Reviewerly/exHarmony huggingface dataset:
A Framework for Mask-Wearing Recognition in Complex Scenes for Different Face Sizes

People are required to wear masks in many countries, now a days with the Covid-19 pandemic. Automated mask detection is very crucial to help identify people who do not wear masks. Other important applications is for surveillance issues to be able to detect concealed faces that might be a safety threat. However, automated mask wearing detection might be difficult in complex scenes such as hospitals and shopping malls where many people are at present. In this paper, we present analysis of several detection techniques and their performances. We are facing different face sizes and orientation, therefore, we propose one technique to detect faces of different sizes and orientations. In this research, we propose a framework to incorporate two deep learning procedures to develop a technique for mask-wearing recognition especially in complex scenes and various resolution images. A regional convolutional neural network (R-CNN) is used to detect regions of faces, which is further enhanced by introducing a different size face detection even for smaller targets. We combined that by an algorithm that can detect faces even in low resolution images. We propose a mask- wearing detection algorithms in complex situations under different resolution and face sizes. We use a convolutional neural network (CNN) to detect the presence of the mask around the detected face. Experimental results prove our process enhances the precision and recall for the combined detection algorithm. The proposed technique achieves Precision of 94.5%, and is better than other techniques under comparison.
How to teach databases in a student-centered environment - Oracle Academy program experiences

Today's global economy daily collects and produces enormous amounts of data. Making strategic and informed decisions based on data storage and analysis is the key to a successful business. That is why there is a growing demand in the market today for employees who have acquired appropriate competencies in databases during their education. Current higher education development policies emphasize the benefits of new teaching and learning approaches, such as student-centered learning. This paper describes teaching databases' experience using Oracle Academy program resources to create an innovative student-centered educational environment at the Polytechnic of Šibenik. Students' attitudes towards such a way of learning were also researched and presented.
Sentiment Forecasting Method on Approach of Supervised Learning by News Comments

Sentiment analysis is a process for mining opinion from a text. A lot of work has been done on this field for English language but the numbers don't rise high for the Bengali language. Though Bengali is the seven largest spoken language but a few works have been done for Bengali language all because of the lack of a perfect model and dataset. Everyday not only in our country but also around the world a lot of incidents happen. Because we are currently living in a global community where the news can impact a person's mind beyond borderline of countries. For this aspect it's a great opportunity to analyze the sentiment of the news. To train the model we classified the emotions into three classes. We have tried to classify a person's emotions into three different classes (happy, sad, neutral) for news published on the internet by online newspapers. For Deep learning Recurrent neural network was applied and for Machine learning, we used methods like Multinomial naive Bayes, K-nearest neighbor, Random Forest Classifier, Decision tree classifier, Support vector machine, Logistic Regression to train our model. Among those RNN obtain 95% accuracy.
Design Thinking and Emotional Intelligence in UI/UX Design of Website-Based Online Foreign Service Travel Expenses (BPDL) Applications

UI/UX design in applications is very important because a good design can meet user needs and make users feel comfortable and understand the use of the application. An example is an online application for website-based Foreign Service Travel Expenses (BPDL). In making this application focused on potential users of this application, if the user cannot understand the application created, it will have an impact on the user and the system. Such as data entry errors in the service application process, wrong service timings, etc. The purpose of this UI/UX design is to overcome any problems that may arise. When implementing this design, the design thinking method used can influence the way decisions are made, generate new innovative ideas, and make it easier for users to use BPDL applications.
Light4AR: a Shadow-based Estimator of Multiple Light Sources in Interactive Time for More Photorealistic AR Experiences

We present Light4AR, a light source estimation solution based on the detection of real cast shadows in an image captured by a mobile device. Given the camera pose of the image and a local 3D model of the scene, the approach consists in analyzing shadows cast by a reference object onto its supporting plane to determine the 3D position and the intensity of multiple light sources. By creating virtual point lights based on the resulting parameters and adding them to the AR scene, all the virtual objects can be illuminated and cast virtual shadows consistent with the real environment lighting, thereby enhancing user experience and object presence. In addition to offering the ability to share results over multiple users while preserving device resources, the server-based GPU implementation provides results in interactive time and makes photorealistic AR experiences accessible to most mobile devices, not limiting them to recent ones or specific OS. The proposed approach requires small manual input, limited to placing in the scene a reference object of known geometry, then selecting a region including the shadows cast by this object. We show the potential of this approach on several challenging scenes with various lighting configurations and background textures.
Forward Design Method of Avionics System Based on Communication

With the rapid development of computer technology, electronic technology, and control technology, communication-based avionics systems can provide greater potential for the improvement of aircraft combat capabilities. Moreover, during the flight, passengers have an increasing demand for space communications. How to provide passengers with a communication-based aerospace electronic system that is indistinguishable from the ground during the flight is a problem that needs to be solved urgently. The research on the forward design method of this system has been highly valued by experts at home and abroad. This article is a communication-based forward design method for avionics systems. Based on the development of aerospace data communication technology, this article discusses the advantages of the application of the CCSDS (Consultative Committee for Space Data Systems) protocol in the design of aerospace electronic communication systems. This research designs the technical scheme of the entire aerospace communication electronic system and refers to the relevant CCSDS protocol to complete the design of the distributor’s fast transmission telemetry package and the distributor’s slow transmission telemetry package. This article introduces the two existing architectures of the integrated avionics system, compares and analyzes them, and then elaborates the integrated avionics system architecture based on trusted computing and the trust chain transfer mechanism. The experimental results show that the method designed in this paper uses the NBSP basic protocol in an aeronautical communication environment with a nesting depth of 2 s. When the data transmission delay is 2 s, the NBSP scheme is 0.9 and the NERON scheme is 0.4. This shows that the design method of this study is able to meet the requirements of the system and technical indicators.
Demak Kingdom: Study of Environmental Condition and Geographical

Many experts have written about the history of the Demak kingdom, therefore the role of this kingdom in politics, economics and especially in the spread of Islam in Indonesia in the past. With the geophysiographical, historical and archaeological approach used in this study, it can be revealed about the environmental conditions and the location of the palace and the royal city of Demak. The research methods used in this research are observation, geophysiographical (drilling, geoelectrical measurement), historical (reading of documents) and archaeological (test pit excavations) methods, to obtain credible conclusions. The result is that the kingdom, which has a fairly large influence in the archipelago, is located on the island of Demak between Java and the Muria peninsula, which is still in a separate condition, which is surrounded by swamps, rivers and the sea. the tombs of kings, the names of places/villages (toponyms), have proven that the existence of the central city of the kingdom is concentrically arranged and the settlement patterns are grouped according to position, profession, ethnicity and religion.
FPGA implementation of eye movement detection algorithm

In this paper, we propose an simple and efficient VLSI hardware architecture is implemented for eye movement detection. For Eye movement detection reading activity Electrooculography (EOG) signal is considered. Here, for denoising the noisy EOG signal efficient FIR filter and for decomposition of denoised EOG signal an efficient Haar wavelet transform architecture is used respectively. The modified VLSI hardware architecture method detected the saccade (left movement of eye and right movement of eye) and blink efficiently. The hardware architecture of the eye movement detection algorithm functionality is verified by using Xilinx System Generator hardware co-simulation tool. The eye movement detection algorithm is implemented on the ZedBoard FPGA using Xilinx Vivado design suite.
Stochastic Gradient Descent with Selfish Mining Attack Parameters on Dash Difficulty Adjustment Algorithm

A selfish mining attack allows miners to gain more block mining rewards since they release the secretly mined blocks in public only when the honest miners release their mined blocks. The computation work of the honest miners gets wasted. The profitability of selfish mining attacks with different cryptocurrencies such as bitcoin, bitcoin cash, monero, dash, Zcash, Litecoin, and their corresponding difficulty adjustment algorithms has already been explored in previous works. Here the study of the profitability of selfish mining attacks with the dash difficulty adjustment algorithm is considered with gradient descent, mini-batch descent, and stochastic gradient descent algorithms which have not been explored in previous works. Here using stochastic gradient descent, the minimum error percentage of 21.9% is achieved.
A new data classification improvement approach based on kernel clustering

Abstract Data classification is one of the most critical issues in data mining with a large number of real-life applications. In many practical classification issues, there are various forms of anomalies in the real dataset. For example, the training set contains outliers, often enough to confuse the classifier and reduce its ability to learn from the data. In this paper, we propose a new data classification improvement approach based on kernel clustering. The proposed method can improve the classification performance by optimizing the training set. We first use the existing kernel clustering method to cluster the training set and optimize it based on the similarity between the training samples in each class and the corresponding class center. Then, the optimized reliable training set is trained to the standard classifier in the kernel space to classify each query sample. Extensive performance analysis shows that the proposed method achieves high performance, thus improving the classifier’s effectiveness.
Curriculum Design Assistant System Based on Automatically-Generated Educational Ontologies

This work describes the design and implementation of a functional assistant system to support instructors in designing or re-designing their courses’ curriculum. The system uses automatically generated ontologies from textbooks that reflect the structure of a specific body of knowledge. Twelve instructors participated in the generation of curricula for a programming fundamentals course using the assistant system. An initial evaluation of this tool suggests that the system successfully helped novel instructors design a new curriculum.
Image Resolution Enhancement Using Convolutional Autoencoders with Skip Connections

Improving image resolution, restoring images, denoising images has been a topic of wide study in deep learning domain. Due to the lack of ground truth images in practical scenarios, the enhanced images help tremendously in understanding and studying the phenomenon in an effective and efficient way. The paper presented here uses autoencoders which in turn comprise of encoder and decoder parts. In order to improve performance of autoencoder, skip connections from initial layers of encoder to the final layers of decoder have also been used. The deconvolutional part can be understood as combination of upsampling layers and convolutional layers. The proposed technique achieves impressive performance on a dataset (WHU-RS19) that has images of different geographies and are highly unrelated. The method proposed in this paper that uses symmetric convolutional and deconvolution layers, is able to achieve an accuracy of 89%; showing the merit of proposed network.
SeMPE: Secure Multi Path Execution Architecture for Removing Conditional Branch Side Channels

One prevalent source of side channel vulnerabilities is the secret-dependent behavior of conditional branches (SDBCB). The state-of-the-art solution relies on Constant-Time Expressions, which require high programming effort and incur high performance overheads. In this paper, we propose SeMPE, an architecture support to eliminate SDBCB without requiring much programming effort while incurring low performance overheads. When a secret-dependent branch is encountered, SeMPE fetches, executes, and commits both paths of the branch, preventing the adversary from inferring secret values from the branching behavior of the program. SeMPE outperforms code generated by FaCT, a constant-time expression language, by up to 18×.
The Design and Evaluation of a Chatbot for Human Resources

Technological innovations in artificial intelligence and machine learning enable business operators to engage with their customers 24/7 through chatbots. Many customers expect around-the-clock support which puts a strain on human resources; augmenting human resources with a chatbot can reduce costs for an organization and increase customer satisfaction. This work presents the HR Chatbot: a chatbot that answers general questions about human resource topics (i.e. payroll, benefits) for a private university. The research involves a collaboration between computer scientists, user experience researchers, and human resource administration. This work addresses two research questions: What are employees at a private university looking for from a chatbot for human resources?; and what are the appropriate methods to evaluate and measure the success of a chatbot for human resources? The HR Chatbot uses IBM Watson Assistant services, and an initial prototype was designed from a document of 31 frequently asked questions. Three rounds of user testing were conducted with employees of the university. The initial tests revealed that the chatbot was perceived as useful, but many were dissatisfied with the responses, specifically the lack of responses. Errors in the chatbot were classified into different categories; the most common being that the question was not in the content scope for the chatbot. Thus, data from the initial studies informed the scope of the chatbot; the number of unique questions grew to 157 and the total number of questions increased to 463. The HR Chatbot has 90% accuracy and an average sustained usability score of 69.5, surpassing the benchmark score. Following the initial tests, the HR Chatbot was deployed in real-time on the human resources website. This work describes how a chatbot was created, evaluated, and deployed online. We hope that this work inspires and informs others to explore similar use cases with chatbot technologies.
Personalized Emotion Recognition Utilizing Speech Signal and Linguistic Clues

In this project, non-intrusive videos are taken in real-time, which detect the emotional state of an individual by analyzing the facial expression. We present the Convolution Neural Network (CNN), a machine learning algorithm used for automatic image classification. This supervised classification technique analyses and trains the classifier on the labeled images and extracts the features of the classifier. By using the learned information of the training, the newly provided image will be classified based on the features observed in the image. The prototype which we are going to develop detects whether a person is happy or sad or angry or surprise.
Foundations of Deep Learning

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Video Creation Using Facial Animation and Speech Synthesis

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Identifying Importance of Supporting Policy for Gyeongsangbuk-do Seafood Processing Companies Using AHP

The purpose of this paper is to derive implications for rational priority by identifying the relative importance of the supporting policy for seafood processing companies in Gyeongsangbuk-do. The paper employs AHP(Analytic Hierarchy Process) which composes of upper hierachy(Facility⋅Equipment, Technical R&D, Marketing, Management) and lower hierachy including 17 factors, and 48 samples for seafood processing enterprises, public officials and experts were surveyed using personal interviews. As a result of AHP analysis of the support system for seafood processing companies, support for facilities and equipment(0.358) is the most important for the support system for seafood processing companies, followed by technical support(R&D)(0.315), marketing support(0.257). and management support(0.070). In the results of AHP analysis by each survey group, there is a difference in the perception of importance in support policy for seafood processing companies. Seafood processing companies show high importance in facilities and equipment support(0.589) but low importance in technology(R&D) support(0.078). On the other hand, the official and expert group consider that technical(R&D) is the most important support.
Deep Learning Models for Rubik’s Cube with Entropy Modelling

Rubik’s Cube is a classic and standard puzzle which has a very large state space of 4.3 × 1019 different states in 3 × 3 × 3 cube. However, there is only one terminal state. Recently, there are few contemporary solutions to solve Rubik cube which exploit Machine Learning Techniques. Our goal is to explore and generate Reinforcement Learning, CNN and LSTM techniques for sequence learning of Rubik cube solution with entropy modelling. The entropy is maximum when the cube is in unsolved state and minimum when it’s in solved state. We perform an Entropy traversal modelling for Rubik cube solution.
Student Perception of Software Engineering Factors Supported by Machine Learning

Software engineering education requires training in project management techniques in combination with intercultural communication skills to cooperate effectively in today's software development landscape. This poses a challenge to teach software engineering effectively in undergraduate courses for industry needs. International engineering study programs provide a unique opportunity to experience a software development process with participants from different cultures. For most students, English is not their native language, which reflects many distributed software engineering projects. This study surveyed 28 students from an international undergraduate engineering course in Japan on their perceived importance of software engineering factors and freeform text comments on their success, struggle, and critique within this course. A language model is trained using Word2Vec on the textual data to support the results of the perceived importance of software engineering factors.
LLWM &amp; IR-Mark

While software protection mechanisms, such as DRM and online services, hinder the unrestrained duplication of games and applications, these mechanisms fail at protecting individual software components from reuse by intellectual property thieves. While conceptually watermarking can discourage software misuse and allows proof of ownership, embedding watermarks requires expert knowledge. This is why software watermarks are barely used.
A Fine-Grained Annotated Corpus for Target-Based Opinion Analysis of Economic and Financial Narratives

In this paper about aspect-based sentiment analysis (ABSA), we present the first version of a fine-grained annotated corpus for targetbased opinion analysis (TBOA) to analyze economic activities or financial markets.We have annotated, at an intra-sentential level, a corpus of sentences extracted from documents representative of financial analysts' most-read materials by considering how financial actors communicate about the evolution of event trends and analyze related publications (news, official communications, etc.).Since we focus on identifying the expressions of opinions related to the economy and financial markets, we annotated the sentences that contain at least one subjective expression about a domain-specific term.Candidate sentences for annotations were randomly chosen from texts of specialized press and professional information channels over a period ranging from 1986 to 2021.Our annotation scheme relies on various linguistic markers like domain-specific vocabulary, syntactic structures, and rhetorical relations to explicitly describe the author's subjective stance.We investigated and evaluated the recourse to automatic pre-annotation with existing natural language processing technologies to alleviate the annotation workload.Our aim is to propose a corpus usable on the one hand as training material for the automatic detection of the opinions expressed on an extensive range of domain-specific aspects and on the other hand as a gold standard for evaluation TBOA.In this paper, we present our pre-annotation models and evaluations of their performance, introduce our annotation scheme and report on the main characteristics of our corpus.
Chapter 13. Binomial adverbs in Germanic and Romance Languages

As a special type of multiword expressions, binomials are a frequent phenomenon in many languages. We focus on binomial adverbs that are coordinations of two adverbial constituents. Their syntactic contribution to a sentence is adverbial as well and their semantic contribution is idiomatic. They have many uses, such as to intensify ( first and foremost ), express tendency ( more and more ), frequency ( over and over [again] ), vagueness ( more or less ), determination ( sooner or later ) etc. In this work, we describe our approaches to identify binomial adverbs in a large multiparallel corpus. Alongside the well-known measure of reversibility, we also calculate measures of statistical association and look for single-word translation equivalents in other languages. Combining these features facilitates the identification of binomial adverbs.