Reviewerly/exHarmony huggingface dataset:
Background-foreground segmentation for interior sensing in automotive industry

Abstract To ensure safety in automated driving, the correct perception of the situation inside the car is as important as its environment. Thus, seat occupancy detection and classification of detected instances play an important role in interior sensing. By the knowledge of the seat occupancy status, it is possible to, e.g., automate the airbag deployment control. Furthermore, the presence of a driver, which is necessary for partially automated driving cars at the automation levels two to four can be verified. In this work, we compare different statistical methods from the field of image segmentation to approach the problem of background-foreground segmentation in camera based interior sensing. In the recent years, several methods based on different techniques have been developed and applied to images or videos from different applications. The peculiarity of the given scenarios of interior sensing is, that the foreground instances and the background both contain static as well as dynamic elements. In data considered in this work, even the camera position is not completely fixed. We review and benchmark three different methods ranging, i.e., Gaussian Mixture Models (GMM), Morphological Snakes and a deep neural network, namely a Mask R-CNN. In particular, the limitations of the classical methods, GMM and Morphological Snakes, for interior sensing are shown. Furthermore, it turns, that it is possible to overcome these limitations by deep learning, e.g. using a Mask R-CNN. Although only a small amount of ground truth data was available for training, we enabled the Mask R-CNN to produce high quality background-foreground masks via transfer learning. Moreover, we demonstrate that certain augmentation as well as pre- and post-processing methods further enhance the performance of the investigated methods.
End-to-end speech recognition with Alignment RNN-Transducer

The Recurrent Neural Network Transducer (RNN-T) extends Connectionist Temporal Classification (CTC) by jointly modeling both input-output and output-output dependencies, and it has been successfully applied in end-to-end speech recognition. RNN-T adds language information by expanding its dimensions in this way, but the model training is difficult. And some paths of the model are unreasonable when decoding. Therefore, we present the Alignment RNN Transducer (ART) algorithm, which uses the forward-backward algorithm in CTC to find the best path alignment information as context-related information for training. We not only reduce the training dimensions but also adds context information to the model. And we still use CTC as the loss function to avoid decoding the impossible path in the RNN-T. We verify the algorithm on a Mandarin speech corpus AIShell-1, and it achieves a 13.65% CER, compared with the 16.79% CER given by the RNN-T model.
Forming Predictive Features of Tweets for Decision-Making Support

The article describes the approaches for forming different predictive features of tweet data sets and using them in the predictive analysis for decision-making support. The graph theory as well as frequent itemsets and association rules theory is used for forming and retrieving different features from these datasets. The use of these approaches makes it possible to reveal a semantic structure in tweets related to a specified entity. It is shown that quantitative characteristics of semantic frequent itemsets can be used in predictive regression models with specified target variables.
Upper tail analysis of bucket sort and random tries

Bucket Sort is known to run in expected linear time when the input keys are distributed independently and uniformly at random in the interval [0,1). The analysis holds even when a quadratic time algorithm is used to sort the keys in each bucket. We show how to obtain linear time guarantees on the running time of Bucket Sort that hold with very high probability. Specifically, we investigate the asymptotic behavior of the exponent in the upper tail probability of the running time of Bucket Sort. We consider large additive deviations from the expectation, of the form cn for large enough (constant) c, where n is the number of keys that are sorted. Our analysis shows a profound difference between variants of Bucket Sort that use a quadratic time algorithm within each bucket and variants that use a Θ(blog⁡b) time algorithm for sorting b keys in a bucket. When a quadratic time algorithm is used to sort the keys in a bucket, the probability that Bucket Sort takes cn more time than expected is exponential in Θ(nlog⁡n). When a Θ(blog⁡b) algorithm is used to sort the keys in a bucket, the exponent becomes Θ(n). We prove this latter theorem by showing an upper bound on the tail of a random variable defined on tries, a result which we believe is of independent interest. This result also enables us to analyze the upper tail probability of a well-studied trie parameter, the external path length, and show that the probability that it deviates from its expected value by an additive factor of cn is exponentially small in Θ(n).
Triplet Knowledge Distillation Networks for Model Compression

Knowledge distillation is a widely used neural network model compression technique. In general, the knowledge distillation transfer the knowledge from a large pre-trained teacher network with superior performance to a small student network enables the student network to achieve better performance. This paper proposes a triplet knowledge distillation framework (abbreviated as TKD), which introduces a smaller assistant network into the knowledge distillation structure. The performance of the assistant network is lower than that of the student network. During the training of the TKD, by minimizing the Mean Squared Error(MSE) loss function, the output of the student network will closer to the output of the teacher network and further from that of the assistant network. Therefore, the student network can learn more expressive knowledge from the teacher network while throwing away mistaken knowledge in the assistant network. Finally, the student network achieves a surprising performance even superior to the teacher network. We have demonstrated the effectiveness of TKD by extensive experiments on benchmark datasets(CIFAR-10, CIFAR-100, SVHN, STL-10). When using VGGNet as an experimental model, the student network VGGNet13 achieving 94.29%, 75.30%, 95.53%, and 87.61% accuracy on the CIFAR-10, CIFAR-100, SVHN, and STL-10 datasets, improved by 1.24%, 2.81%, 0.40%, and 2.32%, respectively.
Training Strategies for Convolutional Neural Networks with Transformed Input

Convolutional Neural Network (CNN) are now considered the main tool for image classification. However, most networks studied for classification are large and have extensive computing and storage requirements. Their training time is also usually very long. Such costly computational and storage requirements cannot be met in many applications with simple devices such as small processors or Internet of things (IoTs) devices. Therefore, reducing the size of networks and input sizes become necessary. However, such reductions are not easy and may reduce the classification performance. We examine how domain transforms under different training strategies can be used for efficient size reduction and improvement of classification accuracy. In this paper, we consider networks with under 220K learnable parameters, as opposed to millions in deeper networks. We show that by representing the input to a CNN using appropriately selected domain transforms, such as discrete wavelet transforms (DWT) or discrete cosine transform (DCT), it is possible to efficiently improve the performance of size-reduced networks. For example, DWT proves to be very effective when significant size reduction is needed (improving the result by up to 9%). It is also shown that by tuning training strategies such as the number of epochs and mini batch size, the performance can be further improved by up to 4% under fixed training time.
Tightly secure ring signatures in the standard model

Ring signatures allow a user to sign messages as a member of a set of users, which is called the ring. This primitive ensures that nobody can detect which member in the ring signs the message. Libert, Peters, and Qian (2018) [24] proposed the first tightly secure ring signature scheme with O(log⁡n) signature size in the random oracle model, where n is the size of a ring. To our knowledge, a tightly secure ring signature scheme has never been reported without depending on the random oracle methodology. In this paper, we propose two generic constructions of tightly secure ring signatures in the standard model. Our first (resp., second) construction is secure in the common reference string model (resp., the plain model). Both of our constructions are secure under the decisional linear assumption over the pairing groups. Our first generic construction has a more efficient instantiation than our second one. While our second generic construction does not have an efficient instantiation, its signature size achieves O(log⁡n) asymptotically, which is the same as one of the Libert et al.'s scheme.
JomAR Purchasing Furniture in Augmented Reality Experiences

In the modern world, online shopping is a common activity that people do. They buy all things even large-size product such as furniture. People used to measure the product traditionally using tape measure. However, people can take advantages of the virtual reality products such as augmented reality. This study used mixed methodology involving 105 respondents. The augmented reality will have benefit to reduce the cost since the customer have no need to measure traditionally and to increase efficiency to buy the product. Additionally, the customer will have more trust towards the seller. The augmented reality can help user to understand the product and match the product with the user’s surroundings. This augmented reality will potentially grow and adapt to the user needs as the user might explore more with the image projection.
NPLP: A Noisy Pseudo-Label Processing Approach for Unsupervised Domain-Adaptive Person Re-ID

Most of the existing unsupervised cross-domain person re-identification (re-ID) methods utilize pseudo-labels estimation to cast the unsupervised problem into a supervised problem, whose performance is limited by the quality of pseudo-labels. To address the problem, we propose a noisy pseudo-label processing (NPLP) approach to suppress the pseudo-labels noise and improve the performance of the person re-ID model. Specifically, we first summarize two types of pseudo-label noise that lead to the collapse of the re-ID model, as defined as mixed noise and fragmented noise. Secondly, we propose a different method which is composed of Startup Stage and Correcting Stage for pseudo-labels estimation to relieve these two types of noise respectively. The Startup Stage aims to decrease the ratio of the fragmented noise by increasing the recall of the clustering results. At the Correcting Stage, we evaluate the quality of the pseudo-labels and correct those low-quality pseudo-labels to suppress the mixed noise and generate more reliable pseudo-labels for the re-ID model to learn. At last, we build a feature learning strategy for unsupervised re-ID task and learn from the denoised pseudo-labels iteratively. Extensive evaluations on three large-scale benchmarks show that the NPLP is competitive with most state-of-the-art unsupervised re-ID methods.
Irrelevant Racist Tweets Identification Using Data Mining Techniques

In recent times, Twitter is one of the major sources to access information. Its feature of the hashtag is something that grabs more attention from the users. One can write one’s mind and heart out on Twitter at any given minute. Due to which there is a rapid increase in the generation of irrelevant content on Twitter. Lately, a new hashtag called “#whitelivesmatter” was used as a counter for another hashtag “#blacklivesmatter”. A lot of anti-government protests and various other violent activities were conducted, recorded, and posted on Twitter with this hashtag. A lot of Kpop fans had taken over this hashtag and flooded Twitter with extremely irrelevant content. Due to which the main and important content of the protests was drowned in these irrelevant tweets, which made it extremely hard for the officials to find and reinforce the law and order. This paper aims at building a model that helps in finding the relevance of text content in the tweet and its hashtag #whitelivesmatter in specific. In this paper, supervised data analysis techniques like text classification are used to get the required output.
Visual Distortion and Simulated Partial Seizures using Augmented Reality

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Future Prospective of Domestic and Commercial Photovoltaic System Installation in Estonia: A Techno-Economic Analysis

Nordic countries have a good potential for solar photovoltaic (PV) energy generation as on average 15 hours of sunlight in summer is available. Another potential option is to encourage the construction of nearly zero-energy buildings (NZEBs) as per EU framework. This study focuses on solar irradiance and energy generation potential in different regions of Estonia. A techno-economic analysis is presented on possible solutions of using differently rated domestic and commercial PV systems’ feasibility and payback periods. The results illustrate that all PV systems studied in the research are self-sufficient while selling excess energy to the grid with a nominal payback period. Furthermore, for short-term energy management, we developed an efficient deep learning-based forecasting algorithm. Apart from the inherent non-linear nature of solar energy data, what makes forecasting particularly challenging is to efficiently cope with the issue of data regression and random noise. The RNN-LSTM algorithm is chosen for the prediction of solar energy. This is the first comprehensive report, which can encourage potential users in Estonia to invest in solar PV systems and gain economic benefits. The results presented in this study cover a broader perspective and are more useful keeping in view the real market situation of Baltic countries.
Multi-Material Interface Extraction and its Topological Relationship Expression for 3-D Image Data

The interface extraction and its topological relationship is a critical step toward the visualization, recognition and feature retrieval of multi-material objects. The existing methods only pay attention to the interface visualization and extraction, while ignoring the representation of the interface topological relationship. In this paper, we proposed a multi-material interface extraction and its topological relationship expression method for 3D image data. Firstly, we extend the 2D Canny detector to 3D for extracting the interface-points of 3D image data. Secondly, we introduce an efficient clustering method to cluster interface-points for obtaining the sub-interfaces. Thirdly, the neighboring relationship of each sub-interface is calculated and the directed skeleton tree with global topological feature is constructed. Finally, we propose a matrix representation to explicitly express the topological relationship of interfaces. Experimental results demonstrate that our method can efficiently extract interfaces for a variety of synthetic and raw scanned 3D image data, even in the presence of noisy.
An approach to explore sequential interactions in cognitive activities of software engineering

: The study of cognitive aspects around software activities can provide valuable insights to improve software engineering practices. Objective: This paper presents an approach based on distributed cognition and sequential analysis to explore cognitive activities in the software development context by analyzing the interactions between software practitioners and the resources used to support them. Method: We conducted nine laboratory-based observation sessions to record qualitative audio/video data of interactions between the study participants and at-hand resources during the planning and managing of software analytics tasks. Results: The interaction strategies of the resources model included 21 emergent actions, and the sequential analysis revealed two different patterns of interaction over time. Conclusion: Our approach has been useful for evaluating how well an artifact works to support a team in software analytics activities. Furthermore, it can be applied to explore and discover interaction patterns in different software activities.
A Structural Transformer with Relative Positions in Trees for Code-to-Sequence Tasks

We suggest two approaches to incorporate syntactic information into transformer models encoding trees (e.g. abstract syntax trees) and generating sequences. First, we use self-attention with relative position representations to consider structural relationships between nodes using a representation that encodes movements between any pair of nodes in the tree, and demonstrate how those movements can be computed efficiently on the fly. Second, we suggest an auxiliary loss enforcing the network to predict the lowest common ancestor of node pairs. We apply both methods to source code summarization tasks, where we outperform the state-of-the-art by up to 6 % F1. On natural language machine translation, our models yield competitive results. We also consistently outperform sequence-based transformers, and demonstrate that our method yields representations that are more closely aligned with the AST structure.
Concept Maps as a Method of Cognitive Learning in Foreign Language Classes in High School

The study aims to identify the didactic potential of concept maps in teaching a foreign language using a cognitive approach. In the article, the author relies on the works of domestic and foreign researchers and substantiates the use of concept maps from the standpoint of cognitive learning. The scientific originality of the research lies in the hypothesis of the possibility of using concept maps for the development of various types of foreign language speech activity. As a result of the study, the connection between the drawing up of concept maps and the effectiveness of the educational activity of students in the study of a foreign language was confirmed.
Influence of light source and detector’s imperfections on the photon counting statistics

Abstract Photon counting provides a valuable tool for measuring quantum properties of light. Unavoidable imperfections of photon detectors lead to discrepancies between the statistics of photons and statistics of photon counts. Here we consider dead-time corrections and influence of intensity fluctuations for simulated Poissonian statistics of photo counts for the pulsed or continuous wave coherent light sources. We analyse dependence between the statistical moments of corrected distribution and the mean photon number, dead-time, and level of intensity fluctuations.
Automatizing the Khasra Maps Generation Process Using Open Source Software: QGIS and Python Coding Language

Humans are trying to acquire a piece of land from the time they have come into existence. In modern era, the management of land and its ownership is taken up by the Land and Revenue Department of the State. In order to do that, they need maps with specific objectives, so that even a laymen can understand and use it. The process explained in this paper automate the process of map making after getting the digitized shapefile of the khasra (property identification number), as a single village is divided into numerous grids and it is a tedious work and can have lots of errors while doing it manually. So in order to do the process in swift manner and without having any errors, the process was developed using the Quantum Geographic Information System (QGIS) and Python. The proposed method involves making the use of models built in QGIS along with the Python console. It helps to run the whole process on its own with taking the required input parameters and storing the outputs in a specific folder designed for them. The requirement of the project was to do the same operations on a village file and to get the final khasra map from the village polygon file. Depending upon the village area and its dimensions, the numbers of grids for a particular village is decided and the same GIS tools need to be run on each grid files which make this process a tedious work and more prone to errors. By making use of the method suggested in the paper, all the work can be done error proof with the use of Python. The use of Python code helps to do work in just couple of seconds which would have taken days to complete.
A Bi-modal Automated Essay Scoring System for Handwritten Essays

During the past few decades, Automated Essay Scoring (AES) technology has been widely used to alleviate the workload of teachers and improve the feedback cycle in educational systems. However, the scoring of handwritten essays poses great challenges for existing systems, since most of them only take text as input without consideration of errors or bias which may be introduced by Optical Character Recognition (OCR) as a necessary pre-processing step. This paper proposes VisualAES, a bimodal automated essay scoring system that utilizes both textual and visual features for handwritten essay scoring. Specifically, we first employ three powerful pre-trained transformer-based models as the backbone, and extend them to take both textual features and visual features extracted by Faster R-CNN. Then, a stacking ensemble model is subsequently adopted to robustly map their outputs to a final score. We evaluate VisualAES with the public Automated Student Assessment Prize (ASAP) dataset and our proposed handwritten Chinese Students' handwritten essay dataset (ChnStd). Results show that the proposed VisualAES outperforms all state-of-the-art methods on both datasets. More importantly, by incorporating handwritten image information, we also achieve a further performance improvement on ChnStd and reduce the side effects of OCR.