Reviewerly/exHarmony huggingface dataset:
Spot What Matters: Learning Context Using Graph Convolutional Networks for Weakly-Supervised Action Detection

The dominant paradigm in spatiotemporal action detection is to classify actions using spatiotemporal features learned by 2D or 3D Convolutional Networks. We argue that several actions are characterized by their context, such as relevant objects and actors present in the video. To this end, we introduce an architecture based on self-attention and Graph Convolutional Networks in order to model contextual cues, such as actor-actor and actor-object interactions, to improve human action detection in video. We are interested in achieving this in a weakly-supervised setting, i.e. using as less annotations as possible in terms of action bounding boxes. Our model aids explainability by visualizing the learned context as an attention map, even for actions and objects unseen during training. We evaluate how well our model highlights the relevant context by introducing a quantitative metric based on recall of objects retrieved by attention maps. Our model relies on a 3D convolutional RGB stream, and does not require expensive optical flow computation. We evaluate our models on the DALY dataset, which consists of human-object interaction actions. Experimental results show that our contextualized approach outperforms a baseline action detection approach by more than 2 points in Video-mAP. Code is available at \url{https://github.com/micts/acgcn}
WITHDRAWN: Preservation of data using magic squares in Asymmetric key cryptography

In data communication the information that is being communicated should be confidential. Many algorithms have been designed to keep the data secure. But malicious attacks on these algorithms to break its security have made it necessary to design new algorithms. To overcome this problem a new method using magic squares has been proposed in this paper. The magic square is constructed on constraints such as order of the matrix n and starting number a.
Nearly Monotone Neural Approximation with Quadratic Activation Function

Abstract Quadratic functions give good rates of approximation when used as activation functions of feedforward neural networks. Also, monotonicity is important to describe the function behavior, so the behavior of its constrained approximation. Previously, the degree of approximation by feedforward neural networks with quadratic activation function is proved to be within no less than the second order modulus of smoothness. In this paper, we discuss whether the improvement of the above estimates for Lebesgue integrable functions is possible or not. By nearly monotone approximation, it is possible to talk about a higher order modulus of smoothness, while it is not for just monotone functions. We get a nearly monotone function approximation by splitting the interval [0,1] into a partition with infinitely small lengths and then excluding intervals near the endpoints of the partition’s subintervals. However, counter examples cut hope for any more improvement outside that restricted interval. All the results are proved in the Lp-space with p &lt; 1.
Ensemble Flower-Pollination Algorithm for Automatic Breast Cancer Classification using Haar Features

Abstract Breast cancer is a deadly disease which reports a higher mortality rate, every year. This type of cancer is more common in women of developed countries while compared with the incident rate in developing countries. The objective of this work is to propose a methodology to increase earlier breast cancer detection. The methodology makes use of ensemble-based classifiers namely histogram-based Gradient Boosting Machine (hbGBM), Gradient Boosting with Light GBM (lGBM), and Gradient Boosting with CatBoost (GBCB) algorithms. The paper adopts the Mammogram Image Analysis Society (MIAS) database for testing the effectiveness of the algorithms. Haar wavelet transform is used for feature extraction and Flower-Pollination Algorithm (FPA) is employed for selecting the best features. Thus, a flower-pollination based haar wavelet feature with three different ensemble-based classifiers is proposed for the automatic mammogram classification. The performance results are then compared briefly by using some benchmark metrics such as specificity, sensitivity, precision, accuracy, and precision, F1 score, and Matthews Correlation Coefficient (MCC) analysis. The result reveals that the highest classification accuracy is obtained for ensemble GBCB based FPA algorithm i.e., 91% when comparing with other existing models.
Design and Realization of Replaying Record Data Software of Central Ship System of Survey Ship

This article aims at the situation that the verification methods of survey ship's central computer software are limited, and there are many cooperation positions required, the center computer system recording data replay software is developed under the Linux operating system. The replay software can distribute all kinds of data to the corresponding group address as required for the central computer software to read, process and display, it has enriched software verification and troubleshooting methods, and it does not require the cooperation of other positions, which greatly improves the efficiency of software verification.
Quadtree Compression Based Method for High Capacity Reversible Data Hiding in Encrypted JPEG Images

The existing methods of reversible data hiding in encrypted JPEG images vacates space for data hiding after encrypting the image. This results in information loss as entropy in encrypted images is more. The complexity of image reconstruction is higher in such cases. The proposed method therefore finds space for data hiding before image encryption using the method of quadtree compression. Then the image is encrypted using RC4 encryption algorithm. The secret data bits are embedded into the MSB bit of pixels obtained after quadtree compression with the help of data hiding key. The data extraction and image recovery is made separable at the receiver side such that the secret data can be extracted without knowing the image contents. Original pixel restoration is done using prediction error method. The experimental results show that the proposed method is able to achieve better data hiding capacity and recovered image quality within an acceptable complexity range.
Chapter 9. Face, conflict, and adaptability in mediated intercultural invitations

In a world of increasing national and international mobility, people are progressively more engaged in communication over multiple types of media. This paper discusses the question of adaptability in mediated intercultural communication, considering the perspectives of a group of international graduate students and their peers in a graduate program in a North American university. The paper explores the relational aspects of technologically mediated “invitations” as potential face threatening acts made more complex by the unequal social positions of the participants regarding ethnicity, gender, nationality, and age. It also problematizes the nature of mediated communication and choices of communicative tools, the contradictory assessments by users as to the value of specific semiotic information, and the chronotopes of mediated invitations that intensify facework.
Neural Network Compression by Filter Similarity Detection and Visualization

This paper presents an Automated Filter Similarity Detecting and Pruning System with deep visualization technique. During vision tasks, neural networks are found to converge to similar filters leading to significant increase in redundancy in parametric space resulting in high consumption of time and processing power. Moreover, the inner-working of neural networks are not transparent, so it is not tractable which features are extracted in which layer, how they are extracted and how much viable those extracted features are for final output. In order to mitigate the parametric redundancy, we introduce a technique to visualize and detect the similar filters based on similarity metric. And also we implemented the compressed and efficient architecture following pruning, and we observe no decline in learning performance when applied on standard image dataset.
Human-System Interaction Interface Utilizing 3D Gesture Recognition Techniques based on Wearable Technology

Working with robots has the risk of safety and security of human interaction that limits the robots fine motion within a factory floor. Thus, it requires the improving human system interaction interface in a robotic assembly line. Gesture recognition can be used as an assistive tool to reduce the interaction complexities associated during human intervention with robots and machines. The aim of this research is to recognize 3D gesture using wearable devices in order to improve human-robot/machine collaboration through better interaction interface. The derived systems consist of Data Classification, Recognition, and interaction. The novel system proposed improves human-machine interaction interface and reduces the system complexity through improving the interaction system leading to better working environment. The recognition of gestures helps to improve the interaction between humans and robots and helps in performing different robotic tasks. The method proposed in this research proves better performance compared to the currently existing gesture recognition methods. The data for gesture recognition is taken using different wearable devices, pressure and motion sensors. Reducing the interaction complexity will help to provide better working environment and gives assurance to worker about their security and safety with better working environment.
Defending Against Adversarial Attacks in Deep Learning with Robust Auxiliary Classifiers Utilizing Bit Plane Slicing

Deep Neural Networks (DNNs) have been widely used in variety of fields with great success. However, recent researches indicate that DNNs are susceptible to adversarial attacks, which can easily fool the well-trained DNNs without being detected by human eyes. In this paper, we propose to combine the target DNN model with robust bit plane classifiers to defend against adversarial attacks. It comes from our finding that successful attacks generate imperceptible perturbations, which mainly affects the low-order bits of pixel value in clean images. Hence, using bit planes instead of traditional RGB channels for convolution can effectively reduce channel modification rate. We conduct experiments on dataset CIFAR-10 and GTSRB. The results show that our defense method can effectively increase the model accuracy on average from 8.72% to 85.99% under attacks on CIFAR-10 without sacrificina accuracy of clean images.
JPEG Dimple-Based Fingerprint Under Erasure Channel

A new JPEG fingerprint code exploiting the periodic JPEG quantization artifacts, JPEG dimples, is proposed in this paper. Robustness against collusion attack is then been discussed. We investigate both the erasures of the JPEG content and the removal of the embedded fingerprint. Without knowing the specific collusion strategies, the proposed tracing algorithm can estimate the number of malicious users. Experimental results demonstrate the effectiveness of the proposed algorithm.
Implementation of High-Level JSON Schema Change Operations Using JUpdate

Currently, JSON and JSON Schema languages are being widely used by NoSQL database designers, administrators and application developers. However, there is neither a standard JSON update language (like the XQuery Update Facility language in the XML world), nor a standard JSON Schema change language (like the SQL-DDL language in the relational setting). For that reason, we proposed in (Brahmia et al.: JUpdate: A JSON Update Language. Submitted for Publication, 2019) a JSON instance update language, named JUpdate, and in (Brahmia et al. in Int. J. Cloud Comput. 10(5–6), 2021) a JSON Schema change language. Each one of these languages consists of a complete set of high-level operations. Moreover, in (Brahmia et al. in JUpdate: A JSON Update Language. Submitted for Publication, 2019), we introduced a complete and minimal set of low-level JSON instance update operations, which were used to define the semantics and to provide a basis for the implementation of the high-level JSON instance update operations of JUpdate. In this work, owing to the fact that a JSON Schema file is also a plain JSON file, we use the high-level JSON instance update operations of JUpdate to define the semantics and to provide a basis for the implementation of the high-level JSON Schema change operations of our language previously presented in (Brahmia et al. in Int. J. Cloud Comput. 10(5–6), 2021).
Risk Treatment: An Iterative Method for Identifying Controls

Due to the increasing number of security incidents in the last years, the consideration of security during software development becomes more and more important. A certain level of security can be achieved by applying suitable countermeasures. The ISO 27001 standard demands a risk-based selection of countermeasures, i.e. controls, for information security. Risk serves as a prioritization criterion for selecting controls. To reduce the development effort, security should be addressed as early as possible in the software development lifecycle.In this paper, we present an iterative and risk-based method to select controls during requirements engineering, following the principle of security-by-design. We select controls based on unacceptable risks and the related functional requirements. Each risk and control is described by attributes that allow an evaluation of the control’s effectiveness based on the Common Vulnerability Scoring System. The evaluation is supported by a web-based tool. A distinguishing feature of our method is that during iteration, we consider new incidents that may occur when applying a control. For documenting the results, we present a metamodel that ensures consistency and traceability between requirements and security aspects.
MIRAI: A Modifiable, Interpretable, and Rational AI Decision Support System

With the recent advancements and results obtained by Deep Learning, several corporations are eager to begin adapting AI into their workflow to benefit from these systems, especially with the emergence of Industry 4.0. However, decision makers find themselves unable to fully trust AI systems from evaluation metrics alone and require some more transparency in their systems. As such, research has gone in the direction of Explainable AI (XAI), where the inner mechanics and reasoning processes of these learning systems are presented in such a way that it is interpretable by humans, opening the Black Box of opaque AI algorithms. This study intends to develop a Big Data and Online Learning-based Explaining AI architecture which utilizes a novel approach of combining Rule-Based Reasoning methodology alongside an explainable Deep Learning framework in order to create a system capable of a higher level of reasoning, or ‘Deep Understanding’. Evaluation for this system is to take place by means of standard Machine Learning evaluation metrics such as F1-Scores, Precision, Recall, and ROC Curves. Alongside this, user-feedback evaluation forms from relevant personnel will be used to verify how useful the explanations are in production.
A Kenyan Village As An Innovative Learning Environment

Cultural sensitivity and improved communication and assessment skills for undergraduate nursing students are the primary aims of a medical mission experience based on an innovative learning environment in a rural African village. Nursing faculty implement components of Irby's Conceptual Framework for Learning Environments to facilitate students' integration and understanding of didactic information and the holistic practice of nursing while decreasing students' dependence on technology.
Development of student entrepreneurial competence using the resources of social partnership

The paper brings to the fore the need for developing entrepreneurial competences in school students. It describes the concept of entrepreneurial competence as an ability to put forward original ideas and innovate along with communication, organisation, project management, action planning, and risk-taking competences, receptivity, and also knowledge and skills required for starting a new business and implementing practical ideas to proceed successfully. The case is made for developing courses in entrepreneurship and the criteria of efficiency are determined as follows: clearly set objectives, balanced theoretical and practical content, focus on learning by doing, voluntary approach and motivation, social partnership involving businessmen, financial institutions, and municipal government bodies, openness of the educational environment, availability of training for teachers and contacts with businessmen, and opportunity to present the outcomes. A description is provided of the model and algorithm of creating a school business incubator using the resources of social partnership with businessmen, financial institutions, and municipal governance bodies. The outcomes in entrepreneurship education in the context of a business incubator include the defence of a business project under the supervision of a mentor conducted in a competitive form in two stages, namely, the defence of the business idea and the project itself. Various procedures are used to assess business incubator performance, such as questionnaires for students, teachers, and mentors and expert assessment of students’ entrepreneurial competence acquisition levels by teachers and mentors.
Determine effective pedestrian paths as evacuation routes for landslide disaster (case study: manyaran and kembangarum villages)

Abstract Central Java is one of the provinces in Indonesia that is vulnerable to natural disasters, such as earthquakes, volcanic eruptions, and landslides. Landslide incidents in Central Java from 2011-2015 have the highest frequency compared to other disasters, i.e. 568 incidents. Primarily, for landslide, the data shows that the incidents in the city of Semarang have a high frequency. The previous study about the readiness index of several villages in the city of Semarang in facing the landslide disaster has been conducted and the result indicated that 2 out 19 surveyed villages belong to the category not ready for facing the landslides, namely Manyaram and Kembangarum. One of the causes of the lack of readiness is they don’t have an evacuation route. So, based on this condition, the purpose of this study is to determine an effective pedestrian path as evacuation routes for landslide disaster for Manyaram and Kembangarum Villages. In this case, the effective pedestrian path will be determined based on the shortest, fastest, and inexpensive route parameters. The method used in this study is Network Analysis with ArcGIS10.3 applications. The result of this study will display the route map and trip descriptions of the shortest, fastest, and inexpensive route.
Solution to 8 Queen’s Problem Using SOAR Cognitive Architecture

In our work we consider SOAR cognitive architecture with its various types of memories. Here , 8 Queen's problem is solved by SOAR agent using multiple memories with residing productions using given initial and goal board positions and also study how to improve problem solving mechanism by reducing the processing time to reach goal. By this intelligence of agent also get improved as complexity (time) decreases.
Enhancing Enterprise Business Processes Through AI Based Approach for Entity Extraction – An Overview of an Application

While Industries are growing strong with their digital transformation, advanced analytics are making them stronger through data driven decisions. At the same time traditional automation is getting matured and emerging as cognitive automation. In the era of Industry 4.0, handshake of business process automation, advance analytics and cognitive services have laid down a strong platform for ‘Cognitive Bots’. Enterprises can leverage the advent of powerful technologies and approaches, anticipating ultimate goal of the business through more adaptive, self-learning, and contextual applications. This paper explains one of such cognitive bot for finance department where invoices are of utmost importance for the function. The mentioned bot is intended for amount detection and verification; additionally, it can also extract various entities like organization name, location and date which contributes to perform analytics to a great extent. The application reward business in reducing turnaround time and human errors. The accuracy of specially customized trained neural model has achieved state of the art results on the current set of learning data. The proposed framework makes use of Optical Character Recognition and PDFMiner for text extraction from scanned invoices. A Quality Classifier that will reject hand written invoices for any further processing. A spaCy’s Name-Entity-Recognition predicts the amount, date, organization name and location from extracted unstructured text.
Collaborative Learning With a Multi-Branch Framework for Feature Enhancement

Feature representation is highly important for many computer vision tasks. A broad range of prior studies have been proposed to strengthen representation ability of architectures via built-in blocks. However, during the forward propagation, the reduction in feature map scales still leads to the lack of representation ability. In this paper, we focus on boosting the representational power of a convolutional network by the multi-branch framework that we term the BranchNet. Each branch is directly supervised by label information to enrich the hierarchy features in BranchNet. Based on this framework, we further propose a collaborative learning loss and a soft target loss to transfer knowledge from deeper layers to shallow layers. BranchNet is an efficient training framework without extra parameters introduced in inference and can be integrated in existing networks, e.g., VGG, ResNet, and DenseNet. We evaluate BranchNet on all of these models and find that our method outperforms the baseline models on the widely-used CIFAR and ImageNet datasets. In particular, on the CIFAR-100 dataset, the classification error of ResNet-164 with BranchNet decreases by 4.51 percent. We also conduct experiments on the representative computer vision tasks of instance segmentation and class activation mapping, further verifying the superiority of BranchNet over the baseline models. Models and code are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zyyupup/BranchNet/</uri> .
An Approach to Extract the Relation and Location from the Short Stories

Stories are the most wonderful and fascinating thing of childhood. It creates a whole new world, each character has a different impact and the end which always states the goodness, helpfulness, courage always empowers and win on evil things. From childhood we are emotionally bonded with different types of stories. Stories are Motivational, Funny, Comedy, Puzzled, and Happiest etc. Each and every story taught us many lessons. Stories are one of the important medium to share knowledge and express the emotions. It can be classified into some types described by Christopher Booker such as Overcoming the Monster, Rags to Riches, The Quest, Voyage and Return, Comedy, Tragedy and Rebirth etc. Stories are not just a text but a powerful tool, they make us understand the moral of life, helps to explain difficult or complex event in a simple and understandable manner. This paper takes into account the stories as the main domain for work and tries to produce a summary of it in the present work extraction of important aspects i.e. feature so that a meaningful summary could be generated. The use of POS tag and NER based relation are used first to extract feature which we decided as Actor, Relation, Location and Event. For all this main terms a rule based extraction is written for proper extraction. The extracted terms are used to extract the sentences and finally sentences are ranked and summary is generated.