Reviewerly/exHarmony huggingface dataset:
Search templates for real-world objects in natural scenes

Decades of research highlight the importance of bottom-up stimulus-driven guidance (e.g., saliency) and top-down user-driven factors (e.g., target surface features, prior knowledge of likely target locations) in visual search. While these factors can be experimentally manipulated in simple abstract search arrays, it has been difficult to empirically derive unique predictions for distinct top-down factors in real-world scenes. As a first step toward addressing this issue, we developed two new approaches based on convolutional neural network models. The first extends the class activation mapping (CAM) approach (Zhou, Khosla, Lapedriza, Oliva & Toraralba, 2015) to compute “Average-CAM” maps which capture variability in the diagnostic value of scene regions in predicting scene category membership. The second approach, which we term “Patch-Match”, maps the relatedness of scene regions to category-level (e.g. any clock) target and visual search target template (e.g. a specific clock) activations. We demonstrate the value of these approaches by showing these maps explain unique variance (beyond that explained by saliency models) in human gaze patterns in visual search tasks for real-world targets embedded in natural scenes.
Stroke-based semantic segmentation for scene-level free-hand sketches

Sketching is a simple and efficient way for humans to express their perceptions of the world. Sketch semantic segmentation plays a key role in sketch understanding and is widely used in sketch recognition, sketch-based image retrieval, or editing. Due to modality difference between images and sketches, existing image segmentation methods may not perform best, which overlook the sparse nature and stroke-based representation in sketches. The existing sketch semantic segmentation methods are mainly designed for single-instance sketches. In this paper, we present a new stroke-based sequential-spatial neural network (S $$^3$$ NN) for scene-level free-hand sketch semantic segmentation, which leverages a bidirectional LSTM and graph convolutional network to capture the sequential and spatial features of sketches. In order to address the data lacking issue, we propose the first scene-level free-hand sketch dataset (SFSD). SFSD is composed of 12K sketch-photo pairs over 40 object categories, where the sketches were completely hand-drawn and each contains 7 objects on average. We conduct comparative and ablative experiments on SFSD to evaluate the effectiveness of our method. The experimental results demonstrate that our method outperforms state-of-the-art methods. The code, models, and dataset will be made public after acceptance.
Creating and evaluating uncertainty estimates with neural networks for environmental-science applications

Neural networks (NN) have become an important tool for prediction tasks – both regression and classification – in environmental science. Since many environmental-science problems involve life-or-death decisions and policy-making, it is crucial to provide not only predictions but also an estimate of the uncertainty in the predictions. Until recently, very few tools were available to provide uncertainty quantification (UQ) for NN predictions. However, in recent years the computer-science field has developed numerous UQ approaches, and several research groups are exploring how to apply these approaches in environmental science. We provide an accessible introduction to six of these UQ approaches, then focus on tools for the next step, namely to answer the question: Once we obtain an uncertainty estimate (using any approach), how do we know whether it is good or bad? To answer this question, we highlight four evaluation graphics and eight evaluation scores that are well suited for evaluating and comparing uncertainty estimates (NN-based or otherwise) for environmental-science applications. We demonstrate the UQ approaches and UQ-evaluation methods for two real-world problems: (1) estimating vertical profiles of atmospheric dewpoint (a regression task) and (2) predicting convection over Taiwan based on Himawari-8 satellite imagery (a classification task). We also provide Jupyter notebooks with Python code for implementing the UQ approaches and UQ-evaluation methods discussed herein. This article provides the environmental-science community with the knowledge and tools to start incorporating the large number of emerging UQ methods into their research.
Timelike correlations and quantum tensor product structure

The state-space structure for a composite quantum system is postulated among several mathematically consistent possibilities that are compatible with a local quantum description. For instance, the unentangled Gleason's theorem allows a state space that includes density operators as a proper subset among all possible composite states. However, bipartite correlations obtained in Bell-type experiments from this broader state space are, in fact, quantum simulable [Barnum et al., Phys. Rev. Lett. 104, 140401 (2010)], and hence, such spacelike correlations are no good for making a distinction among different compositions. In this work we analyze the communication utilities of these different composite models and show that they can lead to distinct utilities in a simple communication game involving two players. Our analysis thus establishes that a beyond quantum composite structure can lead to beyond quantum correlations in the timelike scenario and hence welcomes new principles to isolate the quantum correlations from the beyond quantum ones. We also prove a no-go theorem that the classical information carrying capacity of different such compositions cannot be greater than that of the corresponding quantum composite systems.
Revision of Slovenian Normative Guide: Scientific Basis and Inclusion of the Public

Contemporary Slovenian language standardisation includes the revision of the normative guide, a process taking place since 2013 within the Commission on Orthography. This article presents an overview of the scientific basis of this process as well as describes the systematic inclusion of different segments of the public in the phase of assessing the suitability of current orthographic rules and formulating new ones. This is due to an awareness that a normative guide can be accepted by the wider language community only through a convergence of differing opinions and codification based on arguments.
A hierarchical GAN method with ensemble CNN for accurate nodule detection

Lung cancer can evolve into one of the deadliest diseases whose early detection is one of the major survival factors. However, early detection is a challenging task due to the unclear structure, shape, and the size of the nodule. Hence, radiologists need automated tools to make accurate decisions.This paper develops a new approach based on generative adversarial network (GAN) architecture for nodule detection to propose a two-step GAN model containing lung segmentation and nodule localization. The first generator comprises a U-net network, while the second utilizes a mask R-CNN. The task of lung segmentation involves a two-class classification of the pixels in each image, categorizing lung pixels in one class and the rest in the other. The classifier becomes imbalanced due to numerous non-lung pixels, decreasing the model performance. This problem is resolved by using the focal loss function for training the generator. Moreover, a new loss function is developed as the nodule localization generator to enhance the diagnosis quality. Discriminator nets are implemented in GANs as an ensemble of convolutional neural networks (ECNNs), using multiple CNNs and connecting their outputs to make a final decision.Several experiments are designed to assess the model on the well-known LUNA dataset. The experiments indicate that the proposed model can reduce the error of the state-of-the-art models on the IoU criterion by about 35 and 16% for lung segmentation and nodule localization, respectively.Unlike recent studies, the proposed method considers two loss functions for generators, further promoting the goal achievements. Moreover, the network of discriminators is regarded as ECNNs, generating rich features for decisions.
The Study of the Educational Program Competence Model Based on the Intellectual Analysis of the Labor Market Professional Requirements

The educational standards of higher education — FGOS3++ require designing educational programs (EP) based on professional competencies in demand in the labor market. It is rec ommended to use professional standards. This way has certain disadvantages. For example, professional standards describe labor functions, but in the EP competencies are required. The use of professional standards in the development of the EP obviously determines a certain lag in the reaction in the labor market changes. An alternative to such EP design is a direct analysis of market requirements. Intelligent Analysis (IA) tools: data mining and machine learning can facilitate such analysis. An overview of developments in this direction is given. The results of the application of IA based on the KNIME analytical platform for determining the competence model of the EP in the feld of project management are presented. Automation aﬀected:the collection of requirements for more than 6000 vacancies of employers of the Head Hunter online resource; the analysis of texts presented in natural language (descriptions of EP of diﬀerent universities, professional standards and guidance materials of professional associations — SOVNET-Agile, IPMA OCB&amp;ICB). Based on these data sets was carried out: tokenization, collocation of terms and topics, clustering of topics, the cross-classifcation of professional standards and guidance materials based on a pre-trained competence model of the EP. The results confrmed the efciency of the used technique. Such analytics allows us to dynamically systematize descriptions of professional activities and formulate considerations about the elements, structure, and mutual correspondence of the EP competence models and the competencies natural models on the labor market. In combination with the traditional expert assessment, it can contribute to the formation of a more complete isomorphism between the qualifcations of EP and professional activity.
Jute Fiber Reinforced Hydrogel Composite for Removal of Methylene Blue Dye from Water

In this study, a hydrogel composite based on natural jute fiber (JF) has been prepared and employed for the removal of methylene blue (MB) dye from aqueous solution. Polyacrylamide (PAAm) hydrogel was synthesized by thermal polymerization in presence of N,N□-methylene bisacrylamide (MBAA) as the cross-linker and potassium per sulfate (KPS) as the initiator. JF reinforced hydrogel (JFRH) composite was prepared by incorporating JF into PAAm matrix prior to the polymerization. The prepared JFRH was characterized by FTIR spectroscopy and scanning electron microscope. It was found that, JFRH composite demonstrated strong mechanical properties with a compressive strength of 1.5 MPa at a deformation of above 80%. The prepared JFRH composite can remove more than 90% of MB from an aqueous solution and be separated easily from the solution after the adsorption process. Therefore, the synthesized JF based hydrogel composite could significantly expand the use of our natural golden fiber for reinforcement of composite materials and removal of dyes from water bodies. Dhaka Univ. J. Sci. 70(2): 59-64, 2022 (July)
Traffic-Net: 3D Traffic Monitoring Using a Single Camera

Computer Vision has played a major role in Intelligent Transportation Systems (ITS) and traffic surveillance. Along with the rapidly growing automated vehicles and crowded cities, the automated and advanced traffic management systems (ATMS) using video surveillance infrastructures have been evolved by the implementation of Deep Neural Networks. In this research, we provide a practical platform for real-time traffic monitoring, including 3D vehicle/pedestrian detection, speed detection, trajectory estimation, congestion detection, as well as monitoring the interaction of vehicles and pedestrians, all using a single CCTV traffic camera. We adapt a custom YOLOv5 deep neural network model for vehicle/pedestrian detection and an enhanced SORT tracking algorithm. For the first time, a hybrid satellite-ground based inverse perspective mapping (SG-IPM) method for camera auto-calibration is also developed which leads to an accurate 3D object detection and visualisation. We also develop a hierarchical traffic modelling solution based on short- and long-term temporal video data stream to understand the traffic flow, bottlenecks, and risky spots for vulnerable road users. Several experiments on real-world scenarios and comparisons with state-of-the-art are conducted using various traffic monitoring datasets, including MIO-TCD, UA-DETRAC and GRAM-RTM collected from highways, intersections, and urban areas under different lighting and weather conditions.
A Study of Conventional and Learning-Based Depth Estimators for Immersive Video Transmission

Obtaining an accurate depth map of a scene is very important for major applications like immersive video, robotics, autonomous driving, and many more. The different methods to estimate depths can be classified as conventional and learning-based methods. While these methods have been studied for their depth accuracy, less attention has been paid to studying their performance in the use case of depth image-based rendering (DIBR). Here we study and evaluate two conventional methods and five learning-based methods for a real-world use case of immersive video transmission in the context of MPEG-I. The user-requested views are synthesized using Test Model for Immersive Video (TMIV) from the depth maps obtained by all methods and original texture views. The synthesized images are compared with their original counterparts using various quality metrics.
Multimodal interaction and IoT applications

Multimodal interaction enables multiple modes for users to interact with the system. Whereas the multimodal interaction with IoT applications depends on multimedia systems’ input/output which may limit their scalability and expressiveness. Furthermore, multimodal interaction with IoT applications could also be limited by input/output integration, security, privacy, and portability issues. This special issue focuses on recent state-of-the-art advances in multimodal interaction and IoT applications that could address these challenges. We aim at bringing together the latest industrial and academic progress, research, and development efforts within the rapidly maturing multimodal interaction and IoT applications. This article summarizes the research contribution of the accepted papers (27.94% acceptance rate) along with possible future directions emanating from these papers.
E-Ensemble: A Novel Ensemble Classifier for Encrypted Video Identification

In recent years, video identification within encrypted network traffic has gained popularity for many reasons. For example, a government may want to track what content is being watched by its citizens, or businesses may want to block certain content for productivity. Many such reasons advocate for the need to track users on the internet. However, with the introduction of the secure socket layer (SSL) and transport layer security (TLS), it has become difficult to analyze traffic. In addition, dynamic adaptive streaming over HTTP (DASH), which creates abnormalities due to the variable-bitrate (VBR) encoding, makes it difficult for researchers to identify videos in internet traffic. The default quality settings in browsers automatically adjust the quality of streaming videos depending on the network load. These auto-quality settings also increase the challenge in video detection. This paper presents a novel ensemble classifier, E-Ensemble, which overcomes the abnormalities in video identification in encrypted network traffic. To achieve this, three different classifiers are combined by using two different combinations of classifiers: the hard-level and soft-level combinations. To verify the performance of the proposed classifier, the classifiers were trained on a video dataset collected over one month and tested on a separate video dataset captured over 20 days at a different date and time. The soft-level combination of classifiers showed more stable results in handling abnormalities in the dataset than those of the hard-level combination. Furthermore, the soft-level classifier combination technique outperformed the hard-level combination with a high accuracy of 81.81%, even in the auto-quality mode.
The Impact of the Marketing Mix on Online Business: A Study in Sylhet City

Since the beginning of the twenty-first century, online business has been getting more popular. Especially during the pandemic period, online business was a blessing for people to survive. Although nowadays, people get used to the pandemic, still online business is a vital part of daily life. For making online business more useful for both parties, i.e., sellers and buyers, it is important to undertake academic research. The current study aims to find out the impact of the elements of the Marketing Mix on online business in Sylhet City. A mixed questionnaire was served to the members of different online business groups for collecting data. In total 256 persons participated in the survey. It is found that the elements of the Marketing Mix have a significant impact on online business.
Cloud Data Security through BB84 Protocol and Genetic Algorithm

In the current digitalized world, cloud computing becomes a feasible solution for the virtualization of cloud computing resources. Though cloud computing has many advantages to outsourcing an organization’s information, but the strong security is the main aspect of cloud computing. Identity authentication theft becomes a vital part of the protection of cloud computing data. In this process, the intruders violate the security protocols and perform attacks on the organizations or user’s data. The situation of cloud data disclosure leads to the cloud user feeling insecure while using the cloud platform. The different traditional cryptographic techniques are not able to stop such kinds of attacks. BB84 protocol is the first quantum cryptography protocol developed by Bennett and Brassard in the year 1984. In the present work, three ways BB84GA security systems have been demonstrated using trusted cryptographic techniques like an attribute-based authentication system, BB84 protocol, and genetic algorithm. Firstly, attribute-based authentication is used for identity-based access control and thereafter BB84 protocol is used for quantum key distribution between both parties and later the concept of genetic algorithm is applied for encryption/decryption of sensitive information across the private/public clouds. The proposed concept of involvement of hybrid algorithms is highly secure and technologically feasible. It is a unique algorithm which may be used to minimize the security threats over the clouds. The computed results are presented in the form of tables and graphs.
PENDAMPINGAN PENYUSUNAN ANGGARAN BERBASIS KINERJA SMK TRIATMA JAYA

This community service is carried out in collaboration with Triatma Jaya High School in the context of assisting in the preparation of performance-based budgets. The aim is to provide school administrators with the ability to prepare performance-based budgets and improve the quality of their financial reporting. This activity was attended by three units of SMK, including Triatma Jaya Badung Vocational School, Tabanan Triatma Jaya Vocational School and also Triatma Jaya Singaraja Vocational School which in its implementation were represented by three people each consisting of the Principal, Deputy Head and also the school treasurer. The method used in the community service program uses the RAD (Rapid Application Development) method, which is a method that prioritizes partner involvement in the use of a series of systems, where the series functions to create a more effective prototype. After the dedication, partners understand and understand more about performance-based budgeting and excel-based financial management. this can be seen from the results of the pretest an average score of 60 and after training and mentoring the pastest score is 90.
Mind Your Path: On (Key) Dependencies in Differential Characteristics

Cryptanalysts have been looking for differential characteristics in ciphers for decades and it remains unclear how the subkey values and more generally the Markov assumption impacts exactly their probability estimation. There were theoretical efforts considering some simple linear relationships between differential characteristics and subkey values, but the community has not yet explored many possible nonlinear dependencies one can find in differential characteristics. Meanwhile, the overwhelming majority of cryptanalysis works still assume complete independence between the cipher rounds. We give here a practical framework and a corresponding tool to investigate all such linear or nonlinear effects and we show that they can have an important impact on the security analysis of many ciphers. Surprisingly, this invalidates many differential characteristics that appeared in the literature in the past years: we have checked differential characteristics from 8 articles (4 each for both SKINNY and GIFT) and most of these published paths are impossible or working only for a very small proportion of the key space. We applied our method to SKINNY and GIFT, but we expect more impossibilities for other ciphers. To showcase our advances in the dependencies analysis, in the case of SKINNY we are able to obtain a more accurate probability distribution of a differential characteristic with respect to the keys (with practical verification when it is computationally feasible). Our work indicates that newly proposed differential characteristics should now come with an analysis of how the key values and the Markov assumption might or might not affect/invalidate them. n this direction, more constructively, we include a proof of concept of how one can incorporate additional constraints into Constraint Programming so that the search for differential characteristics can avoid (to a large extent) differential characteristics that are actually impossible due to dependency issues our tool detected.
Decomposing Linear Layers

There are many recent results on reverse-engineering (potentially hidden) structure in cryptographic S-boxes. The problem of recovering structure in the other main building block of symmetric cryptographic primitives, namely, the linear layer, has not been paid that much attention so far. To fill this gap, in this work, we develop a systematic approach to decomposing structure in the linear layer of a substitutionpermutation network (SPN), covering the case in which the specification of the linear layer is obfuscated by applying secret linear transformations to the S-boxes. We first present algorithms to decide whether an ms x ms matrix with entries in a prime field Fp can be represented as an m x m matrix over the extension field Fps . We then study the case of recovering structure in MDS matrices by investigating whether a given MDS matrix follows a Cauchy construction. As an application, for the first time, we show that the 8 x 8 MDS matrix over F28 used in the hash function Streebog is a Cauchy matrix.