Reviewerly/exHarmony huggingface dataset:
Progressive Motion Representation Distillation With Two-Branch Networks for Egocentric Activity Recognition

Video-based egocentric activity recognition involves fine-grained spatio-temporal human-object interactions. State-of-the-art methods, based on the two-branch-based architecture, rely on pre-calculated optical flows to provide motion information. However, this two-stage strategy is computationally intensive, storage demanding, and not task-oriented, which hampers it from being deployed in real-world applications. Albeit there have been numerous attempts to explore other motion representations to replace optical flows, most of the methods were designed for third-person activities, without capturing fine-grained cues. To tackle these issues, in this letter, we propose a progressive motion representation distillation (PMRD) method, based on two-branch networks, for egocentric activity recognition. We exploit a generalized knowledge distillation framework to train a hallucination network, which receives RGB frames as input and produces motion cues guided by the optical-flow network. Specifically, we propose a progressive metric loss, which aims to distill local fine-grained motion patterns in terms of each temporal progress level. To further enforce the proposed distillation framework to concentrate on those informative frames, we integrate a temporal attention mechanism into the metric loss. Moreover, a multi-stage training procedure is employed for the efficient learning of the hallucination network. Experimental results on three egocentric activity benchmarks demonstrate the state-of-the-art performance of the proposed method.
Attention-model Guided Image Enhancement for Robotic Vision Applications

Optical data is one of the crucial information resources for robotic platforms to sense and interact with the environment being employed. Obtained image quality is the main factor of having a successful application of sophisticated methods (e.g., object detection and recognition). In this paper, a method is proposed to improve the image quality by enhancing the lighting and denoising. The proposed method is based on a generative adversarial network (GAN) structure. It makes use of the attention model both to guide the enhancement process and to apply denoising simultaneously thanks to the step of adding noise on the input of discriminator networks. Detailed experimental and comparative results using real datasets were presented in order to underline the performance of the proposed method.
The Effect of Instant Message on Chinese Character Handwriting Among Teens: Pinyin Input Experience as a Compensating Role

The relationship between handwriting and instant message (IM) frequency has not been studied in English and the reason may be related to the similar cognitive process of typing IM and handwriting words. The mental processes of producing both handwriting and typing English words are from semantics to phonology and orthography. However, the cognitive process of Chinese Pinyin typing used in IM activity is very different from traditional handwriting, which changes the production of handwritten glyph into the phonetic production and orthography recognition. While the IM activity itself is not conducive to literacy skill, the Pinyin input experience may be helpful for that. If we could separate the independent effects of IM and Pinyin input on handwriting, it would help us to clarify the complexity of the impact of informational and communicational technology (ICT) on literacy skills. This study investigated 449 junior school students aged 12–15 in China and found that IM directly predicts the performance of handwriting characters negatively, meanwhile, it indirectly predicts handwriting performance positively through the mediator of Pinyin input experience. Pinyin input experience compensates the negative effects of IM on handwriting. The implications of this study are that we should look at the individual effects of different types of online activity on children’s literacy, and guide Chinese children to appropriately engage in useful ICT for traditional literacy development.
Design control system using gesture control on the Arduino-based robot warfare

Abstract The development of increasingly advanced technology provides a positive side in various fields, especially the military. This research introduces a different control system in the navigation of robots, especially in the military field. Navigation system used are varied, ranging from robots that are driven using cables to wireless communications that are used to move robots. Various control systems on robots using RC (Remote Control), android, and several electronic devices designed for navigation systems such as joysticks and other control system devices. Researchers designed gesture control on the Arduino nano v3 based and Arduino ATMEGA based combat robots on military equipment, because there is no military equipment that uses a gesture control navigation system. Researchers used an experiment method with an Arduino electronic device as a microcontroller, accelerometer module and MPU6050 gyroscope as a sensor that serves to determine the orientation of motion, NRF24L01 module as telemetry, and engine as drive. The results obtained after designing a gesture control navigation system on a combat robot are very good because it is very easy to operate the robot, and a long duration because using the engine. It was concluded that gesture control was appropriate and safe for use in the military.
Design and Implementation of ADL Content with VR Sensor at a Smart Human-Care Service

This paper deals with the development of the content of Activity of Daily Life (ADL) based on VR (virtual reality) for cognitive function training for the elderly. The proposed ADL content has been developed focusing on a feedback technique based on performance analysis unlike in the existing VR-based cognitive training content wherein the customized training management based on performance evaluation was difficult. Experiments were conducted with 30 elderly people in the welfare center for 3 months. As a result of the effectiveness evaluation, the proposed ADL content showed higher results in all aspects of immersion, satisfaction, and performance. It is expected that user departure from existing ADL content will be prevented, and various artificial intelligence researches will be carried out to improve the technology accuracy to provide customized difficulty which is an important factor.
Watermark Detection and Clearance in Video Using Simple Signal and Image Processing Techniques

A digital watermark is an information or signal inserted into a digital content (image, audio, video, document, software) that can be used to determine its ownership, showing who created or sold it, or to ensure its integrity. Typically, the same watermark is added consistently to mark movie collections. Consequently, media with a visible watermark will be eventually susceptible to the necessity of watermark remission, operation that could be an issue. This paper presents two new and very fast algorithms for watermark detection and removal. The here considered watermarks could be blended, overlaid or added in a continuous form to the digital video on remission treatment. The first of such algorithms use a template matching approach to pre-process the blended watermarks frame-to-frame in the Hue-Saturation-Value (HSV) color space, and then improve the detection using a post-processing step. The second algorithm presents two steps as well. Initially, it estimates the structure of a translucent watermark observing the constancy of its gradient magnitude in the area corresponding to the tenth part of each video. Secondly, it uses a sliding window to detect the watermarked in the frames. The results show that watermark detection by both algorithms has high sensitivity. Moreover, they are very fast when compared with other methods especially those based on deep learning.
Gaslight revisited: Efficient and powerful fuzzing of digital forensics tools

The fields of digital forensics and incident response have seen significant growth over the last decade due to the increasing threats faced by organizations and the continued reliance on digital platforms and devices by criminals. This rise has coincided with a significant and continued increase in the size, complexity, and number of digital forensic investigations that must be performed. In the past, such investigations were performed manually by expert investigators, but this approach is no longer viable given the amount of data that must be processed compared to the relatively small number of trained investigators. These resource constraints have led to the development and reliance on automated processing and analysis systems for digital evidence. Given the central role that such evidence plays in securing organizations and nations against attacks as well as in criminal and civil legal proceedings, it is necessary that such systems are developed in a robust and reliable manner. In this paper, we present our effort to develop a stress testing platform specifically tailored to assess the robustness and reliability of digital forensics tools. For our initial testing, we chose to target The Sleuth Kit framework given its prominence as both as a standalone tool as well as a programming library that is utilized by a large number of open source and commercial filesystem analysis systems. The results of our efforts were the automated discovery of many critical programming errors in The Sleuth Kit framework.
Modeling Long-Term Dependencies from Videos Using Deep Multiplicative Neural Networks

Understanding temporal dependencies of videos is fundamental for vision problems, but deep learning–based models are still insufficient in this field. In this article, we propose a novel deep multiplicative neural network (DMNN) for learning hierarchical long-term representations from video. The DMNN is built upon the multiplicative block that remembers the pairwise transformations between consecutive frames using multiplicative interactions rather than the regular weighted-sum ones. The block is slided over the timesteps to update the memory of the networks on the frame pairs. Deep architecture can be implemented by stacking multiple layers of the sliding blocks. The multiplicative interactions lead to exact, rather than approximate, modeling of temporal dependencies. The memory mechanism can remember the temporal dependencies for an arbitrary length of time. The multiple layers output multiple-level representations that reflect the multi-timescale structure of video. Moreover, to address the difficulty of training DMNNs, we derive a theoretically sound convergent method, which leads to a fast and stable convergence. We demonstrate a new state-of-the-art classification performance with proposed networks on the UCF101 dataset and the effectiveness of capturing complicate temporal dependencies on a variety of synthetic datasets.
Personalised recommendation algorithm based on covariance

Recommender systems are of great significance in predicting the potential interesting items based on the target user's historical selections. However, the recommendation list for a specific user has been found changing vastly when the system changes. So far, a variety of personalised recommendation algorithms have been proposed and most of them are based on similarities, such as collaborative filtering and mass diffusion. Most of the algorithms aimed at recommendation's accuracy. Here, the authors proposed a new Cov (covariance) recommendation method based on correlation coefficients, which can provide precision of recommendation. First, this method can express the positive and negative correlation among random samples without knowing the distribution of items. Then by limiting the popular items in recommendation list, an improved method is proposed, CovH, which can improve the diversity and novelty in recommendation compared with Cov. By applying the two methods on realistic recommendation datasets, such as Movie-Lens, Netflix The authors found that comparing with baseline methods, these two algorithms can not only ensure decent accuracy but also have good performance on diversity and novelty indeed.
Exploring Collections of research publications with Human Steerable AI

Understanding highly-dimensional data sets is a complex task. Traditionally, this problem has been tackled with linear pipelines that rely on mathematical models and algorithms to summarize relationships and structure, producing a visual representation of the data in a collapsed, low-dimensional form. The main issue with these traditional pipelines is that they are driven solely by algorithms or models, and without a human in the loop, they can potentially limit sense-making by masking expected or known structure in the data. Textual data, such as that contained in research publications, is one example of unstructured highly dimensional data, wherein the raw data must be converted to an abstract numeric representation that is highly dimensional.
Image deblurring and super resolution using bilateral filter and sparse representation

Super Resolution based Sparse Representation has as of late demonstrated to perform well for picture rebuilding and deblurring. The proposed approach begins with the sparsest conceivable framework and step by step works its away upwards during deblurring. Reciprocal Filter is utilized for edge safeguarding. Ringing curios can be smothered in the back-projection step. We contrast our calculation and a few best in class picture super-goals calculations. Specifically, we first parcel picture patches into a few gatherings by a fix handling technique dependent on contrast ebb and flow of LR patches. At that point we create High goals patches utilizing the Sparse guess technique. The proposed technique accomplishes much preferred outcomes over many cutting edge calculations as far as both PSNR and visual observation.
Logo detection and brand recognition with one-stage logo detection framework and simplified resnet50 backbone

Logo and brand name are two concepts which are typically studied in many course subjects. In education context, automated logo detection and brand name recognition from digital image or video are very crucial as a learning tool to achieve learning outcomes. One technical issue in the logo detection and brand name recognition is its requirement to develop model that achive fast recognition speed and high recognition accuracy. One-stage detector is a breakthrough and innovative object detection framework; however, long duration and computing power required to carry out training and detection processes using the backbone deep architecture are often considered to be the challenge of this framework. The objective of this study is to propose a novel ResNet variant models using ResNet-50 as the basis. The empiric results showed that the model 2 achieved 0.408 mAP, the best average accuracy with training time 1.41 hour. The original Resnet50 model, in contrast, achieved 0.556 mAP average accuracy with 1.91 hour training time. The detection testing of the proposed Model 2 model was 23.47 fps, while the detection testing of Resnet50 model was 29.33 Fps.
Digital Forensics: Essential Competencies of Cyber-Forensics Practitioners

Technical advancement expands the usage of digital devices in today’s society. As the usage increases, the amount of cyber-crime related to data leakage also substantially increases. To deal with these cyber-crime-related activities, a crucial need of skilled cyber-forensics practitioners is essential. Cyber-forensics practitioners should equip with varied forensics frameworks to meet the technological enhancement in today’s society. This work discusses the various essential competencies required for the practitioners to prepare themselves for forensic investigation. Actually, the experts are expected to be well versed in investigation approach using various resources like computer, IoT, cloud, mobile, etc., and various commercial and open-source forensics tools. An investigation approach is taken up here to measure the effectiveness of a forensics practitioner in targeting the challenges in a forensics laboratory using multi-agent.
Long Short-Term Memory for Human Fall Detection Based Gamification on Unconstraint Smartphone Position

Fall incident can caused health problem in elderly and people with special treatments. Fall detection method is needed to minimized the problem when human fallen and smartphone can be used as the device to detect it. Usually people carry smartphone in any positions and can make fall detection method difficult to detect when fall occurs. Long Short-Term Memory (LSTM) combined with Sigmoid helps to answer the challenge to handle smartphone accelerometer and gyroscope data in many smartphone orientation position. LSTM method on this experiment can achieved 91.67% for the accuracy result. Since the fall data occurs rarely and there may be insufficient data available, the gamification prospect implemented in fall detection application especially on “Bug” bounty can help researcher to enhance the accuracy result to make a better human fall detection method.
GigaHertz: Gesture Sensing Using Microwave Radar and IR Sensor with Machine Learning Algorithms

Intelligent systems have been important in all the industries whether its automotive, medical, automation and smart switches (in our terms replacing traditional switches). The key element of such systems is the user interface that can keep track of human gestures. Hand gesture sensing using a traditional approach i.e. camera has a serious limitation of lightning condition which triggers the use of additional infrared camera making the entire system bulky costly and less secure toward hacking problems. Short-range radar in combination with an IR sensor can sense all those gestures that a camera can by overcoming the drawbacks of the existing traditional approach. We have proposed a novel method to overcome the drawbacks of the existing systems and to use the proposed system GigaHertz for multiple applications, this method is based on the use of Doppler shift using radar and IR sensor for end to end (hardware, interface, and software) radar-IR-based system design to efficiently detect the gesture. The novelty of the proposed method is the collaboration of a radar system with an IR sensor to provide a total of eight gestures which can be configured as per user needs. The main advantage of the proposed system is in the selection of effective and efficient feature selection that enhances the classifier accuracy. A unique algorithm that we have designed to sync the various components and to configure different gestures as per user needs and also achieve high accuracy of 92% with SVM machine learning algorithm which is higher than existing gesture recognition technology. We aim at replacing the traditional switch with the proposed model device making it safer to use for multiple applications controlling a wheelchair, home automation, to control various systems in a vehicle making it safer for the driver to drive without taking eyes off the road, controlling quadcopter and many other such applications.
Faster &amp; strong: string dictionary compression using sampling and fast vectorized decompression

Abstract String dictionaries constitute a large portion of the memory footprint of database applications. While strong string dictionary compression algorithms exist, these come with impractical access and compression times. Therefore, lightweight algorithms such as front coding (PFC) are favored in practice. This paper endeavors to make strong string dictionary compression practical. We focus on Re-Pair Front Coding (RPFC), a grammar-based compression algorithm, since it consistently offers better compression ratios than other algorithms in the literature. To accelerate compression times, we propose block-based RPFC (BRPFC) which consists in independently compressing small blocks of the dictionary. For further accelerated compression times especially on large string dictionaries, we also propose an alternative version of BRPFC that uses sampling to speed up compression. Moreover, to accelerate access times, we devise a vectorized access method, using $$\hbox {Intel}^{\circledR }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mtext>Intel</mml:mtext> <mml:mo>®</mml:mo> </mml:msup> </mml:math> Advanced Vector Extensions 512 ( $$\hbox {Intel}^{\circledR }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mtext>Intel</mml:mtext> <mml:mo>®</mml:mo> </mml:msup> </mml:math> AVX-512). Our experimental evaluation shows that sampled BRPFC offers compression times up to 190 $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> faster than RPFC, and random string lookups 2.3 $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> faster than RPFC on average. These results move our modified RPFC into a practical range for use in database systems because the overhead of Re-Pair-based compression for access times can be reduced by 2 $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> .
Color Video JND Model Using Compound Spatial Masking and Structure-Based Temporal Masking

In order to provide high transmission efficiency and visual quality for the color image/video in internet, effective estimation of its inherent just noticeable distortion (JND) is continuously an important topic. In this paper, a discrete cosine transform (DCT)-based perceptual model for estimating spatio-temporal JNDs of color videos is presented. Firstly, the compound masking adjustment integrated with different spatial masking factors is proposed. Based on the base detection threshold for DCT coefficients in luminance and chrominance components of the color image, the adjustment combined by luminance masking, pattern-based contrast masking, and cross masking is exploited to measure visibility thresholds for luminance component, while the adjustment induced by the variance-based statistical properties is utilized to measure visibility thresholds for chrominance components. Then, the local temporal statistics of luminance component are considered to design the structure-based temporal masking adjustment for further estimating visibility thresholds of video signals. To verify the proposed method, a subjective viewing test is designed and a fair rating process of evaluating the visual quality is carried out under the specified viewing condition. Experimental results show that the proposed method is able to measure more visual distortion tolerance than the existing color-based methods at high visual quality.
Computer Vision for the Ballet Industry: A Comparative Study of Methods for Pose Recognition

The presence of computer vision technology is continually expanding into multiple application domains. An industry and an art form that is particularly attractive for the application of computer vision algorithms is ballet. Due to the well-codified poses, along with the challenges that exist within the ballet domain, automation for the ballet environment is a relevant research problem. The paper proposes a model called BaReCo, which allows for ballet poses to be recognised using computer vision methods. The model contains multiple computer vision pipelines which allows for the comparison of approaches that have not been widely explored in the ballet domain. The results have shown that the top-performing pipelines achieved an accuracy rate of 99.375% and an Equal Error Rate (EER) of 0.119% respectively. The study additionally produced a ballet pose dataset, which serves as a contribution to the ballet and computer vision community. By combining suitable computer vision methods, the study demonstrates that successful recognition of ballet poses can be accomplished.