Reviewerly/exHarmony huggingface dataset:
Generation of Hardware Modules for Comparison in Residue Numeral Systems

The residue numeral system (RNS) is one of the alternative number representation systems which has a strong potential for using it in computing systems. One of the known problems of the RNS is the complexity of the implementation of some basic arithmetic operations, including comparison and division operations. The paper considers the problem of implementing the comparator for the residue number system with one of the popular moduli set {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> +1}. Description of two existing methods and its properties are provided. After that, an experimental evaluation of the described methods using modern EDA tools and analysis of results are presented.
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search

Differentiable neural architecture search (DNAS) is known for its capacity in the automatic generation of superior neural networks. However, DNAS based methods suffer from memory usage explosion when the search space expands, which may prevent them from running successfully on even advanced GPU platforms. On the other hand, reinforcement learning (RL) based methods, while being memory efficient, are extremely time-consuming. Combining the advantages of both types of methods, this paper presents RADARS, a scalable RL aided DNAS framework that can explore large search spaces in a fast and memory-efficient manner. RADARS iteratively applies RL to prune undesired architecture candidates and identifies a promising subspace to carry out DNAS. Experiments using a workstation with 12 GB GPU memory show that on CIFAR-10 and ImageNet datasets, RADARS can achieve up to 3.41% higher accuracy with 2.5X search time reduction compared with a state-of-the-art RL-based method, while the two DNAS baselines cannot complete due to excessive memory usage or search time. To the best of the authors’ knowledge, this is the first DNAS framework that can handle large search spaces with bounded memory usage.
Sentiment Analysis of Chinese Product Reviews Based on BERT Word Vector and Hierarchical Bidirectional LSTM

Sentiment analysis data on Chinese shopping comments has gained much attention in recent years. Many previous studies focus on the relationship between words in a single sentence but ignore the context relationship between sentences. To better serve this problem, we propose a method based on Bidirectional Encoder Representations from Transformers (BERT) pre-training language model, Hierarchical Bi-directional Long Short-Term Memory (Hierarchical Bi-LSTM) and attention mechanism for Chinese sentiment analysis. We first use BERT pretraining language model to obtained word vector, then applies Hierarchical Bi-LSTM model to extract contextual feature from sentences and words. Finally, we inj ect attention mechanism to highlight key information. Base on the experimental results, our method achieves more idealistic performance.
Personalized API Recommendations

Application Programming Interfaces (APIs) play an important role in modern software development. Developers interact with APIs on a daily basis and thus need to learn and memorize those APIs suitable for implementing the required functions. This can be a burden even for experienced developers since there exists a mass of available APIs. API recommendation techniques focus on assisting developers in selecting suitable APIs. However, existing API recommendation techniques have not taken the developers personal characteristics into account. As a result, they cannot provide developers with personalized API recommendation services. Meanwhile, they lack the support for self-defined APIs in the recommendation. To this end, we aim to propose a personalized API recommendation method that considers developers’ differences. Our API recommendation method is based on statistical language. We propose a model structure that combines the N-gram model and the long short-term memory (LSTM) neural network and train predictive models using API invoking sequences extracted from GitHub code repositories. A general language model trained on all sorts of code data is first acquired, based on which two personalized language models that recommend personalized library APIs and self-defined APIs are trained using the code data of the developer who needs personalized services. We evaluate our personalized API recommendation method on real-world developers, and the experimental results show that our approach achieves better accuracy in recommending both library APIs and self-defined APIs compared with the state-of-the-art. The experimental results also confirm the effectiveness of our hybrid model structure and the choice of the LSTM’s size.
Outliers Detection in Unmanned Aerial System Data

Operation of many dynamic systems is connected with data processing and analysis. Presence of single value that is significantly different from other data in the set is one of the main problems of data processing. Presence of outliers in Unmanned Aerial System (UAS) data indicates about system unreliability that can be considered dangerous for all airspace users and reduce the safety of aviation. A set of simple statistical tests for outliers detection in UAS data was considered in the current study. UAS angular data has a cyclic scale and requires a specific approach for its processing. Parametric and non- parametric methods have been studied to detect outliers in UAS parameters with a cyclic scale. Results of k-sigma test for outliers detection in vehicle heading data gives maximal probability of correct detection and minimal values for false alarm and outlier omission.
A Survey on Unsupervised Transfer Clustering

Clustering is widely used in text analysis, natural language processing, image segmentation and other data mining fields. However, traditional clustering algorithms, such as K-means, can produce good clustering result only when the instance size is large enough. When the instance size is insufficient, the clustering result will be poor. One way to solve this problem is transfer learning. At present, researches on transfer learning mainly focus on classification and recognition, while researches on clustering are very limited, but become more and more promising. This survey focuses on categorizing and reviewing the current progress on unsupervised transfer clustering algorithm. We also explore some potential future issues in unsupervised transfer clustering research.
A Novel Method for Laser Peak Detection with Subpixel Accuracy for the Rail Corrugation Measurement

In this article, a new wavelet-based laser peak detection algorithm is proposed having subpixel accuracy. The algorithm provides an accurate and rapid measurement platform for the rail surface corrugation with no need to any image noise elimination. The proposed rail Corrugation Measurement System (CMS) is based on the laser triangulation principle, and the accuracy of such system is mainly affected by the laser peak detection in the captured image. The intensity of each row or column of the image is taken as a 1-D discrete signal. Intensity distribution of a laser stripe in this signal follows a Gaussian pattern contaminated by the white noise. Against usual peak detection algorithms with need to prenoise-filtering process, the proposed method based on the wavelet transform is able to perform these tasks efficiently and robustly. Present wavelet-based methods for the peak detection are at pixel level, but for achieving high accuracy subpixel detection is proposed. Experiments show that the capability of the proposed method for laser peak detection is more accurate and faster than the filter-based methods, especially for low S/N ratios. Also, this technique can be utilized for any application in laser peak detection with subpixel accuracy. A prototype system based on the proposed method for the rail corrugation measurement has been designed and manufactured. Results of the rail corrugation measurement guarantee capability of the proposed methodology for accurate measurement of the rail corrugation and its potential for industrial application.
Secure Multi-party Computation-Based Privacy-Preserving Data Analysis in Healthcare IoT Systems

Recently, many innovations have been experienced in healthcare by rapidly growing Internet-of-Things (IoT) technology that provides significant developments and facilities in the health sector and improves daily human life. The IoT bridges people and information technology and speeds up shopping. For these reasons, IoT technology has started to be used on a large scale. Thanks to the use of IoT technology in health services, chronic disease monitoring, health monitoring, rapid intervention, early diagnosis and treatment, etc., facilitate the delivery of health services. However, the data transferred to the digital environment pose a threat of privacy leakage. Unauthorized persons have used them, and there have been malicious attacks on the health and privacy of individuals. In this chapter, it is aimed to propose a model to handle the privacy problems based on federated learning. Besides, we apply secure multi-party computation. Our proposed model presents an extensive privacy and data analysis and achieves high performance.
DAEMA: Denoising Autoencoder with Mask Attention

Missing data is a recurrent and challenging problem, especially when using machine learning algorithms for real-world applications. For this reason, missing data imputation has become an active research area, in which recent deep learning approaches have achieved state-of-the-art results. We propose DAEMA (Denoising Autoencoder with Mask Attention), an algorithm based on a denoising autoencoder architecture with an attention mechanism. While most imputation algorithms use incomplete inputs as they would use complete data - up to basic preprocessing (e.g. mean imputation) - DAEMA leverages a mask-based attention mechanism to focus on the observed values of its inputs. We evaluate DAEMA both in terms of reconstruction capabilities and downstream prediction and show that it achieves superior performance to state-of-the-art algorithms on several publicly available real-world datasets under various missingness settings.
Exploring How Saliency Affects Attention in Virtual Reality

In this paper, we investigate how changes in the saliency of the Virtual Environment (VE) affect our visual attention during different tasks. We investigate if users are attracted to the most salient regions in the VE. This knowledge will help researchers design optimal VR environments, purposefully direct the attention of users, and avoid unintentional distractions. We conducted a user study (N = 30) where participants performed tasks (video watching, object stacking, visual search, waiting) with two different saliency conditions in the virtual environment. Our findings suggest that while participants notice the differences in saliency, their visual attention is not diverted towards the salient regions when they are performing tasks.
Artificial Intelligence Vision For Visually Impaired

The object detection concept is widely used for automation or tracking purpose; hence, it is used in self-driving cars. There are many applications other than automation that use object detection but are not explored in depth till date. This research involves one such application that uses object detection to help the visually impaired to identify the objects ahead of them for safe navigation. A few attempts have been previously made to implement such a device, but it was only confined to small objects. The results show that the proposed system implements both object detection and an ultrasonic sensor and GPS module to monitor location. SSD- Mobile Net is used to implement the object detection to obtain greater accuracy.
Blockchain-based Consensus Study on Distributed Control Systems

In this paper, two blockchain-based consensus mechanisms are proposed for distributed control systems, those are the proof-of-work-based (PoW-based) and proof-of-stake-based (PoS-based) consensus mechanisms. The basic idea and process of these blockchain-based consensus mechanisms are introduced, giving rise to the dynamic, impartial and efficient consensus compared with the consensus protocol achieved by traditional control theories.
Research on Password Detection Technology of IoT Equipment Based on Wide Area Network

At present, while the Internet of Things (IoT) devices bring convenience to people, security issues have led to an increasing number of threats to IoT security. Since IoT devices have a Web application system for device managers to operate, the system can view device information, control and configure device status, and its security is of great significance. Among the various authentication methods provided by IoT devices, the password information authentication mechanism is still a critical method for Web login. If the IoT device has a weak Web password, once a hacker discovers the device, it is straightforward to be attacked and implanted with malicious code to control the device and attack other devices in the network. In response to this problem, this paper designs a set of automatic detection frameworks for weak passwords for web application systems of IoT devices. Based on this framework, an automated weak password detection system was developed to detect weak Web passwords on IoT devices on the wide-area networks of Beijing, Shandong Province, and Zhejiang Province. A total of 12,179 devices with weak Web passwords were found, accounting for all discovered IoT devices of 7.58%, verifying the effectiveness of the proposed framework.
Machine Learning Approach towards Quality Control of Aerosol-Jet Printed Polymer Optical Waveguides Material

In this work, a Machine Learning approach to predict the quality of the aerosol jet printed polymer optical waveguides material is presented. For the acquisition of necessary data, a video camera system was integrated into the aerosol jet printing. An image processing tool was developed to extract printed line width and to map to the process data of the printing. For modeling, an Automated Machine Learning framework was utilized to reduce development time for a proof of concept. The final ML model reached R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 74 % in predicting line width and surpassed the statistical approaches.
Multi-scale Attention-Based Feature Pyramid Networks for Object Detection

Feature pyramid network (FPN) is widely used for multi-scale object detection. While lots of FPN based methods have been proposed to improve detection performance, there exists semantic difference between cross-scale features. Therefore, simple connections bring spatial or channel information loss, while excessive connections bring extra parameters and inference cost. Besides, the fusion of too many features may lead to the information decay and feature aliasing. To deal with the above problems, we propose the Multi-scale Attention-based Feature Pyramid Networks (MAFPN), to fully exploit spatial and channel information and generate a better feature representation for each level from multi-scale features. Taking scale, spatial and channel information into consideration at the same time, MAFPN can process multi-scale input more comprehensively than most conventional methods. The experimental results show that our MAFPN can improve the detection performance of both two-stage and one-stage detectors with an acceptable increase of inference cost.
Challenges and Key Points for Fraud Detection in Aviation

As most of the fraud detection systems take part on the side of banking sector, aviation industry is one of the important sectors which faces seriously damaging fraud cases. As fraud cases increase exponentially, airline companies started to face with huge financial losses caused by chargeback expenses. Up to the present, rule-based systems have been used widely to struggle with fraud in aviation. However, their inefficacy, especially against zero-day fraud attacks, encourage the researchers to exploit machine learning approach in aviation. In this study, we created a fraud detection mechanism using a suite of machine learning algorithms on an airline payment dataset. We also proposed several techniques to overcome most common challenges of fraud detection and to point some misapplications in literature. We emphasized the importance of chronological order train-test sets with comparative experiments, then we used undersampling technique to resolve imbalanced dataset problem, afterwards the success of fraud detection system was evaluated from a cost-based perspective and cumulative training set technique was applied to determine the optimal timewise distance between train and test sets. Finally, we carried out a set of feature selection trials to observe its impact on final results.
Generating Personalized Summaries of Day Long Egocentric Videos

The popularity of egocentric cameras and their always-on nature has lead to the abundance of day long first-person videos. The highly redundant nature of these videos and extreme camera-shakes make them difficult to watch from beginning to end. These videos require efficient summarization tools for consumption. However, traditional summarization techniques developed for static surveillance videos or highly curated sports videos and movies are either not suitable or simply do not scale for such hours long videos in the wild. On the other hand, specialized summarization techniques developed for egocentric videos limit their focus to important objects and people. This paper presents a novel unsupervised reinforcement learning framework to summarize egocentric videos both in terms of length and the content. The proposed framework facilitates incorporating various prior preferences such as faces, places, or scene diversity and interactive user choice in terms of including or excluding the particular type of content. This approach can also be adapted to generate summaries of various lengths, making it possible to view even 1-minute summaries of one's entire day. When using the facial saliency-based reward, we show that our approach generates summaries focusing on social interactions, similar to the current state-of-the-art (SOTA). The quantitative comparisons on the benchmark Disney dataset show that our method achieves significant improvement in Relaxed F-Score (RFS) (29.60 compared to 19.21 from SOTA), BLEU score (0.68 compared to 0.67 from SOTA), Average Human Ranking (AHR), and unique events covered. Finally, we show that our technique can be applied to summarize traditional, short, hand-held videos as well, where we improve the SOTA F-score on benchmark SumMe and TVSum datasets from 41.4 to 46.40 and 57.6 to 58.3 respectively. We also provide a Pytorch implementation and a web demo at https://pravin74.github.io/Int-sum/index.html.
Secure Transfer of Images Using Pixel-Level and Bit-Level Permutation Based on Knight Tour Path Scan Pattern and Henon Map

During last two decades, security of the digital images apparently increased. This paper involves Knight Tour Path Scan Pattern and Henon Map methods to encrypt an image. The basic idea used in the proposed algorithm is that the plain images go through pixel-level and bit-level shuffling operation followed by diffusion process. With the help of knight tour path scan pattern, each 8*8 block of the plain image arbitrarily permuted and to enhance the security of image bit-level permutation also involved in the proposed algorithm where every pixel bits are shuffled according to mutation rule. In the diffusion operation, a bitwise exclusive-OR operation applied between bit-level permuted image and random sequence generated from henon map. The strength of the proposed algorithm is to reduce the high correlation and produce the secure cipher image.
Basic research on a handwritten note image recognition system that combines two OCRs

In this paper, we propose an OCR system that recognizes the contents of manual writing in handouts. Since the target handouts contain a mixture of Japanese and mathematical formulas, we thought that one type of OCR systems alone would not provide sufficient accuracy. Therefore, in this proposed system, we will consider combining two types of OCR systems. The OCRs to be combined are Tesseract and Mathpix, which are good at recognizing Japanese and mathematical formulas, respectively. When the two OCRs are combined, the final plausible result must be selected, but we wondered if the OCR score could be used as an indicator of selection. The OCR score is a self-evaluation value that is output together with the recognition result. However, there is no guarantee that the OCR score and the actual recognition result show the same tendency. Therefore, in this paper, after classifying the written contents into three categories, words, sentences, and mathematical formulas, we verified whether the recognition results of each OCR can be appropriately selected based on the OCR score. At this point, the result is that further improvement is needed to make the selection based on the OCR score alone. However, if it becomes possible to classify handwritten contents with the proposal system by additional learning etc., it will be possible to analyze the handwritten contents efficiently, and it is expected that it will be possible to improve the handouts and provide feedback to the lesson plan.
Speed up Differential Evolution for ranking of items in recommendation systems

Recommendation systems can suggest users list of items they have not yet seen but might be interested in. To improve the quality of the generated recommendations, different techniques are often used which try to personalize recommendations. Usually user preferences are stored in the form of a vector in which individual values describe to what extent a given feature is desired by the user. To find this vector, metaheuristic algorithms can be used, however their main drawback is their computational complexity. Therefore, in this paper, a modification of the Differential Evolution algorithm is proposed to enable faster computation of the ranking score for each item in the system, which is used to create a recommendation list. Experiments have been performed on the current MovieLens 25m database and they show that our modification can significantly speed up the process of finding a preference vector, without losing their quality for the top-N recommendation task. We will also address the vulnerability of recommendation systems to profile injection attacks, as a result of which an attacker can influence the generated recommendations.