Reviewerly/exHarmony huggingface dataset:
Single Bit Fault Detecting ALU Design using Reversible Gates

The semiconductor industry has been experiencing a massive upgradation in terms of speed and device performance due to device scaling since the invention of MOSFETs in 1960s, but simultaneously power dissipation has also been a major concern. With the device scaling having reached its limit, researchers are looking for a breakthrough technology. One of the emerging fields in the VLSI industry is Reversible Computing. This technology has largely attracted the researchers, because of zero power dissipation in reversible logic circuits. The proposed work here is towards designing an ALU using reversible quantum gates, which operate on qubits. The proposed ALU performs both arithmetic and logical operations, either of which can be selected as per the requirement and additionally, it is capable of detecting single bit error. The design uses Fredkin and CNOT gate, which are parity preserving reversible gates. The proposed design is modelled using Verilog HDL and simulated in Modelsim software. The design is focused on reducing the number of gates, quantum cost, Garbage outputs and ancillary inputs or constant inputs.
Policing rural fraud victimisation

Generally, people in rural areas may look out for one another but not to the point that they are seen to be interfering. It is a unique rural dichotomy found at intersection of sense of community and being polite. The sender indicated that a percentage of any monies would be shared with those who provided assistance. This fax was sent to recipients across the continent, and one came to a fax machine in the office of a unique rural farming community. The person who received and viewed the fax was, in many ways, the ‘ideal’ victim: a person with little or no knowledge of even existence of such scams but with direct access to sizeable financial holdings of the entire community. When it comes to fraud prevention resources, training and investigations, people in rural areas remain largely on their own despite the fact that the new digital reality makes rural residents as readily accessible as those who live in cities.
Aspects of Synthesis of Quasi-Orthogonal Code Sequence Sets Using Walsh Functions

In the present work, a number of key aspects of the synthesis process of a set of quasi-orthogonal code binary sequences based on multiplicative compositions of optimal binary code sequences and a subset of Walsh orthogonal functions are developed and proposed. In particular, analytical expressions are proposed for estimating the parameters of periodic auto- and cross-correlation functions of quasi-orthogonal code binary sequences as a function of the structure parameters of the implemented binary sequences. From the obtained analytical expressions, the requirements to the structure of the implemented binary sequences are formulated. The simulation results of the procedure for extracting subsets of implemented binary sequences based on a system of Walsh orthogonal functions are given, examples of specific distinguished subsets that satisfy the above requirements.
Breaking Down and Reduplication of Information in Cloud for Best Overall Performance and Protection

As an increase in the usage of the database, the data security and storage of data become a very big issue. To overcome this, cloud computing comes first. All the data will be stored in a third-party location and retrieve whenever the user wants to access it. In order to achieve this, we came up with “Disintegration and reduplication of data in cloud for safety and security.” In this methodology, when the user sends the file on the cloud server it gets fragmented. Fragmentation is a process of dividing the file into some fragments in a way that it is impossible to attack the total file at a time. Each node stores a single fragment of a particular file. The main aim of this work is to give security, protection, and performance against all types of attacks.
Rothstein’s Code Transformations for Discordant Datasets: Matrix Operations

The Rothstein code is a binary vector representation of the slope of a line or scaling factor that was originally introduced for the scalar transformation of images. The methodology has built-in interpolation with each operation. The technique is underutilized for data operations and its use is amenable for the practical and real-world management of data from different sources, different data sampling methods, and discordant outputs from related processes. This work proposes and demonstrates the use of the code for assimilating data sets with discordant sizes for simple matrix operations. Examples presented are for the calculation of a matrix determinant, adding matrices, and matrix multiplication where the matrix sizes are untenable. The emphasis is on understanding the technique and its potential using simple examples, 2-dimensional matrices, and integer arithmetic.
Analysis of learning media in the development of flood-themed teaching materials for high school students

Abstract Learning media is a very important learning component who use by teachers and students in the learning. The development of information, communication and technology have big contributed for teachers to develop learning media as needed by students in learning. The purpose of the research is to analyze the media used by students in high school physics learning and to determine the right media in the development of flood-themed teaching materials. This research method is descriptive qualitative approach. Descriptive method is a method used to examine the condition of the natural objective. To obtain research data used questionnaire instrument. Instrument for retrieving data in the form of a questionnaire for analysis of learning media filled in by students. Analysis results from quality of content and purpose are in good criteria with a percentage value of 72, Quality of Learning are in good criteria with a percentage value of 76, technical quality are in good criteria with a percentage value of 78, and electronic-based learning media are in good criteria with a percentage value of 78. The result of the study showed that the use of learning media based on electronic can be used in physics learning and develop flood-themed teaching materials based on electronic.
Quantum simulation of electron Coulomb interactions

We review a strategy to use an ion-based analog quantum simulator to study the many-body electron-electron Coulomb interaction of an electron gas. This is made possible by an exact unitary dilatation mapping, which allows an experimentally challenging system to be replaced by an alternative one that may be experimentally more accessible. We show the feasibility of this simulation strategy mathematically. As a bonus, the dilatation transformation does not even need to be physically implemented when only the system spectrum is needed, in which case the simulation induces no experimental complexity overhead. This proposal is dimension-agnostic, and could be generalized as a simulation strategy between systems involving terms with different position or momentum dependence.
Effective Identification of Distinctive Wordmarks

A wordmark or a logotype string is a stylized text used by companies or businesses for the purposes of identification and branding products. Pie face under the product category “baked goods and pastries” and airbus smarter fleet under the product category “data processing and aircraft maintenance software” are examples of wordmarks. The wordmark strings are manually examined by patent officers for their distinctiveness and uniqueness to be accepted as “protected intellectual property” for specific businesses. We address the problem of automatically identifying acceptable English wordmarks based on their textual content. Different from most text mining tasks, our problem involves the classification of a small set of words (often less than five) in context of a specific product description. We handle this sparsity challenge by designing a range of features for characterizing wordmarks using the syntactic and linguistic properties of words as well as by incorporating co-occurrence and similarity information from external resources such as WordNet, Wikipedia, and Word Embeddings. We investigate machine learning models for this novel task and study their classification effectiveness on a large dataset of about 71K wordmarks.
Analytical Techniques Help Enhance the Results of Data Mining: Case Study of Cow Insemination

Once we have the information about the system, information coming from measurements and from expert estimates, we use this information to come up with a model describing the system. The usual way to come up with such a model is to formulate several different hypotheses and to select the one that best fits the data. Techniques for formulating hypotheses based on the available information are known as data mining techniques. When the amount of data is not sufficient to make statistically justified conclusions, the dependencies produced by data mining techniques are often caused by accidental coincidences and do not reflect the actual behavior of the corresponding system.
Digging Deep—Mud as Medium. Playful Encounters with the Soil

Computing increasingly takes place beyond the desktop, as humanity is being permanently surrounded by, and entangled with, technological infrastructure. Against the backdrop of these recent developments, echoing in the emergence of what geologist Peter K. Haff calls the “technosphere”, we are confronted with the necessity to reframe contemporary media technologies beyond the epistemic tradition of modernity. Established dichotomies such as nature/culture, subject/object and inside/outside increasingly fail to diagnose what it means to live in the current condition. Navigating through the possibilities of playful engagement in these media-saturated environments, I will argue for the notion of the “elemental” in media studies, in its first and foremost substance-based meaning, indicating an ecological understanding of technology. In negotiating these potentials, speculative artistic strategies make use of the properties of natural elements in experimental media-technological set ups. They allow for a playful attitude towards technology, e.g. by appropriating the messy characteristics of mud as an interactional device, through the attempt to establish contact with the mineral basis of technological artifacts or by applying the capacities of the soil to demonstrate alternative screen formats. In this sense, the paper serves as a proposal to re-evaluate the media phenomena of the twenty-first century in terms of a techno-ecological paradigm shift in media theory.
Rate Matching Pattern Estimation

Rate matching in 3GPP standard is a process of getting the right amount of bits out of full-rate channel encoded bits, either by repetition or by puncturing, at the transmitter. The rate matching parameters may not be available at the receiver in various non-cooperative communication contexts, and the estimation of the rate matching pattern is required before any channel decoding attempt. A generic rate matching pattern estimation problem is formally formulated for repetitive rate matching. A set of ML estimators are derived based on the conditional probability of the observed bit-repetition index over a perfect channel as well as over BSC channel. Their performances are explored through simulations. It was found that the detection error rate of the proposed ML estimator was relatively high when very few bits are repeated, but was reduced by our ML estimator exploiting multiple observations.
Fuzzy Model of Quality Control of the Finished Software Product

When developing a software product, the testing phase is the most important. Testing takes the most significant time of the entire development, so the automation of the testing process is an urgent task. Within the scope of the application, one can imagine the testing phase as a set of input linguistic variables, namely functional suitability, accuracy, interoperability, compliance, security, where the degree of confidence in the quality of the software product depends on the assessment of each variable. In this paper, you consider a model for checking the quality of a software product after its development. A fuzzy model is developed that was used to model the iterative life cycle of a software product. This approach is universal and can be applied to models of another category, for example, cascading due to a softer repetition of the testing stage. This knowledge and model can be applied in the field of automation of the testing process. This approach will significantly increase the speed of passage of this stage, reduce the influence of the human factor, minimize the possible error and optimize the development process.
A New Quick View Retrieval Method for GF-6 Remote Sensing Images

Abstract As of November 3, 2019, all seven satellites for project of the high-resolution earth observation system have been launched. We have ushered in an era of massive remote sensing data. When we do large-scale forest land mapping, such as national and provincial scale, we need to retrieve images that meet the image quality requirements, are as cloudless as possible, cover the research area, and have as few images as possible. The conventional practice is that the application unit searches the web page provided by the remote sensing data provider for the remote sensing data it needs, and orders to obtain the data. It is often time-consuming and laborious to query data that meet the needs. This paper puts forward a new selection process method of GF-6, GF-1 and GF-2 satellite data. This is a step-by-step method. First, the cloud coverage, time and area are selected according to the metadatas provided by the data provider to meet the requirements. Then, according to the image quality and cloud amount set by the program, it is selected again. Finally, in this image set, the final selected image is given according to the requirements of maximum coverage of the research area and minimum overlap of the image itself. The method can greatly save the time for manually selecting images and improve the working efficiency. This paper designs a program, which can be used conveniently by users. The paper introduces the idea of program design in detail.
Research on safety measures for secondary maintenance of Intelligent Substation

There are many problems in the secondary maintenance of intelligent substation, such as the long time of checking SCD documents;the unclear mutual influence of operation process interval, etc. Especially in the interval expansion, the above problems are obvious. This paper uses graph signal processing technology to decompose station information monitoring model into SCD static configuration model and dynamic timing model. For the static model, machine learning is used to automatically compare the incremental point table to realize the automatic maintenance function of the station endpoint table;for the dynamic time series model, a prefabricated rule safety measure sequence reference database is established, and self-learning fuzzy algorithm is used to realize the self-learning of safety measure template. Finally, the paper builds an online monitoring platform for safety measures, uses the technology of state perception and real-time scene monitoring to dynamically track the implementation of safety measures and prevent misoperation. The effectiveness and practicability of the proposed safety measures for secondary maintenance are proved by engineering examples and applications.
Bayesian-Network Time-Series Analysis Based on Employee’s Sales Skills

It is important to provide services to suit individual characteristic. However, it is difficult to improve productivity in the service sector because the value of a service is determined largely by the situation or the subjective perception of the customer, and the quality of service depends on the experience and institution of the service provider. This study analysed and examined sale strategies pertaining to what product categories should be presented next in what month to the customers who made a purchase in a particular product category in a particular month, by creating Bayesian network time-series models of customer purchases by considering employees' sales skills. In addition, we created Bayesian network models for presenting products or services to suit values and needs of customers based on the questionnaire survey.
Adversarial Models for Deterministic Finite Automata

We investigate a finer-grained understanding of the characteristics of particular deterministic finite automata (DFA). Specifically, we study and identify the transitions of a DFA that are more important for maintaining the correctness of the underlying regular language associated with this DFA. To estimate transition importance, we develop an approach that is similar to the approach widely used to expose the vulnerability of neural networks with the adversarial example problem. In our approach, we propose an adversarial model that reveals the sensitive transitions embedded in a DFA. In addition, we find for a DFA its critical patterns where a pattern is a substring that can be taken as the signature of this DFA. Our defined patterns can be implemented as synchronizing words, which represent the passages from different states to the absorbing state of a DFA. Finally, we validate our study through empirical evaluations by showing that our proposed algorithms can effectively identify important transitions and critical patterns. To our knowledge, this is some of the first work to explore adversarial models for DFAs and is important due to the wide use of DFAs in cyberphysical systems.
Development of a Health Charting Application using a Human Centered Design Approach

This paper describes a work in progress of a collaborative effort between Shinshu University and a local nursing home to find a solution for simplifying the health charting activities which need to be done on a daily basis for tens of residents using a limited number of staff. We adopted an iterative development approach that involved all stakeholders from the beginning stages of work for two purposes: 1) to take into consideration the needs of the current nursing home staff members who include non-digital native users and future staff who might include non-native language users in the nursing home environment and 2) to give the student designers and developers an opportunity to experience the different aspects of problem solving which arise in human centered design.
The Effect of Development and Application Music Programs Using Arts PROPEL on Musical Interest, Self-Efficacy, and Music Cognition of Middle school students

본 연구는 우리나라 중등학교 음악과 교육과정에서 아츠 프로펠을 활용한 음악과 프로그램을 개발 및 적용하여 이에 대한 효과성을 목적으로 둔다. 실제 음악교육현장에서 발생하는 문제들과 한계점을 해결하고자 한 실행연구로써 혼합연구방법으로 설계하여 개발 된 프로그램 적용 유무에 따라 통제반과 실험반으로 나누어 음악적 흥미도, 자기효능감, 음악인지능력의 사전·사후 검사하였으며, 양적연구 대상자의 동일 학생들을 면밀하게 참여관찰하고 16명의 학생들을 선정하여 심층면담 하였으며 학습 과정을 담은 프로세스-폴리오를 분석하였다. 양적데이터 분석 결과 자기효능감, 음악인지능력 향상에 실험반과 통제반의 유의한 차이가 검증되었다. 질적 연구 분석 결과 학생들은 모둠원 간 소통하며 학습 참여도가 향상되고 학습에 대한 자신감이 신장되어 음악적 자기효능감이 향상되는 것으로 나타났다. 학생들은 전문음악인처럼 인지적으로 사고하여 음악 과제를 해결하려는 학습 태도 변화가 나타났으며, 의존도가 높은 통제반에 비하여 실험반은 독립적으로 자유롭게 창작곡을 제작하였다. 본 연구를 통해 우리나라 정규수업의 음악과 교육과정에서 아츠 프로펠을 적용한 음악과 프로그램이 긍정적인 가능성이 있다는 것을 보았으며, 학습 수행의 접근 방법에 따른 학생들 능력 향상에 차이가 나는 것을 알 수 있었다. 본 연구가 학생들의 음악적 성장에 도움이 되길 희망하며, 더 나아가 장기적이고 지속적인 실행연구가 이루어지길 바란다.The purpose of study is the effectiveness of music program applied with Arts PROPEL by developing and carrying out it in music curriculum in secondary education in national level, in order to pursue music education appropriate to this era. A mixed method was chosen for this purpose, and quantitative and qualitative research analysis was comprehensively interpreted to draw conclusions as follows. First, Arts PROPEL consisting of various musical activities in the areas of singing, playing instrument, listening, and creating music for 3rd graders in middle school considering the curriculum in national level was developed through the processes of domain projects focusing on production, perception, and reflection. Second, it was verified that Arts PROPEL music program had significant differences between the experiment group and the control group for improving self-efficacy and music cognition ability. Third, as a result of the analysis of the participatory observing note, interview materials, and process-folio based on the evaluation criteria and rubric, the students improved their participation in music learning and increased their interest in music as they communicated among the members and planned how to study. Through this study, it was confirmed that there would be positive possibilities of Arts PROPEL in regular music curriculum in Korea, and that there were differences of developing talents of students depending on learning methods. It is expected that the study will be effective method for students musical development, and furthermore, it is hoped that long-term and practical studies will continue.
Adaptive Cubature Kalman filter for Bearing only Tracking with non-additive sensor noise

This paper presents a nonlinear estimation algorithm entitled adaptive Cubature Kalman filter for Bearing only Tracking problem in presence of non-additive sensor noise. The proposed nonlinear sigma point filter also incorporates adaptation algorithm with which it can take care of the critical situations where the covariance of non-additive sensor noise remains unknown. The adaptation algorithm is designed for automatic tuning of the unknown sensor noise covariance to ensure satisfactory estimation performance of the filter. From Monte Carlo simulation superiority of the proposed filter has been demonstrated over its non-adaptive counterpart for the Bearing only Tracking (BOT) problem and indicates its suitability for on board implementation.
An Investigation on Ability of Pre-trained Convolutional Neural Networks Trained on ImageNet to Classify Melanoma Images Without Re-training

Deep learning, particularly with Convolutional Neural Network based implementations for medical diagnostics using images is widely acclaimed for assisting doctors. Medical image processing serves as a second opinion for doctors particularly for diseases like Meleroma. Several deep learning paradigms have proved their ability and advantages interns of reducing the training time which is crucial for medical image processing. Using a pre-trained deep architecture is always advantages and one such successful and widely used CNN based deep learning architecture is ResNet50.This paper tries to explore the ability of ResNet for classification of Melodrama images to articulate the possibility using pre-trained deep architectures in healthcare decision making systems. The dataset used in this paper is provided by International Skin Imaging Collaboration, as part of ISIC challenge 2019.Experiments are performed using pre-trained ResNet50 with and without retraining and using multiple sets of test data with different sample sizes. The experiment results shows that by a simple retraining with a small number of samples, the classification accuracies can be improved which is 13.41% for the set of experiment conducted using a sample size of 6000 images. Further, it is noteworthy to observe that ResNet was able to provide a classification accuracy of 61.39% without any re-training.
High-speed stereo imaging for the characterization of anisotropic viscoelastic media

This thesis proposes an experimental method for observing and characterizing the viscoelastic properties of anisotropic media using high-speed white light stereo imaging. The method uses short-time ...