Reviewerly/exHarmony huggingface dataset:
Vision-based Obstacle Avoidance Algorithm for Mobile Robot

The obstacle avoidance technology of mobile robots is the key to affect the reachable space and working ability of mobile robots, and is also a research hotspot in the field of robots. In this paper, under the environment of ROS (Robot Operating System) platform environment, aiming at the shortcomings of traditional mobile robot obstacle avoidance methods, an obstacle avoidance method based on machine vision for dynamic obstacle detection is proposed. In the aspect of vision, this method uses the improved object detection algorithm based on YOLO-v3 to detect dynamic obstacles in real time, and combined with the dynamic window approach (DWA) to improve the local path obstacle avoidance algorithm. So that it can avoid sudden mobile obstacles after detection, increase the ability of local dynamic obstacle avoidance, and improve the local obstacle avoidance performance of mobile robots. Finally, the feasibility and effectiveness of the algorithm are verified in the actual environment.
Fault warning method based on extreme learning regression and fuzzy reasoning

Aiming at the problem of condition monitoring of thermal power units, a fault early warning method based on fuzzy learning machine is proposed. The extreme learning regression model between monitoring parameters is established by using real-time data. Then the estimated value is fuzzed and used for fuzzy reasoning, finally, the fault diagnosis results of the unit under small abnormal state are obtained. The simulation data of a 1000 MW unit is used for verification test and results show that the proposed method is reliable and accurate which is suitable for thermal unit state warning.
Efficient One-Shot Video Object Segmentation

Video object segmentation is the problem of labelling the foreground object of interest that has widespread applications. We reevaluate One-shot Video Object Segmentation (OSVOS), a simple method that adapts VGG to image segmentation using a structure similar to a Fully Convolutional Network. We propose a range of improvements to make OSVOS competitive to newer methods while keeping its simplicity. Specifically, we replace VGG with EfficientNet, and adopt the U-net architecture. We also utilize Focal Loss and Dice Loss to handle the imbalanced binary classification, and finally we remove the boundary snapping module. With our amendments, we achieve 82.4% J&F on DAVIS 2016 validation set, an improvement over the original 80.2% of OSVOS. We also achieve much faster inference time per frame than OSVOS.
Research on Artificial Intelligence Machine Learning Character Recognition Based on Online Machine Learning Method

The development of artificial intelligence technology has completely transformed human daily life and industrial production mode. Due to the improvement of scientific level and the progress of Internet technology, people have gradually entered the information society, and information technology plays an indispensable role in many application scenarios of daily life and production. With the progress of social science, artificial intelligence technology has also developed rapidly, and people have made breakthrough progress in the research of machine learning. In the past, character recognition methods were based on the image representation of characters, and then the differences between characters were analyzed and processed. With the development of artificial intelligence technology, researchers have studied it more widely and deeply. As a new technical field in artificial intelligence technology, machine learning generally plays an important role in network search, language recognition and other scenarios. This paper will discuss and study the research of character recognition method based on machine learning, and make corresponding arrangement and summary.
Coherence of PRNU weighted estimations for improved source camera identification

Abstract This paper presents a method for Photo Response Non Uniformity (PRNU) pattern noise based camera identification. It takes advantage of the coherence between different PRNU estimations restricted to specific image regions. The main idea is based on the following observations: different methods can be used for estimating PRNU contribution in a given image; the estimation has not the same accuracy in the whole image as a more faithful estimation is expected from flat regions. Hence, two different estimations of the reference PRNU have been considered in the classification procedure, and the coherence of the similarity metric between them, when evaluated in three different image regions, is used as classification feature. More coherence is expected in case of matching, i.e. the image has been acquired by the analysed device, than in the opposite case, where similarity metric is almost noisy and then unpredictable. Presented results show that the proposed approach provides comparable and often better classification results of some state of the art methods, showing to be robust to lack of flat field (FF) images availability, devices of the same brand or model, uploading/downloading from social networks.
A Geometric Approach to Linear Cryptanalysis

A new interpretation of linear cryptanalysis is proposed. This ‘geometric approach’ unifies all common variants of linear cryptanalysis, reveals links between various properties, and suggests additional generalizations. For example, new insights into invariants corresponding to non-real eigenvalues of correlation matrices and a generalization of the link between zero-correlation and integral attacks are obtained. Geometric intuition leads to a fixed-key motivation for the piling-up principle, which is illustrated by explaining and generalizing previous results relating invariants and linear approximations. Rank-one approximations are proposed to analyze cell-oriented ciphers, and used to resolve an open problem posed by Beierle, Canteaut and Leander at FSE 2019. In particular, it is shown how such approximations can be analyzed automatically using Riemannian optimization.
Rank Aggregation Based Multi-filter Feature Selection Method for Software Defect Prediction

With the variety of different filter methods, selecting the most appropriate filter method which gives the best performance is a difficult task. Filter rank selection and stability problems make the selection of filter methods in SDP a hard choice. The best approach is to independently apply a mixture of filter methods and evaluate the results. This study presents a novel rank aggregation-based multi-filter feature selection method to address high dimensionality and filter rank selection problems in software defect prediction. The proposed method combines the rank list generated by individual filter methods from the software defect dataset using a rank aggregation mechanism into a single aggregated rank list. The proposed method aims to resolve the filter selection problem by using multiple filter methods of diverse computational characteristics to produce a more stable (non-disjoint) and complete feature rank list better than individual filter methods employed. The effectiveness of the proposed method was evaluated by applying with Decision Tree (DT) and Naïve Bayes (NB) models on defect datasets from NASA repository. From the experimental results, the proposed method had a superior effect (positive) on the prediction performance of selected prediction models than other experimented methods. This makes the combining of individual filter rank methods a viable solution to the filter rank selection problem and enhancement of prediction models in SDP.
Face Recognition Using 3D CNNs

The area of face recognition is one of the most widely researched areas in the domain of computer vision and biometric. This is because the non-intrusive nature of face biometric makes it comparatively more suitable for application in area of surveillance at public places such as airports. The application of primitive methods in face recognition could not give very satisfactory performance. However, with the advent of machine and deep learning methods and their application in face recognition, several major breakthroughs were obtained. The use of 2D convolution neural networks(2D CNN) in face recognition crossed the human face recognition accuracy and reached to 99%. Still, robust face recognition in the presence of real-world conditions such as variation in resolution, illumination and pose is a major challenge for researchers in face recognition. In this work, we used video as input to the 3D CNN architectures for capturing both spatial and time domain information from the video for face recognition in real-world environment. For the purpose of experimentation, we have developed our own video dataset called CVBL video dataset. The use of 3D CNN for face recognition in videos shows promising results with DenseNets performing the best with an accuracy of 97% on CVBL dataset.
Identifying Irrelevant Answers in Web Based Question Answering Systems

Answer re-ranking and quality tagging are two major techniques used to address irrelevant answers in web-based community question answering systems (CQA). However, answer re-ranking is memory inefficient, and quality tagging lacks the ability to predict partially relevant responses. The reported precisions of both mechanisms are also low. Those facts emphasize the importance of finding alternative techniques for identifying irrelevant answers. In this paper, we have analyzed the capability of three widely popular deep learning models (CNN, LSTM and CLSTM) in the NLP literature to identify irrelevant answers in factoid and non-factoid systems. Further, we studied the ability of the same deep learning models to detect partially relevant answers in non-factoid systems. According to the results, the CLSTM model performed over CNN and LSTM in detecting irrelevant answers.
Collective Sensitivity of Artificial Hair Sensors to Flow Direction

We study the collective response of synthetic hair sensors to changes in the airflow direction. Specifically, we examine whether an array of isotropic sensors, which are unable to detect the direction of an incident flow when operating in isolation, can together sense not only the magnitude but also the orientation of a freestream flow passing over and through them. To conduct our inquiry, we consider a fundamental scenario involving a boundary-layer flow past a flat plate decorated with a array of sensors. Numerical modeling is used to simulate the interaction between the fluid flow and flexible hair sensors. We find that, indeed, it is feasible to achieve collective directional sensitivity by engineering the arrangement of the sensors. We also propose a supervised machine learning approach capable of deducing the direction of the freestream flow from the deformation signals outputted by individual sensors in the array.
Utilizing Deep Learning Technology to Segment Pectoral Muscle in Mediolateral Oblique View Mammograms

Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death among women all over the world. Accurate pectoral muscle segmentation in mediolateral oblique(MLO) view mammograms is an important pre-processing step for subsequent analysis in computer aided diagnosis (CAD) systems. In this paper, a deep learning based approach for pectoral muscle segmentation is proposed. The method includes the following steps. First, the artifacts in the mammogram is eliminated by applying connected component labeling. Second, a pre-trained U-Net model is utilized to get the initial pectoral muscle segmentation result. Finally, an improved scanline filling method is employed to solve the under-segmentation problem existing in initial segmentation result. Preliminary investigation on a test dataset demonstrated that the proposed method yielded average values of 93.5 ± 3.0% and 88.0 ± 5.0% for the Dice and Jaccard similarity metrics, respectively.
An Architectural Pattern to Realize Multi Software Product Lines in Java

We present a realization of multi software product lines in the Java programming language that permits full interoperability and hierarchical dependencies among multiple product variants. This concept, called variability modules (VM), is implemented in terms of an architectural pattern in Java and does not require any pre-processing or language extension. It can be used with any Java development environment. The VM architectural pattern comes with a dedicated UML profile, which makes it possible to present variability to non-technical stakeholders. We evaluate our approach with the help of a real-world case study.
Novel fault attack resistant architecture for elliptic curve cryptography

Hardware implementations of cryptosystems are susceptible to fault attacks. By analyzing the side channel information from implementation, the attacker can retrieve the secret information. Generally, in the hardware implementations, validations of results are reported at the end of the computation. If faults are injected at the input side of computation, all the computations performed afterward are wasteful and this is a potential situation which can leak the secret key information using side channel attacks. The current work proposes fault attack resistant implementation of an elliptic curve cryptosystem using a shared point validator unit, zero-one detector, and double coherence check by modified Montgomery Powering Ladder Algorithm. The architecture is robust to fault attacks along with power and area efficiency.
Deformation monitoring and numerical simulation analysis of ultra-deep working well

Abstract Taking an ultra-deep working well in Jinan as the background, a three-dimensional finite element model of the excavation of the foundation pit was established using nonlinear finite element analysis software MADIS GTS/NX. The numerical simulation of the excavation process of the foundation pit, combined with the actual measured data, studied the deformation law of the envelope structure under the underground continuous wall and internal support support scheme. The results show that the three-dimensional finite element model can better consider the influence of the spatial effect of the foundation pit on the underground continuous wall and the surface deformation around the foundation pit. The calculated value and the measured value have a small gap, and the development trend is almost consistent. The optimization design and deformation control of the supporting structure of the project provide a basis and also have reference value for the design and construction of similar projects.
Closed-set speaker identification system based on MFCC and PNCC features combination with different fusion strategies

In this chapter, we investigate novel fusion strategies for text-independent speaker identification. In this context, we present four main simulations for speaker identification accuracy (SIA) including different fusion strategies such as feature-based early fusion, score-based late fusion, early-late fusion (combination of feature- and score-based), late fusion for concatenated features, and statistically independent normalized scores fusion for all the previous scores. The Gaussian mixture model (GMM) with the universal background model (UBM) is used for voice modeling. For the original clean speech recording, Mel frequency cepstral coefficient (MFCC) features are utilized. power normalized cepstral coefficient (PNCC) features are employed in the noisy environment due to their efficiency with noise. Hence, to produce a robust speaker identification system, the MFCCs and PNCCs are combined. In addition, cepstral mean and variance normalization (CMVN) and feature warping (FW) are used in order to mitigate possible linear channel effects, as they are robust for channel and handset mismatch and additive noise. We used the TIMIT database to evaluate the closed-set speaker identification. Results show the highest SIA (95%) at a mixture size of 512 using the late fusion approach. Moreover, a fusion-based technique for MFCC and PNCC features results in better accuracy than using each feature alone.
Evaluating Animated Transitions between Contiguous Visualizations for Streaming Big Data

An approach to analyzing Streaming Big Data as it comes in while maintaining the proper context of past events is to employ contiguous visualizations with an increasingly aggressive aggregation degree. This allows for the most recent data to be displayed in detail, while older data is shown in an aggregated form according to how long ago it was received. However, the transitions applied between visualizations with different aggregations must not compromise the understandability of the data flow. Particularly, new data should be perceived considering the context established by older data, and the visualizations should not be perceived as independent or un-connected. In this paper, we present the first study on transitions between two contiguous visualizations, focusing on time series data. We developed several animated transitions between a scatter plot, where all data points are individually represented as they arrive, and other visualizations where data is displayed in an aggregated form. We then conducted a user evaluation to assess the most appealing and effective transitions that allow for the best comprehension of the displayed data for each visualization pair.
LSB Substitution Image Steganography Based on Randomized Pixel Selection and One-Time Pad Encryption

Digital communication is an inseparable part of almost every individual and involved community on the plant. Different algorithms use to secure the transmission media such as image and sound. Using steganography as essential tools to keep the integrity and confidentiality of data, it has become a norm in modern digital communication era. Four different images used in Experimental approach. The result shows the average Peak Signal-Noise Ratio with a value of 83.26753978, average Mean Square Error with value0.000310368, and average Structural Similarity Index with a value of 0.999997914. Furthermore, the histogram analysis of the cover image and steganography image has shown little difference. The outcome of experimental assessment demonstrates that the approach has high impressibility.
Acquiring sentiment towards information security policies through affective computing

Information security policies (ISPs) are formalised rules and regulations to which users or employees are required to adhere to in order to safeguard information technology assets. It is an important tool to influence information security behaviour and is mandatory in most organisations. Given the importance of these policies, there is still a significant number of studies that report on the noncompliance to ISPs. Many research projects are conducted to explain this phenomenon -however, little or no attention is given to the opinion of users and employees about ISPs and the contents thereof. Surveys to obtain the opinion of employees on organisation and information security policies often resulted in response bias problems where answers are faked in order to provide an expected opinion. The purpose of this paper is to propose the use of affective computing and sentiment analysis to address this problem of response bias and to contribute to the evaluation of the quality of ISPs. An illustrative example is presented where a dataset based on facial expressions, derived from video recordings, was used to perform sentiment analysis to obtain an opinion. A deep learning neural network model was constructed to perform the sentiment analysis. This model was able to accurately classify opinions as positive, neutral or negative based on the original facial expressions. The paper is concluded with brief remarks and comments on the relevance of the study for ISPs and the opportunities created for further education of users and employees.
Mass surveillance: A study of past practices and technologies to predict future directions

Abstract This paper aims to dispassionately examine the techniques and technologies of mass surveillance. We will study different approaches and technologies, including what each one surveils and how. We will also observe patterns and changes over time, and in doing so, we will seek to model the attributes exhibited by various methods. Finally, we will predict what next generation surveillance methods will look like, based on the attributes we have observed in past and current methods. Our key insights include: (a) mass surveillance methods share a number of common attributes, (2) we can analyze the expression of these attributes to predict future methods of mass surveillance, and (3) a likely next generation method will entail the monitoring of a communication platform with third‐party assistance to collect large amounts of private communication data for future analysis.
Performance Analysis of DWT Families

Discrete wavelet transform (DWT) is a technique which is used to acquire wavelets from image pixels. It transforms a discrete time signal to discrete wavelet representation. The DWT has shown great performance in digital image compression, demodulation, demultiplexing and denoising applications. In this paper, a comparison among different wavelet transforms is made. These wavelets include; Discrete Meyer, Biorthogonal, Fejer-korovkin, Daubechies, Coiflets, Symlets and Reverse Biorthogonal wavelets. Each wavelet is implemented for upto six levels of wavelet decomposition. The reconstructed images are then compared with the original images and their performance is evaluated with the help of Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE). It has been observed in all wavelets that as we increase the decomposition level, MSE increases and PSNR decreases. In this paper, a comparison has been made between the different wavelet families, keeping the filter length fairly same in all the wavelets. This paper will help the new researchers of this field to choose the suitable wavelet transform for image processing applications.
Language Identification with Unsupervised Phoneme-like Sequence and TDNN-LSTM-RNN

A novel language identification (LID) method is proposed that accepts the architecture of time delay neural network (TDNN) followed by long short term memory (LSTM) recurrent neural network (RNN) to learn long-term phonetic patterns and model the phonetic dynamics for different languages. Instead of the linguistic phonemes, the phoneme-like speech units are used to train the TDNN-LSTM-RNN, which can be found without prior linguistic knowledge and manual transcriptions. Compared with PPRLM, the experiment results show that the phoneme-like speech units by unsupervised discovering and the linguistic phonemes by manual annotation have the same effect in the LID task. Furtherly, the proposed LID method is built and reported the test results on the NIST LRE07 and the task of dialect identification. We compare the proposed LID method with other state-of-the-art methods, including the acoustic feature based LID methods and the phonetic feature based LID methods. The experimental results show that our method provides competitive performance with the existing methods in the LID task. In particular, our method helps to capture robust discriminative information for short duration language identification and high accuracy for dialect identification.