Reviewerly/exHarmony huggingface dataset:
A Comparative Study on the Loss Functions for Image Enhancement Networks

Image enhancement and image retouching processes are often dominated by global (shift-invariant) change of colour and tones. Most &#x201C;deep learning&#x201D; based methods proposed for image enhancement are trained to enforce similarity in pixel values and/or in the high-level feature space. We hypothesise that for tasks, such as image enhancement and retouching, which involve a significant shift in colour statistics, training the model to restore the overall colour distribution can be of vital importance. To address this, we study the effect of a Histogram Matching loss function on a state-of-the art colour enhancement network &#x2014; HDRNet. The loss enforces similarity of the RGB histograms of the predicted and the target images. By providing detailed qualitative and quantitative comparison of different loss functions on varied datasets, we conclude that enforcing similarity in the colour distribution achieves substantial improvement in performance and can play a significant role while choosing loss functions for image enhancement networks.
UML4NoSQL: A Novel Approach for Modeling NoSQL Document-Oriented Databases Based on UML

The adoption of Big Data systems by the companies is relatively new, although the data modeling and system design are ages old. Despite the fact that traditional databases are built on solid foundations, they cannot handle the swift and massive flow of data coming from multiple different sources. Herein, NoSQL databases are an inevitable alternative. However, these systems are schemaless compared to traditional databases. It is important to emphasize that schemaless does not mean no-schema which would mean that NoSQL databases do not need modeling. Hence, there is a need for conceptual models to define the data structure in these databases. This paper sheds a light on the importance of the UML in showing how to store Big Data described through meta-models within NoSQL databases. We propose a novel Big Data modeling methodology for NoSQL databases called UML4NoSQL, which is independent of the target system, and taking into account the four Big Data characteristics: Variety, Volume, Velocity, and Veracity (4 V's). The approach relies on the UML blocks with a data-up technique; it starts with a use-case and the class diagram resulting from the understanding of the data at hand and the definition of the developer's strategies while focusing on the user's needs. To illustrate our approach, we take a case study from health care domain. We show that our approach produces designs that can be implemented on NoSQL document-oriented system with respect to Big Data 4 V's.
Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance

Test, Evaluation, Verification, and Validation (TEVV) for Artificial Intelligence (AI) is a challenge that threatens to limit the economic and societal rewards that AI researchers have devoted themselves to producing. A central task of TEVV for AI is estimating brittleness, where brittleness implies that the system functions well within some bounds and poorly outside of those bounds. This paper argues that neither of those criteria are certain of Deep Neural Networks. First, highly touted AI successes (eg. image classification and speech recognition) are orders of magnitude more failure-prone than are typically certified in critical systems even within design bounds (perfectly in-distribution sampling). Second, performance falls off only gradually as inputs become further Out-Of-Distribution (OOD). Enhanced emphasis is needed on designing systems that are resilient despite failure-prone AI components as well as on evaluating and improving OOD performance in order to get AI to where it can clear the challenging hurdles of TEVV and certification.
Hypersuccinct Trees -- New universal tree source codes for optimal compressed tree data structures and range minima

We present a new universal source code for distributions of unlabeled binary and ordinal trees that achieves optimal compression to within lower order terms for all tree sources covered by existing universal codes. At the same time, it supports answering many navigational queries on the compressed representation in constant time on the word-RAM; this is not known to be possible for any existing tree compression method. The resulting data structures, "hypersuccinct trees", hence combine the compression achieved by the best known universal codes with the operation support of the best succinct tree data structures. We apply hypersuccinct trees to obtain a universal compressed data structure for range-minimum queries. It has constant query time and the optimal worst-case space usage of $2n+o(n)$ bits, but the space drops to $1.736n + o(n)$ bits on average for random permutations of $n$ elements, and $2\lg\binom nr + o(n)$ for arrays with $r$ increasing runs, respectively. Both results are optimal; the former answers an open problem of Davoodi et al. (2014) and Golin et al. (2016). Compared to prior work on succinct data structures, we do not have to tailor our data structure to specific applications; hypersuccinct trees automatically adapt to the trees at hand. We show that they simultaneously achieve the optimal space usage to within lower order terms for a wide range of distributions over tree shapes, including: binary search trees (BSTs) generated by insertions in random order / Cartesian trees of random arrays, random fringe-balanced BSTs, binary trees with a given number of binary/unary/leaf nodes, random binary tries generated from memoryless sources, full binary trees, unary paths, as well as uniformly chosen weight-balanced BSTs, AVL trees, and left-leaning red-black trees.
A Learning-to-Rank Approach for Spare Parts Consumption in the Repair Process

The repair process of devices is an important part of the business of many original equipment manufacturers. The consumption of spare parts, during the repair process, is driven by the defects found during inspection of the devices, and these parts are a big part of the costs in the repair process. But current Supply Chain Control Tower solutions do not provide support for the automatic check of spare parts consumption in the repair process. In this paper, we investigate a multi-label classification problem and present a learning-to-rank approach, where we simulate the passage of time while training hundreds of Logistic Regression Machine Learning models to provide an automatic check in the consumption of spare parts. The results show that the trained models can achieve a mean NDCG@20 score of 81% when ranking the expected parts, while also marking a low volume of 10% of the consumed parts for alert generation. We briefly discuss how these marked parts can be aggregated and combined with additional data to generate more fine-grained alerts.
Using The “Triad” of Uav-Tem, Uav-Magnetic Prospecting and Uav-Gamma-Spectrometry: Case of Prospecting for Blind Ore Deposits

Summary In order to successfully prospect for new deposits, in most cases it is necessary to use a set of geophysical methods that allow both studying the surface, and creating models of the deep structure of the near surface part of the earth’s crust. The classic universal combination is the «triad» of methods “magnetic prospecting+gamma-spectrometry+electromagnetic prospecting”, this option is used in most geophysical systems based on manned helicopters. In recent years, magnetic prospecting and gamma survey methods have already been widely used in the UAV version, so UAV-based electromagnetic soundings are of maximum interest at this stage of geophysics. In this paper, we consider a case of combining all three UAV-based methods in the case of prospecting for blind deposits of sandstone-type uranium, with special attention being paid to the use of the current version of SibGIS UAV-TEM system for time-domain electromagnetic sounding, which is significantly improved compared to the first prototypes. According to the authors, this case proves that unmanned systems, which allow performing geophysical surveys with a set of methods at low altitudes and with precise terrain drape, make it possible to almost completely abandon ground-based surveys, at present - and in the case of transient electrical prospecting too.
Graph-based Multi-View Fusion and Local Adaptation: Mitigating Within-Household Confusability for Speaker Identification

Speaker identification (SID) in the household scenario (e.g., for smart speakers) is an important but challenging problem due to limited number of labeled (enrollment) utterances, confusable voices, and demographic imbalances. Conventional speaker recognition systems generalize from a large random sample of speakers, causing the recognition to underperform for households drawn from specific cohorts or otherwise exhibiting high confusability. In this work, we propose a graph-based semi-supervised learning approach to improve household-level SID accuracy and robustness with locally adapted graph normalization and multi-signal fusion with multi-view graphs. Unlike other work on household SID, fairness, and signal fusion, this work focuses on speaker label inference (scoring) and provides a simple solution to realize household-specific adaptation and multi-signal fusion without tuning the embeddings or training a fusion network. Experiments on the VoxCeleb dataset demonstrate that our approach consistently improves the performance across households with different customer cohorts and degrees of confusability.
Modelling a cause-and-effect relationship of resilience promotion by fuzzy DEMATEL- trapezoidal structure

Although widely used, standard game-theoretic approaches to security games face severe shortcomings, being the common knowledge assumption a critical one. Adversarial Risk Analysis (ARA) is an alternative modeling framework that mitigates such limitations. However, from a computational perspective, ARA is much more involved than its game theoretical counterparts. We propose an approach for finding ARA solutions to security games represented as bi-agent influence diagrams that is based on augmented probability simulation. We motivate this approach using two simple cases: sequential and simultaneous defend-attack models. We next provide the general framework and illustrate it in handling risks in a cybersecurity setting.
A Comprehensive Study on Sign Language Recognition for Deaf and Dumb people

There are roughly 72 million ‘hard of hearing’ individuals all over the planet, and more than 80% of them live in developing countries, as indicated in a review by the World Federation for the Deaf. Their lives are hindered by hearing distortions which bar them from showing full interest in the public besides taking pleasure in enjoying identical privileges. Motion based communication is common for the people with hearing and speaking impairments. Communication through signs is a successful choice rather than talking, where the former is replaced by hand flags. One solution to this problem is to study text comprehension tasks for hearing impaired localities using Sign Language Recognition. Gesture-based communication is the most significant and centered approach of communication for deaf and dumb individuals. This paper gives a concise review of different examination works conducted thus far in this field.
Fault Prediction of Power Electronics Module based on RELM-AdaBoost

This paper introduces the extreme learning machine (ELM) optimization theory based on the regularization coefficient, and the framework and theory of AdaBoost integrated learning algorithm. A fault prediction method based on RELM-AdaBoost is proposed. For insulated gate bipolar transistor (IGBT), a standard power unit in power electronics, the monitoring data of the module is collected by accelerating degradation experiment to form a data set. We carry out the remaining useful life (RUL) prediction experiment of the IGBT module with the method proposed in this paper. The experimental results show that the RELM-AdaBoost model can reasonably predict the remaining practical life of the IGBT module under overload conditions. The prediction curve of RUL is consistent with the actual fault occurrence. The approach in this article can support subsequent evaluation and maintenance decisions.
Teachers' perception of higher education in a transition scenario

Distance and online teaching and learning have become common forms during the COVID-19 pandemic that forced the change of pedagogical and didactical approaches from residential to virtual and online settings. Given that these novel forms of teaching and learning possess advantages beyond the needs of this contingent situation, it is reasonable to suppose that they will be used also after the end of the pandemic. In light of this, it is important for all the stakeholders to possess digital skills fine-tuned to education, in order to teach and learn at best, even more so in international contexts such as those regarding security and defence. This is in accordance with the more general setting of future skills, that considers personal development related to what the world is requiring. In this paper, we investigate changes from before to during the pandemic in the perception of teachers working in programs or courses concerning security and defence on several aspects, such as the relation with their students and the time required to carry out various tasks. Our investigation, conducted through the analysis of an online anonymous questionnaire, triggers the suggestion that teachers need a specific training in digital skills, since they experience difficulties in relating with students and in performing tasks without excessive time consumption. Our analysis took place in accordance with international partners from the European project DIGICODE, which aims at improving the security and defence education quality by using digital tools in the didactic activities and by developing teachers' digital competencies.
Dynamic Environment-based Visual User Interface System for Intuitive Navigation Target Selection for Brain-actuated Wheelchairs

Visual user interface paradigms are one of the key modules for brain-actuated wheelchairs, which are a class of promising assistive devices that can increase the autonomy and mobility of people suffering from severe motor impairments. This work proposes a Dynamic Environment-based Visual Interface System (DEVIS) for intuitive navigation target selection for brain-actuated wheelchairs. It is composed of a novel Dynamic Visual Interface (DVI), an RGB image-based perception module (indoor scene classification, object detection and classification, and object tracking), and a P300-based BCI. The DVI displays potential navigation goals in three forms of visual cues: an RGB camera image streaming with object bounding boxes overlaid on objects detected and tracked, three global points of interest (indoor places), and two static commands. Hence, the DVI allows a user to select, through the P300-based BCI, navigation targets/commands that are flashing independently and randomly to create an oddball paradigm. Experimental evaluations were carried out in a dynamic setting with 5 participants, who were asked to select targets displayed in the DVI. The dynamic setting was obtained using RGB image sequences of the ISR-RGB-D Dataset, which represents a mission performed by a mobile robotic platform. The target selection was performed through a P300-based BCI, using non-self-paced and self-paced modalities. The obtained results show that the proposed DEVIS can run in real-time and that users are able to correctly select targets with high BCI accuracy rates.
Impact of the LCD monitor locations on a novel alignment method: the combination of deflectometry and the sine condition test

We have proposed a new alignment method which is the combination of deflectometry and the sine condition test. One of the great advantages of the new approach is that we need a camera and an LCD monitor larger than the clear aperture of the telescope instead of an interferometer and a return flat. To determine the state of the alignment, we have to place the monitor at two different locations: ideally at the rear principal plane of the telescope and a few meters displaced from the rear principal plane. However, for practical reasons, we may have to place the monitor closer to the telescope. We have simulated how changing the monitor location impacts the alignment, and we show the consequences of variations in the LCD locations on the alignment of a telescope using the new method.
Hint-Based Image Colorization Based on Hierarchical Vision Transformer

Hint-based image colorization is an image-to-image translation task that aims at creating a full-color image from an input luminance image when a small set of color values for some pixels are given as hints. Though traditional deep-learning-based methods have been proposed in the literature, they are based on convolution neural networks (CNNs) that have strong spatial locality due to the convolution operations. This often causes non-trivial visual artifacts in the colorization results, such as false color and color bleeding artifacts. To overcome this limitation, this study proposes a vision transformer-based colorization network. The proposed hint-based colorization network has a hierarchical vision transformer architecture in the form of an encoder-decoder structure based on transformer blocks. As the proposed method uses the transformer blocks that can learn rich long-range dependency, it can achieve visually plausible colorization results, even with a small number of color hints. Through the verification experiments, the results reveal that the proposed transformer model outperforms the conventional CNN-based models. In addition, we qualitatively analyze the effect of the long-range dependency of the transformer model on hint-based image colorization.
Estimating linear mixed effects models with truncated normally distributed random effects

Linear Mixed Effects (LME) models have been widely applied in clustered data analysis in many areas including marketing research, clinical trials, and biomedical studies. Inference can be conducted using maximum likelihood approach if assuming Normal distributions on the random effects. However, in many applications of economy, business and medicine, it is often essential to impose constraints on the regression parameters after taking their real-world interpretations into account. Therefore, in this paper we extend the classical (unconstrained) LME models to allow for sign constraints on its overall coefficients. We propose to assume a symmetric doubly truncated Normal (SDTN) distribution on the random effects instead of the unconstrained Normal distribution which is often found in classical literature. With the aforementioned change, difficulty has dramatically increased as the exact distribution of the dependent variable becomes analytically intractable. We then develop likelihood-based approaches to estimate the unknown model parameters utilizing the approximation of its exact distribution. Simulation studies have shown that the proposed constrained model not only improves real-world interpretations of results, but also achieves satisfactory performance on model fits as compared to the existing model.
Attribute-Based Collaborative Access Control Scheme with Constant Ciphertext Length for Smart Grid

Attribute based encryption (ABE) is usually used for authorization to ensure data confidentiality because it can provide fine-grained access control. However, the data of smart grid is not completely divided, and there are many cases of collaborative access to data. The existing ABE based collaboration schemes have the defect that the ciphertext increases with the increase of attributes. Due to the limited storage and transmission capacity of smart grid equipment, the previous schemes are not suitable for smart grid. An attribute based collaborative access control scheme with constant ciphertext length for smart grid is proposed. By extending the collaborative nature to ABE with constant ciphertext length, the vacancy of collaborative ABE is made up. Performance analysis shows that this scheme is significantly better than the traditional collaborative ABE scheme in terms of storage and computing overhead. Security analysis shows that the scheme can ensure the confidentiality of data and support the revocation of collaborative at any time.
Development of Evaluation System for VIMS by Machine Learning of Eye Movement Data

The prevention of traffic accidents involving elderly drivers is an important issue. Research on elderly drivers often uses the driving simulator (DS). Visually induced motion sickness (VIMS) has been pointed out as a problem in DS experiments. Although there are many methods to evaluate VIMS, reducing the burden on the experimental collaborators is an issue. As a method of measuring physiological indices that is less burdensome to the participants and has many applications, the use of noncontact eye-tracking device has been mentioned. This study developed a VIMS evaluation system using data collected with a noncontact eye-tracking device for DS experiments. The participants included eight elderly people with visual and balance functions that did not interfere with their daily life. The participants' gaze data were measured, and they answered the simulator sickness questionnaire (SSQ) before and after experiment. Gaze measurements were taken at rest. The participants were divided into two groups on the basis of their SSQ results. One group experienced VIMS during the DS trial (four people; average age, 79.0 years), whereas the other group did not experience it (four people; average age, 72.0 years). Machine learning analysis was performed on the gaze data from the 1st and 5th DS trials. we used the leave one out method for the verification of training and test data in machine learning; in this method, verification is repeated so that the data of all experimental participants become test cases. The results of the learning model's validation showed a high rate of correct answers. The results suggested that the learning model obtained using machine learning was an effective evaluation system for VIMS.
A High-throughput Hardware Implementation of ZUC-256 Stream Cipher

The ZUC-256 stream cipher algorithm has been incorporated into the security portfolio of 3GPP LTE-Advanced. Therefore, it is important to study its high-performance implementation. In this paper, we present a high-throughput hardware implementation of the ZUC-256 cryptographic algorithm for ASICs and FPGAs. By using a fully pipelined design scheme, the critical path of the algorithm in both operating modes is significantly shortened and the operating frequency is increased. The throughput of ZUC-256 is up to 160Gpbs with the area 15500GEs under TSMC 12nm technology. In comparison with other work, the area efficiency of the ASIC implementation is improved by 1.61 times and that of FPGA is improved by 1.06 times while the throughput rate is significantly improved.