal parameters of Spark that can improve the performance by adding nodes.
Abnormal Behavior Detection in Crowd Scene Using YOLO and Conv-AE

This paper proposes a weighted convolutional autoencoder (Conv-AE) and a novel regularity score based on the results of You Only Look Once (YOLO) network to detect abnormal behavior in crowd scenarios. The weighted Conv-AE extracts spatial features of video frames. In the training process, a weighted loss function is proposed based on the YOLO detection results, which emphasizes the foreground part, and thus overcomes the impact of complex background. In addition, a novel regularity score is put forward in the anomaly detection process. The regularity score takes into account the three factors of reconstruction errors obtained from weighted Conv-AE, speed information and category of objects detected by YOLO. Three scores respectively based on these factors are integrated to obtain anomaly detection results. The experimental results on UCSD ped1 and ped2 dataset verify that the proposed method achieves better performance than the most of semi-supervised methods.
Tracking Nonlinear Model Predictive Control for Obstacle Avoidance

This work proposes a single-layer nonlinear finite- horizon optimal control strategy to solve the autonomous navigation problem while providing obstacle avoidance feature in cluttered environments with unknown obstacles. Inspired by the tracking model predictive control framework, the central idea of including artificial variables into the control problem is considered. This approach allows to address the problem of combining different objectives and provide the closed- loop system with an enlarged domain of attraction and with feasibility insurances in the face of any changing reference. This idea is considered together with an avoidance cost functional to establish the basis of the obstacle avoidance feature of the strategy, while providing feasibility insurance in the presence of pop-up obstacles. Finally, numerical results for a quadrotor UAV are provided to corroborate the proposed strategy.
A new robust Kalman filter with measurement loss based on mixing distribution

A new robust Kalman filter (KF) based on mixing distribution is presented to address the filtering issue for a linear system with measurement loss (ML) and heavy-tailed measurement noise (HTMN) in this paper. A new Student’s t-inverse-Wishart-Gamma mixing distribution is derived to more rationally model the HTMN. By employing a discrete Bernoulli random variable (DBRV), the form of measurement likelihood function of double mixing distributions is converted from a weighted sum to an exponential product, and a hierarchical Gaussian state-space model (HGSSM) is therefore established. Finally, the system state, the intermediate random variables (IRVs) of the new STIWG distribution, and the DBRV are simultaneously estimated by utilizing the variational Bayesian (VB) method. Numerical example simulation experiment indicates that the proposed filter in this paper has superior performance than current algorithms in processing ML and HTMN.