Reviewerly/exHarmony huggingface dataset:
Non-central panorama indoor dataset

Omnidirectional images are one of the main sources of information for learning-based scene understanding algorithms. However, annotated datasets of omnidirectional images cannot keep the pace of these learning-based algorithms development. Among the different panoramas and in contrast to standard central ones, non-central panoramas provide geometrical information in the distortion of the image from which we can retrieve 3D information of the environment. However, due to the lack of commercial non-central devices, up until now there was no dataset of these kind of panoramas. In this data paper, we present the first dataset of non-central panoramas for indoor scene understanding. The dataset is composed of 2574 RGB non-central panoramas taken in around 650 different rooms. Each panorama has associated a depth map and annotations to obtain the layout of the room from the image as a structural edge map, list of corners in the image, the 3D corners of the room and the camera pose. The images are taken from photorealistic virtual environments and pixel-wise automatically annotated.
Learning by Interpreting

This paper introduces a novel way of enhancing NLP prediction accuracy by incorporating model interpretation insights. Conventional efforts often focus on balancing the trade-offs between accuracy and interpretability, for instance, sacrificing model performance to increase the explainability. Here, we take a unique approach and show that model interpretation can ultimately help improve NLP quality. Specifically, we employ our learned interpretability results using attention mechanisms, LIME, and SHAP to train our model. We demonstrate a significant increase in accuracy of up to +3.4 BLEU points on NMT and up to +4.8 points on GLUE tasks, verifying our hypothesis that it is possible to achieve better model learning by incorporating model interpretation knowledge.
Forecasting of depth and ego-motion with transformers and self-supervision

This paper addresses the problem of end-to-end self-supervised forecasting of depth and ego motion. Given a sequence of raw images, the aim is to forecast both the geometry and ego-motion using a self supervised photometric loss. The architecture is designed using both convolution and transformer modules. This leverages the benefits of both modules: Inductive bias of CNN, and the multi-head attention of transformers, thus enabling a rich spatio-temporal representation that enables accurate depth forecasting. Prior work attempts to solve this problem using multi-modal input/output with supervised ground-truth data which is not practical since a large annotated dataset is required. Alternatively to prior methods, this paper forecasts depth and ego motion using only self-supervised raw images as input. The approach performs significantly well on the KITTI dataset benchmark with several performance criteria being even comparable to prior non-forecasting self-supervised monocular depth inference methods.
Pedagogical conditions for the development of the technical creativity of students in college

The article tells about the results of an investigation of the problem of the development of technical creativity of college students as a significant part of modern life. Particularly the pedagogical conditions which determine the effectiveness of such development are scientifically proved, the experimental base of the investigation on which they have been tested is given, guidances are given on their use in the broad educational practice of the domestic vocational education.
The way we cite: common metadata used across disciplines for defining  bibliographic references

Current citation practices observed in articles are very noisy, confusing, and not standardised, making identifying the cited works problematic for hu-mans and any reference extraction software. In this work, we want to investigate such citation practices for referencing different types of entities and, in particular, to understand the most used metadata in bibliographic refer-ences. We identified 36 types of cited entities (the most cited ones were articles, books, and proceeding papers) within the 34,140 bibliographic references extracted from a vast set of journal articles on 27 different subject ar-eas. The analysis of such bibliographic references, grouped by the particular type of cited entities, enabled us to highlight the most used metadata for de-fining bibliographic references across the subject areas. However, we also noticed that, in some cases, bibliographic references did not provide the essential elements to identify the work they refer to easily.
Usability of the super-vowel for gaze-based text entry

We tested experimentally the idea of reducing the number of buttons in the gaze-based text entry system by replacing all vowels with a single diamond character, which we call super-vowel. It is inspired by historical optimizations of the written language, like Abjar. This way, the number of items on the screen was reduced, simplifying text input and allowing to make the buttons larger. However, the modification can also be a distractor that increases the number of errors. As a result of an experiment on 29 people, it turned out that in the case of non-standard methods of entering text, the modification slightly increases the speed of entering the text and reduces the number of errors. However, this does not apply to the standard keyboard, a direct transformation of physical computer keyboards with a Qwerty button layout.
APLIKASI PERINGKASAN DOKUMEN MENGGUNAKAN METODE MAXIMUM MARGINAL RELEVANCE (MMR)

making a summary application to help readers who do not like to read long and thick news articles which take a relatively long time and can cause readers to be lazy to read the news articles. This summary application is used to summarize news articles, in making the application using ASP.Net. In this summary, the Maximum Marginal Relevance (MMR) method is used. In this study, you can use articles on the website, and do it. Articles are processed in the form of a file (single document) with a txt extension. The summary process goes through the preprocessing stage, which consists of sentence segmentation, case folding, tokenizing, filtering, stemming.
Analysis of web data classification methods based on semantic similarity measure

In this survey, 60 research papers are reviewed based on various web data classification techniques, which are used for effective classification of web data and measuring the semantic relatedness between the two words. The web data classification techniques are classified into three types, such as semantic-based approach, search engine-based approach, and WordNet-based approach, and the research issues and challenges confronted by the existing techniques are reported in this survey. Moreover, the analysis is carried out based on the research works using the categorized web data classification techniques, dataset, and evaluation metrics are carried out. From the analysis, it is clear that semantic-based approach is the widely used techniques in the classification of web data. Similarly, Miller-Charles dataset is the most commonly used dataset in most of the research papers, and the evaluation metrics, like precision, recall, and F-measure are widely utilized in web data classification. The insights from this manuscript can be utilized to understand various research gaps and problems in this area. Those can be considered in the future by developing novel optimization algorithms, which might enhance the performance of web data classifications.
A Framework on Online Reviews Ranking Based on Set Theory for Mining Using Automated Pipeline

With the rapid rise of e-commerce, a big number of things are being sold online, and a growing number of people are making purchases online. Users can get valuable information from online reviews before purchasing a product or making a purchase. We investigate the peculiarities of their behaviour based on their early reviews. In this study, customer feedback linked with various products is collected from several online shopping websites in order to forecast product ratings based on user feedback utilising opinion mining. We classified the product's lifespan into three segments at first (Early, majority and laggards). A person who posts a review at an early stage is considered to be an early responder to the product. The product reviews are analysed using machine learning techniques. They give comments, and products are subsequently recommended for purchase and sale based on that factor. Users can provide product reviews on popular e-commerce platforms like Flipkart, Myntra, Amazon, and many others. To purchase a product, the consumer will investigate to gain a deeper grasp of the product and how it works. The interpretation will be a very straightforward product with inferior, superior, and neutral product checks. This experiment is carried out using machine learning techniques. Sentiment Analysis is a type of market research in which customers are aware of their reaction to a product. Individual decision-makers, businesses, and governments can all benefit from the awareness of feeling.
A STUDY ON IMPACT OF SKILLS AND TRAINING ON EMPLOYEE PROFICIENCY

An employee proficiency uses standardized testing to measure a potential employee's skill set and knowledge within a specialized area. For example, a business might test an employee's language skills for a position that involves writing, or spreadsheet skills for a bookkeeping position. These aptitude tests can be given prior to employment but also at certain points in an employee's time with the organization, such as during a review. There are many good reasons to use employee proficiency testing when evaluating potential job candidates. Proficiency can weed out the candidates who don't possess the knowledge or skills necessary to meet the job requirement, which in turn saves the company the frustration of hiring someone unable to do the job. Proficiency tests also help identify a candidate's strengths and weaknesses and help determine if additional training may be needed or if there is another position within the organization they may be better suited for. The way in which the company conducts the proficiency exam usually depends on the skills it seeks.
Feature selection, feature reassembly, and the role of Universal Grammar: The acquisition of <i>wh</i>-questions by Japanese and Chinese learners of English

This dissertation investigates second-language (L2) acquisition of wh-questions by Japanese- and Chinese-speaking learners of English and attempts to specify the role of Universal Grammar (UG) in L2 acquisition.
The Effectiveness of Get Skado App (Gadget Srabut Kelapo Dorong) to Increase Traffic Knowledge In Children

Increasing knowledge about traffic is one solution to reduce the number of traffic accidents, especially for children. This research aimed to figure to examine the effectiveness of Get Skado App to increase traffic knowledge in children. The method used was a quasi-experiment with a pre-post control group design. The population of this research consists of 18 kindergarten students which were split into an experimental group and a controlled group. The experimental group was given Get Skado treatment. To measure the result, there was a traffic knowledge scale used in the pretest and posttest. The result was analyzed by using an independent t-test sample and paired t-test sample. The result of the research showed that there was significant traffic knowledge between pretest and post-test scores within the experimental group. Meanwhile, within the controlled group, no significant traffic knowledge was found. It can be concluded that Get Skado app can increase the traffic knowledge in children. Keywords: trafficÂ knowledge, Children, get skado
Secured and Adaptive Space-Time Two-Path OFDM Systems

In this paper, a secured 2x2 space-time (ST) orthogonal frequency division multiplexing (OFDM) system is proposed with parallel cancellation (PC) two-path communication scheme to form a secured 2x2 STPC system in communication networks. These 2x2 STPC-OFDM systems not only secured but also mitigate the inter-carrier interference (ICI) in the mobile channels with a better bit error rate (BER) than that of a conventional 2x2 ST-OFDM system. The design is intelligently adaptive between two secured operation modes: either high data rate with ST-OFDM or high reliability with STPC in mobile fading channels having lower bit error rate (BER). The security is obtained via precoders of the transmitter on each path since the receiver (or users) must know a prior and perform the reversed operation. The intelligent decision of switching is obtained via channel estimation. It offers higher diversity order and better BER performance. Two orthogonal transforms, such as Zadoff-Chu transform (ZCT) and Walsh-Hadamard transform (WHT), are used as the precoder for security and simultaneously enhance the BER further. Although the proposed STPC-OFDM systems require two-path communication with the cost of reducing bandwidth efficiency, simulations demonstrate that the proposed 2x2 secured STPC-OFDM systems are robust to various channel environments. Moreover, by employing a larger signal alphabet size, it regains the bandwidth efficiency and has a better BER performance for fair data rate comparison with 2x2 ST-OFDM.
Goodness-of-fit graphical assessment for a broad family of unimodal distributions

A Skewed Exponential Power Distribution is used to construct a bidimensional array of graphics in which kurtosis varies when moving horizontally and skewness when moving vertically. The Normal distribution lies in the center of the mosaic. Given a sample, the null hypotheses that it comes from each distribution in the mosaic are tested and the cell of each distribution is filled with a color that is a function of the obtained p-value. In this way, a dark area is obtained on the mosaic to indicate the distributions with which the sample is compatible.
Website for Fully Customised Pizza Ordering System

The online Pizza delivery system is the need of hour because of the recent changes and the adding use of the internet. BIG SLICE is a website designed primarily for use in the customised Pizza delivery. Through these services caffs can vend and distribute their coffers at minimum resource operation effectively with high gains by gaining the client trust. This Online Pizza order system database will be helpful for the business possessors to extend their business just by placing the orders online and not visiting the restaurant. To create this application, the database is the most important component, which will interface with the application to retrieve information. The Online Pizza Ordering Database will be built using PHP as the basis. Customers can place orders from a variety of pizzas in the database, and restaurant employees will process and deliver the specified order.
Using The Quizizz as an Assessment of Students' English Learning

Education is becoming more advanced with increasingly sophisticated technology. The use of education-based ICT has evolved since the mid-twentieth century promoting e-learning as an alternative to facilitating student learning. The purpose of this study is to explore students' views on using the Quizizz application as an assessment tool for students' English learning. The design of this research is a qualitative approach. The sample of this research is 3 students of SMKN 1 Pakisjaya. Data was collected through observation, interviews and documentation. The results of the study showed students' perspectives on the use of Quizizz. In conclusion, the students strongly agree that the Quizizz application is a medium for learning and assessing English because this application has an attractive appearance.Keywords: Assessment, Quizizz, English Learning
Multi-Secret Image Sharing Scheme by Boolean Operations

Multi-secret image sharing technology shares multiple secret images among participants. The proposed (k, n, m) multi-secret image sharing scheme shares m secret images among n participants and gathering k participants' shared images perfectly recovers these m secret images. The usage of sharing matrix based strategy acquires perfect reconstruction of the secret images under (k, n) thresholds. The proposed source-random- separate (SRS) method exhibits good sharing on multiple secret images. Experimental results show that the proposed scheme performs well on sharing efficiency and sharing security.
A Comparative Study on Single-handed Keyboards on Large-screen Mobile Devices

Many questions regarding single-hand text entry on modern smartphones (in particular, large-screen smartphones) remain under-explored, such as, (i) will the existing prevailing single-handed keyboards fit for large-screen smartphone users? and (ii) will individual-customization improve single-handed keyboard performance? In this paper we study single-handed typing behaviors on several representative keyboards on large-screen mobile devices. We found that, (i) the user-adaptable-shape curved keyboard performs best among all the studied keyboards; (ii) users’ familiarity with the Qwerty layout plays a significant role at the beginning, but after several sessions of training, the user-adaptable curved keyboard can have the best learning curve and performs best; (iii) generally the statistical decoding algorithms via spatial and language models can well handle the input noise from single-handed typing.
ANALISIS KEPUASAN PENGGUNAAN APLIKASI SHOPEE MENGGUNAKAN ALGORITMA NAÏVE BAYES

The Covid-19 pandemic that attacks the world's health conditions, including Indonesia, greatly affects daily life. The impacts include the need to maintain distance and avoid crowds, which forces people to reduce interactions between people. Therefore, people need other ways to be able to meet their daily needs. One alternative that can be done is to use e-commerce applications to conduct online transactions or also known as online shopping. It was reported on September 30, 2021 that shopee occupies the most superior e-commerce in Indonesia with the number of monthly visitors which currently reaches 93 million subscribers, followed by Tokopedia with 86 million subscribers and in third place with 35 million customers occupied by Bukalapak. Therefore, an analysis of satisfaction with the use of e-commerce applications with the Shopee case study will be carried out to determine the response from users to the applications that are declared the most desirable. The application will be made based on a website to make it easier to use and use the Naïve Bayes algorithm as the analysis method. The Naïve Bayes algorithm is one method that can classify data which in this case are comments from application users into three types of comments, namely positive, negative and neutral. The process that is run also includes a preprocessing task as raw data processing into ready-to-use data and a confusion matrix as a calculation of the accuracy of the resulting application. Testing of the application is carried out in two ways, namely using a confusion matrix using a system with 80% results and a user acceptance test with 94.8% results, namely very good predicate. From the analysis, it can be concluded that Shopee users are satisfied with the application used and the classification of user reviews using the Naïve Bayes Algorithm produces a fairly good accuracy.
Information System of Muara Badak Village Culinary Sales Using Laravel Web-Based

The world of technology is developing very fast. In line with this, the fast-growing business growth and development is driven by various supporting factors including promotions and advertising boards. As one of the villages, Muara Badak Village has many tourist attractions to visit this village. One of the biggest attractions is the culinary field and beach tourism, so there are so many culinary places that are very diverse. Therefore, the purpose of this research is to utilize existing knowledge in college to build an application, namely information media that provides sales services through culinary applications by looking at newcomers and tourists who do not understand the culinary specialties of Muara Rhino Village and view an environment that can be free. from the Covid-19virus through online buying and selling so researchers are trying to make the application. The results of this study are the birth of an application called MB FOOD not only to take advantage of the application but how to make a bridge between consumers and sellers to make it easier to connect people around, by looking at problems and technological developments now how to find ideas that are useful for the community. It is hoped that this research can be used as a reference for the next time and the progress of future technological developments by looking at people's current income so that the government can see people's income and participate in developing the MB FOOD application to help each other and learn together further technological developments.
Using canonical shapes (images) for camera characterization

An accurate prediction of the number of pixels on a target is critical in modeling the performance of a cameras ability to perform a task. This requires an accurate knowledge of the angle subtended by a pixel of interest, which can be calculated from a specification sheet or lens prescription. When such information is not available, it can be retrieved through a measurement of a known sized target at a known distance. In this correspondence, we utilize canonical images (ideal simple functions) together with non-linear optimization to provide sub-pixel target localization. This allows for accurate and repeatable measurement of the angular sampling of a camera. Additionally, the use of well-defined shapes and accurate location determination can be used to determine the blur, rotation, motion, contrast, distortion, and other camera metrics.
MOCCA: A Process Variation Tolerant Systolic DNN Accelerator using CNFETs in Monolithic 3D

Hardware accelerators based on systolic arrays have become the dominant method for efficient processing of deep neural networks (DNNs). Although such designs provide significant performance improvement compared to its contemporary CPUs or GPUs, their power efficiency and area efficiency are greatly limited by the large computing array and on-chip memory. In this work, we demonstrate that we can further improve the efficiency of systolic accelerators using emerging carbon nanotube field-effect transistors (CNFETs) by stacking the computing logic and on-chip memory on multiple layers and utilizing monolithic 3D (M3D) vias for low-latency communication. We comprehensively explore the design space and present MOCCA, the first process variation tolerable CNFET-based systolic DNN accelerator. We validate MOCCA against previous 2D accelerators on state-of-the-arts DNN models. On average, MOCCA achieves the same throughput with 6.12× and 2.12× improvement respectively on performance and power efficiency in a 2× reduced chip footprint.