Miracle-dz/UniFAR-FTUs huggingface dataset:
ActKnow: Active External Knowledge Infusion Learning for Question Answering in Low Data Regime

Deep learning models have set benchmark results in various Natural Language Processing tasks. However, these models require an enormous amount of training data, which is infeasible in many practical problems. While various techniques like domain adaptation, fewshot learning techniques address this problem, we introduce a new technique of actively infusing external knowledge into learning to solve low data regime problems. We propose a technique called ActKnow that actively infuses knowledge from Knowledge Graphs (KG) based"on-demand"into learning for Question Answering (QA). By infusing world knowledge from Concept-Net, we show significant improvements on the ARC Challenge-set benchmark over purely text-based transformer models like RoBERTa in the low data regime. For example, by using only 20% training examples, we demonstrate a 4% improvement in the accuracy for both ARC-challenge and OpenBookQA, respectively.
Graph Model-Based Lane-Marking Feature Extraction for Lane Detection

This paper presents a robust, efficient lane-marking feature extraction method using a graph model-based approach. To extract the features, the proposed hat filter with adaptive sizes is first applied to each row of an input image and local maximum values are extracted from the filter response. The features with the maximum values are fed as nodes to a connected graph structure, and the edges of the graph are constructed using the proposed neighbor searching method. Nodes related to lane-markings are then selected by finding a connected subgraph in the graph. The selected nodes are fitted to line segments as the proposed features of lane-markings. The experimental results show that the proposed method not only yields at least 2.2% better performance compared to the existing methods on the KIST dataset, which includes various types of sensing noise caused by environmental changes, but also improves at least 1.4% better than the previous methods on the Caltech dataset which has been widely used for the comparison of lane marking detection. Furthermore, the proposed lane marking detection runs with an average of 3.3 ms, which is fast enough for real-time applications.
Scaled 360 layouts: Revisiting non-central panoramas

From a non-central panorama, 3D lines can be recovered by geometric reasoning. However, their sensitivity to noise and the complex geometric modeling required has led these panoramas being very little investigated. In this work we present a novel approach for 3D layout recovery of indoor environments using single non-central panoramas. We obtain the boundaries of the structural lines of the room from a non-central panorama using deep learning and exploit the properties of non-central projection systems in a new geometrical processing to recover the scaled layout. We solve the problem for Manhattan environments, handling occlusions, and also for Atlanta environments in an unified method. The experiments performed improve the state-of-the-art methods for 3D layout recovery from a single panorama. Our approach is the first work using deep learning with non-central panoramas and recovering the scale of single panorama layouts.
Modeling and Analysis on Minimum Safe Distance for Platooning Vehicles Based on Field Test of Communication Delay

Vehicle platooning is a perspective technique for intelligent transportation systems (ITS). Connected and automated vehicles (CAVs) use dedicated short-range communication (DSRC) to form a convoy, in which the following vehicles can receive the information from their preceding vehicles to achieve safe automated driving and maintain a short headway. Consequently, a vehicle platoon can improve traffic safety and efficiency, further reducing fuel consumption. However, emergency braking inevitably occurs when the platoon meets an accident or a sudden mechanical failure. It is more critical when the wireless communication got delays. Therefore, “how to predefine a minimum safe distance (MSD) considering communication delay” is a challenging issue. To this end, a series of field tests were carried out to measure the communication delay of IEEE 802.11p that is the underlying protocol of DSRC. Subsequently, MSD is modeled and analyzed when the platoon travels at accelerating, cruising, and decelerating states. More importantly, the results of field tests are applied in the models to investigate the impact of communication delay on MSD in practice. The simulation results verify that the proposed model can effectively maintain the platooning vehicles’ safety even if emergency braking happens with certain communication delays.
Is It Time to Redeﬁne the Classiﬁcation Task for Deep Learning Systems?

Many works have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial examples. A deep learning system involves a couple of elements: the learning task, data set, deep model, loss, and optimizer. Each element may cause the vulnerability of the deep learning sys-tem, and simply attributing the vulnerability of the deep learning system to the deep model may impede addressing the adversarial attack. So we redeﬁne the robustness of DNNs as the robustness of the deep neural learning system, and we experimentally ﬁnd that the vulnerability of the deep learning system also roots in the learning task itself. In detail, this paper deﬁnes the interval-label classiﬁcation task for the deep classiﬁcation sys-tem, whose labels are predeﬁned non-overlapping intervals instead of a ﬁxed value (hard label) or probability vector (soft label). The experimental results demonstrate that the interval-label classi-ﬁcation task is more robust than the traditional classiﬁcation task while retaining accuracy.
Comprehensive transcriptome analysis of peripheral blood unravels key lncRNAs implicated in ABPA and asthma

Allergic bronchopulmonary aspergillosis (ABPA) is a complex hypersensitivity lung disease caused by a fungus known as Aspergillus fumigatus. It complicates and aggravates asthma. Despite their potential associations, the underlying mechanisms of asthma developing into ABPA remain obscure. Here we performed an integrative transcriptome analysis based on three types of human peripheral blood, which derived from ABPA patients, asthmatic patients and health controls, aiming to identify crucial lncRNAs implicated in ABPA and asthma. Initially, a high-confidence dataset of lncRNAs was identified using a stringent filtering pipeline. A comparative mutational analysis revealed no significant difference among these samples. Differential expression analysis disclosed several immune-related mRNAs and lncRNAs differentially expressed in ABPA and asthma. For each disease, three sub-networks were established using differential network analysis. Many key lncRNAs implicated in ABPA and asthma were identified, respectively, i.e., AL139423.1-201, AC106028.4-201, HNRNPUL1-210, PUF60-218 and SREBF1-208. Our analysis indicated that these lncRNAs exhibits in the loss-of-function networks, and the expression of which were repressed in the occurrences of both diseases, implying their important roles in the immune-related processes in response to the occurrence of both diseases. Above all, our analysis proposed a new point of view to explore the relationship between ABPA and asthma, which might provide new clues to unveil the pathogenic mechanisms for both diseases.
New records of moths (Lepidoptera) from Novaya Zemlya, Arctic Russia, with a supplement of DNA barcoding data

How to cite this article: Spitsyn V.M. , Kondakov A.V. , Tomilova A.A. , Spitsyna E.A. , Potapov G.S. 2021. New records of moths (Lepidoptera) from Novaya Zemlya, Arctic Russia, with a supplement of DNA barcoding data // Russian Entomol. J. Vol.30. No.2. P.178–181. doi: 10.15298/rusentj.30. 2.12 New records of moths (Lepidoptera) from Novaya Zemlya, Arctic Russia, with a supplement of DNA barcoding data
A Combined Neural Network and CRF Framework for Stereo Depth Estimation Anonymous CVPR 2021 submission

We propose a generic end-to-end framework for stereo depth estimation that combines neural networks and conditional random ﬁelds. We explain how our approach improves in key ways over existing combined approaches and demonstrate empirically that the CRF acts as an effective regularizer. Through an extensive evaluation on a variety of real and synthetic data sets, we show that plugging existing deep neural network architectures into our framework yields signiﬁcant performance improvements, some-times more than 20%, over pure deep neural network solutions. Finally, we
A FORECASTING MODEL IN MANAGING FUTURE SCENARIOS TO ACHIEVE THE SUSTAINABLE DEVELOPMENT GOALS OF THAILAND’S ENVIRONMENTAL LAW: ENRICHING THE PATH ANALYSIS-VARIMA-OVI MODEL

The objective of this study is to develop a forecasting model for causal factors management in the future in to order to achieve sustainable development goals. This study applies a validity-based concept and the best model called “Path analysis based on vector autoregressive integrated moving average with observed variables” (Path Analysis-VARIMA-OV i Model). The main distinguishing feature of the proposed model is the highly efficient coverage capacity for different contexts and sectors. The model is developed to serve long-term forecasting (2020-2034). The results of this study show that all three latent variables (economic growth, social growth, and environmental growth) are causally related. Based on the Path Analysis-VARIMA-OV i Model, the best linear unbiased estimator (BLUE) is detected when the government stipulates a new scenario policy. This model presents the findings that if the government remains at the current future energy consumption levels during 2020-2034, constant with the smallest error correction mechanism, the future CO 2 emission growth rate during 2020-2034 is found to increase at the reduced rate of 8.62% (2020/2034) or equivalent to 78.12 Mt CO 2 Eq. (2020/2034), which is lower than a carrying capacity not exceeding 90.5 Mt CO 2 Eq. (2020-2034). This outcome differs clearly when there is no stipulation of the above scenario. Future CO 2 emission during 2020-2034 will increase at a rate of 40.32% or by 100.92 Mt CO 2 Eq. (2020/2034). However, when applying the Path Analysis-VARIMA-OV i Model to assess the performance, the mean absolute percentage error (MAPE) is estimated at 1.09%, and the root mean square error (RMSE) is estimated at 1.55%. In comparison with other models, namely multiple regression model (MR model), artificial neural network model (ANN model), back-propagation neural network model (BP model), fuzzy analysis network process model (FANAP model), gray model (GM model), and gray-autoregressive integrated moving average model (GM-ARIMA model), the Path Analysis-VARIMA-OV i model is found to be the most suitable tool for a policy management and planning to achieve a sustainability for Thailand.
Nonlinear Convex Quadrature Surrogate Hyperparameter Optimization

Deep learning is a cornerstone of today’s renaissance in artiﬁcial intelligence and an enabler for next-generation technologies in a variety of applications. Many of these successes are attributed to ﬁnely tuned hyperparameters. Despite the importance of optimizing hyperparameters for a given problem, it remains a difﬁcult task. Many optimization techniques are performed in a naive manner or require assumptions regarding the smoothness and continuity of the loss function. In this work, we introduce a new hyperparameter optimization method using a data-driven approach based on a convex quadrature surrogate. We demonstrate the effectiveness and practicality of our new method on continuous domain spaces against state-of-the-art optimization techniques with higher efﬁciency while maintaining comparable validation, testing and accuracy performance scores on a multitude of datasets for the challenging scenario of open set recognition.
Impact of Customer Service Techniques on Customer Satisfaction in Retail Bank

The study sought to assess the impact of customer care delivery on customer satisfaction in retail banks. The specific objectives of this study are to examine 1. The impact of customer care delivery the quality of service, effectiveness, complaint handling and interpersonal relationship on customer satisfaction. 2. Examine the mediating role of interpersonal relationships between quality of service, effectiveness, complaint handling and customer satisfaction. The study uses quantitative approach with total of 395 questionnaires was collected from retail bank personnel. The result was analyzed using Structural Equation Modeling. The results of the study indicate that customer care delivery factors significantly impact customer satisfaction. Also, has an impact on Tanzania retail banking sector. In contrast, complaint handling does not impact customer satisfaction for Tanzania retail banks and interpersonal relationship does not have mediating effect on complaint handling and customer satisfaction. However, this study focuses on Tanzania retail banks but the study offers wide knowledge to banking professionals globally so as to improve and widen the scope of quality of customer services delivery for purpose of satisfying customer needs and demands in banking sector.
Encrypted Web Traffic Classification Using Deep Learning

Traffic classification is essential in network management for operations ranging from capacity planning, performance monitoring, volumetry, and resource provisioning, to anomaly detection and security. Recently, it has become increasingly challenging with the widespread adoption of encryption in the Internet, e.g., as a de-facto in HTTP/2 and QUIC protocols. In the current state of encrypted traffic classification using Deep Learning (DL), we identify fundamental issues in the way it is typically approached. For instance, although complex DL models with millions of parameters are being used, these models implement a relatively simple logic based on certain header fields of the TLS handshake, limiting model robustness to future versions of encrypted protocols. Furthermore, encrypted traffic is often treated as any other raw input for DL, while crucial domain-specific considerations exist that are commonly ignored. In this thesis, we design a novel feature engineering approach that generalizes well for encrypted web protocols, and develop a neural network architecture based on Stacked Long Short-Term Memory (LSTM) layers and Convolutional Neural Networks (CNN) that works very well with our feature design. We evaluate our approach on a real-world traffic dataset from a major ISP and Mobile Network Operator. We achieve an accuracy of 95% in service-level classification with less raw traffic and smaller number of parameters, out-performing a state-of-the-art method by nearly 50% fewer false classifications. We show that our DL model generalizes for different classification objectives and encrypted web protocols. We also evaluate our approach on a public QUIC dataset with finer and application-level granularity in labeling, achieving an overall accuracy of 99%.
Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation (FSS) aims to segment unseen class objects given very few densely-annotated support images from the same class. Existing FSS methods find the query object by using support prototypes or by directly relying on heuristic multi-scale feature fusion. However, they fail to fully leverage the high-order appearance relationships between multi-scale features among the support-query image pairs, thus leading to an inaccurate localization of the query objects. To tackle the above challenge, we propose an end-to-end scale-aware graph neural network (SAGNN) by reasoning the cross-scale relations among the support-query images for FSS. Specifically, a scale-aware graph is first built by taking support-induced multi-scale query features as nodes and, meanwhile, each edge is modeled as the pairwise interaction of its connected nodes. By progressive message passing over this graph, SAGNN is capable of capturing cross-scale relations and overcoming object variations (e.g., appearance, scale and location), and can thus learn more precise node embeddings. This in turn enables it to predict more accurate foreground objects. Moreover, to make full use of the location relations across scales for the query image, a novel self-node collaboration mechanism is proposed to enrich the current node, which endows SAGNN the ability of perceiving different resolutions of the same objects. Extensive experiments on PASCAL-5i and COCO-20i show that SAGNN achieves state-of-the-art results.
Semantic Segmentation of Remote Sensing Image Based on Convolutional Neural Network and Mask Generation

High-resolution remote sensing images usually contain complex semantic information and confusing targets, so their semantic segmentation is an important and challenging task. To resolve the problem of inadequate utilization of multilayer features by existing methods, a semantic segmentation method for remote sensing images based on convolutional neural network and mask generation is proposed. In this method, the boundary box is used as the initial foreground segmentation profile, and the edge information of the foreground object is obtained by using the multilayer feature of the convolutional neural network. In order to obtain the rough object segmentation mask, the general shape and position of the foreground object are estimated by using the high-level features in the process of layer-by-layer iteration. Then, based on the obtained rough mask, the mask is updated layer by layer using the neural network characteristics to obtain a more accurate mask. In order to solve the difficulty of deep neural network training and the problem of degeneration after convergence, a framework based on residual learning was adopted, which can simplify the training of those very deep networks and improve the accuracy of the network. For comparison with other advanced algorithms, the proposed algorithm was tested on the Potsdam and Vaihingen datasets. Experimental results show that, compared with other algorithms, the algorithm in this article can effectively improve the overall precision of semantic segmentation of high-resolution remote sensing images and shorten the overall training time and segmentation time.
Reversible Data Hiding using Block-wise Histogram Shifting and Run-length Encoding

Histogram shifting-based Reversible Data Hiding (RDH) is a well-explored information security domain for secure message transmission. In this paper, we propose a novel RDH scheme that considers the block-wise histograms of the image. Most of the existing histogram shifting techniques will have additional overhead information to recover the overflow and/or the underflow pixels. In the new scheme, the meta-data that is required for a block is embedded within the same block in such a way that the receiver can perform image recovery and data extraction. As per the proposed data hiding process, all the blocks need not be used for data hiding, so we have used marker information to distinguish between the blocks which are used to hide data and the blocks which are not used for data hiding. Since the marker information needs to be embedded within the image, we have compressed the marker information using runlength encoding. The run-length encoded sequence is represented by an Elias gamma encoding procedure. The compression on the marker information ensures a better Embedding Rate (ER) for the proposed scheme. The proposed RDH scheme will be useful for secure message transmission also where we are also concerned about the restoration of the cover image. The proposed scheme's experimental analysis is conducted on the USC-SIPI image dataset maintained by the University of Southern California, and the results show that the proposed scheme performs better than the existing schemes. Keywords—Histogram shifting; run-length encoding; secure message transmission; overflow; Elias gamma
A Preliminary Study of Japanese Co-Workers’ Attitudes Towards Migrant Workers’ Japanese Language Skills in Blue-Collar Workplaces

Background: This is a preliminary study of Japanese co-workers’ attitudes towards migrant workers’ Japanese language skills in blue-collar workplaces. This is an important topic, seeing as Japanese co-workers’ attitudes can result in inequalities at the workplace, directly impacting migrant workers’ everyday lives. Understanding these attitudes and addressing them can improve migrant workers’ experiences. Methods: The study is based on content analysis of documents taken from English-language sources. Results: The author found that Japanese co-workers and employers assume ownership of the means of communication, expecting only Japanese to be used at the workplace. They also underrate migrant workers’ Japanese proficiency. Conclusions: Although the data has its limitations, the study provides some insight and can potentially serve as a base for further research.
Droplet-microarray on superhydrophobic–superhydrophilic patterns for high-throughput live cell screenings

Cell-based high-content phenotypic screenings are widely used in fundamental research, pharmaceutical industry, and healthcare in order to simultaneously evaluate the effects of multiple compounds or gene overexpressions/knockdown on the phenotype of cells. Most screenings, particularly in the industrial sector, are performed using the microplate technology, which relies on high reagent and cell consumption, as well as on expensive liquid-handling robotics. Developing miniaturized screening platforms has been an important topic in the past decade. In this study, we demonstrate the applicability of the droplet-microarray platform based on superhydrophobic–superhydrophilic patterning for cell-based high-throughput screenings. We show the homogeneous seeding of cells and culturing of different adherent cell lines in individual droplets of different sizes. We demonstrate pipetting-free medium exchange, enabling cell culture in miniaturized droplet arrays for up to 5 days. We establish the methods of reverse transfection and reverse drug screening in individual nanoliter-sized droplets by printing transfection mixtures or drug molecules directly onto superhydrophilic spots prior to cell seeding.
Use of Nanomaterials for the Immobilization of Industrially Important Enzymes

Immobilization enables enzymes to be held in place so that they can be easily separated from the product when needed and can be used again. Conventional methods of immobilization include adsorption, encapsulation, entrapment, cross linking and covalent binding. However, conventional methods have several drawbacks including reduced stability, loss of biomolecules, less enzyme loading or activity and limited diffusion. The aim of this study is the evaluation of importance of nanomaterials for the immobilization of industrially important enzymes. Nano materials are now in trend for the immobilization of different enzymes due to their physiochemical properties. Gold nanoparticles, silver nanoparticles, nano diamonds, graphene, carbon nanotubes and others are used for immobilization. Among covalent and non-covalent immobilization of enzymes involving both single and multiwalled carbon nanotubes, non-covalent immobilization with functionalized carbon nanotubes is superior. Therefore, enzymes immobilized with nanomaterials possess greater stability, retention of catalytic activity and reusability of enzymes