Reviewerly/exHarmony huggingface dataset:
Spectral Leakage and Rethinking the Kernel Size in CNNs

Convolutional layers in CNNs implement linear filters which decompose the input into different frequency bands. However, most modern architectures neglect standard principles of filter design when optimizing their model choices regarding the size and shape of the convolutional kernel. In this work, we consider the well-known problem of spectral leakage caused by windowing artifacts in filtering operations in the context of CNNs. We show that the small size of CNN kernels make them susceptible to spectral leakage, which may induce performance-degrading artifacts. To address this issue, we propose the use of larger kernel sizes along with the Hamming window function to alleviate leakage in CNN architectures. We demonstrate improved classification accuracy on multiple benchmark datasets including Fashion-MNIST, CIFAR-10, CIFAR-100 and ImageNet with the simple use of a standard window function in convolutional layers. Finally, we show that CNNs employing the Hamming window display increased robustness against various adversarial attacks. Our code is available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Learning Algorithm Recommendation Framework for IS and CPS Security

Artificial intelligence and machine learning have recently made outstanding contributions to the performance of information system and cyber--physical system security. There has been a plethora of research in this area, resulting in an outburst of publications over the past two years. Choosing the right algorithm to solve a complex security problem in a very precise industrial context is a challenging task. Therefore, in this paper, we propose a Learning Algorithm Recommendation Framework that, for a clearly defined situation, guides the selection of learning algorithm and scientific discipline (e.g. RNN, GAN, RL, CNN,...) which have sparked great interest to the scientific community and which therefore offers preponderant elements and benefits for further deployments. This framework has the advantage of having been generated from an extensive analysis of the literature, as illustrated by this paper for the recurrent neural networks and their variations.
Integration of Machine Learning in Education: Challenges, Issues and Trends

Machine learning has had a wide range of applications in almost every aspect of human endeavor including health care, businesses, agriculture and finance among others. But the application of machine learning does not stop here. In education, machine learning has been used for modeling intelligent tutoring systems for students, making recommendations about possible future career paths, improving the curriculum, accurately predicting grades of students and much more. However, while implementation of machine learning does have the potential to enhance the learning experience, teachers and students alike must understand the implications that these systems will have on education. This article provides a review of the implications of advancements in machine learning and its impact on the education sector. In this article, we first highlight the vast opportunity machine learning can create in the educational environment. Many current educational systems are deploying the applications of machine learning along with other technologies to provide fast, efficient and quality learning experiences. Finally, we cover some of the major challenges and issues educators and developers of such systems encounter while trying to move toward a fully technology-driven education system.
Teaching traditional Yao dance in the digital environment: Forms of managing subcultural forms of cultural capital in the practice of local creative industries

The use of digital environment technologies, such as virtual reality, is a modern way to teach dance. The use of virtual reality, as a new method of digital interaction between human and machine, makes it possible to solve the problems associated with the lack of feedback, as, for example, in video lessons. It provides users with a three-dimensional virtual world that has immersive and interactive characteristics, solves the problem of time and place limitations, tracks student actions, and monitors and corrects them, thereby enabling students and teachers to implement learning even under challenging pandemic constraints. Innovative pedagogical approaches also generate additional interest among today's students, which makes it possible to use them to make students more interested in studying traditional dance as a national and subcultural form of cultural capital. The research purpose is an experimental test study of the digital technology capabilities of a virtual reality system with motion capture for teaching dance (using the example of the traditional Chinese Yao dance). Eighty-three people (N = 83) took part in the study, randomly assigned to the experimental and control groups. Preliminary tests of the experimental and control groups were compared using a two-sample t-test. There was no significant difference in the initial scores of the experimental and control groups. As a result of the analysis, it was revealed that there was a significant difference between the progress indicators in the experimental and control groups. The average improvement in the experimental group (11.48) was significantly higher than in the control group (2.86). A survey of students showed that 100% of the experimental group participants and 86.5% of the control group participants are ready to recommend the proposed teaching technology. Thus, it was found that the feedback offered to experimental group students facilitates the students' task of learning dance.
Realizing self-adaptive systems via online reinforcement learning and feature-model-guided exploration

A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptive system, software developers codify knowledge about the system and its environment, as well as how adaptation actions impact on the system. However, the codified knowledge may be insufficient due to design time uncertainty, and thus a self-adaptive system may execute adaptation actions that do not have the desired effect. Online learning is an emerging approach to address design time uncertainty by employing machine learning at runtime. Online learning accumulates knowledge at runtime by, for instance, exploring not-yet executed adaptation actions. We address two specific problems with respect to online learning for self-adaptive systems. First, the number of possible adaptation actions can be very large. Existing online learning techniques randomly explore the possible adaptation actions, but this can lead to slow convergence of the learning process. Second, the possible adaptation actions can change as a result of system evolution. Existing online learning techniques are unaware of these changes and thus do not explore new adaptation actions, but explore adaptation actions that are no longer valid. We propose using feature models to give structure to the set of adaptation actions and thereby guide the exploration process during online learning. Experimental results involving four real-world systems suggest that considering the hierarchical structure of feature models may speed up convergence by 7.2% on average. Considering the differences between feature models before and after an evolution step may speed up convergence by 64.6% on average. [...]
Polarimetric Helmholtz Stereopsis

Helmholtz stereopsis (HS) exploits the reciprocity principle of light propagation (i.e., the Helmholtz reciprocity) for 3D reconstruction of surfaces with arbitrary reflectance. In this paper, we present the polarimetric Helmholtz stereopsis (polar-HS), which extends the classical HS by considering the polarization state of light in the reciprocal paths. With the additional phase information from polarization, polar-HS requires only one reciprocal image pair. We formulate new reciprocity and diffuse/specular polarimetric constraints to recover surface depths and normals using an optimization framework. Using a hardware prototype, we show that our approach produces high-quality 3D reconstruction for different types of surfaces, ranging from diffuse to highly specular.
Towards a Two-Tier Architecture for Privacy-Enabled Recommender Systems (PeRS)

The current surge in recommender systems research is impressive, but it has highlighted a number of concerns, including users’ privacy and data security. Although various solutions to these privacy breaches have been proposed, the existing solutions fall short of directly addressing the real issues, and most of them continue to rely on third parties. Moreover, giving third parties access to users’ personally identifiable information (PII) is a cause for concern. In this paper, we have suggested a two-tiered architecture. The identity of the users is anonymized in the first tier, in the next tier, homomorphic encryption is exploited for the purpose of randomization of the users’ data. With the help of proposed solution, on one hand, third-party involvement can be eliminated. On the other hand, there can be improvement in the privacy mechanism by providing a two-tier architecture for user data security. The suggested framework is intended to serve as a baseline for safeguarding users’ privacy and integrity when conducting online purchases and other associated activities.
Finger motion detection based on optical fiber Bragg grating with polyimide substrate

Gestures play an important role in human-computer interaction, which provides a natural way for human-machine communication. In this paper, we proposed a new finger motion detection method based on optical fiber Bragg grating (FBG) with polyimide substrate. Several FBGs with polyimide substrate were fixed on the forearm to monitor the deformation around the forearm caused by finger motion. Firstly, the sensors layout was determined according to the distribution of forearm muscles which control finger motions, and the arrangement of FBGs was optimized. Then, through a series of tests, the response characteristics and applicability of the proposed FBG with polyimide substrate were verified. The tests results show that FBG with polyimide substrate has a high sensitivity, good linearity, low hysteresis error and good creep resistance compared with other two substrates. In the experiment of finger motion detection, test data based on six static gestures was adopted to evaluate the availability of the FBGs. The experimental results show that the proposed method can detect six static gestures effectively, and the FBG output signal has good repeatability under the same gesture. This is the first attempt to use FBGs to detect fingers’ monitor by monitoring deformations at the forearm, which is a technique with wide application prospects.
A Multilevel Transfer Learning Technique and LSTM Framework for Generating Medical Captions for Limited CT and DBT Images

Medical image captioning has been recently attracting the attention of the medical community. Also, generating captions for images involving multiple organs is an even more challenging task. Therefore, any attempt toward such medical image captioning becomes the need of the hour. In recent years, the rapid developments in deep learning approaches have made them an effective option for the analysis of medical images and automatic report generation. But analyzing medical images that are scarce and limited is hard, and it is difficult even with machine learning approaches. The concept of transfer learning can be employed in such applications that suffer from insufficient training data. This paper presents an approach to develop a medical image captioning model based on a deep recurrent architecture that combines Multi Level Transfer Learning (MLTL) framework with a Long Short-Term-Memory (LSTM) model. A basic MLTL framework with three models is designed to detect and classify very limited datasets, using the knowledge acquired from easily available datasets. The first model for the source domain uses the abundantly available non-medical images and learns the generalized features. The acquired knowledge is then transferred to the second model for the intermediate and auxiliary domain, which is related to the target domain. This information is then used for the final target domain, which consists of medical datasets that are very limited in nature. Therefore, the knowledge learned from a non-medical source domain is transferred to improve the learning in the target domain that deals with medical images. Then, a novel LSTM model, which is used for sequence generation and machine translation, is proposed to generate captions for the given medical image from the MLTL framework. To improve the captioning of the target sentence further, an enhanced multi-input Convolutional Neural Network (CNN) model along with feature extraction techniques is proposed. This enhanced multi-input CNN model extracts the most important features of an image that help in generating a more precise and detailed caption of the medical image. Experimental results show that the proposed model performs well with an accuracy of 96.90%, with BLEU score of 76.9%, even with very limited datasets, when compared to the work reported in literature.
A Modified Ant Lion Optimization Method and Its Application for Instance Reduction Problem in Balanced and Imbalanced Data

Instance reduction is a pre-processing step devised to improve the task of classification. Instance reduction algorithms search for a reduced set of instances to mitigate the low computational efficiency and high storage requirements. Hence, finding the optimal subset of instances is of utmost importance. Metaheuristic techniques are used to search for the optimal subset of instances as a potential application. Antlion optimization (ALO) is a recent metaheuristic algorithm that simulates antlion’s foraging performance in finding and attacking ants. However, the ALO algorithm suffers from local optima stagnation and slow convergence speed for some optimization problems. In this study, a new modified antlion optimization (MALO) algorithm is recommended to improve the primary ALO performance by adding a new parameter that depends on the step length of each ant while revising the antlion position. Furthermore, the suggested MALO algorithm is adapted to the challenge of instance reduction to obtain better results in terms of many metrics. The results based on twenty-three benchmark functions at 500 iterations and thirteen benchmark functions at 1000 iterations demonstrate that the proposed MALO algorithm escapes the local optima and provides a better convergence rate as compared to the basic ALO algorithm and some well-known and recent optimization algorithms. In addition, the results based on 15 balanced and imbalanced datasets and 18 oversampled imbalanced datasets show that the instance reduction proposed method can statistically outperform the basic ALO algorithm and has strong competitiveness against other comparative algorithms in terms of four performance measures: Accuracy, Balanced Accuracy (BACC), Geometric mean (G-mean), and Area Under the Curve (AUC) in addition to the run time. MALO algorithm results show increment in Accuracy, BACC, G-mean, and AUC rates up to 7%, 3%, 15%, and 9%, respectively, for some datasets over the basic ALO algorithm while keeping less computational time.
Disentangled High Quality Salient Object Detection

Aiming at discovering and locating most distinctive objects from visual scenes, salient object detection (SOD) plays an essential role in various computer vision systems. Coming to the era of high resolution, SOD methods are facing new challenges. The major limitation of previous methods is that they try to identify the salient regions and estimate the accurate objects boundaries simultaneously with a single regression task at low-resolution. This practice ignores the inherent difference between the two difficult problems, resulting in poor detection quality. In this paper, we propose a novel deep learning framework for high-resolution SOD task, which disentangles the task into a low-resolution saliency classification network (LRSCN) and a high-resolution refinement network (HRRN). As a pixel-wise classification task, LRSCN is designed to capture sufficient semantics at low-resolution to identify the definite salient, background and uncertain image regions. HRRN is a regression task, which aims at accurately refining the saliency value of pixels in the uncertain region to preserve a clear object boundary at high-resolution with limited GPU memory. It is worth noting that by introducing uncertainty into the training process, our HRRN can well address the high-resolution refinement task without using any high-resolution training data. Extensive experiments on high-resolution saliency datasets as well as some widely used saliency benchmarks show that the proposed method achieves superior performance compared to the state-of-the-art methods.
Salient Object Ranking with Position-Preserved Attention

Instance segmentation can detect where the objects are in an image, but hard to understand the relationship between them. We pay attention to a typical relationship, relative saliency. A closely related task, salient object detection, predicts a binary map highlighting a visually salient region while hard to distinguish multiple objects. Directly combining two tasks by post-processing also leads to poor performance. There is a lack of research on relative saliency at present, limiting the practical applications such as content-aware image cropping, video summary, and image labeling.In this paper, we study the Salient Object Ranking (SOR) task, which manages to assign a ranking order of each detected object according to its visual saliency. We propose the first end-to-end framework of the SOR task and solve it in a multi-task learning fashion. The framework handles instance segmentation and salient object ranking simultaneously. In this framework, the SOR branch is independent and flexible to cooperate with different detection methods, so that easy to use as a plugin. We also intro-duce a Position-Preserved Attention (PPA) module tailored for the SOR branch. It consists of the position embedding stage and feature interaction stage. Considering the importance of position in saliency comparison, we preserve absolute coordinates of objects in ROI pooling operation and then fuse positional information with semantic features in the first stage. In the feature interaction stage, we apply the attention mechanism to obtain proposals’ contextualized representations to predict their relative ranking orders. Extensive experiments have been conducted on the ASR dataset. Without bells and whistles, our proposed method outperforms the former state-of-the-art method significantly. The code will be released publicly available on https://github.com/EricFH/SOR.
Loss-Based Performance Assessment and Seismic Network Optimization for Earthquake Early Warning

ABSTRACT The goal of earthquake early warning (EEW) is to issue an alert before the damaging seismic waves of an earthquake hit a given exposure. We develop a framework to evaluate the EEW performance in a loss-based context. We use warning time as a key performance indicator and determine statistics of warning time by loss severity to assess the rate and consistency with which an EEW system can deliver timely alerts. In the second part of this work, we develop a Genetic Algorithm approach to optimize an existing sensor network by proposing sites for new stations to enhance the EEW performance in damaging earthquakes. We demonstrate this framework for Switzerland using 2000 realizations of a 50-yr-long stochastic earthquake catalog, which samples the earthquake rate forecast of the Swiss Hazard Model in space and time. For each of the almost 24k earthquake scenario ruptures (5.0 ≤ M ≤ 7.4), we predict shaking intensities and losses (here, fatalities and injuries) at the largest Swiss cities. We find that the current Swiss Seismic Network could provide positive warning times to the affected sites for about 80% of very damaging earthquakes (≥100 fatalities) and for around 85% of earthquakes with ≥10 fatalities. Warning times of &amp;gt;5 s could be achieved for about 40%–55% of very damaging earthquakes and &amp;gt;10 s for about 35%. For around 50% of events with ≥1 fatality (≥10 injuries), EEW could provide &amp;gt;15 s of warning. The greatest benefit of EEW is expected in Zürich, where the population density and, consequently, absolute long-term expected losses are highest and the warning times for damaging earthquakes are often long (&amp;gt;15 s). Densifying the Swiss Seismic Network with additional stations can increase warning times in selected scenarios by up to 5 s. However, because it is already very dense (7 ± 5 km interstation distance), decreasing data latencies (currently ∼2 s) may be more important.
PRNU-Based Video Source Attribution: Which Frames Are You Using?

Photo Response Non-Uniformity (PRNU) is reputed the most successful trace to identify the source of a digital video. However, its effectiveness is mainly limited by compression and the effect of recently introduced electronic image stabilization on several devices. In the last decade, several approaches were proposed to overcome both these issues, mainly by selecting those video frames which are considered more informative. However, the two problems were always treated separately, and the combined effect of compression and digital stabilization was never considered. This separated analysis makes it hard to understand if achieved conclusions still stand for digitally stabilized videos and if those choices represent a general optimum strategy to perform video source attribution. In this paper, we explore whether an optimum strategy exists in selecting frames based on their type and their positions within the groups of pictures. We, therefore, systematically analyze the PRNU contribute provided by all frames belonging to either digitally stabilized or not stabilized videos. Results on the VISION dataset come up with some insights into optimizing video source attribution in different use cases.
End-to-end person re-identification: Real-time video surveillance over edge-cloud environment

In this work, a real-time end-to-end person re-identification (Re-ID) system is developed for a realistic unconstrained open-world environment. The raw surveillance videos are analyzed to provide the ranked list of person images matched with the query at the user-end. Two frameworks based on distributed-computing and edge-based computing that utilize the NVIDIA Jetson Nano and TX2, respectively have been developed to realize the objective. Various practical aspects of deploying an end-to-end Re-ID system over edge-cloud environment, such as resource constraints at the edge, hardware allocation to individual tasks and their acceleration, communication costs and scalability are dealt with. An improvised omni-scale network (iOSNet) with a powerful feature aggregation scheme has been proposed to handle more complex variations in the appearance of a person. The performance of the system has been evaluated on our own dataset and compared with other existing datasets, where state-of-the-art accuracy is achieved on all the datasets.