Reviewerly/exHarmony huggingface dataset:
3D Face Reconstruction with Geometry Details from a Single Color Image Under Occluded Scenes

3D face reconstruction technology aims to generate a face stereo model naturally and realistically. Previous deep face reconstruction approaches are typically designed to generate convincing textures and cannot generalize well to multiple occluded scenarios simultaneously. By introducing bump mapping, we successfully added mid-level details to coarse 3D faces. More innovatively, our method takes into account occlusion scenarios. Thus on top of common 3D face reconstruction approaches, we in this paper propose a unified framework to handle multiple types of obstruction simultaneously (e.g., hair, palms and glasses et al.). Extensive experiments and comparisons demonstrate that our method can generate high-quality reconstruction results with geometry details from captured facial images under occluded scenes.
Demodulation method based on a component synthesis and gradient projection for phase-shifting interferometry

In order to reduce the vibration error of phase-shifting interferometry (PSI) and improve the demodulation accuracy of the wavefront phase, a demodulation method based on a component synthesis and gradient projection for PSI is proposed. For two sequences of interference images with a phase shift of 0, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>π<!-- π --></mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:math> , the method first corrects the vibration error by the pre-processing mechanism and multi-component synthesis. At the same time, two frames of interference images with a phase shift of 0, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>π<!-- π --></mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:math> are synthesized. Then the gradient projection algorithm is used to filter the background light of the synthesized image, and the wavefront phase is demodulated based on the arctangent operation. The experimental results show that the percentage of pixels demodulated effectively at the truncated interface is more than 90%. Compared with the traditional demodulation method, the number of pixels demodulated effectively (demodulation accuracy) is improved.
On Explainability in AI-Solutions: A Cross-Domain Survey

Artificial Intelligence (AI) increasingly shows its potential to outperform predicate logic algorithms and human control alike. In automatically deriving a system model, AI algorithms learn relations in data that are not detectable for humans. This great strength, however, also makes use of AI methods dubious. The more complex a model, the more difficult it is for a human to understand the reasoning for the decisions. As currently, fully automated AI algorithms are sparse, every algorithm has to provide a reasoning for human operators. For data engineers, metrics such as accuracy and sensitivity are sufficient. However, if models are interacting with non-experts, explanations have to be understandable. This work provides an extensive survey of literature on this topic, which, to a large part, consists of other surveys. The findings are mapped to ways of explaining decisions and reasons for explaining decisions. It shows that the heterogeneity of reasons and methods of and for explainability lead to individual explanatory frameworks.
Establishment of Wildfire Monitor-Repeater-EOC System Based on TOPSIS and Genetic Algorithm

To get the frequency and magnitude of forest fires in Victoria, the Forest Fire Danger Index (FFDI) is used to characterize some parameters of fires.First, an FFDI calculation model is established based on meteorological data.For the necessary parameter drought factor DF, we used the TOPSIS method to estimate and finally calculated the FFDI value of weather stations in Victoria.Based on the FFDI, we established a single-stream hierarchical facility location model based on genetic algorithm distance constraints.Finally, we got the distribution of fire-prone points and the distribution of monitor-repeater-EOC.
Analysis and Evaluation of Railway Accounting Information System

With the rapid development of China's economy and the deepening of reform and opening up, the development of enterprises has many favorable platforms and faces more opportunities.This question conducts literature research on the basic theories and the current situation of domestic and foreign research in the fields of enterprise financial management, information system integration, information system evaluation, etc.The analysis of the current situation of the road accounting information system summarizes the characteristics of the railroad accounting information system, and on this basis, the railroad accounting information system evaluation index system is constructed, from the level of infrastructure, the level of accounting information system function, the level of accounting information system performance The evaluation index system of railway accounting information system is constructed, and the railroad accounting information system is comprehensively evaluated in five aspects: infrastructure level, accounting information system function level, accounting information system performance level, accounting information security level, and accounting information performance level.
Improved Rank Pooling Strategy for Action Recognition

In the field of action recognition, it is crucial to capture the temporal evolution of video content compactly and effectively. One of the solutions is the rank pooling method that enables acquiring the evolution of video content. To further enhance temporal discrimination of the rank pooling, we proposed two improved rank pooling strategies, named the Minimum Volume Enclosing Ellipsoids (MVEE) and the Temporal Minimum Volume Enclosing Ellipsoids (TMVEE). The proposed methods are compatible with rank pooling and characterize the data distribution in other orthogonal directions to improve the temporal discrimination. We performed experiments on the ChaLearn gesture recognition and HMDB51 database, the results reveal that our proposed methods outperform other mainstreaming methos.
Yakov L. Shrayberg in the context of modernity

The article coincides with the jubilee of Yakov L. Shrayberg, a prominent Russian scientist, Doctor of Science in Engineering, Professor, Honored Worker of Culture of the Russian Federation, corresponding member of Russian Academy of Education, founder and president of the International Association of Users and Developers of Electronic Libraries and New Information Technologies (ELNIT Association) National Library Association “Libraries of the Future” (NALF). The author reviews various aspects of Dr. Shrayberg’s professional activities, in particular, development of International Library, Information and Analytical Center (ILIAC) in Russia and the USA, planning and orchestration of international scientific and practical conferences “Crimea” and LIBCOM, and, within their framework, the intellectual show game “What? Where? When? in Library, Information, Book and Publishing Space”, and authoring the memorial three-volume book “My friend Katya Genieva”, etc. The author recalls with warm feelings the milestones of his joint work with the celebrant and congratulates him on his birthday.
A novel restricted Boltzmann machine-based temporal-spatial correlation method for student behaviour recognition in depth video

Human behaviour recognition is an important research hotspot in the field of artificial intelligence. Current behaviour recognition methods have low recognition accuracy under different viewing angles, therefore, this paper proposes a novel restricted Boltzmann machine (RBM)-based temporal-spatial correlation method for student behaviour recognition in depth video. The RBM is used to map the human behaviour from different viewing angles to the high-dimensional space. The time level pooling function is applied in the time series activated by each neuron to realise the encoding of the video time sub-series. Finally, behaviour recognition and classification experiments are conducted on different public datasets and real classroom student behaviour datasets with other methods. The results show that the proposed method improves the accuracy of depth video recognition under different viewing angles and has good generalisation performance. The data analysis of abnormal behaviour in class can play an auxiliary role in dynamic classroom management.
Analytical Comparison Between the Pattern Classifiers Based upon a Multilayered Perceptron and Probabilistic Neural Network in Parallel Implementation

It is well known that, while the training mode of probabilistic neural network is quickly completed by a straightforward manner of allocating the units in a single hidden layer, its utility in the reference mode is slow in ordinary serial computing situations. In order to alleviate the slow operation, the parallel implementation is thus considered to be a desirable option. In this paper, we first quantify the overall amount of the step-wise operations required for the reference mode of a probabilistic neural network and that of a multilayered perceptron (or deep) neural network, both implemented in a parallel environment. Second, we derive the necessary condition for the reference mode of a probabilistic neural network to yield an equally fast as or faster operation than that of a deep neural network. Based upon the condition so derived, we next deduce a comparative relation between the training mode of a probabilistic neural network, where k-means clustering algorithm is applied to reduce the number of the hidden units, and that of a deep neural network operated in parallel. It is then shown that both the training and testing modes of the compact-sized network meeting these criteria can be run in a parallel environment as fast as or faster than those of a feed-forward deep neural network, while keeping a reasonably high classification performance.
O-LGMD: An Opponent Colour LGMD-Based Model for Collision Detection with Thermal Images at Night

It is an enormous challenge for intelligent robots or vehicles to detect and avoid collisions at night because of poor lighting conditions. Thermal cameras capture night scenes with temperature maps, often showing different pseudo-colour modes to enhance the visual effects for the human eyes. Since the features of approaching objects could have been well enhanced in the pseudo-colour outputs of a thermal camera, it is likely that colour cues could help the Lobula Giant Motion Detector (LGMD) to pick up the collision cues effectively. However, there is no investigation published on this aspect and it is not clear whether LGMD-like neural networks can take pseudo-colour information as input for collision detection in extreme dim conditions. In this study, we investigate a few thermal pseudo-colour modes and propose to extract colour cues with a triple-channel LGMD-based neural network to directly process the pseudo-colour images. The proposed model consists of three sub-networks—each dealing with one specific opponent colour channel, i.e. black-white, red-green, or yellow-blue. A collision alarm is triggered if any channel’s output exceeds its threshold for a few successive frames. Our experiments demonstrate that the proposed bio-inspired collision detection system works well in quickly detecting colliding objects in direct collision course in extremely low lighting conditions. The proposed method showed its potential to be part of sensor systems for future robots or vehicles driving at night or in other extreme lighting conditions—to help avoiding fatal collisions.
Explaining Neural Matrix Factorization with Gradient Rollback

Explaining the predictions of neural black-box models is an important problem, especially when such models are used in applications where user trust is crucial. Estimating the influence of training examples on a learned neural model's behavior allows us to identify training examples most responsible for a given prediction and, therefore, to faithfully explain the output of a black-box model. The most generally applicable existing method is based on influence functions, which scale poorly for larger sample sizes and models. We propose gradient rollback, a general approach for influence estimation, applicable to neural models where each parameter update step during gradient descent touches a smaller number of parameters, even if the overall number of parameters is large. Neural matrix factorization models trained with gradient descent are part of this model class. These models are popular and have found a wide range of applications in industry. Especially knowledge graph embedding methods, which belong to this class, are used extensively. We show that gradient rollback is highly efficient at both training and test time. Moreover, we show theoretically that the difference between gradient rollback's influence approximation and the true influence on a model's behavior is smaller than known bounds on the stability of stochastic gradient descent. This establishes that gradient rollback is robustly estimating example influence. We also conduct experiments which show that gradient rollback provides faithful explanations for knowledge base completion and recommender datasets.
Statistical Characterization of Groundwater Geochemistry in Vein-Type Gold Mines in Korea

A dataset of chemical composition of groundwater in Korea including that in vein-type gold mines was compiled through literature review.Groundwaters were grouped, according to location, into 'adit groundwater' in the immediate vicinity of gold veins, 'surrounding groundwater' spatially separated from the veins in gold mines, 'normal groundwater' from non-mineralized granitic rocks, and 'deep groundwater' from a depth of 500 m or deeper.ANOVA indicated that adit groundwater showed statistically higher values of TDS, Ca, and SO 4 than the other groups.High SO 4 in the adit groundwater is likely due to sulfide minerals, and high Ca may be attributed to the occurrence of calcite as a fracture-filling mineral in the mines.Classification functions derived from discriminant analysis could distinguish between adit, surrounding, and normal groundwaters with a hit ratio > 92%.The classification functions may serve as an indicator for the exploration of unknown vein-type gold deposits through chemical analysis of the groundwater.
Features of the coagulating and peptizing action of weakly basic and strongly basic cationic polyelectrolytes on rosin emulsions

It has been shown for the first time that cationic polyelectrolytes (weakly basic (polyamide-polyamine-epichlorohydrin resin (PPER)) and strongly basic (polydimethyldiallylammonium chloride (PDMDAAC) and copolymer of acrylamide with methylene chloride dimethylamminopropylacrylamide (CA MC DMAPA)), widely used in paper and cardboard technology as flocculants (existing technology), additionally participate in the processes of coagulation and peptization (proposed technology). It has been established that the coagulating ability of polyelectrolytes decreases in the series CA MC DMAPA &gt; PDMDAAC &gt; PPER, while the peptizing ability, on the contrary, increases. Peptizable coagulants with the size and ξ-potential that do not exceed 5200 nm and +15 mV, respectively, are formed in the presence of PPER, when its content in the dispersed system does not exceed 0.15 parts by weight / parts by weight of the dispersed phase of the rosin emulsion (RE). The first region of electrolytic coagulation RE (proposed technology) corresponds to the ratio of RE : electrolyte in the range from 1: 0.3 to 1: 0.6, while with an increased content of electrolyte in the dispersed system (existing technology), which is in the range of 1: 2.4 to 1: 3.0 , the coagulation process proceeds in the second region (the resulting coagulates are electrically neutral and coarse (size exceeds 5200 nm) and are not able to peptize). The peptization process proceeds in the dispersed system “RE (1.0 wt. parts) – PPER (0.04 wt. parts) – electrolyte (0.6 wt. parts)”. The presence of PPER promotes an increase in the ξ-potential of peptized particles from +35 to +50 mV. Unlike coagulates, such particles provide a shift in the sizing process from the traditional mode of homocoagulation to a more efficient mode of heteroadagulation. This results in an increase in the hydrophobicity and strength of paper (cardboard) by 29–36 and 28–38 %, respectively, as well as in an improvement in the printing properties of coated products by 12–15 %.
A Short Survey on Constructing an IoT based Intelligent Road System

Road construction is a crucial component of modern infrastructure since it greatly facilitates travel between various areas.Sustainability, Progress, and Transformation refer to the upgrading of roads employing communication, lighting, and control transmission mechanisms that may promote sustainability, road progress, and a better driving experience for users.Smart roads that are connected to the Internet of Things (IoT) devices make it possible to drive more efficiently, sustainably, and safely.For this reason, the range of smart road technology like actuators, sensors, and solar power along with software infrastructures like Artificial Intelligence and big data are now made standard in all new roads. This article provides a framework for patients to employ speech-to-text chatbots to conduct treatment. Using chat-box technology, we have implemented cognitive therapy as a solution.
Overall Satisfaction with the Emergency Remote Teaching during the COVID-19 Pandemic from the Perspective of Higher Education Students in Portugal

Objective - The COVID-19 pandemic led to an abrupt transition to online classes in higher education, with no consideration for the specific students' conditions in Portugal and other countries. Therefore, this study aims to assess the students' perspectives on online learning classes during the lockdown in 2020. Methodology/Technique - The analysis is based on 2,107 valid answers from students of seven Portuguese higher education institutions that took synchronous online classes. Principal component analysis and binary logistic regression were the main quantitative methods used. Findings – This study identified five factors that, in all cases, significantly explain the students' perspective on the transition to online classes. These factors include not only pedagogical matters (teachers' overall quality) but also elements related to personal or motivational students' characteristics (performance and autonomy; constraints and obstacles to socialization; self-confidence while attending online classes) and technological issues (Internet access conditions). Notwithstanding, self-confidence is not a relevant factor for students with technical limitations concerning infrastructural matters. Novelty - Besides the contributions of this study, it is important to emphasize its relevance by combining the critical success factors in distance learning with different demographic variables, which are usually less explored by this literature. Notwithstanding, less robust and inconsistent results were found for those variables. Type of Paper: Empirical JEL Classification: I18, I23, I28. Keywords: COVID-19; emergency remote teaching; higher education; technical infrastructures; students' perspective. Reference to this paper should be made as follows: Albuquerque, F; Santos, P.G.D; Martinho, C. (2022). Overall Satisfaction with the Emergency Remote Teaching during the COVID-19 Pandemic from the Perspective of Higher Education Students in Portugal, J. Mgt. Mkt. Review, 7(3), 116 – 128. https://doi.org/10.35609/jmmr.2022.7.3(4)
Analysis of Building Integrated Photovoltaic Application for Apartment Building in Jakarta

In sustainable development concept, energy efficiency and conservation measurements are paramount to reduce the level of building energy consumption. Utilization of such device diminishes the building dependency to the grid which is still dominated by energy from fossil fuels. Implementation of BIPV (Building Integrated Photovoltaic) system is expected to be one of the best possible choices for a tropical country such as Indonesia with abundance of solar radiation. With the existence of BIPV system, it is also expected to reduce the burden of electricity cost for the building management and occupants. This research is considerably new for government building and it also generates a formula to calculate minimum area and the amount of PV panels needed for apartment in Jakarta. Pasar Jumat Apartment is selected to be a subject for this research as the apartment is classified government building which was built and managed by Ministry of Public Work and Housing. Moreover, the apartment has modelled with BIM and already had green building concept. It is expected that BIPV system can enhance the building performance through energy efficiency. However, more in-depth research work to the efficiency and economic feasibility of BIPV system installation are required before implementing the system on the building. Several options of PV panel installation are measured to figure out the most optimal PP (Payback Period) and BCR (Benefit Cost Ratio). Based on the physical feasibility and financial analysis, BIPV system is found to be more promising and profitable if it is implemented at "Pasar Jumat Apartment" for a long term in the future.
Missing Data Imputation for Categorical Variables

Dealing with missing data is a crucial part of everyday data analysis. The IMIC algorithm is a missing data imputation method that can handle mixed numerical and categorical datasets. However, the categorical data are crucial for this work. This paper proposes the new improvement of the IMIC algorithm. The two proposed modifications consider the number of categories in each categorical variable. Based on this information, the factor, which modifies the original measure, is computed. The factor equation is inspired by the Eskin similarity measure that is known in the hierarchical clustering of categorical data. The results show that as the missing value ratio in the dataset grows, better results are achieved using the second modification. The paper also shortly analyzes the advantages and disadvantages of using the IMIC algorithm.
Transit facility allocation: Hybrid quantum-classical optimization

An essential consideration in urban transit facility planning is service efficiency and accessibility. Previous research has shown that reducing the number of facilities along a route may increase efficiency but decrease accessibility. Striking a balance between these two is a critical consideration in transit planning. Transit facility consolidation is a cost-effective way to improve the quality of service by strategically determining the desirable allocation of a limited number of facilities. This paper develops an optimization framework that integrates Geographical Information systems (GIS), decision-making analysis, and quantum technologies for addressing the problem of facility consolidation. Our proposed framework includes a novel mathematical model that captures non-linear interactions between facilities and surrounding demand nodes, inter-facility competition, ridership demand and spatial coverage. The developed model can harness the power of quantum effects such as superposition and quantum tunnelling and enables transportation planners to utilize the most recent hardware solutions such as quantum and digital annealers, coherent Ising Machines and gate-based universal quantum computers. This study presents a real-world application of the framework to the public transit facility redundancy problem in the British Columbia Vancouver metropolitan area. We demonstrate the effectiveness of our framework by reducing the number of facilities by 40% while maintaining the same service accessibility. Additionally, we showcase the ability of the proposed mathematical model to take advantage of quantum annealing and classical optimization techniques.