Reviewerly/exHarmony huggingface dataset:
Research on Applicability of Sensitivity Table Method in Optical System Alignment

Abstract Sensitivity table method (STM), which is a most widely used computer-aided alignment technology in optical system alignment, faces a problem that is how to determine the correctness of the applied object and established sensitivity table. In order to solve the problem, two criteria for applying STM correctly are concluded, one is determining the linearity relation between misalignments and wavefront aberration, the other one is establishing the effective sensitivity table by monitoring its condition number. Two optical systems are tested in this paper, and experimental results show the correctness of the proposed method.
Examining Hyperparameters of Neural Networks Trained Using Local Search

Deep neural networks (DNNs) have been found useful for many applications. However, training and designing those networks can be challenging and is considered more of an art or an engineering process than rigorous science. In this regard, the important process of choosing hyperparameters is relevant. In addition, training neural networks with derivative-free methods is somewhat understudied. Particularly, with regards to hyperparameter selection. The paper presents a small-scale study of 3 hyperparameters choice for convolutional neural networks (CNNs). The networks were trained with two single-candidate optimization algorithms: Stochastic Gradient Descent (derivative-based) and Local Search (derivative-free). The CNN is trained on a subset of the FashionMNIST dataset. Experimental results show that hyperparameter selection can be detrimental for Local Search, especially regarding network parametrization. Moreover, the best hyperparameter choices didn't match for both algorithms. Future investigation into the training dynamics of Local Search is likely needed.
Heat Kernel Smoothing on Manifolds and Its Application to Hyoid Bone Growth Modeling

We present a unified heat kernel smoothing framework for modeling 3D anatomical surface data extracted from medical images. Due to image acquisition and preprocessing noises, it is expected the medical imaging data is noisy. The surface data of the anatomical structures is regressed using the weighted linear combination of Laplace-Beltrami (LB) eigenfunctions to smooth out noisy data and perform statistical analysis. The method is applied in characterizing the 3D growth pattern of human hyoid bone between ages 0 and 20 obtained from CT images. We detected a significant age effect on localized parts of the hyoid bone.
Anomaly Detection Method for Operation and Maintenance Data Based on One-Class Learning

Abstract Due to frequent changes in system services, the amount of data collected in a certain state is insufficient, which causes problems such as insufficiency of normal sample data, scarcity of fault sample data, and lack of prior knowledge. Aiming at the problem of anomaly detection of small sample operation and maintenance data lacking negative samples, this paper proposes an operation and maintenance data anomaly detection method based on one-class learning, which uses SVDD (support vector data description) method to eliminate abnormal data in the collected operation and maintenance data. Then, we can better analyze the subsequent data. The experiments show that the proposed method is reasonable and effective.
A Fast Interactive Geometric Shape Recognition Method

This paper focuses on a simple, fast and highly accurate procedure for recognizing 2D geometric shapes such as rectangle, trapezium, acute triangle, etc. thereupon providing an interactive platform for the children. Proposed method for recognition is based on fundamental digital image processing and basic metrics of the shapes. On the basis of the pixels position of the shape's image on a predefined bounding-box and geometric logic, broadly 7 features have been addressed for recognizing 10 shapes. Defined novel features and use of decision tree not only simplify the process of recognition but also lower the response time. For experimental evaluation, two different types of dataset consisting a total of 4,725 shapes of 10 different 2D geometric shapes together are used and the overall accuracy of the recognition process for the datasets are 98.79% and 99.39% respectively.
Extracting Relevant Information Using Handheld Augmented Reality

Augmented Reality (AR) technology is being incorporated into education materials to attract students and to make the learning experience more engaging. This study focuses on the development of 3D object, audio-visual and interaction in Handheld AR. This research aims to bridge that gap using Handheld AR for a magazine, which allows students to get an overview and interact with the 3D model of the campus, view general information and events of the university. This magazine also benefits students that live outside Dhaka, who are unable to visit the campus beforehand. The users can use their Android phone camera for real-time video capture and render virtual objects in the augmented environment through Vuforia and Unity engine integration. To evaluate system effectiveness and user satisfaction, a survey is conducted. The survey consists of user background information, functionality tests and a user feedback questionnaire. The outcome of the survey shows satisfactory of the successful implementation of 3D and multimedia modules. This paper also discusses the future scopes and summarizes how to extract relevant information for students to gain knowledge and get entertainment by using handheld AR.
Code-based Pseudorandom Generator for the Post-Quantum Period

This paper focuses on research of a provably secure code-based pseudorandom sequence generators whose cryptanalysis problem equals to syndrome decoding (belonging to the NP-complex class). It was found that generated sequences of such well-known Fischer-Stern code-based generator don't have a maximum period, the actual period is much lower than expected. In our work, we have created a new generator scheme. It retains all advantages of the Fisher-Stern algorithm and provides pseudorandom sequences which are formed with maximum period. Also comparative analysis of proposed generator and popular generators was conducted.
A Model-Driven Engineering Approach for Business Process Based SaaS Services Composition

Nowadays, tremendous number of enterprises are looking to outsource their business processes in order to gain in productivity, reduce cost, and enhance performance. Outsourcing business processes to cloud computing and more specifically to SaaS services, is actually among the most prominent opportunities regarding the benefits that this paradigm offers. However, matching the business process activities with the suitable SaaS services is not a trivial task regarding the diversity of both, functional requirements of business processes and the functionalities offered by SaaS services. Furthermore, once the business process execution is entirely supported by the SaaS services, the process owner, lacking in general the required expertise related to SaaS domain, needs to be aware of how the business process is handled by the SaaS. This is done principally through transforming the business process model to the SaaS model. This paper proposes a model-based approach using SAT-based formal verification to select and validate the most suitable SaaS services to support the business process achievement. Furthermore, a model to model transformation from business process to SaaS is proposed. This transformation is based on (i) a lightweight extension of the business process meta-model of ISO/CEI 19510 and (ii) our proposition of the SaaS meta-model.
Design of Quality Control System for Fighting Machine Operation Based on Beidou Navigation

Abstract In view of the serious waste and pollution of pesticide spraying, this study developed a sprayer flow control system based on the high-precision Beidou satellite navigation system software. The system uses a high-precisbion Beidou satellite navigation system R21 positioning receiver. Acquires the vehicle in real-time through the built-in GNSS board of the vehicle. The speed and position information are analyzed and processed by the vehicle intelligent control terminal, and sent to the flow control system corresponding control commands, so that the flow rate of the spray can be controlled according to the speed of the vehicle to realize the electrification control of the spray operation machine. Based on the field operation requirements of the fight drugs machine, an automatic navigation system path tracking control algorithm is designed to make the system complete the route of the line and curve. The real-time information collection of the work is carried out by using the APP carried by the industrial tablet, and the vehicle inherent parameter information is used to interface with the agricultural information platform through the Internet to realize the online monitoring of the spraying operation parameters. At the variable forward speed, tested in a farmland environment. The test results show that the designed automatic navigation path control system and spray flow control system have good working accuracy values and stable operation, which can meet the requirements of farmland environment spraying operation.
An Integrity Protection Scheme Based on Binary Multi-granularity Incremental Tree for Embedded Computing Platform

Data integrity is an effective approach to protect the off-chip memory from active attacks such as data modification, injection and malicious code execution. However, in the existing integrity checking schemes, constructing the checking tree and operating massive computation occupied excessive memory space during checking the integrity of the memory data, and those schemes cannot be directly applied to the embedded system due to the limited system resources. A memory integrity protection scheme based on a binary multi-granularity incremental hash tree (MIT) was proposed. By applying this scheme, there are three improvements: reducing the scale of the node of the checking tree, optimizing the structure of the checking tree and accelerating the verification rate. The proposed memory integrity protection scheme was quantitatively evaluated by the SimpleScalar architecture simulation tool. The experimental results show that, compared with the traditional hash method, the rate of the overhead memory occupation and the average system performance are decreased by 48.72% and 32.62% respectively, which have better performance than the existing methods.
Probabilistic Action/Observation Planning for Playing Yamakuzushi

We propose a method for observation and action planning for manipulating piled objects under uncertainty focusing on a game of extracting one object from the pile with one finger, which we call Yamakuzushi. In cluttered scenes, an object is often occluded by the others, and it is difficult to recognize objects correctly and to manipulate an object as we want. By discretizing the object state, we plan both the observation pose and sliding action of an object based on a partially observable Markov decision process (POMDP), which can handle uncertainty. We experimentally confirmed that a robot plays Yamakuzushi by finding the correct observation pose and correct action to the stacked pieces and by taking one of the pieces out safely.
Privacy Preserved Ranking of Industrial Sensing Services Using Topological Information

The sensing-as-a-service paradigm has realized a number of contemporary industrial Internet-of-things applications. In this article, we have considered an order-driven scenario of a smart factory, where wearable devices provide various services and communicate with the service providers. The discovery process generates numerous smart factory services. The selection of an appropriate (or a set of) service(s) remains a challenge while preserving the service data privacy. It is required to involve an anonymous communication mechanism to design a privacy-preserved ranking model. The prevalent techniques for the prioritization of semantically equivalent sensor-provided services rely on quality-of-service (QoS) information. However, the QoS information is not always readily available at the node level. Moreover, the existing topological information-based (i.e., node importance and energy) solutions do not consider imperative features, such as a degree. The objective of this study is to design a privacy-preserved ranking model based on the onion routing technique and features along with the stochastic shortest route. Onion routing enables anonymous communication and prevents unauthorized entities from accessing ranking results. The weighted valuation is then derived to compute the cost of the homogeneous and dynamic sensor-provided services. Finally, the ranking is computed based on each service cost. The proposed model is extensively evaluated in two different network configurations of varying sizes. The evaluation results show that the proposed method performs 10% better in terms of ranking quality and 32% in terms of energy efficiency across different network configurations, as compared with the existing cost-based method while offering a desired level of privacy.
Introduction of Interstitial Model and a Sumkey to Address the Key Challenges with Blockchain

The Internet is an incredible platform available for sharing information and keeping track of data, but due to all that information and data floating around on the Internet and passing through multiple distinctive IoT devices, people can be tempted to steal that information for malicious use. With the help of 5G technology, we can reduce latency, increase speeds, and capacity for IoT devices for extensively used. Concurrently, these devices can power the security, decentralization, immutability, and consensus by using blockchains as foundational layers.In this paper, we discuss security issues in blockchain and IoT devices, and we propose the use of an interstitial model for flexibility and xtensibility of the layered architecture for the growth of blockchain, and a SumKey algorithm to resolve the subset sum problem for whole numbers by combining with the partition problem. This method applies to entire sequential numbers in a multiset cardinality. In ERP Financial Systems, the Chartfield can be used for this purpose.
Toward Deep and Handcraft Features for Detecting Violent Behaviors

Violent and abnormal behaviors detecting has been an interesting field recently. Many types of research have been done to accurately predict the behaviors of human whether it’s violent or not. However, it requires a lot of modern and strong infrastructure to complete the task. To fill the gap, in this research, we propose a skeleton-based method that doesn’t require much of infrastructure but very fast and accurate to detect violent behaviors. Our method contains two stages: extracting deep features from image frames to estimate human pose, and then we define four handcraft features to classify whether the act is violent or not. We tested our method on UT-Interaction [1] dataset which result is very tempting and promising
Obstacle Avoidance Method for Highly Redundant Robotic Arms

Abstract Obstacle avoidance is an important concept to be considered when a robotic system is installed in an environment. Obstacles within the working space of a robotic system can prevent the robot from performing the tasks assigned to it properly; hence, the designer of the robotic system must program the robot to follow an emergency strategy that enables it to avoid any contact with a probable obstacle anywhere anytime within the working space of the robot. For mobile robots this task is relatively easier to accomplish since this type of robots have the flexibility to change their routes and take alternatives that are free of obstacles. For fixed robotic arm manipulators the situation is much more complex. In this paper we are only concerned with obstacle avoidance techniques and present a real-time obstacle avoidance method for fixed robotic arms that combines the benefits of both global and local techniques in that it can be used for both static and dynamic environments with a reduced computational effort.
Deep Learning for an Anti-Jamming CPM Receiver

A novel continuous phase modulation (CPM) receiver model is proposed in this paper that employs deep learning (DL) techniques to improve the signal recovery and synchronization performance under heavy jamming conditions for frequency hopping (FH) based waveforms. A hybrid deep neural network is used to implement the DL in the receiver model. The simulation results show that the proposed receiver with DL boosting is robust under tone jamming which is a worst case scenario for a CPM receiver. The model achieves 3 - 5dB improvement under single-tone jamming, in terms of bit-error-rate (BER), and 2dB improvement under multi-tone jamming, compared with a receiver without DL.
Leonardo and the Design of Machines

Leonardo’s machine designs are an extraordinary testimony to the technologies developed during the Renaissance. Leonardo’s Codices contain a repertoire of machines of all types and for different uses. Many of these projects are shown by Leonardo with extreme attention to details and a sophisticated level of technical representation. Leonardo also analytically dissects the individual mechanical elements to recompose them in the design of complex machines. Leonardo studied the individual elements of a machine and their functions with an analytical and systematic approach. The mechanical parts will then be recomposed for the design of its more complex devices such as automata.
A linear code based on resilient Boolean maps whose dual is a platform for a robust secret sharing scheme

A linear code based on resilient maps on vector spaces over finite fields is introduced and lower bounds for its minimal distance are given. The dual code is used to define a secret sharing scheme (SSS) according to Massey construction. Since the dual code satisfies the conditions stated by Padró and Sáez, the generalization to a vector space SSS is robust against cheaters.
SFV-CNN: Deep Text Sentiment Classification with Scenario Feature Representation

In this paper, we present a deep learning approach to represent the scenario-related features of a sentence for text classification, and also demonstrate an interesting application which shows the nearest scenarios for a sentence. In order to improve the performance of text classification, it is necessary to make them be aware of the scenario switching at the background of the texts. We propose a CNN based sentiment analysis model named SFV-CNN for sentence classification. The proposed model can be improved by assigning suitable window for each scenario corpus in scenario word embedding training. Our experiments demonstrate that SFV-CNN brings an improvement in accuracy and also shows more obvious advantages when test on datasets across scenarios.
A Rescoring Method Using Web Search and Word Vectors for Spoken Term Detection

We propose a rescoring method using words related to a query obtained by Web search and word vectors for spoken term detection (STD). In this paper, we assume that words associated with the topic in speech data and co-occurring with the query are called “words related to the query”, and that the related words appear multiple times in the speech data. To identify the words related to the query, we introduce distributed expression of words obtained by Word2vec [1] [2], and first convert each word in the word-recognition results of speech data into a word vector. Each word vector is then compared with a word vector of the query. Words related to the query are determined by calculating the degree of similarity between the two word vectors. However, a word vector of an out-of-vocabulary (OOV) query cannot be obtained in this manner, since OOV queries do not appear in word-recognition results. For such OOV queries, we perform a Web search using the query, whereupon texts including the query are extracted. Recognition results of the speech data and the extracted texts are then combined and used for training of Word2vec. In this manner, a word vector of the OOV query can be obtained. Distances to all candidates in the document, including words related to the query, are used advantageously. Experiments are conducted to evaluate the performance of the proposed method using open test collections of the NTCIR-10[3] and NTCIR-12[4] workshops. For retrieval accuracy, an improvement of 3.2 points in mean average precision was achieved using the proposed method.
A Learning Framework for Versatile STL Controller Synthesis

In this paper, we aim towards providing a practical framework for learning to satisfy signal temporal logic (STL) task specifications for systems with partially unknown dynamics. We consider STL tasks whose satisfaction can be guaranteed by enforcing a priori known temporal specifications imposed on the atomic propositions that compose them. First, a neural network is trained offline as a control policy to satisfy such temporal specifications while also minimizing a target cost, such as the input energy of the system. The obtained controller then serves as a guide that aids exploration while learning to satisfy any specific STL task optimally using policy improvement, greatly increasing the sample efficiency of the procedure. The promise of the approach towards a versatile STL learning framework is demonstrated through simulations.
Acquisition of Knowledge with Time Information from Twitter

In this paper, we propose a knowledge acquisition method for non-task-oriented dialogue systems. Such dialogue systems need a wide variety of knowledge for generating appropriate and sophisticated responses. However, constructing such knowledge is costly. To solve this problem, we focus on a relation about each tweet and the posted time. First, we extract event words, such as verbs, from tweets. Second, we generate frequency distribution for five different time divisions: e.g., a monthly basis. Then, we remove burst words on the basis of variance for obtaining refined distributions. We checked high ranked words in each time division. As a result, we obtained not only common sense things such as “sleep” in night but also interesting activities such as “recruit” in April and May (April is the beginning of the recruitment process for the new year in Japan.) and “raise the spirits/plow into” around 9 AM for inspiring oneself at the beginning of his/her work of the day. In addition, the knowledge that our method extracts probably contributes to not only dialogue systems but also text mining and behavior analysis of data on social media and so on.
Internet Traffic Classifier Using Artificial Neural Network and 1D-CNN

Internet Traffic frequently expands in realm and complexity. Its rate of evolution is dwarfing the ability to manage and control the Network Traffics which will make it very difficult for the ISPs to optimizing network performance and security issues. One of the major issue is Network Traffic Classification, which is the area of major apprehension these days because of the accretion of networks. There are many primeval methods for Internet Traffic Classification like port and payload based classifiers. But many Internet applications do not use standard port numbers, moreover many application use identical port number and payload method uses packet inspection which erodes privacy of user. This lead to the advancement towards Deep Learning(DL) approach for quality Internet Traffic Classification. This paper proposes DNN approach which predict network traffics based on Fully Connected Neural Network(FCNN) and 1DConvolutional Neural Network(1D-CNN) model. The accuracy of proposed solutions on Andrew Moore's dataset is 98.34% for 1D-CNN and 96.97% for FCNN which proves their effectiveness as internet traffic classifier.