Reviewerly/exHarmony huggingface dataset:
Automatic Severity Evaluation of Articulation Disorder in Speech using Dynamic Time Warping

Articulation disorder is a speech disorder condition where a person has an obstruction to correctly utter certain sounds. In the case of children, the early assessment and medical treatment for this disorder are most important. An Automatic Speech Recognition(ASR) based system has already been developed for assessment of articulation disorder by the Centre for Advanced Signal Processing (CASP), Department of Electronics, RIT Kottayam in collaboration with All India Institute of Speech and Hearing (AIISH), Mysore. Even though the classification of disordered speech into mild/moderate/severe categories are successfully done by the system, the numerical measure obtained for severe cases is not satisfactory. In this paper, an additional effective method to calculate a numerical measure of articulation disorder is proposed. The first method is to find out the similarity measure between articulation disordered words and their corresponding normal words by Dynamic Time Warping (DTW) of corresponding spectrogram images. Computation of Log Cepstral Distance (LCD) between disordered and normal speech is also done. Evaluation of the system is done using disordered speech in the collected dataset. It is found that spectrogram-based measure gives better results, and hence can be combined with the existing ASR-based system for better objective evaluation of articulation disorder.
Words Matter: The Need for Environmental Awareness-Raising Content in Middle School English Textbooks in Korea

This study analyses six Middle School English textbooks accredited by the Ministry of Education in Korea, focusing on their representation of environmentally related issues and the human-induced climate change crisis in particular. Having identified a lack of content analysis research regarding the structuring of learning around real-world issues pertinent to the students, the authors examined the books for: (1) climate change related ideas that are misleading or ill-informed; (2) the extent to which the books promote environmental awareness; and (3) how their content might be altered to reflect current calls for an interdisciplinary approach to environmental education. The findings reveal a lack of attention to the causes and ramifications of climate change and their impact on climate, water, food, human health, the natural environment, and infrastructure. In view of the urgent need to address these problems, this study recommends that the world view projected in English textbooks be expanded through a content-based approach, to include attention to the anthropocentric issues students will face throughout their lives. It is also recommended that future research explore student perceptions regarding appropriate content and educational approaches for Middle School English textbooks.
Smartphones-Based Non-contact Children’s Posture Evaluation

Early detection of poor posture which might incur cervical spondylosis or lumbar vertebrae disease is of great significance to the healthy growth of children. Most of human body posture measurement methods require various sensors and wearable devices. However, devices are easily damaged and complicated to use, making these methods difficult to promote in daily life. In this paper, we propose a smartphones-based non-contact children’s posture evaluation method based on computer vision, which is more convenient and accurate in contrast to sensor-based ones. First, we take children’s standing posture images and record accelerometry data by smartphone at the same time. Then, the skeleton in the image is recognized through the children’s skeleton keypoints recognition model, which is retrained in named children dataset by fine tuning of transfer learning. Next, the keypoints is corrected by the accelerometry data to solve the tilt problem in the smartphone shooting. Finally, the relations between keypoints, termed as Joint Angle, is used to evaluate the posture. Experiments results verify that the average accuracy rate of proposed method for evaluating a single part of body can reach 94.78%.
Intra-Pulse Feature Extraction of Radar Emitter Signals Based on Complex Network

The electromagnetic signal environment of the modern battlefield is complex. The radar signal recognition technology based on the traditional five parameters cannot meet the actual needs. For this reason, it is urgent to explore new identification methods to improve the technical level of existing radar countermeasure equipment in electronic warfare. Aiming at the research challenge of low-accuracy intra-pulse waveform recognition in a dense modern electronic warfare environment, In this paper, we propose a new method to extract the characteristics of radar emission signals. This method first reconstructs the radar transmitter signal into a complex network, and then extracts the characteristics of the complex network. In our algorithm, the features are extracted from network topology statistics with each vector point of the reconstructed phase space represented by a single node and edge determined by the phase space distance. Through analyzing the global properties of network nodes, we found that the network constructed by the method described in this article inherited the dynamic characteristics of all aspects of the intentional intra-pulse modulation type of radar signals in time domain. Furthermore, we investigate the stability and sensibility of feature parameters with the variation range from 5dB to 20dB.
Introduction

This Chapter starts with a brief introduction to the state-of-the-art in cryptography. Thereafter, this Chapter connects the following Chapters of the book.
Character Recognition Based on Composite Convolution Neural Networks and Coordinate Attention

Text is ubiquitous in our daily life. There are texts in road signs, billboards, posters and license plates, which are important elements of scene understanding. However, due to the complexity of the scene, the calculation of the recognition model is complicated, the amount of parameters is large, and the accuracy is not high. To solve this problem, this paper proposes a text recognition algorithm based on composite convolution and coordinate attention. Firstly, the depth separable convolution is used to replace the conventional convolution, which greatly reduces the parameters and speeds up the training speed. Then the two-scale convolution is added to obtain different receptive fields, and the feature fusion is carried out to highly extract the text features in the picture. Finally, the attention modules coordinate attention embedded in the model can obtain stronger channel information without losing the location information, which makes the network model more focused on useful text features. Experimental results show that compared with the traditional mainstream framework and other feature extraction networks, this method has a smaller model, fewer parameters, and higher accuracy.
Histopathological Cancer Detection using Deep Learning

Deep learning is a sophisticated machine learning technique that teaches machines what to try and do that comes naturally to humans like understanding patterns and recognizing, analyzing things. Nowadays, health care has become an associate trade that uses deep learning the foremost. Deep learning intending offers pathbreaking applications. Deep learning collects large amounts of information as well as patient records, medical reports, and insurance records, and applies your neural network to produce the most effective results. In this paper, the histopathology of detecting bodily fluid nodes is predicted using a deep learning model of cancer scanning. This will be useful to society as a timely detection and alert system to detect cancer trends.
A Data Security Scheme for Cloud Storage Based on Data Segmentation and Trusted Third Party Supervision

In order to improve the security of data access process in cloud storage environment, an association structure based on data security level and data segmentation strategy is proposed. Design a reliable cloud storage data security scheme with the technical support of Trusted Third Party control and data encryption. In this scheme, the key-partition-machine corresponding logic relation of the three-level association map is constructed by selecting the data segmentation strategy with different security intensity according to the data of different privacy and security level, the data is fragmented and stored in separate physical database nodes. Using data fragmentation can not only solve the bottleneck of storage performance, achieve the goal of uniform data distribution and load balance, but also improve the confidentiality and stability of data cloud storage, effective prevention of single database breach brings the problem of data leakage. In addition, Trusted Third Party provides key distribution and management, digital certificate signing and identity authentication functions, and use data encryption techniques in user upload and storage processes, further enhance the data in the cloud storage process of controllability, data integrity and confidentiality, greatly improve the security performance of cloud storage.
Adaptive Eigenmodes for Robust Object Tracking

Discriminative correlation filters based algorithms have attracted extensive attention due to their strong tracking capability. However, object tracking still faces many challenges due to object appearance variations, background clutter, occlusion, plane rotation, etc. In this paper, to better express the object, multiple features are integrated to make full use of the advantage of different features. Furthermore, the adaptive eigen-decomposition and reconstruction ("eigenmodes") method is applied to carry out the integrated-feature decomposition, and optimal expression of the object is established through simple eigen-relationships. It has proved experimentally that the predicted value after eigenmodes is closer to the groundtruth than before. Furthermore, to solve the tracking failure caused by interfering objects or background clutters and improve the tracking accuracy, the average peak-correlation energy (APCE) method is utilized as an optimized update strategy in this paper. A large number of experimental results on the known reference datasets indicate that our algorithm has good performance compared to the existing tracking methods.
Fault Aware BAT Algorithm for Task Scheduling in Cloud

Distributed resources in a cloud environment come with issues of reliability and heterogeneous performance of every data center. This behavior of the cloud comes with many issues like trust, fault, and reliability in the cloud. So, to overcome this, reliable task scheduling is required, which considers all the conduct of the cloud. In such a situation, the position, and the executives of the allocation of resources between on-premises, datacenters and cloud data centers is a fundamental problem. Distributing in the cloud places a heavy burden on the execution of the cloud and the supervision of resource and task performance. Various static and dynamic algorithms may be used to resolve this problem. For better cloud presentation in terms of several tasks failed, execution duration, and start time, a fault-aware bat algorithm inspired by undertaking distribution algorithm for cloud infrastructure is suggested in this paper.
Financial Data Sharing Mechanism Under the Background of Big Data

In recent years, with the widespread application of emerging technologies such as big data and AI, the Internet has played an irreplaceable role in the economic development. In the context of digital economy, the personal information data of financial consumers has become an increasingly important asset for financial institutions to pursue business interests. The fragmentation and dispersion of financial data have always been a feature of the development of the financial industry, while the introduction of big data and other emerging technologies in data sharing can effectively resolve the characteristics of traditional financial data and has a far-reaching impact on financial development. Based on the characteristics of big data, this paper discusses the scientific mechanism of financial institutions’ data sharing under the background of digital economy and proposes relevant rules and regulations for the increasingly efficient sharing mechanism.
Towards an AI playing Touhou from pixels: a dataset for real-time semantic segmentation

When playing from pixels, AIs share some of the struggles that humans face when playing a game, namely not knowing its internal state. In this paper we begin the exploration of the AI-playing-from-pixels problem for Touhou, a bullet hell game. Albeit being a massively popular game in some niches, the community has yet to produce an AI capable of beating it, while looking only at pixels, in Lunatic mode, the hardest difficulty. We propose, as a first step, to build a semantic segmentation model to create a bridge to the internal-state-looking AIs. To achieve this, we created a dataset to train models for this task. This dataset is procedurally generated using manually labeled assets from classic era Touhou games. After selecting five state-of-the-art real-time semantic segmentation networks, we trained them using our dataset. The results indicate that the models produced have a high classification performance over the validation set. However all models, but one, are too slow to run in real-time at the game's target frame rate. On real game footage the models show promising results, but the dataset needs to be strengthen to account for noise sources in the real game.
The Role of Explanations in Human-Machine Learning

In this paper, we study explanations in a setting where human capabilities are in parity with Machine Learning (ML) capabilities. If an ML system is to be trusted in this situation, limitations in the trained ML model’s abilities have to be exposed to the end-user. A majority of current approaches focus on the task of creating explanations for a proposed decision, but less attention is given to the equally important task of exposing limitations in the ML model’s capabilities, limitations that in turn affect the validity of created explanations. Using a small-scale design experiment we compare human explanations with explanations created by an ML system. This paper explores and presents how the structure and terminology of scientific explanations can expose limitations in the ML models knowledge and be used as an approach for research and design in the area of explainable artificial intelligence.
BDLedger: A Scalable Distributed Ledger for Large-Scale Data Recording

Nowadays, blockchain becomes a hot topic in academics and industry. Traditionally, due to the “Impossible Triangle”, most existing blockchain systems prefer decentralization and security to scalability, which causes their limited throughput. However, it is undeniable that there exist scenarios that require extremely high scalability of blockchain, such as the on-chain record of vehicle driving data. In this paper, we propose a new blockchain-like distributed ledger called BDLedger that can realize an approximately linear increase of throughput to solve the above challenge. In BDLedger, we design a novel consensus mechanism called Random Witness Consensus and organize blocks as a DAG structure. To achieve the linear increase of throughput, we make sacrifices in two aspects: 1) abandoning the consensus on the order of transactions and keep only the consensus on the content of transactions, which means that there are no conflict transactions so that all transactions can be processed in parallel. 2) The content of each block only exists in a constant number of random nodes, which ensures that the overall storage and bandwidth costs will not increase rapidly as the number of nodes increase. We discuss the rationality of the above approach and introduce the design of BDLedger in detail. Experimental results show that BDLedger can achieve an approximately linear increase of throughput, from about 20000 TPS for 10 nodes to about 160000 TPS for 100 nodes.
Insights of Deep Learning Applications

Deep learning is the latest innovation in the fields of Machine Learning and Artificial Intelligence. Deep learning tries to mimic the human brain, it can intake and process lots of amount of data and also performs complex tasks on them. This report discusses the applications of deep learning and how it can be applied shortly. Deep learning is introduced and how it differs from machine learning is studied and then some of its applications in various fields is explored.
Modeling Object’s Affordances via Reward Functions

Object affordance learning is the ability to process information about objects and how to use them. Embedding this knowledge in robots is an essential step for the development of intelligent and truly autonomous agents. This work proposes the development of a framework for learning affordances for robotic manipulation. The proposed approach was implemented as a reinforcement function in a network with a Soft Actor-Critic (SAC) algorithm and trained in simulation with a humanoid robot. Among different affordance complexities (touching and grabbing the object), the results show a rate of up to 95% correctness in the best scenario, with the agent properly performing all desired actions. Such results suggest that it is possible to define reward functions representing the object’s affordances for different objects.
Performing Analysis, Performing Metal

Light shows at contemporary rock concerts generally create an immersive, multi-sensory experience. In their most sophisticated forms, however, they provide a visual analysis of the music as it unfolds. This paper presents a case study of what I call the analytical light show, by examining how the intricate light shows of extreme metal band Meshuggah contribute an interpretive layer that not only promotes multi-sensory engagement, but also actively guides listeners through songs’ formal structures. Meshuggah’s light shows, created by lighting designer Edvard Hansson, are exhaustively synchronized to the rhythmic patterns of the guitars and drums. Meticulous use of color, brightness, directionality, placement pattern, and beam movement provide additional information about gesture, articulation, and pitch. These analytical light shows provide a three-dimensional visual score that dramatizes rhythms while guiding listeners through each riff. Through this lighting, spatial and bodily metaphors of musical movement—high and low, moving and holding still—are transmuted into visual representation. By presenting analysis and performance simultaneously and as each other, Meshuggah combines technical virtuosity with rock authenticity, and provides another example of what I have called “coercive synesthesia” (Lucas 2014), as the lighting becomes an inextricable part of the musical experience. Beyond the confines of metal culture, I study the analytical light show as an expression of vernacular musical analysis that combines specific analytical and technical expertise with the intuitive, embodied knowledge that experienced music listeners possess.
Endorsement Just a Hashtag +1 Away

One of the major reasons of delayed development project deliveries is the time taken to resolve critical or non-trivial issues. Time also gets consumed in developing the Proof of Concept before final development where the area is niche, and the procedure is not known. Critical system setups also consume time. Imagine, a platform that can easily tell you if the issue you are facing has already been faced and resolved, a concept you are building using new approach has already been achieved by someone. Some tapping into the potentials of this platform can reduce project impediments and speed up the development. This paper explains in detail that a huge per- centage of people who solve critical issues skip writing blogs or articles on open forums as it requires time for content writing and due to which these experts are not visible to the world for help. This forum allows the users to quickly share their knowledge without writing lengthy blogs and get recognized as experts for helping others in resolving critical issues. It also let other users to search for experts and save time for further development. We have named this forum as Endorsement—just a #tag +1 away! because via this forum getting endorsed on a skillset requires only creating a hashtag and getting an upvote on providing the right solution to the query asked.
Context-BMN for Temporal Action Proposal Generation

Temporal Action Proposal Generation (TAPG) is a challenging task which locates the pair of starting and ending point of each action in an untrimmed video. In this article, a novel network named Context-BMN is proposed to better take advantage of the contextual semantic information in action features. To explicitly encode the starting/ending point of an action, context-BMN introduces extra proposal-level action features in addition to local-level action features for action feature extraction. To balance the positive/negative training samples, a novel loss function called Free-Focal Loss is proposed for the training of Context-BMN which utilizes different IOU interval of the sample to judge its contribution in the loss function. To address the problem that large gradients of difficult samples are detrimental to co-training of classification and regression, a “Balanced L1 Loss” is proposed to improve the regression gradients of specific samples. Comprehensive experiments are conducted on the well-known ActivityNet-1.3 dataset. The experimental results demonstrate the effectiveness of Context-BMN. Specifically, Context-BMN promotes the AR@100 from 75.01% to 76.56%, which is comparable to the state-of-the-art performance [] on this dataset (76.75%).
Promote an Abstract Understanding of the Problem-Solving Knowledge in the Theory of Computation Through Visualization-Based Intelligent Tutor System

Just like heart is to the body, so is theory of computation for computer science and engineering discipline. Course deals with designing of abstract machines like finite automata, push down automata and Turing machines. It not only involves designing but also theoretical concepts like theorems, proofs and examples and to deal with examples accordingly. This course involves visualization, imagination which is difficult for students in the physical classrooms and teacher needs to blend traditional black board teaching method with active learning strategies to make learning easy. A visualization-based intelligent tutor system (ITS) is a computer system that aims to provide immediate and customized instruction to learners, usually without intervention from a human teacher. This instructional strategy is helpful for enabling learning in positive manner as students can “see” what is happening and gets feedback. The active way of learning se using Java Formal Languages and Automata Package (JFLAP) tool. It is a preexisting software tool created by Professor Susan Rodger and her students at Duke University. The control group and experimental group results are presented in this paper partially; supporting the effect of the usage of the visualization-based intelligent tutor system on student learning. The experiment shows that use of VIT enhance learning ability and interest in the course. The usefulness is supported with survey questionnaire.