Reviewerly/exHarmony huggingface dataset:
Convolutional Autoencoder For Single Image Dehazing

In this paper, we present a Convolutional AutoEncoder (CAE) for single image dehazing. Our CAE makes use of Densely Connection Networks as its encoder and decoder. It is trained with the corresponding hazy and clean images at the input and output, enabling it to remove the haze without having to rely on an atmospheric scattering model. The CAE is trained and tested with the RESIDE dataset. Experiment results show that this CAE outperforms eight state-of-art methods. The trained CAE is also applied to some real-life hazy images, and decent dehazing results are obtained. Moreover, our method is computationally efficient enough to run on computers without GPU units.
A Log-Gabor Feature-Based Quality Assessment Model for Screen Content Images

In this paper, an image quality assessment (IQA) model for conducting objective evaluations of screen content images (S-CIs) is proposed, called the log-Gabor feature-based model (LGFM). From the standpoint of signal representation, the log-Gabor filters outperform the classical Gabor filters since the outputs of the log-Gabor filters are more consistent with the perception of visual cortex in human visual system (HVS). Furthermore, the following two remarkable characteristics of the log-Gabor filters are highly beneficial to develop a more accurate IQA model; i.e., (i) zero response at the DC, and (ii) stronger response at high frequencies. In our proposed L-GFM, the log-Gabor filters are used to extract features from the luminance of the reference SCIs and that of the distorted SCIs for measuring their degree of similarity. Together with the measurements from the other two chrominance components, the final LGFM score will be arrived at the output of the pooling stage. Extensive simulation results have shown that our proposed LGFM is highly consistent with the human perception, compared to other state-of-the-art IQA models.
What Quality Attributes Can We Find in Product Backlogs? A Machine Learning Perspective

Automatically identifying quality attributes (e.g., security, performance) in agile user stories could help architects reason about early architecture design decisions before analyzing a product backlog in detail (e.g., through a manual review of stories). For example, architects may already get the "bigger picture" of potential architectural key drivers and constraints. Applying a previously developed method to automatically identify quality attributes in user stories, in this paper we investigate (a) what quality attributes are potentially missed in an automatic analysis of a backlog, and (b) how the importance of quality attributes (based on the frequency of their occurrence in a backlog) differs to that of quality attributes identified in a manual review of a backlog. As in previous works, we analyzed the backlogs of 22 publicly available projects including 1,675 stories. For most backlogs, automatically identified quality attributes are a subset of quality attributes identified manually. On the other hand, the automatic identification would usually not find more (and therefore potentially irrelevant) quality attributes than a manual review. We also found that the ranking of quality attributes differs between the automatically and manually analyzed user stories, but the overall trend of rankings is consistent. Our findings indicate that automatically identifying quality attributes can reduce the effort of an initial backlog analysis, but still provide useful (even though high-level and therefore potentially incomplete) information about quality attributes.
Cascade Attention Network for Person Re-Identification

Person re-identification is a challenging task due to the viewpoint, illumination and pose variations. Recent works focus on extracting part-level features to offer beneficial fine-grained information. However, the part misalignment as well as the multi-stage training process limits their performance. Inspired by the human visual attention mechanism, this paper builds a cascade attention network(CAN) to learn the discriminative person features in a coarse-to-fine manner. Firstly, we employ the human semantic parsing module to generate coarse-grained part-level attention, which corresponds to the division of human body parts and can effectively filter the background noise. Then, to extract the local detailed features within each part, we introduce spatial-channel attention module to generate fine-grained pixel-level attention, which can further highlight the distinctive characteristics and repress the irrelevant ones. Finally, we can obtain an efficient person feature descriptor by combining both the global and local features. The whole learning process is conducted end-to-end. Experimental results show that the proposed method not only considerably outperforms its counter part but also achieves competitive performance on Market-1501 and DukeMTMC.
Medical Diagnosis Under Uncertain Environment Through Bipolar-Valued Fuzzy Sets

In medical field, theDutta, Palash necessary data or information provided by patients to medical experts are more often uncertain or vague. Fuzzy set theory is explored to deal with these types of uncertain environment and toDoley, Dhanesh analyze the data explained by the patients. Numerous studies have been done in the field of medicine using fuzzy sets, in particular, medical diagnosis is a prominent one. However, most of the studies leads to counterintuitive results. Therefore, in this paper, a medical diagnosis has been made using the composition of bipolar-valued fuzzy sets. It is observed that the present approach has the ability to address the medical diagnosis problem so effectively, which tallies with analytical results along with human intuitions.
Stochastic Cloning for Robust Fusion of Multiple Relative and Absolute Measurements

Fusing multiple sensors is vital for the precise online localization of mobile robots. The Extended Kalman filter is well suited for this purpose and allows to directly integrate multiple absolute measurements in the filter. However, multiple relative state measurements cannot be fused directly into the filter because they measure differences between a past state and the current state, which introduces correlations. These interdependencies can be modeled by stochastic cloning, which introduces a state augmentation by cloning the respective state estimates connected by a relative measurement. This paper investigates the impact of multiple mixed relative and absolute updates on the estimated state. An approach to feed back information from absolute updates to the cloned state by explicit cloning is presented and compared to existing implicit methods.
Visual exploration of an ensemble of classifiers

Inspecting the outputs of classification algorithms is becoming progressively difficult due to the increase in both scale and complexity of both the data and the algorithms. This has led to research efforts to develop new techniques to interpret the behavior of these algorithms and to facilitate the understanding of their results. A common classification approach is the “ensemble of classifiers”, where a set of classifiers c ∈ C is trained on the input data set and the final classification is computed by “voting”, i.e., ranking their results. One of the issues with this approach, however, is that instead of having only one classifier to analyze, now there are |C|, each with its characteristics. Thus, there is a demand for methods that provide insights into the results of an ensemble of classifiers and at the same time allow a detailed analysis of each classifier in the ensemble. Our work proposes to draw on dimensionality reduction techniques to provide visual tools to interpret the results of an ensemble of classifiers, while also giving insights into how each classifier contributes to the final results. Our approach also presents a measure of classification uncertainty by highlighting regions where there is a divergence among the classifiers in the ensemble, allowing one to focus their analysis on these regions. We tested our approach using the Digits MNIST and Fashion MNIST data sets. Through the use of maps that provide an overview of a classifier behavior to instance-based visualizations, we show how our approach can assist in the interpretation of why a specific decision (classification) was made.
Control-Theoretic Approaches to Cyber-Security

In this chapter, we discuss the control-theoretic approach to cyber-security. Under the control-theoretic approach, the defender prescribes defense actions in response to security alert information that is generated as the attacker progresses through the network. This feedback information is inherently noisy, resulting in the defender being uncertain of the underlying status of the network. Two complementary approaches for handling the defender’s uncertainty are discussed. First, we consider the probabilistic case where the defender’s uncertainty can be quantified by probability distributions. In this setting, the defender aims to specify defense actions that minimize the expected loss. Second, we study the nondeterministic case where the defender is unable to reason about the relative likelihood of events. The appropriate performance criterion in this setting is minimization of the worst-case damage (minmax). The probabilistic approach gives rise to efficient computational procedures (namely sampling-based approaches) for finding an optimal defense policy, but requires modeling assumptions that may be difficult to justify in real-world cyber-security settings. On the other hand, the nondeterministic approach reduces the modeling burden but results in a significantly harder computational problem.
A Calibration Method For Automotive Augmented Reality Head-Up Displays Based On A Consumer-Grade Mono-Camera

In this paper, we propose a simple mono-camera based calibration method for automotive augmented reality head-up display (AR-HUD) systems. The concept is inspired by the conventional pinhole camera calibration, and the calibration camera can be merely a consumer-grade digital camera or even a smartphone, while the accuracy remains at a high level in the evaluation. Therefore, it is highly applicable to the calibration of automotive AR-HUDs in the production as well as the aftermarket. We further combine AR-HUD calibration with head tracking results for multiple viewpoints to calibrate the pose of head tracker.
Learning to Validate the Predictions of Black Box Machine Learning Models on Unseen Data

When end users apply a machine learning (ML) model on new unlabeled data, it is difficult for them to decide whether they can trust its predictions. Errors or shifts in the target data can lead to hard-to-detect drops in the predictive quality of the model. We therefore propose an approach to assist non-ML experts working with pretrained ML models. Our approach estimates the change in prediction performance of a model on unseen target data. It does not require explicit distributional assumptions on the dataset shift between the training and target data. Instead, a domain expert can declaratively specify typical cases of dataset shift that she expects to observe in real-world data. Based on this information, we learn a performance predictor for pretrained black box models, which can be combined with the model, and automatically warns end users in case of unexpected performance drops. We demonstrate the effectiveness of our approach on two models -- logistic regression and a neural network, applied to several real-world datasets.
Study on Collaborative Algorithm for a Spherical Multi-robot System based on Micro-blockchain

Spherical multi-robot system is mainly used to perform field tasks in harsh environments. How to improve the communication distance and task duration, or if the multi-robot system adopts centralized control mode, once the Byzantine general problem arises and the enemy invades the control center, how to make control power of the whole multi-robot system be guaranteed, is the main study of this paper. The Blockchain technology, as a new application of computer science, such as distributed storage, point-to-point transmission, consensus plugin and encryption algorithm, was widely used for financial industry. At the same time, LORA communication technology was also widely used in the field of Internet of Things for its low power consumption, remote transmission distance, and powerful anti-jamming performance and so on. Via the decentralized architecture and LORA communication technology, which can improve flexibility and reliability of the multi-robot system and ensure the control power on battlefield. This paper proposed the Micro-blockchain and collaborative algorithm based on decentralized architecture which can be used for the spherical multi-robot system. On the basis of the theory, an experimental platform was built up and the result shown that it was practicable to set up the Micro-blockchain based on LORA and through this collaborative algorithm, multi-robot system can complete more complex tasks.
Bidirectional LSTM Tagger for Latvian Grammatical Error Detection

This paper reports on the development of a grammar error labeling system for the Latvian language. We choose to label six error types that are crucial for understanding a text as noted in a survey by native Latvian speakers. The error types are the following: an incorrect use of a preposition, an incorrect agreement in a phrase, an incorrect verb form, an incorrect noun form, an incorrect choice of the definite/indefinite ending of an adjective, and a missing comma. For neural network model training, a large amount of error-annotated training data is required. We generate artificial errors in a correct text to cope with the lack of manually annotated data. As a bidirectional Long Short-Term Memory neural network algorithm is considered the best for erroneous word detection by several authors, we chose this architecture. We train several models – models labeling a single type of error and models labeling all six types of errors. The precision for all types of errors reaches 94.61%, the recall – 94.08%.
Precise Adjacent Margin Loss for Deep Face Recognition

Softmax loss is arguably one of the most widely used loss functions in CNNs. In recent years some Softmax variants have been proposed to enhance the discriminative ability of the learned features by adding additional margin constraints, which significantly improved the state-of-the-art performance of face recognition. However, the `margin' referenced in these losses does not represent the real margin between the different classes in the training set. Furthermore, they impose a margin on all possible combinations of class pairs, which is unnecessary. In this paper we propose the Precise Adjacent Margin loss (PAM loss), which gives an accurate definition of `margin' and has precise operations appropriate for different cases. PAM loss has better geometrical interpretation than the existing margin-based losses. Extensive experiments are conducted on LFW, YTF, MegaFace and FaceScrub datasets, and results show that the proposed method has state-of-the-art performance.
Source-Constraint Adversarial Domain Adaptation

Adversarial adaptation has made great contributions to transfer learning, while the adversarial training strategy lacks of stability when reducing the discrepancy between domains. In this paper, we propose a novel Source-constraint Adversarial Domain Adaptation (SADA) method, which jointly use adversarial adaptation and maximum mean discrepancy (MMD) so that the method can be easily optimized by gradient descent. Furthermore, motivated by metric learning, our method introduces metric loss to constrain the structure of source domain, which explicitly increases the inter-class distance and decreases the intra-class distance. As a result, SADA can not only reduce the domain discrepancy, but also make the extracted features become more domain-invariant and discriminative. We show that our model yields state of the art results on standard datasets.
Novel Quaternary Quantum Reversible Half Adder and Full Adder Circuits

One of the major problems in designing highly compact digital systems is the rate of power consumption and internal heat generated by the loss of information during the computation. The tendency of current circuit design technologies is towards Nanoscale. Quaternary reversible logic is an emerging technology for the future designing of the quantum systems since quantum reversible circuits can be realized in Nanoscales. The adder building block is an essential part of any computer system. This block must be designed efficiently. We proposed a new quaternary quantum reversible half adder with the quantum cost of 17 and a quaternary quantum reversible full adder with a quantum cost of 23. For the first time in the literature, we report that we proposed quaternary quantum reversible half adder as well as full adder with an optimum number of constant inputs. As the circuit lines in the domain of quantum computation are considered to be a very limited resource, the achievements in this paper are promising. The proposed circuits are efficient in terms of all figures of merits in comparison to the existing counterparts. To design these adder circuits, we used 1-qudit and 2-qudit quaternary gates.
A Deterministic Key-Frame Indexing and Selection for Surveillance Video Summarization

Video data is voluminous and impacts the data storage devices as there are CCTV surveillance videos being created every minute and stored continuously. Due to this increase in data there is a need to create semantic information out of the frames that are being stored. Video Summarization is a process that continuously monitors changes and helps in reducing the number of frames being stored. This work enables summarization to be carried out based on selecting threshold-based system that can select key-frames ideally suit for storage and further analysis. Initially a Global threshold based on Otsus method is carried out for all frames of a surveillance video and based on the set threshold a retrospective comparison is done on each frame based on statistical methods to converge on determining the keyframes. A similarity index is generated based on the iterative comparison of frames based on global and local threshold comparison. The local threshold is indexed based on Analysing Method Patterns to Locate Errors(AMPLE), An-derbergs D(AbD), Cohens Kappa(CK), Tanimoto Similarity(TS), Tversky feature contrast model(TFCM), Pearson coefficient of mean square contingency(Pmsc). The Global threshold is updated each time a keyframe is selected based on the comparison of local and global threshold. The results are compared with five surveillance videos and six methods to identify keyframes Selection Rate is the metric used for calculating the performance.
Problems of Semantics of Words of the Kazakh Language in the Information Retrieval

The theme of this research is intelligent search engines that can search and extract new information from text data in Kazakh language and education. The significance of the research topic due to the growing amount of data represented in digital form, which provide the ability to access various sources of electronic documents. The use of intelligent search engines will allow you to meet the information needs of users. In this regard, the development of information-analytical search engines that allows you to work with data in Kazakh language is relevant. The goal of this research is to develop efficient algorithms and models for intelligent search systems, based on modern technologies in the field of information retrieval and natural language processing teaching them.
Context-Based News Headlines Analysis Using Machine Learning Approach

An increasing number of people are changing their way of thinking by reading news headlines. The interactivity and sincerity present in online news headlines are becoming influential to society. Apart from that, news websites build efficient policies to catch people’s awareness and attract their clicks. In that case, it is a must to identify the sentiment polarity of the news headlines for avoiding misconception. In this paper, we analyze 3383 news headlines generated by five major global newspapers during a minimum of four consecutive months. In order to identify the sentiment polarity (or sentiment orientation) of news headlines, we use 7 machine learning algorithms and compare those results to find the better ones. Among those Bernoulli Naïve Bayes technique achieves higher accuracy than others. This study will help the public to make any decision based on news headlines by avoiding misconception against any leader or governance and will help to identify the most neutral newspaper or news blogs.
Towards Tactical Maneuver Detection for Autonomous Driving Based on Vision Only

The detection of tactical maneuvers performed by other traffic participants is a key component for modern autonomous driving systems. We present a novel vision-based approach to detect tactical maneuvers for vehicles, pedestrians, and bicycles. Our approach uses neural networks with temporal convolutions to incorporate temporal information into the detection. The training and evaluation of the presented architecture is performed on a video dataset obtained in a city and industrial environment spanning 3.5 hours. Our approach detects nine distinct maneuver classes with an average accuracy of 54.21%, with single detection accuracies of up to 88.17%.
Exploiting label information to improve auto-encoding based classifiers

In this paper, we propose to provide more information to Stacked Denoising Auto-Encoding classifiers in order to increase their performance. Specifically, we use the output of an auxiliary classifier to extend the input to those machines, and carry out the layer-by-layer auto-encoding training considering the input recovering and the label errors by means of a convex combination whose parameter is selected by conventional cross-validation. Extensive experiments support the effectiveness of this proposal, showing that the resulting machines offer better results than standard designs in all the cases, as well as a reduced sensitivity to the design parameters. The main conclusion of this study plus a number of avenues for further research close this contribution.
Fire Risk Factor Analysis of High-Rise Building Based on Bayesian Network and Fuzzy Fault Tree

The factors of fire accident in high-rise buildings are complex and unpredictable, which are greatly affected by personnel operation and environment. Therefore, it is difficult to predict the exact probability of accidents statistically. Based on the influencing factors of fire accidents in high-rise buildings, a fuzzy fault tree was established. The advantage of bayesian network in dealing with uncertain problems was used to model and analyze, and the fuzzy problems in reality can be solved well. In the bayesian network model analysis, the fire accident of high-rise apartment building on yuyao road in Shanghai was taken as an example to verify. The prior probability came from Statistics of fire accidents of high-rise buildings over the past years and fuzzy probability given by experts. It was fuzzified by using the fuzzy mathematics principle, and the posterior probability obtained by bayesian tool was combined to understand the relatively important factors affecting high-rise building fire, so as to provide reference for fire risk factor evaluation of high-rise building.