Reviewerly/exHarmony huggingface dataset:
Transforming Haptic Storyboards into Diagrammatic Models: The Scene2Model Tool

Haptic storyboarding tools supporting storytelling as a Design Thinking approach, enable early exploration and validation of design alternatives regarding services, new product (features), innovative processes and disruptive business models.They do however not communicate the exact meaning and the importance of each object nor do they show relationships between them.Yet when aiming to materialize an innovative idea these aspects need to be unambiguously described.Diagrammatic models play an essential role here as they capture different aspects of the problem.When computed by means of software they also explicitly show details which in haptic storyboards users implicitly fill with their own world-understanding, thus fostering a clear and transparent representation of the problem space.In addition, diagrammatic models can be enriched by semantics and subsequently be machine-queried, -analysed and -processed.The paper at hand shows the DIGITRANS project approach for an automated transformation of haptic storyboards into diagrammatic models and their provision in a computer-aided design environment.
Permuted Pattern Matching Algorithms on Multi-Track Strings

A multi-track string is a tuple of strings of the same length. Given the pattern and text of two multi-track strings, the permuted pattern matching problem is to find the occurrence positions of all permutations of the pattern in the text. In this paper, we propose several algorithms for permuted pattern matching. Our first algorithm, which is based on the Knuth–Morris–Pratt (KMP) algorithm, has a fast theoretical computing time with O ( m k ) as the preprocessing time and O ( n k log σ ) as the matching time, where n, m, k, σ , and denote the length of the text, the length of the pattern, the number of strings in the multi-track, the alphabet size, and the number of occurrences of the pattern, respectively. We then improve the KMP-based algorithm by using an automaton, which has a better experimental running time. The next proposed algorithms are based on the Boyer–Moore algorithm and the Horspool algorithm that try to perform pattern matching. These algorithms are the fastest experimental algorithms. Furthermore, we propose an extension of the AC-automaton algorithm that can solve dictionary matching on multi-tracks, which is a task to find multiple multi-track patterns in a multi-track text. Finally, we propose filtering algorithms that can perform permuted pattern matching quickly in practice.
Online Siamese Network for Visual Object Tracking

Offline-trained Siamese networks are not robust to the environmental complication in visual object tracking. Without online learning, the Siamese network cannot learn from instance domain knowledge and adapt to appearance changes of targets. In this paper, a new lightweight Siamese network is proposed for feature extraction. To cope with the dynamics of targets and backgrounds, the weight in the proposed Siamese network is updated in an online manner during the tracking process. In order to enhance the discrimination capability, the cross-entropy loss is integrated into the contrastive loss. Inspired by the face verification algorithm DeepID2, the Bayesian verification model is applied for candidate selection. In general, visual object tracking can benefit from face verification algorithms. Numerical results suggest that the newly developed algorithm achieves comparable performance in public benchmarks.
A mapping study about the standard ISO/IEC29110

The ISO/IEC29110 has been studied since its release in 2011, and its impact and evaluation over the recent years have been quite diverse. This standard is structured in five parts describing the business terms, the main Very Small Entities (VSE) profile concepts, process assessment guidelines, specification of all the generic profile group, and implementation management and engineering guide for entry and basic profiles. The main purpose of this work is to provide an analysis of the research carried out about the ISO/IEC 29110 during the last ten years, and the literature that has developed around it. Literature is analyzed by using the traditional mapping study of the ISO/IEC29110 and its parts. All these studies are categorized in a set of topics where authors have been contributing. This work helps us on the identification of the main research topics within the primary studies. The mapping study is conducted as a traditional systematic mapping with a categorization of the primary studies. The main search is enhanced with additional searches for each member of the ISO/IEC 29110 series. A search strategy is defined to conduct this mapping study. 184 papers were retrieved from the literature and selected as primary studies. Our study identifies the reference studies in this area, it characterizes them, and identifies which aspects have been treated. The results of this mapping reveal that ISO/IEC 29110 has been used in a broad range of small contexts, and the main contributions are basically from research experiences during the recent last ten years. The literature around this standard is classified based on a well-known classification schema, the activity around this standard, and what types of studies have been carried out. Research topics are diverse, and we have identified the research methods used by the primary studies. As conclusion, more research and experimental outcomes are needed in order to observe how VSEs behave under specific circumstances.
The photocurrent generated by photon replica states of an off-resonantly coupled dot-cavity system

Abstract Transport properties of a quantum dot coupled to a photon cavity are investigated using a quantum master equation in the steady-state regime. In the off-resonance regime, when the photon energy is smaller than the energy spacing between the lowest electron states of the quantum dot, we calculate the current that is generated by photon replica states as the electronic system is pumped with photons. Tuning the electron-photon coupling strength, the photocurrent can be enhanced by the influences of the photon polarization, and the cavity-photon coupling strength of the environment. We show that the current generated through the photon replicas is very sensitive to the photon polarization, but it is not strongly dependent on the average number of photons in the environment.
An FPGA-Based Real-Time Human Eye Pupil Detection System Using E2V Smart Camera

Threshold based pupil detection algorithm was found tobe most efficient method to detect human eye. An implementation of a real-time system on an FPGA board to detect and track a human's eye is the main motive to obtain from proposed work. The Pupil detection algorithm involved thresholding and image filtering. The Pupil location was identified by computing the center value of the detected region. The proposed hardware architecture is designed using Verilog HDL and implemented on aAltera DE2 cyclone II FPGA for prototyping and logic utilizations are compared with Existing work. The overall setup included Cyclone II FPGA, a E2V camera, SDRAM and a VGA monitor. Experimental results proved the accuracy and effectiveness of the hardware realtime implementation as the algorithm was able to manage various types of input video frame. All calculation was performed in real time. Although the system can be furthered improved to obtain better results, overall the project was a success as it enabled any inputted eye to be accurately detected and tracked.
An Enhanced Differential Evolution Algorithm with Multi-mutation Strategies and Self-adapting Control Parameters

Differential evolution (DE) is a stochastic population-based optimization algorithm first introduced in 1995.It is an efficient search method that is widely used for solving global optimization problems.It has three control parameters: the scaling factor (F), the crossover rate (CR), and the population size (NP).As any evolutionary algorithm (EA), the performance of DE depends on its exploration and exploitation abilities for the search space.Tuning the control parameters and choosing a suitable mutation strategy play an important role in balancing the rate of exploration and exploitation.Many variants of the DE algorithm have been introduced to enhance its exploration and exploitation abilities.All of these DE variants try to achieve a good balance between exploration and exploitation rates.In this paper, an enhanced DE algorithm with multi-mutation strategies and self-adapting control parameters is proposed.We use three forms of mutation strategies with their associated self-adapting control parameters.Only one mutation strategy is selected to generate the trial vector.Switching between these mutation forms during the evolution process provides dynamic rates of exploration and exploitation.Having different rates of exploration and exploitation through the optimization process enhances the performance of DE in terms of accuracy and convergence rate.The proposed algorithm is evaluated over 38 benchmark functions: 13 traditional functions, 10 special functions chosen from CEC2005, and 15 special functions chosen from CEC2013.Comparison is made in terms of the mean and standard deviation of the error with the standard "DE/rand/1/bin" and five state-of-theart DE algorithms.Furthermore, two nonparametric statistical tests are applied in the comparison: Wilcoxon signed-rank and Friedman tests.The results show that the performance of the proposed algorithm is better than other DE algorithms for the majority of the tested functions.
Anomaly Detection Approach for Urban Sensing Based on Credibility and Time-Series Analysis Optimization Model

Urban sensor networks often consist of a large number of low-cost sensor nodes. Due to the constrained resource devices and hazardous deployment, urban sensing is vulnerable to interference and destruction of external factors or the impact of external environmental emergencies. Abnormal data, outliers, or anomalies have affected the utility in various domains seriously. Timely and accurate detection of unexpected events, monitoring of network performance, and anomaly detection of data flow are of great significance to improve the decision-making ability of the system. In this paper, we propose an anomaly detection method for urban sensing based on sequential data and credibility. First, based on Bayesian methods, a reputation model is established for the selection of credible sample points. Second, aiming at the problem that the threshold range is difficult to determine in the traditional method, the pivot quantity is defined by using the median of the credible sample, and the confidence interval can be estimated to quantify the deviation degree of the sensor data. Finally, an anomaly data identification and source verification approach is proposed to distinguish errors and events accurately. The evaluation results on both the detection rate and the false positive rate demonstrate a better performance of our approach than the other existing methods.
Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network

The aim of machine learning is to develop algorithms that can learn from data and solve specific problems in some context as human do. This paper presents some machine learning models applied to the intrusion detection system in WiFi network. Firstly, we present an incremental semisupervised clustering based on a graph. Incremental clustering or one-pass clustering is very useful when we work with data stream or dynamic data. In fact, for traditional clustering such as K-means, Fuzzy C-Means, DBSCAN, etc., many versions of incremental clustering have been developed. However, to the best of our knowledge, there is no incremental semisupervised clustering in the literature. Secondly, by combining a K-means algorithm and a measure of local density score, we propose a fast outlier detection algorithm, named FLDS. The complexity of FLDS is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>O</mml:mi><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:msup><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mn>1.5</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mfenced></mml:mrow></mml:math> while the results obtained are comparable with the algorithm LOF. Thirdly, we introduce a multistage system-based machine learning techniques for mining the intrusion detection data applied for the 802.11 WiFi network. Finally, experiments conducted on some data sets extracted from the 802.11 networks and UCI data sets show the effectiveness of our new proposed methods.
The Development of STEM Mobile Learning Package Ekosistem

This study aims to: develop and test the validity, legibility and effectiveness of STEM Mobile Learning Package ecosystems on studentsâ€™ science and technology literacy using R &amp; D research design. STEM Mobile Learning Package was validated by media experts and material experts. The level of legibility is measured by questionnaire through the results of a small-scale trial of a Biology student class. The effectiveness of the application was measured in wide-scale test for biology and natural science students. Data were analyzed using descriptive quantitative.The results showed the the validity on learning package from experts showed valid in 83,6%. Some revisions are needed especially on video aspect which will be better filled with not only text but also voice. Base on data collected from 47 students who have completely filled 12 statements in questionnaire, about 78,7% students stated they could understand the questions very well. Most of students (97,8%) agreed that the use of STEM enhanced their science literacy. Percentage of students that agreed on 12 statements ranged from 74,5% to 100%. Thus those result indicated a high level of legibility Although learning packages can be studied independently, certain topics still requiredto be directly discussed. This learning package is effective on student scientific literacy ranging from 64.6 to 98.6. While the highest achievement of student technology literacy is 92 with an average of 70.32. This study concludes that STEM Mobile Learning Package Ecosystem has good validity and legibility, as well as effective on studentsâ€™ science and technology literacy.
A Fault-Tolerant Data Fusion Method of MEMS Redundant Gyro System Based on Weighted Distributed Kalman Filtering

The application of the Micro Electro-mechanical System (MEMS) inertial measurement unit has become a new research hotspot in the field of inertial navigation. In order to solve the problems of the poor accuracy and stability of MEMS sensors, the redundant design is an effective method under the restriction of current technology. The redundant data processing is the most important part in the MEMS redundant inertial navigation system, which includes the processing of abnormal data and the fusion estimation of redundant data. A developed quality index of the MEMS gyro measurement data is designed by the parity vector and the covariance matrix of the distributed Kalman filtering. The weight coefficients of gyros are calculated according to this index. The fault-tolerant fusion estimation of the redundant data is realized through the framework of the distributed Kalman filtering. Simulation experiments are conducted to test the performance of the new method with different types of anomalies.
High ISO JPEG Image Denoising by Deep Fusion of Collaborative and Convolutional Filtering

Capturing images at high ISO modes will introduce much realistic noise, which is difficult to be removed by traditional denoising methods. In this paper, we propose a novel denoising method for high ISO JPEG images via deep fusion of collaborative and convolutional filtering. Collaborative filtering explores the non-local similarity of natural images, while convolutional filtering takes advantage of the large capacity of convolutional neural networks (CNNs) to infer noise from noisy images. We observe that the noise variance map of a high ISO JPEG image is spatial-dependent and has a Bayer-like pattern. Therefore, we introduce the Bayer pattern prior in our noise estimation and collaborative filtering stages. Since collaborative filtering is good at recovering repeatable structures and convolutional filtering is good at recovering irregular patterns and removing noise in flat regions, we propose to fuse the strengths of the two methods via deep CNN. The experimental results demonstrate that our method outperforms the state-of-the-art realistic noise removal methods for a wide variety of testing images in both subjective and objective measurements. In addition, we construct a dataset with noisy and clean image pairs for high ISO JPEG images to facilitate research on this topic.
Building Capacity for Cyberbiosecurity Training

Cyberbiosecurity lies at the intersection of cybersecurity and biosecurity and addresses the protection of valuable biological material and associated information. As an emerging concept, cyberbiosecurity requires the integration of training strategies targeted to both current and future professionals; as well as an increased awareness in the wider stakeholder community. As the discrete discipline of cyberbiosecurity continues to develop, initial training efforts are likely to include workshops and specialized training that bridge the disciplines of information technology (IT) and life sciences. Potential threats, risks, and vulnerabilities will be defined, cooperative relationships formed, and collaborative solutions developed. As the scope of the training framework for assessing potential threats is adapted to various audiences, in-service trainings will ensure awareness and understanding of threats relevant to specific industries. This framework may also be incorporated into existing curricula across IT and science fields. The scope of potential threats is vast, and eventual specialization will likely fall within the realm of IT professionals, who carry the capability for action. In this paper, we identify stakeholders in the development of cyberbiosecurity training; discuss current training methods, educational requirements, and credentialing for professionals in cybersecurity, biosecurity, and life sciences; suggest mechanisms for integration of cyberbiosecurity training into existing training approaches; and discuss potential for future development of specialized professionals.
Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback

In many anomaly mining scenarios, a human expert verifies the anomaly at-the-top (as ranked by an anomaly detector) before they move on to the next. This verification produces a label—true positive (TP) or false positive (FP). In this work, we show how to leverage this label feedback for the top-1 instance to quickly re-rank the anomalies in an online fashion. In contrast to a detector that ranks once and goes offline, we propose a detector called OJRank that works alongside the human and continues to learn (how to rank) on-the-job, i.e., from every feedback. The benefits OJRank provides are two-fold; it reduces (i) the false positive rate by ‘muting’ the anomalies similar to FP instances; as well as (ii) the expert effort by elevating to the top the anomalies similar to a TP instance. We show that OJRank achieves statistically significant improvement on both detection precision and human effort over the offline detector as well as existing state-of-the-art ranking strategies, while keeping the per feedback response time (to re-rank) well below a second.
Code Search Based on Alteration Intent

Code search is to retrieve the method according to the user needs.Many people think of the query as the only one to reflect the user needs.So they start with the query and find the synonyms, semantically similar to the query terms, for query expansion.However, they overlook a fact: the retrieved methods still need to be altered because it does not meet the user needs directly.This implies that the alteration made in the retrieved methods also reflects the user needs.In this paper, we start with the alteration intent (the possible alterations after retrieving methods) and propose to predict the alteration intent and use it for query expansion.The experiment results show that our approach outperforms CodeHow, the approach to use APIs for query expansion, by 59.8% with a precision score of 0.815.
E-government recommendation algorithm based on probabilistic semantic cluster analysis in combination of improved collaborative filtering in big-data environment of government affairs

In order to solve the problem of information overload and satisfy their individual needs in the use of e-government system, a personalized e-government recommendation algorithm combining probabilistic semantic cluster analysis and collaborative filtering is proposed to recommend the items most associated with users. Firstly, the static basic attributes and dynamic behavior attributes of the users and items are modeled by probabilistic semantic cluster analysis separately. Secondly, based on the user’s historical record and attribute similarity for user community discovery, the user set most similar to the target user is pre-filtered by collaborative filtering algorithm to improve the diversity of the recommended results and reduce the computational amount of the core recommendation process. Finally, the associated sequence mining of the items was taken full account of the business characteristic of e-government, and the item sequence mining with time dimension was added to further improve the accuracy of the recommended results. The simulation experiments were carried out with the information after desensitization of users on the Spark platform. The experimental results show that our proposed e-government recommendation algorithm is suitable for the recommendation of items with sequence or process characteristic and has higher recommendation accuracy compared with traditional recommendation algorithms. The multi-community attribution factor of the user increases the diversity of the recommended results.
The Weighted Fractional Fourier Transform and Its Application in Image Encryption

With the rapid development of information, the requirements for the security and reliability of cryptosystems have become increasingly difficult to meet, which promotes the development of the theory of a class of fractional Fourier transforms. In this paper, we present a review of the development and applications of the weighted fractional Fourier transform (WFRFT) in image encryption. Relationships between the algorithms are established using the generalized permutation matrix group in theoretical analysis. In addition, the advantages and potential weaknesses of each algorithm in image encryption are analyzed and discussed. It is expected that this review will provide a clear picture of the current developments of the WFRFT in image encryption and may shed some light on future developments.
REALISTIC NATURAL INTERACTION WITH VIRTUAL STATUES IN X-REALITY ENVIRONMENTS

Abstract. Augmented, Virtual, and Mixed Reality have successfully transitioned from the laboratory environment to becoming mainstream technologies available to almost everyone, mainly due to important advancements in terms of hardware. These technologies, along with recent advancements in Artificial Intelligence, have the potential to deliver compelling experiences. In the Cultural Heritage domain, they can be used to achieve natural interaction with virtual statues, making visitors feel as if physical statues “come to life” and can engage in dialogue with them. This paper presents the fundamental technological components towards this vision and how they can be orchestrated through the description of a conceptual architecture and a use case example. Open research issues are also discussed, providing a roadmap for future research in this area.