Reviewerly/exHarmony huggingface dataset:
Contrast transfer function of de-noising algorithms

This paper presents a comprehensive study on the contrast transfer function of de-noising algorithms. In order to cover a broad variety of methods, 45 de-noising algorithms are chosen considering their recognized efficiency in the different application domains of image processing. Advanced methods are targeted: wavelet transform-based algorithms with Daubechies, symlets, curvelets, contourlets, patch-based methods such as BM3D, NL-means algorithms and deep learning approaches; in addition, classical spatial filtering methods are considered, such as Wiener, median, Gauss filtering, and adaptive filtering approaches such as anisotropic diffusion and synthetic aperture radar filtering. The contrast transfer function is provided for each algorithm. Ranking of the set of de-noising algorithms is established according to proposed metrics. The paper provides practical methodology and novel results dedicated to the evaluation of the contrast transfer function of de-noising approaches from literature.
Hardware Implementation of Video Processing Device using Residue Number System

This paper considers the creation of video signal processing device. Important performance criteria of these devices are speed and power consumption. We used an Alinx AX309 board containing FPGA Xilinx Spartan6-xc6s1x9 as a hardware basis for the implementation of the system. OV7670 video camera was used to obtain a video signal. The output of the processed video was carried out on the Alinx AN430 LCD display and on the standard VGA port. We used the Residue Number System to accelerate calculations. It allowed to improve device speed by 28% compared to using traditional two's complement number system.
Multi-scale Scene Text Detection via Resolution Transform

Scene text detection is a challenging task because there are many small text targets in the natural scene and the size of scene text varies greatly. For the current popular scene text detection methods, such as EAST and Textboxes, it is difficult to detect small text and text with large differences in size well at the same time. To solve the problem, we propose a novel multi-scale scene text detection method based on EAST. The proposed method extracts high-resolution feature maps at multiple scales via resolution transform and detects text on these feature maps. Through the resolution transform module, the proposed method can detect both kinds of text well. Besides, we use aggregated feature pyramid module to efficiently pass both low-level and high-level information to feature maps at each scale. Experiments on datasets ICDAR2015 and COCO-Text demonstrate that the proposed method has a comparable performance with state-of-the-art methods and it is more efficient. For ICDAR2015 and COCO-Text datasets, the proposed method achieves an F-score of 0.84 and 0.42 respectively.
Smart Trolley with Automatic Master Follower and Billing System

In the modern era, every supermarket makes use of shopping trolleys in order to help customers carry products which they wanted to purchase. The customers have to take the products which they wish to purchase and should drop them into the shopping trolley and then they have to reach the billing section. The billing process is quite hectic and it consumes more time and consequently it has created the need for shops to employ more human resources in the billing section, but still this problem is not resolved and some customers even face the problem of pushing the trolley forward when it is loaded with huge quantity. "Smart Shopping Trolley" is going to be a better solution to it where the trolley is made to follow the customer automatically using a "Kinect Sensor". Then, an automatic billing system is also incorporated where the items will be scanned by a Radio Frequency Identification reader and the amount will be summed accordingly so that the final amount is paid without standing in a large queue.
Bicycle Behavior Recognition Using 3-Axis Acceleration Sensor and 3-Axis Gyro Sensor Equipped with Smartphone

It becomes possible to prevent accidents beforehand by predicting dangerous riding behavior based on recognition of bicycle behaviors. In this paper, we propose a bicycle behavior recognition method using a three-axis acceleration sensor and three-axis gyro sensor equipped with a smartphone when it is installed on a bicycle handlebar. We focus on the periodic handlebar motions for balancing while running a bicycle and reduce the sensor noises caused by them. After that, we use machine learning for recognizing the bicycle behaviors, effectively utilizing the motion features in bicycle behavior recognition. The experimental results demonstrate that the proposed method accurately recognizes the four bicycle behaviors of stop, run straight, turn right, and turn left and its F-measure becomes around 0.9. The results indicate that, even if the smartphone is installed on the noisy bicycle handlebar, our proposed method can recognize the bicycle behaviors with almost the same accuracy as the one when a smartphone is installed on a rear axle of a bicycle on which the handlebar motion noises can be much reduced.
Combined Layer GAN for Image Style Transfer

Image style transfer is an increasingly interesting topic in computer vision where the goal is to map images from one style to another. In this paper, we propose a new framework called Combined Layer GAN as a solution of dealing with image style transfer problem. Specifically, the edge-constraint and color-constraint are proposed and explored in the GAN based image translation method to improve the performance. The motivation of the work is that color and edge are fundamental vision factors for an image, while in the traditional deep network based approach, there is a lack of fine control of these factors in the process of translation and the performance is degraded consequently. Our experiments and evaluations show that our novel method with the edge and color constrains is more stable, and significantly improves the performance compared with the traditional methods.
TDMentions: A Dataset of Technical Debt Mentions in Online Posts

The term technical debt is easy to understand as a metaphor, but can quickly grow complex in practice. We contribute with a dataset, TDMentions, that enables researchers to study how developers and end users use the term technical debt in online posts and discussions. The dataset consists of posts from news aggregators and Q&A-sites, blog posts, and issues and commits on GitHub.
Identifying Scalability Debt in Open Systems

Architectural technical debt can be generated by changes in the business and the environment of an organization. In this paper, we emphasize the change in scalability requirements due to new regulations. Scalability is the ability of a system to handle an increased workload. For complex systems that are abruptly exposed via open interfaces and hence a greater workload, the scalability requirements may quickly increase, leading to technical debt. We term this scalability debt. This paper describes scalability triage, a light-weight, novel technique for identifying scalability threats as a form of technical debt. We illustrate this technique with an open banking case from a large software organization. Open banking is partly caused by the new European PSD2 regulative that enforce banks to open interfaces to unknown third-party actors. Banking systems are well-established, mature systems. However, with the advent of open banking and PSD2, the workload may quickly rocket. This leads to tougher scalability requirements and accumulated architectural debt, despite previously sound architectural decisions. Using scalability triage, such risks may be identified fast. It will then be possible to prevent this form of technical debt with timely reengineering.
Emancipatory accounting and corporate governance: Critical and interdisciplinary perspectives

The rise of big data has led to profound changes to the dynamics of accumulation and profiteering. Today, data is captured, produced, and reproduced with such regularity that its collection, utility, and value can go largely unnoticed, giving rise to “surveillance capitalism” (Zuboff, 2019a). This paper explores emerging forms of exploitation within the data economy, including the rise of “instrumentarian power” (Zuboff, 2019a), opacity surrounding data collection and use, and the impact of data breaches on our capacity to function within the information economy. We consider whether new forms of extended responsibility reporting may help to disrupt the trajectory of surveillance capitalism and democratise participation in the digital economy (Crawford, 2021). We draw on the accounting literature on organisational disclosures to consider whether the disclosure of data breaches might enhance accountability by making aspects of the surveillance economy knowable to us. Empirically, our analysis considers the various rules currently governing the disclosure of data breaches in Australia, the US, the EU, and Canada, and the application of these rules in practice. While regulation of the digital economy is developing, laws governing the disclosure of data breaches are highly dependent on an organisation’s judgement. As a consequence, the nature, scale, and timeliness of these disclosures vary significantly, and the lack of clear routines makes it difficult for stakeholders to assess data risks. In response, we consider whether a mandatory disclosure framework might contribute usefully to the public “naming and taming” of surveillance capitalism (Zuboff, 2019a) and the democratisation of our digital future.
Real-time Sound Detection and Regeneration Based on Optical Flow Algorithm of Laser Speckle Images

Sound detection with optical means is an appealing research topic. Laser speckle is one effective method to detect the sound due to its sensitivity to tiny motion. The traditional laser-speckle sound recovery methods require a high-speed camera to record fast-moving speckle images and then recover the original sound signals from the motion information of the captured speckle images. In this manuscript, a laser microphone system is proposed to detect and regenerate the sound signal in real time. In the proposed system, only a small part of the imaging sensor is used to ensure a high sampling rate with a common industrial camera and reduce the computation time consumption. Meanwhile, optical flow algorithm is employed to obtain the motion information of captured speckle images and regenerate the sound. These two points allow us to capture images from the camera and regenerate sound in real time without storing any data into the computer, which greatly increases the speed of the system and achieves a microphone-like functions. Experiments are conducted to show that the proposed system can detect and regenerate the sound signal in real-time with a high quality.
The Analysis of Text Categorization Represented With Word Embeddings Using Homogeneous Classifiers

Text data mining is the process of extracting and analyzing valuable information from text. A text data mining process generally consists of lexical and syntax analysis of input text data, the removal of non-informative linguistic features and the representation of text data in appropriate formats, and eventually analysis and interpretation of the output. Text categorization, text clustering, sentiment analysis, and document summarization are some of the important applications of text mining. In this study, we analyze and compare the performance of text categorization by using different single classifiers, an ensemble of classifiers, a neural probabilistic representation model called word2vec on English texts. The neural probabilistic based model namely, word2vec, enables the representation of terms of a text in a new and smaller space with word embedding vectors instead of using original terms. After the representation of text data in new feature space, the training procedure is carried out with the well-known classification algorithms, namely multivariate Bernoulli naïve Bayes, support vector machines and decision trees and an ensemble algorithm such as bagging, random subspace and random forest. A wide range of comparative experiments are conducted on English texts to analyze the effectiveness of word embeddings on text classification. The evaluation of experimental results demonstrates that an ensemble of algorithms models with word embeddings performs better than other classification algorithms that uses traditional methods on English texts.
Least Significant Bit Hash Algorithm for Digital Image Watermarking Authentication

Recent advancements in data security like images, audio, and video have shifted the focus of security from using cryptography to steganography or both. Steganography, the most widely used technique is the Least Significant Bit method which is simply and yet vulnerable to attacks. However, the study of modifying the Least Significant Bit method would be challenging or impractical to look at the hidden message domain where the process of extensive study must be done. However, due to its simplicity of the Least Significant Bit method, this technique could be easily modified, but the challenge is how you will modify with the digital image will be safety and integrity which is the top issue on information explosion. This study provides novelty on digital watermarking using least significant bit hash algorithm and is analyzed using data capacity analysis, histogram analysis, and hamming distance.
Cloud-Based In-Memory Columnar Database Architecture for Continuous Audit Analytics

ABSTRACT This study introduces the database architecture that manages data in the main physical memory using columnar format. It proposes a conceptual framework for applying the in-memory columnar database systems to support high-speed continuous audit analytics. To evaluate the proposed framework, this study develops a prototype and conducts simulation tests. The test results show high computational efficiency and effectiveness of the in-memory columnar database relative to the conventional ERP system. Furthermore, the deployment of the in-memory columnar database to the cloud shows the promise of scaling up the in-memory columnar database for continuous audit analytics.
Sequential Behavior Modeling for Next Micro-Video Recommendation with Collaborative Transformer

Micro-video recommendation is important for micro-video social platform, which provides its users with micro-videos they may interested in. In this paper, we propose a new variant of Transformer, alleviating the drawbacks of Recurrent Neural Networks (RNN) which tend to compress all history records in a fixed hidden representation, to model users' sequential behavior for next micro-video recommendation. Firstly, we employ self-attention to capture micro-video's multi-modal features of different importance. Secondly, we make use of multi-head attention to learn users' preference from historical records. We show how the Transformer combined with Collaborative Filtering (CF) and user-video sequential interaction can be used to perform next micro-video recommendation. Extensive experiments are conducted on two collected micro-video datasets, i.e., Toffee and TikTok. The experimental results demonstrate the proposed method is more effective compared with several state-of-the-art sequential recommendation methods.
Continuous Bidirectional Optical Flow for Video Frame Sequence Interpolation

Existing optical flow-based frame interpolation frameworks usually suffer from two problems. First, it is difficult to accurately estimate both large motion and fine motion in the optical flow estimation stage. Second, the hole problem and occlusion problem cannot be efficiently solved in the pixel synthesis step. In this paper, we propose a novel optical flowbased frame interpolation framework, which consists of two submodules: optical flow network and pixel synthesis network. In the optical flow network, we estimate bidirectional optical flow sequences iteratively, which makes full use of the continuity of motion and therefore improves the accuracy of the optical flow estimation. Besides, a novel multi-scale architecture is developed to capture finer motions. In the pixel synthesis network, we fuse the statistical information generated during forward warping to solve the hole problem and the occlusion problem. Experimental results demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods.
Automatic landmark discovery for learning agents under partial observability

Abstract In the reinforcement learning context, a landmark is a compact information which uniquely couples a state, for problems with hidden states. Landmarks are shown to support finding good memoryless policies for Partially Observable Markov Decision Processes (POMDP) which contain at least one landmark. SarsaLandmark, as an adaptation of Sarsa(λ), is known to promise a better learning performance with the assumption that all landmarks of the problem are known in advance. In this paper, we propose a framework built upon SarsaLandmark, which is able to automatically identify landmarks within the problem during learning without sacrificing quality, and requiring no prior information about the problem structure. For this purpose, the framework fuses SarsaLandmark with a well-known multiple-instance learning algorithm, namely Diverse Density (DD). By further experimentation, we also provide a deeper insight into our concept filtering heuristic to accelerate DD, abbreviated as DDCF (Diverse Density with Concept Filtering), which proves itself to be suitable for POMDPs with landmarks. DDCF outperforms its antecedent in terms of computation speed and solution quality without loss of generality. The methods are empirically shown to be effective via extensive experimentation on a number of known and newly introduced problems with hidden state, and the results are discussed.
An efficient GPU approach for designing 3D cultural heritage information systems

We propose a new architecture for 3D information systems that takes advantage of the inherent parallelism of the GPUs. This new solution structures information as thematic layers, allowing a level of detail independent of the resolution of the meshes. Previous proposals of layer based systems present issues, both in terms of performance and storage, due to the use of octrees to index information. In contrast, our approach employs two-dimensional textures, highly efficient in GPU, to store and index information. In this article we describe this architecture and detail the GPU algorithms required to edit these layers. Finally, we present a performance comparison of our approach against an octree-based system.
A Chaotic Block Cryptographic System Resistant to Power Analysis Attack

The securities of chaotic cryptographic systems are widely evaluated by conventional tests such as the character frequency test, entropy test and avalanche test. However, when the chaotic cryptosystem is in operation, side channel information such as power consumption, and electromagnetic radiation is leaked. The side channel information can be used to attack the cryptosystem, e.g. the side channel attack (SCA), which is a threat for the security of chaotic cryptographic systems. This paper proposes a chaotic block cryptographic algorithm that can resist the SCA, with the aim of enhancing the security of chaotic cryptosystems. Masking and hiding mechanisms are used in this work. By using the former, the intermediate data correlated with the plaintexts/keys are masked by random numbers, thus no direct correlation exists between the power consumption and the plaintexts/keys and the first order SCA can be counteracted. By using the latter, additional noise is added to the side channel information by randomizing the operation sequence of the algorithm. Combining these two methods, the higher order SCA can be counteracted. To evaluate the security of the proposed system, the correlation power analysis attacks are carried out based on the target device of an Atmel XMEGA microcontroller. For the proposed system, the correlation coefficient calculated from the correct key is not larger than the incorrect keys. However, for the unprotected cipher system, the correlation coefficient calculated from the correct key is 0.8 and the coefficients calculated from the incorrect keys are less than 0.5, i.e. the system can be attacked. Experimental results demonstrate that the proposed cryptosystems can counteract the correlation power analysis attacks and maintain the security performance for the chaotic cryptographic systems.
Knowing User Better: Jointly Predicting Click-Through and Playtime for Micro-Video

Most micro-video recommender systems use the click-through to measure user satisfaction. However, the amount of time that users spend on a video, the playtime, measures user engagement on video contents and should be used as a complement to the click based signals. In this paper, we propose a coarse-to-fine multi-task jointly optimizing model to predict click-through and playtime. Following the click-through prediction, the playtime is first discretized into several intervals and classified into a specific one with a proposed ordered-balanced cross entropy loss. Then, to further improve upon coarse estimates, we learn a subtle offset with a regressor and produce a fine-grained playtime estimation. To make mutual promotion between click-through and playtime predictors, we optimize them jointly in a multi-task manner. Experimental results show that we achieve state-of-the-art performance on recommendation task and demonstrate effectiveness on playtime prediction at the same time.
A Privacy-Preserving Sensor Aggregation Model Based Deep Learning in Large Scale Internet of Things Applications

Privacy-preservation in data aggregation in large scale Internet of Things applications is challenging. Sensitive data from a result of collecting sensor data needs attentions to address privacy issues. We present a privacy-preserving model to protect data aggregation between sensor gateway and storage servers. Our proposed scheme is designed for decentralized networks and passwordless by obfuscating sensor data. We design, implement and evaluate a practical privacy-preserving system using deep learning autoencoder with convolutional neural network architecture. We do a statistical analysis and perform simulation on computer and IoT board machines. Evaluation process involves training and testing phases with a dataset. We measure system accuracy and computation time. The simulation and experimental results show that privacy-preserving-based deep learning model can address privacy issues on data aggregation and guarantee scalability and performance on applications.
Energy-efficient continual learning in hybrid supervised-unsupervised neural networks with PCM synapses

Artificial neural networks (ANNs) can outperform the human ability of object recognition by supervised training of synaptic parameters with large datasets. Contrarily to the human brain, however, ANNs cannot continually learn, i.e. acquire new information without catastrophically forgetting previous knowledge. To solve this issue, we present a novel hybrid neural network based on CMOS logic and phase change memory (PCM) synapses, mixing a supervised convolutional neural network (CNN) with bio-inspired unsupervised learning and neuronal redundancy. We demonstrate high classification accuracy in MNIST and CIFAR10 datasets (98% and 85%, respectively) and energy-efficient continual learning of up to 30% of non-trained classes with 83% average accuracy.