Reviewerly/exHarmony huggingface dataset:
A Study on Convolutional Neural Networks with Active Video Tubelets for Object Detection and Classification

Rajkumar,R. Arunnehru,J.Convolutional neural networks are a powerful learning model inspired from biological concept of neurons. This deep learning model allows us to replicate the complex neural structure seen in living beings to be applied on data sets and to structure convulsions consisting of several layers. A study on convolutional neural networks have been proven to be an effective class of models for object recognition, taking those results into consideration we intend to apply convolutional neural networks for video classification in two different ways. Generalization of the results obtained by the application of convolutional neural networks on existing data sets for videos, namely Sports 1-M and YouTube object data set (YTO) and their implementation of two distinct CNNs.
Visualization Methods for Spacecraft Telemetry Data Using Change-Point Detection and Clustering

For secure operation of spacecraft, automatic or assistive health monitoring systems utilizing telemetry data are important. However, it is difficult to utilize them comprehensively because they consist of myriad heterogeneous variables. Although various monitoring systems focusing on only a few variables or homogeneous variables have been suggested, a definitive method to deal with the relationship among multiple heterogeneous variables has not yet. This paper proposes a new visualization framework that aims to show the correlation rules underlying multiple variables of spacecraft telemetry data. The proposed framework consists of a change-point detection algorithm based on subspace identification, clustering methods using dimensionality reduction, and a visualization method using heatmaps. In experiments conducted with real telemetry data obtained from JAXA spacecraft SDS-4, the proposed framework demonstrated effective visualizations that reflected the correlations among variables expected from mechanical characteristics of the satellite. Despite differences in scales and/or units, this framework succeeded in visualizing dynamic correlations not only among continuous variables but also among continuous and discrete variables. This framework can be utilized as an initial stage of anomaly detection focusing on the relationship among multiple variables, as well as a method to perceive the overall state of the spacecraft at a glance.
Artificial Bee Colony Algorithm Based on Uniform Local Search

Although Artificial Bee Colony (ABC) algorithm is simple and efficient, it also has some disadvantages too. For example, the ABC is good at exploration but poor at exploitation and easily falls into local optimum. In order to overcome these shortcomings and improve the efficiency of the algorithm, the Uniform Local Search Artificial Bee Colony (UGABC) algorithm has been proposed in this paper. The algorithm greatly improves the exploitation ability. For the purpose of comparison, we used four algorithms to experiment. The experimental results show that the UGABC has the best accuracy and the fastest convergence rate among four algorithms.
Real-time Tracker with Fast Recovery from Target Loss

In this paper, we introduce a variation of a state-of-the-art real-time tracker (CFNet), which adds to the original algorithm robustness to target loss without a significant computational overhead. The new method is based on the assumption that the feature map can be used to estimate the tracking confidence more accurately. When the confidence is low, we avoid updating the object's position through the feature map; instead, the tracker passes to a single-frame failure mode, during which the patch's low-level visual content is used to swiftly update the object's position, before recovering from the target loss in the next frame. The experimental evidence provided by evaluating the method on several tracking datasets validates both the theoretical assumption that the feature map is associated to tracking confidence, and that the proposed implementation can achieve target recovery in multiple scenarios, without compromising the real-time performance.
Optimum shape synthesis of path generating mechanisms using wavelet descriptors

The discrete wavelet transform is a well-known mathematical tool for shape recognition. In this study, a wavelet descriptor with two shape signatures, i.e., centroid distance and triangular centroid area, is developed for shape synthesis for path generation of planar mechanisms. This study is the first to apply the discrete wavelet transform for direct shape synthesis with the two-phase synthesis method for path generation problems. The problem of invariance in the similarity transform for the proposed wavelet descriptor is introduced. The proposed wavelet descriptor contains not only the approximation coefficients but also all the detail coefficients up to a prescribed decomposition level. A geometric-based approach which has been discussed in the literature is used for the scale-rotation-translation synthesis. The differential evolutionary algorithm with a combined mutation strategy is used to solve the optimization problems for shape synthesis. The proposed wavelet-based shape synthesis method is applied to solve five difficult path generation problems for the special paths generated by geared five-bar mechanisms discussed in the literature. The effects of the Haar, Daubechies-1, Daubechies-2 and Daubechies-3 wavelets as well as the number of levels from 1 to 3 on the synthesis results are investigated. Finally, the proposed wavelet-based shape synthesis method successfully solves the path generation problems. The generating coupler curves approximate the desired paths quite well except for one of the five paths. If only the approximation coefficients or only the detail coefficients are employed, the obtained optimum shape may appear similar but does not match the prescribed pattern, e.g. has an incorrect aspect ratio. The most suitable decomposition level depends on the nature of discrete shape signatures and the type of wavelet. The present synthesis results are compared to those obtained with other methods described in the literature.
Threshold-directed signature scheme based on hybrid number theoretic problems

Directed signature is a type of function-based signature with the property that the signature only can be verified by a designated verifier and at certain times, the verifier should be able to convince anyone about the validity of the signature without revealing any secret information about the signature to the public. Taking into consideration the involvement of group decision making, some threshold directed signature schemes based on single number theoretic problems, such as integer factorization, discrete logarithm problem, and elliptic curve discrete logarithm problem, have been developed by cryptographers. Although the single-problem-based schemes are still invincible because there is still no cryptanalyst to find the solution to the problems, in the future, if the enemy or attacker manages to get the polynomial algorithm to solve the single problems, the schemes will no longer be practiced and applied. For such reason, in this paper, we propose a new threshold-directed signature scheme based on integer factorization and discrete logarithm problems. The advantage of our scheme is based on the assumption that it is very unlikely to solve two hard number theoretic problems simultaneously. We also show that our scheme is secured against some cryptographic attacks and also significantly efficient compared with threshold signature scheme based on single problem.
From Stigma to Objects of Desire: Participatory Design of Interactive Jewellery for Deaf Women

Quietude [1] is an EU funded project that aims at creating interactive fashion accessories and jewellery for deaf women to experience and make sense of sounds. Through Participatory Design, a series of prototypes were developed to scaffold design inquiry and develop human-centred solutions. Deaf women and women with different levels of hearing impairment were involved in various activities throughout the design process, from in depth interviews, inspirational workshops, and co-design activities, through to body storming with experienciable prototypes. Each design iteration consolidated the theoretical grounding and the definition of new forms of design support. The latest suite of accessories addresses a number of needs of deaf people, from functional needs like ambient awareness and safety, to hedonic needs like aesthetics, curiosity, possibility to express a personal sense of style when accessorizing the body.
On Minimality Attack for Privacy-Preserving Data Publishing

Preserving privacy while publishing data is an important requirement in many practical applications. Information about individuals and/or organizations are collected from various sources which are being published, after applying some kinds pre-processing logic, that may lead to leaking sensitive information of individual. Anonymization is a widely used technique to suppress or generalize data so that essence of data can be hidden to a certain degree. In this paper, we present an analysis of some well-known anonymization-based privacy preserving schemes such as k-anonymity and l-diversity to show how these schemes suffer from the minimality attack that can lead to potential information leakage from the published data. We present a mitigation mechanism, NoMin algorithm, to address the minimality attack in anonymization-based privacy preserving schemes. The proposed NoMin algorithm uses random sample of spurious records in an equivalence class of actual records such that an adversary cannot figure out an individual from the published data. The analysis and experimental results of the proposed algorithm illustrate its strengths, practicality and limitations with respect to minimality attacks on anonymization-based published data.
Dissemination, Access, Preservation: A Case Study of Publications from the Organic Agriculture Research and Extension Initiative

The Organic Agriculture Research and Extension Initiative, administered by USDA-NIFA, is the major federal funder of organic agricultural research. Analysis of 733 publications produced during the initiative’s first five years explored the dissemination of this research, and accessibility to and preservation of the publications. Publications associated with conferences (e.g., abstracts) were most numerous (36%). Many publications (69%) were openly accessible online in 2017 but fewer than 10% of these appeared in a stable digital repository. In four of the eight publication categories, access disappeared over time. No program exists to systematically collect and preserve these outputs of organic agricultural research.
Comprehensive evaluation of the appearance design of rescue engineering vehicle based on fuzzy mathematics

Purpose Aim at demonstrating the superiority of multifunctional bionic rescue engineering vehicles through fuzzy mathematics comprehensive evaluation. Methods take the design scheme and two selected types of rescue vehicles as examples, analyze the three rescue vehicles comprehensively through the fuzzy mathematics evaluation method constructed by the membership function, and analyze the superiority of the product under different living conditions by adjusting the weights of the evaluation indicators. Conclusion The calculation of the examples verifies the superiority of the multifunctional bionic rescue vehicle under various living conditions, and the superiority will become more obvious with the decrease of the production cost. The comprehensive evaluation method based on fuzzy mathematics can optimize the design scheme and improve the rationality and scientificity of the designer’s scheme selection.
Understanding Adversarial Space Through the Lens of Attribution

Neural networks have been shown to be vulnerable to adversarial perturbations. Although adversarially crafted examples look visually similar to the unaltered original image, neural networks behave abnormally on these modified images. Image attribution methods highlight regions of input image important for the model’s prediction. We believe that the domains of adversarial generation and attribution are closely related and we support this claim by carrying out various experiments. By using the attribution of images, we train a second neural network classifier as a detector for adversarial examples. Our method of detection differs from other works in the domain of adversarial detection [3, 4, 10, 13] in the sense that we don’t use adversarial examples during our training procedure. Our detection methodology thus is independent of the adversarial attack generation methods. We have validated our detection technique on MNIST and CIFAR-10, achieving a high success rate for various adversarial attacks including FGSM, DeepFool, CW, PGD. We also show that training the detector model with attribution of adversarial examples generated even from a simple attack like FGSM further increases the detection accuracy over several different attacks.
Active Aging by Continuous Learning: A Training Environment for Cultural Visits

The “Città Educante” project aims at radically rethinking the learning environments through the application of the most advanced ICT technology. Among the different aspects of the project, the LECTurE module aims, by exploiting artificial intelligence techniques, at proposing contextualizedCesta, Amedeo lessons, as well asCortellessa, Gabriella interaction requests, to the involved users. In particular the lesson’s content, is specifically tailored for the older adults and personalized by taking into account users’ psycho-physiological aspects as well as geo-localization informationDe Benedictis, Riccardo and temporal constraints. In this paper, after a generic introduction to the “Città Educante” project, the LECTurE module is presented and instantiated in two relevant use cases: the on-site training, in whichFracasso, Francesca the system is used as a support to the classical teaching methodologies within a classroom, and the distributed training, in which the technology aims at moving and animating the teaching experience, also, “outside the classroom”, with additional stimuli for the users during a practical experience (e.g., a real visit in a museum to complement a theoretical art history lesson). The paper describes, then, the choices made for the realization of a first prototype and its embodiment into a concrete scenario which, by implementing a “treasure hunt” like game, aims at fostering older adults’ physical and cognitive activity.
An Investigation into Students Responses to Various Phishing Emails and Other Phishing-Related Behaviours

Reports continue to testify that the problem of phishing remains pertinent in many industries today. This descriptive study investigated 126 university students' responses to various forms of phishing emails and other security-related behaviours through a self-designed questionnaire. The majority of the participants reported having an average experience in using computers and the Internet. Most participants chose to respond to phishing emails purportedly originating from Facebook and university contexts thus supporting that users are more likely to fall victim to phishing if the message is of interest or has relevance to their context. However, susceptibility was significantly reduced when users were presented with emails that imitate well-known South African banking institutions. This may suggest that users are either aware of phishing schemes that impersonate banking institutions, or they feel uncomfortable giving up personal information when they feel more at risk to be affected financially. The results from this study offer insights on behavioural aspects that can assist the information security community in designing and implementing more efficient controls against phishing attacks. Furthermore, this study suggests that researchers should consider exploring the behaviour of social media users as they can be vulnerable to phishing.
Ontology-Based Context Agent for Building Energy Management Systems

Replacing existing electrical grids by future smart grids opens a several opportunities for energy-efficient operation of buildings and cities as well as improved coordination of energy load and supply. Future energy systems will be managed by smart controllers. These smart devices will have to interoperate, they need to exchange information with each other in order to cooperate over complex control tasks. Current data and communication technology provides a suitable basis for the bidirectional flow of information between smart buildings and other smart grid stakeholders. Interoperability will only be achieved when Smart Grid appliances share common semantics on the data they exchange. However, a common concept of shared real energy state and knowledge is essential in order to unify heterogeneous grid contexts, incorporate information of smart grid participants, and process this information in building energy management systems. In this work, a context agent based on an OWL ontology is presented that enables semantic representation of knowledge for interaction between building energy management systems and smart grids and end prosumers. A well-proven methodology is used to develop this ontology. Furthermore, the ontology application into building energy management systems and smart grid environments is figured, and the functional capabilities of this approach are shown.
Detecting Parallel-Moving Objects in the Monocular Case Employing CNN Depth Maps

This paper presents a method for detecting independently moving objects (IMOs) from a monocular camera mounted on a moving car. We use an existing state of the art monocular sparse visual odometry/SLAM framework, and specifically attack the notorious problem of identifying those IMOs which move parallel to the ego-car motion, that is, in an ‘epipolar-conformant’ way. IMO candidate patches are obtained from an existing CNN-based car instance detector. While crossing IMOs can be identified as such by epipolar consistency checks, IMOs that move parallel to the camera motion are much harder to detect as their epipolar conformity allows to misinterpret them as static objects in a wrong distance. We employ a CNN to provide an appearance-based depth estimate, and the ambiguity problem can be solved through depth verification. The obtained motion labels (IMO/static) are then propagated over time using the combination of motion cues and appearance-based information of the IMO candidate patches. We evaluate the performance of our method on the KITTI dataset.
Functional Modeling of Human Perceptual Guidance using Constraint Set Analysis

Humans exhibit a wide range of adaptive and robust dynamic motion behavior that is yet unmatched by computational autonomous control systems for real-time behavior generation in cluttered environments. Recent work suggests that task structure learning and ecological cognition in the form of perceptual guidance enables this performance. This work first describes an embedded agent-environment perceptual control model, and uses it to investigate experimental vehicle guidance behavior. Results show that constraint transitions within the embedded model indicate changes in an agent’s perceptual guidance control mode. These mode transitions divide perception and behavior data into elemental segments of interaction, revealing the structure in how humans organize their perceptions and actions, and the specific perceptual guidance relationships that generate motion.
Quantum‐inspired optimisation algorithm via energy equipartition and elitists preservation

Optimisation problems exist widely in real-life applications. A quantum-inspired optimisation algorithm under the harmonic oscillator well transforms the function optimisation into solving the ground state wave function through the transformation of the Schrödinger equation. A novel approach using energy equipartition for the exchange of energy information is present to construct the candidate solutions. Elitists preservation is utilised to preserve dominant energy information for the improvement of search efficiency. Experiments on benchmark test functions for the 30D problem show that the proposed approaches are more effective than compared algorithms.
A Quantitative and Qualitative Characterization of k-mer Based Alignment-Free Phylogeny Construction

The rapidly growing volume of genomic data, including pathogens, both invites exploration of possible phylogenetic relationships among unclassified organisms, and challenges standard techniques that require multiple sequence alignment. Further, the ability to probe variations in selection pressure e.g. among viral outbreaks, is an important characterization of the life of a virus in its biological reservoir. In this paper, we derived the probability distribution of k-mer alignment lengths between random sequences for a given optimized score to quantify the probability that a given alignment was not better than chance, and applied it to Human Papiloma Virus (HPV), primate mtDNA, and Ebola. Even for highly variable HPV types, the number of k-mers required to significantly distinguish an alignment of related genomes from random sequences was reduced from 64 for 1-mers to 6 for 3-mers and 4 for 4-mers, indicating k-mers provide sufficient specificity to be able to characterize differences in sequences by their k-mer frequencies, allowing distances based on the k-mer frequencies to proxy for evolutionary distance. We computed mtDNA coding sequence and Ebola phylogeny construction. Primate mtDNA coding region k-mer UPGMA phylogenies reproduced most of the expected primate phylogeny. The Mantel test, applied to RAxML and Bayesian phylogenetic distances between Ebola samples versus 3-mer frequency distances, was highly significant ( $$\le 1\times 10^{-5}$$ ). We characterized differences in selection pressure between coding and non-coding regions, and of selection in early cell cycle vs. late genes in Ebola. Coding versus non-coding regions showed evidence of purifying selection, while the early vs. late cell cycle proteins showed differences with late cycle proteins resembling influenza like immunological response, noting the g-proteins are among the late genes.
A Parallel Incremental Frequent Itemsets Mining IFIN+: Improvement and Extensive Evaluation

In this paper, we propose a shared-memory parallelization solution for the Frequent Itemsets Mining algorithm IFIN, called IFIN+. The motivation for our work is that commodity processors, nowadays, are enhanced with many physical computational units, and exploiting full advantage of this is a potential solution to improve computational performance in single-machine environments. The portions in the serial version are improved in means which increases efficiency and computational independence for convenience in designing parallel computation with Work-Pool model, be known as a good model for load balance. We conducted extensive experiments on both synthetic and real datasets to evaluate IFIN+ against its serial version IFIN, the well-known algorithm FP-Growth and other two state-of-the-art ones, FIN and PrePost+. The experimental results show that the running time of IFIN+ is the most efficient, especially in the case of mining at different support thresholds within the same running session. Compare to its serial version, IFIN+ performance is improved significantly.