Reviewerly/exHarmony huggingface dataset:
5 The Role of the Ministry of Public Works in Designing Engineering Education in Meiji Japan: Reconsidering the Foundation of the Imperial College of Engineering (Kobu-dai-gakko)

5 The Role of the Ministry of Public Works in Designing Engineering Education in Meiji Japan: Reconsidering the Foundation of the Imperial College of Engineering (Kobu-dai-gakko) was published in Accessing Technical Education in Modern Japan on page 88.
Tech for Facilitating Creativity with Small Ensembles

This chapter begins with an overview of the gear needed to run a live show of any variety of small group from rock bands, to cyphers, virtual instrument bands, or jazz bands. Troubleshooting issues that commonly occur during the use of sound systems are given attention. Cables and microphones are compared and discussed. How to approach creativity using technology with a variety of ensemble types is explored with a particular focus on how to get a jam session going with a popular music ensemble. Soundpainting is explored as a possibility for creating authentic jam-like sessions for orchestras or bands when using technology.
Realizing number recognition with simulated quantum semi-restricted Boltzmann machine

Abstract Quantum machine learning based on quantum algorithms may achieve an exponential speedup over classical algorithms in dealing with some problems such as clustering. In this paper, we use the method of training the lower bound of the average log likelihood function on the quantum Boltzmann machine (QBM) to recognize the handwritten number datasets and compare the training results with classical models. We find that, when the QBM is semi-restricted, the training results get better with fewer computing resources. This shows that it is necessary to design a targeted algorithm to speed up computation and save resources.
Perancangan System Crawler dengan MenerapkanArsitektur Distributed Task

PDC Media Group is a company engaged in online trading. The need for data insight in the online marketplace is very important. Likewise, how to get quite a lot of data, of course, requires automation such as crawling data on the marketplace website. Due to the large amount of data, crawler systems are often not optimal in crawling data. The application of distributed tasks on the crawler system provides convenience in scaling the server both vertically and horizontally. Therefore, the large and growing data can be handled by the crawler system. The application was developed with the Python language, with the application server using Google Cloud Computing. In a distributed task architecture requires a component in the form of a message broker. The message broker used in designing this system is RabbitMQ. Testing the crawler system uses 3 scenarios, namely with 1 worker, 2worker, and 3 worker. The results for the 1 worker scenario are 19.3 requests per second and 332 ms for response time. The results for the 2 worker scenario are 41.4 requests per second and 328 ms for response time. While the results for the 3 worker scenario are 60 requests per second and 331 ms for response time.
Black-Box Tuning for Language-Model-as-a-Service

Extremely large pre-trained language models (PTMs) such as GPT-3 are usually released as a service. It allows users to design task-specific prompts to query the PTMs through some black-box APIs. In such a scenario, which we call Language-Model-as-a-Service (LMaaS), the gradients of PTMs are usually unavailable. Can we optimize the task prompts by only accessing the model inference APIs? This paper proposes the black-box tuning framework to optimize the continuous prompt prepended to the input text via derivative-free optimization. Instead of optimizing in the original high-dimensional prompt space, which is intractable for traditional derivative-free optimization, we perform optimization in a randomly generated subspace due to the low intrinsic dimensionality of large PTMs. The experimental results show that the black-box tuning with RoBERTa on a few labeled samples not only significantly outperforms manual prompt and GPT-3's in-context learning, but also surpasses the gradient-based counterparts, i.e., prompt tuning and full model tuning.
Large Neural Networks Learning from Scratch with Very Few Data and  without Regularization

Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes up to 95% accuracy with only 20 samples per class.
An Efficient Technique for Passive Image Forgery Detection Using Computational Intelligence

This chapter proposes a scheme for the identification of copy-move forgery by inducing adaptive over-segmentation and matching of feature points with the help of discrete cosine transform (DCT). Copy-move forging is an image tampering technique that involves concealing undesired things or recreating desirable elements within the same image to create modified tampered images. Traditional methods added a large number of false matches. To conquer this problem, a new algorithm is proposed to incorporate an adaptive threshold method. So, the block feature matching mechanism is used, and the matching feature blocks classify the feature points using patch matching and Hough transform. Forged regions are detected with the help of the newly proposed algorithm. The results of the proposed method show that it can substantially reduce the number of false matches that lead to improvements in both performance and computational costs. This demonstrates the suggested algorithm's resistance against a variety of known attacks. Comparative results are presented for a better evaluation.
Recognition method of container number in complex scene based on improved EAST network

With the rapid development of global shipping industry, intelligent recognition of container number has become a hot spot of application. However, the containers are usually outdoors, which often can't reach the normal recognition environment of characters. For complex scenes such as corrosion, tilt, blur and uneven characters, the traditional text recognition methods are not ideal due to high time-consuming and multi-stage reasons. In this paper, a new method for recognition of container number of port is proposed. Based on the deep learning algorithm, the multi-layer neural network is used to identify features of multiple dimensions, enhancing the algorithm's ability to identify container number in complex scene. The method consists of four parts, the original container image is processed by the improved EAST network, AddMargin, CRNN network and calculation operation of check digit, the accurate container number can be obtained as the result. Through experiments, 3961 samples of container images were tested, and the accurate rate of recognition of container number could reach 94.14% (3729/3961), and the detection speed was 1.20 seconds per image.
ASSESSING A SCHEMATA-DESIGNED READING MATERIAL IN AN EFL CLASS: STUDENTS’ NEEDS AND TEACHER EDUCATORS’ VOICE

This paper aims at assessing students’ needs of designed reading material for senior high school in Indonesia in improving their reading skills using schemata-based efl reading material. Designed in design-based research, this study involved EFL students and teacher educators as participants who recruited to participate voluntarily in this study. In designing the schemata-designed reading materials, the reading learning materials were revised based on the experts’ suggestions. The learning materials consist of three lessons. The first topic is about mammals. The second topic is about technology and the last is about landmarks. All of the lessons are about factual report text. The result reveals that students found their reading materials need to upgraded and teacher educators as experts’ judgment shows the designed-based reading material is categorized good.
ACM -- Attribute Conditioning for Abstractive Multi Document Summarization

Abstractive multi document summarization has evolved as a task through the basic sequence to sequence approaches to transformer and graph based techniques. Each of these approaches has primarily focused on the issues of multi document information synthesis and attention based approaches to extract salient information. A challenge that arises with multi document summarization which is not prevalent in single document summarization is the need to effectively summarize multiple documents that might have conflicting polarity, sentiment or subjective information about a given topic. In this paper we propose ACM, attribute conditioned multi document summarization,a model that incorporates attribute conditioning modules in order to decouple conflicting information by conditioning for a certain attribute in the output summary. This approach shows strong gains in ROUGE score over baseline multi document summarization approaches and shows gains in fluency, informativeness and reduction in repetitiveness as shown through a human annotation analysis study.
Content Based Image Retrieval Using Artificial Neural Network

Digital Image Processing domain is constantly contributing to make digital era more concise. Specifically Image Retrieval is the trending research area now-a-days. The main problem is, with the increasing volume of digital data the complexity of searching a specific image and retrieving the particular data associated with it is increasing. In recent years the content based image retrieval system has been developing at enormous speed. This paper color , gray level co-occurrence and histogram features are obtain better retrieval efficiency from large database using these feature vectors near about similarly matched images are retrieved. We have retrieved using Euclidian distance and ANN, among of both ANN Shows better result compare to Euclidian Distance based image retrieval.
Know Your Neighbor: Physically Locating Xeon Processor Cores on the Core Tile Grid

The physical locations of the processor cores in multi-or many-core CPUs are often hidden from the users. The current generation Intel Xeon CPUs accommodate many processor cores on a tile grid, but the exact locations of the individual cores are not plainly visible. We present a methodology for physically locating the cores in the Intel Xeon CPUs. Using the method, we collect core location samples of 300 CPU instances deployed in a commercial cloud platform, which reveal a wide variety of core map patterns. The locations of the individual processor cores are not contiguously mapped, and the mapping pattern can be different per each CPU instance. We also demonstrate that an attacker can exploit an inter-core thermal covert channel using the identified core locations. The attacker can increase the channel capacity by strategically placing multiple sender and receiver nodes. Our evaluation shows that up to 15 bps of data transfer is possible with less than 1% of bit error rate on a cloud environment, which is 3 times higher than the previously reported results.
Whiteboard

Meetings and classrooms demand the presence of the person in the room if he wants to participate. A tool widely used in such places is a whiteboard. A whiteboard has limited creativity and interactivity options and hence it needs to be replaced with better tools. In this project we develop a web application to increase collaboration without restricting him to any location, operating system platform or device. Our application allow user to interact and share information. Users only need access to web browsers and internet to make use of the application. We have implemented real-time collaborative drawing whiteboard by using HTML5, DOM, JavaScript, CSS, Canvas.
A Flexible Teaching Model with Digital Transformation Competences for Structural Engineering Courses

The main objective of this paper is to present the best practices, according to the perception of students, applied during the lock down terms as well as introducing a model that can be applied after the pandemic when students return to campuses gradually, especially in countries like ours where still not all students are on campus as of fall of 2021. In some countries the vaccination efforts are slow and universities will continue to rely on online teaching if not for all, for some courses. This model adapts for either full face to face classes or fully online or hybrid classes. Results show that there are important lessons or things that the lock-down left to universities and educators, and as students confirm their will to continue to have some of their courses in the online format, universities should consider not closing the option of online teaching. The model presented in this paper also takes into account students&#x2019; comments and recommendations about the future implementations of online courses and what they would like to see in them, even if they are going to be on campus.
Smart Door with Biometric Face Recognition and Thermal Screening

Since the outbreak of covid-19 it has become truly challenging to recognize the individuals who visit us at our home, offices, educational instituitions are influenced by the infection or not during this COVID19 pandemic. To tackle this issue as of now, temperature gadgets are regularly used. But these gadgets are not non-contact. With the development of artificial intelligence and computer vision, face recognition has become a hot topic of pattern recognition. While recognizing any individual, the most important attribute is face. It serves as an individual identity of everyone and therefore face recognition helps in authenticating any person’s identity using his personal characteristics. The whole procedure for authenticating any face data is sub-divided into two phases, in the first phase, the face detection is done quickly except for those cases in which the object is placed quite far, followed by this the second phase is initiated in which the face is recognized as an individual. Then the whole process is repeated thereby helping in developing a face recognition model which is considered to be one of the most extremely deliberated biometric technology. Our projects presents a conceptual model for providing entry access to an individual after face recognition and thermal screening. We will interface an IR temperature sensor to detect the temperature of an individual along with face detection to grant entry access. The proposed conceptual model can be helpful at every individual house, hospital to detect the temperature of the in-pateint and at institutions.
Model-Based 6D Visual Object Tracking with Impact Collision Models

3D Object tracking is an essential technique in computer vision and has many fields of application. In this study, the focus lies on tracking objects impacting the environment. We show that state-of-the-art methods lose track of objects in this context and we investigate how to overcome this problem by adding prior information regarding the object and surface where collision is expected to occur. For illustration purposes and application relevance, we focus on the case of a box impacting a surface, which is, e.g., encountered in robot tossing in logistics applications. We model the effects of impacts and friction in a motion model, and consider the state of the box to evolve in a Lie group. We present an object tracking algorithm, based on an Unscented Particle Filter, for systems whose state lives in a Lie group and incorporate this motion model. The observations are taken from a single RGB camera and make use of the known 3D model of the object and color characteristics to predict its appearance in the 2D image. We quantitatively evaluate the effectiveness of our proposed methods by means of simulations on synthetic images.
Geochemical and Hydrogeochemical Characterization of Limestone Deposits of Okeluse Area, Southwestern Nigeria

The geochemical and hydrogeochemical studies were carried out to determine the suitability of Okeluse limestone for cement production and the potability of natural waters in that area. Five limestone samples were collected and subjected to X-Ray fluorescence analysis to determine major oxides and their percentages. Six water samples were collected and were tested for pH, temperature, turbidity, electrical conductivity, TDS, TSS, total hardness, calcium hardness as CaCO3, Ca2+, Mg2+, Na+, K+, Cl-, HCO32-, SO42-, PO43-, NO3-, Fe3+, Pb2+, Cr6+, Mn2+, Cd2+, Ni3+, Zn2+, Cu2+, As2+, and Co2+. Results for major oxides revealed that CaO content ranged from 46.46% - 52.22%, SiO2 from 1.52% - 5.1%. MgO, Fe2O3, and Al2O3 contents were 1.564%, 2.408% and 0.968%, respectively, while MnO, P2O5, K2O, Na2O and SO3 are very low in concentration. CaO shows an inverse relation to SiO2 and MgO, indicating a high degree of purity of the limestone. The limestone here is the magnesian type which is suitable for cement production. Hydrogeochemistry showed that the dissolved constituents originated from the weathering of rocks. The hydrogeochemical facies revealed Na-Cl and K-H-CO3 wat types. Water samples were contaminated by K+, Fe2+ and Cd2+; therefore, water is not potable. It has to be treated before use.
The Improvement of Students' Speaking Skill Through Storytelling

This research was conducted in an Islamic private junior high school in Batam and was conducted on 7th grade students with a total of 10 students. This research includes Classroom Action Research (CAR). Started with the problem faced the student such as lack of vocabulary, lack the proper motivation to study and practice speaking, not confident to speak. Responding to the problems, the right method of learning to speak English should be how to train memory, pronunciation, and hearing simultaneously, namely storytelling. Before the storytelling method was applied, students were given a pre-test to get the average grade for the class. After that, the average value of the class that has been obtained will be measured by the rubric. The total students’ average was 2.2 (poor). After looking at the results of the pre-test, researchers saw that with practice conversation, speaking skills of students can’t develop well. Because of that in the treatment 1, application cycle 1 and also post-test. The post-test results were 2.76. It can be said that the storytelling can help develop students' speaking abilities to improve.
Ranking for Better Indexing in the Hidden Web

WWW is a repository of hyperlinked data sources that contains heterogeneous data in the form of text, audio, video, and metadata. Search engines play an important role in searching information from these data sources. They offer the users an interface to retrieve the data from these resources. The search engines gather, analyze, organize, and handle the data available in these data sources and return thousands of results in the form of web pages. Often the returned web pages include a mixture of relevant and irrelevant information. To return more significant and relevant response pages, a mechanism to rank the returned pages is desirable. Page ranking algorithms play an important role in ranking web pages so that the user could get the relevant result according to the user query. The proposed technique is used to rank the hidden web pages based on the meta information of the pages downloaded by crawler and the calculated value for the chosen parameters.
SYSTEM OF PROPORTIONALITY OF THE SAVIOR TRANSFIGURATION CATHEDRAL IN PERESLAVL- ZALISSKYI

Transfiguration Cathedral in Pereslavl-Zalessky is the oldest white stone church of pre-Mongol Kievan Rus' that survived to this day in its original form. The power of its emotional impact on the viewer was noted by the famous American architect of the XX century Luis Kahn, who travelled around cities of the Golden Ring of Russia in 1973. At the end of this trip, he stated that he was most impressed by the Transfiguration Cathedral.&#x0D; We explored the emotional potential of this unique creation of ancient Russian masters using a distinctive theoretical and information model based on the laws of visual perception. Determining the amount of visual information contained in dimensions of the structure, we found that relations between these dimensions belong to a specific proportional system or sequence of numbers.&#x0D; We have proposed a mechanism for measuring the strength of the proportional relationship of dimensions that could belong to one structural level or several at once.&#x0D; The method of identifying the proportional correlation and strength of proportional relationships is based on determining the information modularity of relations and the values of information modules in the information content of individual relations.&#x0D; During the process of information analysis of the Transfiguration Cathedral, it was found that the vast majority of the ratios belong to the two proportional systems: the golden ratio, with a base of 1,618 and the proportional sequence (progression) with a base of 1,477.&#x0D; According to the findings of the Russian neurophysiologist P.V. Simonov, a member of the Russian Academy of Sciences, the exact numbers were found in relations between the extreme characteristics of the encephalogram of the human brain. These ratios of frequencies, according to Simonov, correspond with the human emotional states of "aesthetic perception" and "creative act". &#x0D; It is a well-known fact that Leonardo da Vinci and his associate Luca Pacioli developed the golden ratio theory in the early XVI century. This makes us wonder how the ratio of 1,618 and its derivatives came to be used in Kievan Rus in the XII century?&#x0D; The construction of temples in Kievan Rus was based mainly on the "time will tell" principle. Keeping that in mind, we suggested that the perfection of the proportional construction of the Transfiguration Cathedral was achieved because of the architects' sharp sensitivity to the visual perception of proportions, supported by various sacred religious practices.&#x0D; The presence of the ratio of "aesthetic perception" and the ratio of "creative act" in the proportional structure of the Transfiguration Cathedral explains the strength of its emotional effect on the famous connoisseur of beauty Louis Kahn.
Research and Analysis on FERN Stack in E-Commerce Web Application Development

With web technology rapidly expands out in recent years, there is a significant trend that Hypertext Markup Language (HTML)5 turns into a worldwide web consortium and leads the front-end development to stand on the front stage of internet history. Whereas data transfer frequency rates increase significantly due to adaptation of dynamic website. However, there are numerous front-end development frameworks and libraries such as React, Angular and Vue. Similarly, in back-end we have MYSQL, MongoDB and Firebase. How to select a suitable framework or library to establish the e-Business and reach out to maximize the user experience becomes a priority operation in full stack web development. This paper starts with introducing an overview of the leading frameworks and libraries in the field of full stack development and examine each performance with respect to FERN stack in web services. How it is implemented in e-Business under separate commercial criteria. Finally, the paper summarizes the contributions and concludes with some possible future of FERN stack development in e-Business.