Reviewerly/exHarmony huggingface dataset:
Protecting personal sensitive data security in the cloud with blockchain

To protect personal sensitive data in cloud computing environment, certain issues need to be addressed, including data ownership, fine-grained access control, transparency and auditability. While many models have been explored to address these issues, among most of which, some components, such as Cloud Service Provider (CSP) are required to be trusted. In this chapter, we introduce a trust-free data access model for personal sensitive data protection in the cloud environment. In our model, an access control mechanism is constructed based on the Ethereum blockchain, which requires no trusted party. The smart contract enables fine-grained access control for cloud data based on the blockchain. Data operations including uploading, updating and downloading can be automated processed and logged in our model to ensure transparency and auditability. Comparisons between our model and existing models show that our trust-free model fulfills all requirements on personal sensitive data protection, and brings no extra security risks. Moreover, our model has less burden for data owner from both the computation perspective and communication perspective.
Detection and Identification of Malicious Cyber-Attacks in Connected and Automated Vehicles’ Real-Time Sensors

Connected and automated vehicles (CAVs) as a part of Intelligent Transportation Systems (ITS) are projected to revolutionise the transportation industry, primarily by allowing real-time and seamless information exchange of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). However, these connectivity and automation are expected to offer vast numbers of benefits, new challenges in terms of safety, security and privacy also emerge. CAVs continue to rely heavily on their sensor readings, the input obtained from other vehicles and the road side units to inspect roadways. Consequently, anomalous reading of sensors triggered by malicious cyber attacks may lead to fatal consequences. Hence, like all other safety-critical applications, in CAVs also, reliable and secure information dissemination is of utmost importance. As a result, real time detection of anomaly along with identifying the source is a pre-requisite for mass deployment of CAVs. Motivated by this safety concerns in CAVs, we develop an efficient anomaly detection method through the combination of Bayesian deep learning (BDL) with discrete wavelet transform (DWT) to improve the safety and security in CAVs. In particular, DWT is used to smooth sensor reading of a CAV and then feed the data to a BDL module for analysis of the detection and identification of anomalous sensor behavior/data points caused by either malicious cyber attacks or faulty vehicle sensors. Our numerical experiments show that the proposed method demonstrates significant improvement in detection anomalies in terms of accuracy, sensitivity, precision, and F1-score evaluation metrics. For these metrics, the proposed method shows an average performance gain of 7.95%, 9%, 8.77% and 7.33%, respectively when compared with Convolutional Neural Network (CNN-1D), and when compared with BDL, the corresponding numbers are 5%, 7.9%, 7.54% and 4.1% respectively.
Fast global SA(2,R) shape registration based on invertible invariant descriptor

Here, we intend to introduce a fast and non-rigid global registration for simple planar closed curves relatively to the planar Special Affine group SA(2,R). In previous work, Ghorbel (1998) has been introduced a complete and stable set of invariant for simple closed planar curves which is invariant jointly under its original parametrization and special planar affine transformations. Such property allows us a robust reconstruction of the considered object up to a special affine transformation. In this paper, several numerical difficulties of the computation of the proposed reconstruction are considered. The robustness of this inverse problem with respect to noise and reasonable deformations of non-rigid shape is demonstrated experimentally. The proposed new registration is based, on the one hand, on the shift theorem relating to the group SA(2,R) and, on the other hand, on the invertibility of the set of invariant. Since this shift theorem allows the extraction of a pose parameters exciting between reference and target objects. The low algorithmic complexity is due to the fact that the computation of the inverse descriptors are based essentially on the Fast Fourier Transformation (FFT) algorithm. Experiments are conducted on different known datasets such as MPEG-7, MCD, Kimia-99, Kimia216, ETH-80 and Swedish leaf datasets. Promising results on the sense of shape retrieval and shape recognition rates will also be demonstrated.
Data Augmentation Using Feature Interpolation of Individual Words for Compound Word Recognition of Sign Language

Sign language recognition is an important topic to improve communication between native signers and native speakers. In this paper, we propose one of the data augmentations to recognize compound words represented by sequences of two individual sign language words. The proposed method generates two-dimensional tracking points of the compound words by concatenating the two individual sign language words' tracking points. Linear interpolation is applied to concatenate the tracking points of the individual sign language words. The evaluation was conducted using a conventional sign feature extraction and recognition model based on the encoder-decoder Recurrent Neural Network with Attention. The minimum average word error rate of the model, which trained with the generated compound words, was 18.85%. Moreover, the minimum average word error rate of the model, which trained with the generated and recorded compound words, was 7.15%, and it was improved by 3.08% from that of the recorded compound words.
Weakly supervised instance segmentation using multi-stage erasing refinement and saliency-guided proposals ordering

Weakly supervised instance segmentation is a new research topic in the field of computer vision. Compared with fully supervised instance segmentation, weakly supervised methods use weaker data annotations such as points, scribbles or class labels which are easy to obtain. Among these annotations, image-level instance segmentation using only class labels as supervision is the most challenging task. In this paper, we propose a novel weakly supervised instance segmentation framework using a multi-stage erasing refinement method and a saliency-guided proposals ordering method. Firstly, the multi-stage erasing refinement method is exploited to enhance the instance representation by iteratively discovering separate object-related regions, so as to obtain more complete discriminative regions. Then, the saliency-guided proposals ordering method utilizes the saliency map to alleviate the background noise and better select the object proposals for generating the instance segmentation result. Experimental results on the PASCAL VOC 2012 dataset and the COCO dataset demonstrate that our framework achieves superior performance compared with the state-of-the-art weakly supervised instance segmentation models and the ablation study shows the effectiveness of the proposed two methods.
LandmarkGAN: Synthesizing faces from landmarks

Face synthesis is an important problem in computer vision with many applications. In this work, we describe a new method, namely LandmarkGAN, to synthesize faces based on facial landmarks as input. Facial landmarks are a natural, intuitive, and effective representation for facial expressions and orientations, which are independent from the target's texture or color and background scene. Our method is able to transform a set of facial landmarks into new faces of different subjects, while retains the same facial expression and orientation. Experimental results on face synthesis and reenactments demonstrate the effectiveness of our method.
Data Streams Are Time Series: Challenging Assumptions

The increasingly relevance of data streams in the context of machine learning and artificial intelligence has motivated this paper which discusses and draws necessary relationships between the concepts of data streams and time series in attempt to build on theoretical foundations to support online learning in such scenarios. We unify the concepts of data streams and time series by assessing their definitions in the literature and discuss the major implications of this claim on the way that data streams research and practice is carried out, showing that many common assumptions are incorrect or unnecessary. We analyzed six data sources typically used in benchmark data-stream classification and found that none of those meet the requirements and assumptions qualifying them for online learning.
#ifdef Directives and Program Comprehension: The Dilemma between Correctness and Preference

Many organizations and open-source projects use the C preprocessor (CPP) to implement configurability in their software systems. Despite extensive research, existing studies on the effects of CPP use on program comprehension are still limited to experiences, opinions, and empirical studies with narrow scopes. So, it is unclear whether the CPP actually leads to what is sometimes referred to as "#ifdef hell." In this paper, we expand the existing evidence on program comprehension in the presence of CPP directives, but we also highlight a surprising dilemma. We conducted an empirical study, including an experiment and a questionnaire, on the impact of refactoring CPP directives with 521 experienced software developers. The results indicate that, in contrast to previous findings, comprehension performance slightly worsened in terms of correctness when our participants worked on code with refactored CPP directives. However, in alignment with previous findings, they preferred the refactored code, considering it more comprehensible and easier to work with. This dilemma of objective performance versus subjective preference is a surprising outcome that has not been found before. We argue that our work motivates the need for more studies to understand this dilemma-which may significantly impact common beliefs in research and practice.
WITHDRAWN: Video teaching of computer web design course based on FPGA and sobel algorithm

A general-purpose programmable logic device that allows Field Programmable Gate Arrays (FPGAs) to be manufactured on-chip from simple logic gate operations, or a Sobel algorithm complex system implementation is done by the customer after configuration. All tools are appropriate for the learning process, which can be accessed or downloaded from all over the world with the use of the Internet. Video materials provide computer courses web design concept, based on the potential problems, and also provide a special auxiliary FPGA, the teaching, problem-based approach which is much easier. Therefore, the purpose of the system is to provide intuitive tools, web design courses, understand the Sobel algorithm test before major physical system real-time video processing algorithms, and the creation and practical implementation based on video quality. The purpose is also to prove the potential purpose of developing FPGA-based systems and Sobel algorithms. Video teaching is a procedure of hiding the message into video in such a way that no one can detect its presence. In proposed work, sobel algorithm namely random scan and edge detection is used for hiding the data into video. The development of practical examples of video lessons and reasons to support real-time video processing has been proposed. It re-programmes the FPGA, allowing you not only to verify integrity of the design itself but also the establishment of an early prototype sub-circuits using the same development board.
Civilian pepper spray for self defense: Understanding user perception and impact of design on user performance

Pepper spray is widely used in the US, and is marketed as an effective self-defense device. While pepper spray can be useful in deterring an attacker, many pepper spray owners do not have any experience using it. There is a dearth of published studies that focus on civilian pepper-spray use, not to mention first time pepper-spray users. A study to analyze pepper-spray designs with first-time users, to see how the design, specifically the safety mechanism, affects response time and overall performance would be helpful. The study was conducted in 2 parts – a survey to understand user perception, and a lab experiment using a 2 × 2 randomized block design with two designs of pepper sprays (side-slide safety and flip-top safety) and the two starting locations for the pepper spray (purse or pocket) as the manipulated factors. Results revealed a significant difference in response times between the flip-top pepper-spray and the side-slide pepper-spray. There was also a significant difference in response times between using the index or thumb as the trigger finger but no significant difference in response times when the pepper-spray for location (purse or pocket). Overall, participants rated the side-slide device as the most effective self-defense device. Because there is no other research in the area of first-time pepper-spray users, let alone civilian pepper-spray users, much can be learned about how well people use these devices in a real-world situation, and when there is a highly stressful situation how well does pepper-spray allow an individual to protect him or herself. This study starts the discussion on some of these topics.
Hair counting with deep learning

We present a set of deep learning models aimed at solving the hair counting problem in human skin images. All the models are end-to-end, providing a mapping from the input image to a single scalar corresponding to the number of hair. The list of models corresponds to the most common deep learning architectures that worked over-time in various applications, where some of the networks were adapted to output the hair count. Results show that autoencoder architectures with skip connections work best for such end-to-end counting task, hinting at increased performance when multi-task learning is used. With the results presented, we speculate on the possibility to remove human annotator from the tedious task of manual counting of skin hair.
Researchers' Toolbox for the Future: Empowering Children to Shape Their Future

There is an increasing emphasis on empowering children through co-design and co-creation of technology that shapes their learning, wellbeing and lifestyle, and everyday lived experiences. However, several questions emerge: what does it really mean to empower children, what have we achieved thus far, and where are we headed now, as we fight the ongoing pandemic, and in the future when the pandemic is behind us? This workshop aims to answer such questions by investigating, through a strategy approach, different forms of empowerment of children and related ethical, practical, political, and technological implications, in line with this year's conference theme of “shaping experiences, shaping society." The outcome of the workshop includes a methodological toolbox for researchers working towards empowerment of children and other vulnerable users, with an aim to enable inquiry, design, and critical examination of children's socio-technological futures.
Integrated processing method for microseismic signal based on deep neural network

SUMMARY Denoising and onset time picking of signals are essential before extracting source information from collected seismic/microseismic data. We proposed an advanced deep dual-tasking network (DDTN) that integrates these two procedures sequentially to achieve the optimal performance. Two homo-structured encoder–decoder networks with specially designed structures and parameters are connected in series for handling the denoising and detection of microseismic signals. Based on the similarity of data types, the output of the denoising network will be imported into the detection network to obtain labels for the signal duration. The procedures of denoising and duration detection can be completed in an integrated way, where the denoised signals can improve the accuracy of onset time picking. Results show that the method has a good performance for the denoising of microseismic signals that contain various types and intensities of noise. Compared with existing methods, DDTN removes the noise with a minor waveform distortion. It is ideal for recovering the microseismic signal while maintaining a good capacity for onset time picking when the signal-to-noise ratio is low. Based on that, the second network can detect a more accurate duration of microseismic signals and thus obtain more accurate onset time. The method has great potential to be extended to the study of exploration seismology and earthquakes.
Dynamic Steganography Least Significant Bit with Stretch on Pixels Neighborhood

Background: The confidentiality of a message may at times be compromised. Steganography can hide such a message in certain media. Steganographic media such as digital images have many pixels that can accommodate secret messages. However, the length of secret messages may not match with the number of image pixels so the messages cannot be inserted into the digital images.Objective: This research aims to see the dynamics between an image size and a secret message’s length in order to prevent out of range messages entered in an image.Methods: This research will combine the Least Significant Bit (LSB) method and the Stretch technique in hiding secret messages. The LSB method uses the 8th bit to hide secret messages. The Stretch technique dynamically enlarges the image size according to the length of the secret messages. Images will be enlarged horizontally on the rightmost image pixel block until n blocks of image pixels.Results: This study compares an original image size and a stego image size and examines a secret message’s length that can be accommodated by the stego image, as well as the Mean Square Error and Structure Similarity Index. The test is done by comparing the size change of the original image with the stego image from the Stretch results, where each original image tested always changes dynamically according to the increasing number of secret message characters. From the MSE and SSIM test results, the success was only with the first image, while the second image to the fourth image remained erroneous because they also did not have the same resolution.Conclusion:The combination of LSB steganography and the Stretch technique can enlarge an image automatically according to the number of secret messages to be inserted. For further research development, image stretch must not only be done horizontally but also vertically.
Classifying Time-Bound Hierarchical Key Assignment Schemes

A time-bound hierarchical key assignment scheme (TBHKAS) ensures time-restricted access to the hierarchical data. There is a large number of such schemes are proposed in the literature. Crampton et al. studied the existing hierarchical key assignment schemes in detail and classify them into generic schemes on the basis of storage and computational requirements. Such generic classification helps the implementers and researchers working in this area in identifying a suitable scheme for their work. This work studies the TBHKAS and classifies them with the same spirit. As best of our knowledge, the proposed classification captures every existing TBHKAS in the literature. Furthermore, the proposed schemes are compared and analyzed in detail.
Video object segmentation based on motion-aware ROI prediction and adaptive reference updating

Video object segmentation (VOS) is a research hotspot in the field of computer vision. Traditional video object segmentation methods based on deep learning have some problems such as difficulty in adapting to the change of object appearance and low segmentation speed. In this manuscript, we propose a robust VOS method based on motion-aware region of interest (ROI) prediction and adaptive reference updating. Firstly, based on the historical movement trajectory of target region to perceive motion trend dynamically, we predict the motion-aware ROI of target object in the current frame and use it as the input of segmentation network. Then, in order to adapt to the appearance changes of target in the video, the adaptive updating strategy of reference is given to dynamically update the reference frame during the segmentation process. Finally, VOS Siamese network is designed for fast segmentation. Experiments on three public benchmark datasets, DAVIS-2016 and DAVIS-2017, show that the proposed method performs better than the state-of-the-art approaches.
DeltaPQ

High dimensional data is ubiquitous and plays an important role in many applications. However, the size of high dimensional data is usually excessively large. To alleviate this problem, in this paper, we develop novel techniques to compress and search high dimensional data. Specifically, we first apply vector quantization, a classical lossy data compression method. It quantizes a high dimensional vector to a sequence of small integers, namely the quantization code. Then, we propose a novel lossless compression algorithm, DeltaPQ, to further compress the quantization codes. DeltaPQ organizes the quantization codes in a tree structure and stores the differences between two quantization codes rather than the original codes. Among the exponential number of possible tree structures, we develop an efficient algorithm, whose time and space complexity are linear to the number of codes, to find the one with optimal compression ratio. The approximate nearest neighbor search query can be processed directly on the compressed data with small space overhead in a few bytes. Many similarity measures can be supported, such as inner product, cosine similarity, Euclidean distance, and Lp-norm. Experimental results on five large-scale real-world datasets show that DeltaPQ achieves a compression ratio of up to 5 (and often greater than 2) on the quantization codes whereas the state-of-art general-purpose lossless compression algorithms barely work.
EASIER system. Language resources for cognitive accessibility.

Difficulties in understanding texts that contain unusual words can create accessibility barriers for people with cognitive, language and learning disabilities. In this work, the EASIER system, a web system that provides various tools which improve cognitive accessibility, is presented. From a text in Spanish, complex words are detected and synonyms, a definition and a pictogram are offered for each complex word detected. Language and accessibility resources were used, such as easy-to-read dictionaries. The web system can be accessed from both desktop computers and mobile devices. Moreover, a browser extension is also offered.
A New Social User Anomaly Behavior Detection System Based on Blockchain and Smart Contract

Inspired from the iForest algorithmic scheme, we propose an iForest-based blockchain social media anomaly behavior detection method via the improved tree algorithm, for the purpose of isolating the anomalous behaviors as an outlier. The model is integrated with the smart contract structure of blockchain. In the overall system, the user data is sent to the intelligent contract for a period of time. After the identification of the abnormal behavior of social media users, the abnormal behavior in blockchain is marked and stored in the abnormal chain. To a certain extent, the scheme protects users' privacy, improves the efficiency and accuracy of iForest anomaly detection, and is more suitable for multi-dimensional heterogenous data-centric social media user behavior detection.