Reviewerly/exHarmony huggingface dataset:
Recurrent Layer Aggregation using LSTM

Standard convolutional neural networks assemble multiple convolutional layers to extract high-level features. Recent efforts keep designing deeper and wider architectures. Even with skip connections applied to combine different layers, the useful low-level features are not effectively utilized. Some deep layer aggregation methods have been proposed to aggregate features of all levels, using simple linear combination or complex non-linear transformation. In this paper, we treat convolutional features as a sequence, and propose our Recurrent Aggregation of Convolutional Neural Network (CNN-RA). Our aggregation method splits a standard CNN into blocks and maps their feature matrices to a sequence of vectors of the same length. LSTM is employed to connect to the sequence and better fuse features across blocks. Our proposed CNN-RA can be directly appended to any standard CNN without any modifications. Experiments show remarkable improvements of CNN-RA over the original architectures across datasets.
Development Of Critical Thinking Of Students In Educational Process

The authors of the article raise the problem of the students’ critical thinking development in the educational process of secondary school. The urgency of the problem is substantiated, the purpose of the research is outlined, the theoretical positions of scientists whose scientific interests lie in the field of solving the problem of students’ critical thinking development are reflected. The basic theoretical positions of scientists underlying the disclosure of the essence of the concept of "critical thinking" are defined here: the definition of the concept of "critical thinking", the stages of formation of the essence of this concept, the functions of critical thinking (evaluative, stimulating, search for truth, etc.), the ways of application of critical thinking, etc. The authors formulate the techniques of the students’ critical thinking development: the use of a graphical image of the structure of arguments for the proof and formulation of the thesis, techniques for contextual retelling, evaluation of the text, narrowing, questions (in the context of the topic of the lesson, the problems of modernity, etc.), techniques of finding of sophisms in the text, etc. The authors give the list of questions for writing a critical analysis, which is based on four laws of logic. The authors offer a sample of a critical analysis of the article "Personality as a subject of history" by P. I. Belyaev for students of the 10th grade (subject: the History of Russia), topic: "Political repressions of the 30s".
Real Time Depth Hole Filling using Kinect Sensor and Depth Extract from Stereo Images

The researcher have suggested real time depth based on frequency domain hole filling. It get better quality of depth sequence generated by sensor. This method is capable to produce high feature depth video which can be quite useful in improving the performance of various applications of Microsoft Kinect such as obstacle detection and avoidance, facial tracking, gesture recognition, pose estimation and skeletal. For stereo matching approach images depth extraction is the hybrid (Combination of Morphological Operation) mathematical algorithm. There are few step like color conversion, block matching, guided filtering, minimum disparity assignment design, mathematical perimeter, zero depth assignment, combination of hole filling and permutation of morphological operator and last nonlinear spatial filtering. Our algorithm is produce smooth, reliable, noise less and efficient depth map. The evaluation parameter such as Structure Similarity Index Map (SSIM), Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) measure the results for proportional analysis.
Relationship between Time Length of E-learning Environment Using and Physical Health of Elementary and Middle School Students

A total of 783 elementary and middle school students in Beijing city and Shandong province were selected for a one-year follow-up investigation, and the impact of time length of using E-learning environment each week on physical health was explored from four aspects as eyesight, respiratory rate, body mass index, sit and reach. Through Covariance Analysis, we found that time length of using E-learning environment each week by elementary and middle school students had a significant impact on their eyesight, but not on the respiratory rate, body mass index, sit and reach. Meanwhile, the comparative results of elementary and middle school students between different genders and learning stages that affected by the time length of E-learning were basically consistent with the findings above. Slight difference was found that the E-learning environment produced significant impact on both left and right eyesight of elementary girls, but on the left eyesight of middle-school boys, and significant effect on the middle-school body mass index. Based on this, we proposed that the time length of E-learning each day for elementary and middle school students should be controlled less than 1.5 hours.
Optimization of a Takagi-Sugeno fuzzy controller for voltage regulation of a DC-AC single-phase inverter

This paper shows the optimization of a PD (Proportional Derivative) fuzzy-type Takagi-Sugeno controller, which is used to control the voltage of a single-phase DC-AC inverter. This approach seeks to establish an initial configuration of the fuzzy system which is used as a starting point for the optimization process and thus improve the controller performance. The fuzzy system is designed aiming to have a low number of parameters to be optimized. The results show that the initial proposed configuration allows the successful optimization of the fuzzy controller.
Automated Resource Management on AWS Cloud Platform

Terraform helps in building an Infrastructure for the Cloud Environment as a code. Management of the cloud infrastructure is an important and difficult task as the environment has a huge number of resources and maintaining, monitoring and cleansing of resources in each and every region will take up a lot of human effort and time. The work environment consists of number of instances and volumes made by many developers but it is necessary to keep a track on which user is making the Resources. This paper describes various automation methods, cloud tools and resources used to automate resource management and monitoring on cloud. The code has been developed on Amazon Web Services (AWS) Lambda for the serverless approach. The Lambda function gets triggered by various CloudWatch Events according to its Rule. The automation is to monitor the EC2 resources and provide Tag to know who has created the instance or EBS Volume and also to notify about the resources as well as deleting the resources. Jenkins is a software which supports continuous integration for faster delivery. The resource management on the Cloud platform is a difficult task. Hence, in the rapid growth on industries resulting in huge environment to be maintained, automated strategies are necessary for management of resources and will also help in Cost optimization of the Industry.
Interactive geo-temporal data visualization for aircraft engine

Identifying the potential factors that contribute to an aircraft engine’s durability is a big challenge. In this paper, we propose an interactive system to link the engine maintenance history data, flight route history data, and volcanic ash eruption data, and then visualize the relations among them. Several kinds of visualization forms are used to present the datasets. We implement the React-Redux framework to manage the visual components in the frontend, along with a backend to handle the database. This highly tailored system shows how datasets of different sources can be linked and visualized with interactivity. Data analysts and developers can benefit from full-stack development process from data to intuition, meeting the requirements of customized visualization.
Design and Implementation of Core Modules of WEB Application Vulnerability Detection Model

In view of the shortcomings of existing vulnerability detection tools, an efficient Web application vulnerability detection system model based on crawler and feature recognition is proposed in this paper. The model is optimized from two aspects: scanning information collection and vulnerability detection efficiency. The network crawler module proposes a multi-threaded crawler mechanism based on breadth-first strategy. It extracts and scans a large number of duplicate and useless links through Hash Table duplicate checking, URL duplicate checking and parameter duplicate links, to improve the efficiency of scanning information collection and detection. The feature recognition module reduces the large amount of information exchange process needed to scan the website through two phases, namely, the identification of known websites and the screening of test objects based on VSM, which improves the scanning efficiency and reduces the false alarm rate to a certain extent.
Tree-Structured Semantic Encoder with Knowledge Sharing for Domain Adaptation in Natural Language Generation

Domain adaptation in natural language generation (NLG) remains challenging because of the high complexity of input semantics across domains and limited data of a target domain.This is particularly the case for dialogue systems, where we want to be able to seamlessly include new domains into the conversation.Therefore, it is crucial for generation models to share knowledge across domains for the effective adaptation from one domain to another.In this study, we exploit a tree-structured semantic encoder to capture the internal structure of complex semantic representations required for multi-domain dialogues in order to facilitate knowledge sharing across domains.In addition, a layer-wise attention mechanism between the tree encoder and the decoder is adopted to further improve the model's capability.The automatic evaluation results show that our model outperforms previous methods in terms of the BLEU score and the slot error rate, in particular when the adaptation data is limited.In subjective evaluation, human judges tend to prefer the sentences generated by our model, rating them more highly on informativeness and naturalness than other systems.
Research on Teaching Model of Oral English Training Based on Streaming Media Technology

This paper makes a comprehensive and in-depth analysis of the existing online spoken language training system and its related protocols, and designs an overall structure and implementation framework of the online English language oral training system. Aiming at the problems of stagnation, discontinuity and asynchronization of multimedia data in current system, this paper proposes an improved algorithm by introducing periodic constants to produce a certain interval between multicast streams of a media program, and provides the formal description and flow chart of the improved algorithm. The practical application and performance test show that the scheme can solve technical problems such as multimedia synchronization, error recovery in network transmission and multimedia transmission in narrowband Internet within a certain range. It also provides strong theoretical proof and practical support for the design and implementation of a language platform based on oral language learning.
Applications of optimization to factorization ranks and quantum information theory

Optimization is a fundamental area in mathematics and computer science, with many real-world applications. In this thesis we study the efficiency with which we can solve certain optimization problems from two different perspectives. Firstly, we study it from the perspective of matrix factorization ranks, which comes with connections to quantum information theory. Secondly, we study it from the perspective of quantum computing. This thesis is accordingly divided into two parts, where we take these perspectives. In the first part of this thesis we study several matrix factorization ranks and their connections to quantum information theory. In Chapter 5, we first give a unified approach to lower bounding matrix factorization ranks, using polynomial optimization techniques. In Chapter 6, we exploit the connection between one particular factorization rank, the completely positive semidefinite rank, and quantum correlations to provide an explicit family of matrices with a large completely positive semidefinite factorization rank. In Chapter 8, we use the same connection to study quantum versions of classical graph parameters from the perspective of polynomial optimization (albeit in noncommutative variables). In Chapter 7 we propose and study a new measure for the amount of entanglement needed to realize quantum correlations. We can approximate this new measure using our approach to matrix factorization ranks, i.e., through polynomial optimization in noncommutative variables. In the second part of this thesis we turn our attention to the question: Can we solve optimization problems faster on a quantum computer? First, in Chapter 10, we look at the problem of evaluating a Boolean function. We give a new semidefinite programming characterization of the minimum number of quantum queries to the input that are needed to determine the corresponding function value. In Chapter 11 we revisit the semidefinite programming problem that plays a crucial role in the rest of the thesis; we provide a quantum algorithm for solving semidefinite programs. Finally, in Chapter 12, we study the general problem of solving convex optimization problems in the oracle model. We provide both upper and lower bounds on the efficiency of various reductions in the quantum setting. In particular, we show that quantum computers are more efficient than classical computers for the task of answering separation queries when access to the convex problem is given through membership queries.
Time Masking: Leveraging Temporal Information in Spoken Dialogue Systems

In a spoken dialogue system, dialogue state tracker (DST) components track the state of the conversation by updating a distribution of values associated with each of the slots being tracked for the current user turn, using the interactions until then. Much of the previous work has relied on modeling the natural order of the conversation, using distance based offsets as an approximation of time. In this work, we hypothesize that leveraging the wall-clock temporal difference between turns is crucial for finer-grained control of dialogue scenarios. We develop a novel approach that applies a time mask, based on the wall-clock time difference, to the associated slot embeddings and empirically demonstrate that our proposed approach outperforms existing approaches that leverage distance offsets, on both an internal benchmark dataset as well as DSTC2.
Web Based-Decision Support System to Diagnose Hepatitis Disease using SAW Method

Hepatitis is a disease caused by some types of viruses that attack and cause inflammation and damage to human liver organ cells. Liver is a very important organ of the human body that has a function as a sieve over any entry of harmful particles entering into a person's body. A person when suffering from the disease is no longer have other part of his body that can resist harmful particles or toxins into the body so that it can pose a risk of death. The quick and precise information needs of a health expert or a specialist in disease are indispensable, the treatment of Hepatitis health solution will be very helpful especially in the process of diagnosing, determining the type of hepatitis and therapies are needed. In the application of web-based technology there are several rules that are often used, one of which is the method of SAW (Simple Additive Weighting) is the calculation of the certainty level to the conclusions obtained based on the value of Probability of illness due to obviously/symptoms. The web system to be made is expected to help someone in diagnosing and determining the hepatitis suffered based on the physical symptoms
Engineering a Showcase of Virtual Reality Exposure Therapy

Numerous research studies and controlled trials have unveiled the potential of serious games in various health-related areas [1]. Their range of application can be even further extended by the use of virtual reality (VR) technology, which allows the realistic representation of interactive contents. Virtual Reality Exposure Therapy (VRET) is a very promising novel use case for the development of serious games. Held in a virtual environment (VE) adaptive to the needs of the patient, this form of therapy can outperform traditional realworld measures [2], [3]. One of its major success factors is the engagement of the patient, which can be increased by an immersive gaming experience. We show a demonstrator of VRET application for a fire-related post-traumatic stress disorder (PTSD). In this demonstrator, features to support actively guided VR experiences are improved on, focusing on the interactive adaptivity of the VE.
The Best Land Selection using Simple Additive Weighting

Pringsewu Regency is an agrarian regency with the main livelihood of the population is from agro-industry agriculture. For on farm farming activities, a lot of cultivated commodities are food crops and rice crops. One of the key factors in creating equitable distribution of development or regional development and the right one result is the agricultural factor. While to know the feasibility of an agricultural area can be done by holding an assessment. The Assessment is made by Agriculture office in deciding the feasibility of agricultural areas include the assessment criteria of a region. The criteria of a region that is the type of soil, rainfall, water, temperature, and soil texture. Utilization of decision support system is very helpful in deciding the feasibility of agricultural area, and accompanied by the method of Simple Additive Weighting (SAW), this method can complete the research by finding the weight value for each attribute, then perform the ranking process that will determine the optimal alternative that is feasible for agriculture. With the decision support system with Simple Additive Weighting (SAW) method and with object oriented design using Unified Modeling Language (UML) this will make it easier for Agriculture Office and other institutions to monitor and to obtain information about agriculture that is useful for agricultural development.
On Smartphone Sensability of Bi-Phasic User Intoxication Levels from Diverse Walk Types in Standardized Field Sobriety Tests

Intoxicated driving causes 10,000 deaths annually. Smartphone sensing of user gait (walk) to identify intoxicated users in order to prevent drunk driving, have recently emerged. Such systems gather motion sensor (accelerometer and gyroscope) data from the users' smartphone as they walk and classify them using machine or deep learning. Standard Field Sobriety Tests (SFSTs) involve various types of walks designed to cause an intoxicated person to lose their balance. However, SFSTs were designed to make intoxication apparent to a trained law enforcement officer who manually proctors them. No prior work has explored which types of walk yields the most accurate results when assessed autonomously by a smartphone intoxicated gait assessment system. In this paper, we compare how accurately Long Short Term Memory (LSTM), Convolution Neural Network (CNN), Random Forest, Gradient Boosted Machines (GBM) and neural network classifiers are able to detect intoxication levels of drunk subjects who performed normal, walk-and-turn and standing on one foot SFST walks. We also compared the accuracy of intoxication detection on the ascending (increasing intoxication) vs descending (decreasing intoxication) limbs of drinking sessions (bi-phasic). We found smartphone intoxication sensing more accurate on the descending limb of the drinking episode and that intoxication detection on the normal walks of subjects were just as accurate as the SFSTs.
Communal Domain Learning for Registration in Drifted Image Spaces

Designing a registration framework for images that do not share the same probability distribution is a major challenge in modern image analytics yet trivial task for the human visual system (HVS). Discrepancies in probability distributions, also known as drifts, can occur due to various reasons including, but not limited to differences in sequences and modalities (e.g., MRI T1-T2 and MRI-CT registration), or acquisition settings (e.g., multisite, inter-subject, or intra-subject registrations). The popular assumption about the working of HVS is that it exploits a communal feature subspace exists between the registering images or fields-of-view that encompasses key drift-invariant features. Mimicking the approach that is potentially adopted by the HVS, herein, we present a representation learning technique of this invariant communal subspace that is shared by registering domains. The proposed communal domain learning (CDL) framework uses a set of hierarchical nonlinear transforms to learn the communal subspace that minimizes the probability differences and maximizes the amount of shared information between the registering domains. Similarity metric and parameter optimization calculations for registration are subsequently performed in the drift-minimized learned communal subspace. This generic registration framework is applied to register multisequence (MR: T1, T2) and multimodal (MR, CT) images. Results demonstrated generic applicability, consistent performance, and statistically significant improvement for both multi-sequence and multi-modal data using the proposed approach (p-value $$<0.001$$ ; Wilcoxon rank sum test) over baseline methods.
Real-world attack on MTCNN face detection system

Recent studies proved that deep learning approaches achieve remarkable results on face detection task. On the other hand, the advances gave rise to a new problem associated with the security of the deep convolutional neural network models unveiling potential risks of DCNNs based applications. Even minor input changes in the digital domain can result in the network being fooled. It was shown then that some deep learning-based face detectors are prone to adversarial attacks not only in a digital domain but also in the real world. In the paper, we investigate the security of the well-known cascade CNN face detection system - MTCNN and introduce an easily reproducible and a robust way to attack it. We propose different face attributes printed on an ordinary white and black printer and attached either to the medical face mask or to the face directly. Our approach is capable of breaking the MTCNN detector in a real-world scenario.
Morphological Reservoir Computing Hardware

In the recent years, Reservoir Computing arises as an emerging machine-learning technique that is highly suitable for time-series processing. In this work, we propose the implementation of reservoir computing systems in hardware via morphological neurons which make use of tropical algebra concepts that allow us to reduce the area cost in the neural synapses. The main consequence of using tropical algebra is that synapses multipliers are substituted by adders, with lower hardware requirements. The proposed design is synthesized in a Field-Programmable Gate Array (FPGA) and benchmarked against a time-series prediction task. The current approach achieves significant savings in terms of power and hardware, as well as an appreciable higher precision if compared to classical reservoir systems.