Reviewerly/exHarmony huggingface dataset:
Study on Microcontroller Board Design Planning for Design Education

Several new design tools have been introduced in design education recently with an emphasis on high-level design applications and a technical viewpoint. For example, when computer-aided design was introduced as a design tool, it was used as a communication tool between the design and technical fields, which efficiently changed the product development process. Therefore, microcontroller-type boards such as Arduino have been recently introduced as design tools to teach electronics. However, they are not actively used because of the difficulty met by design students who lack engineering knowledge. In this study, we designed a microcontroller board operating with an application able to control the input/output (I/O) and produced prototypes. The proposed solution eliminates the difficulties associated with the coding stage and can thus be easily used without any special training. It allows quick prototyping of design ideas during a short design workshop, which can increase awareness regarding prototype usability and interest in acquiring additional knowledge among students. We tested the microcontroller board in an educational process, during which student teams were able to quickly develop their ideas through design workshops, build their designs, and express functions of the final prototypes. Through this process, we were able to confirm the validity and suitability of the proposed solution for short electronics design educational programs that enable students to appreciate the role of design in technical applications.
Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images

We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images captured by wide-baseline cameras under collocated point lighting. We first estimate per-view depth maps using a deep multi-view stereo network; these depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture that is trained to pool features from these coarsely aligned images and predict per-view spatially-varying diffuse albedo, surface normals, specular roughness and specular albedo. We do this by jointly optimizing the latent space of our multi-view reflectance network to minimize the photometric error between images rendered with our predictions and the input images. While previous state-of-the-art methods fail on such sparse acquisition setups, we demonstrate, via extensive experiments on synthetic and real data, that our method produces high-quality reconstructions that can be used to render photorealistic images.
Improved Techniques for Training Single-Image GANs

Recently there has been an interest in the potential of learning generative models from a single image, as opposed to from a large dataset. This task is of practical significance, as it means that generative models can be used in domains where collecting a large dataset is not feasible. However, training a model capable of generating realistic images from only a single sample is a difficult problem. In this work, we conduct a number of experiments to understand the challenges of training these methods and propose some best practices that we found allowed us to generate improved results over previous work in this space. One key piece is that unlike prior single image generation methods, we concurrently train several stages in a sequential multi-stage manner, allowing us to learn models with fewer stages of increasing image resolution. Compared to a recent state of the art baseline, our model is up to six times faster to train, has fewer parameters, and can better capture the global structure of images.
A Study on Multimodal and Interactive Explanations for Visual Question Answering.

Explainability and interpretability of AI models is an essential factor affecting the safety of AI. While various explainable AI (XAI) approaches aim at mitigating the lack of transparency in deep networks, the evidence of the effectiveness of these approaches in improving usability, trust, and understanding of AI systems are still missing. We evaluate multimodal explanations in the setting of a Visual Question Answering (VQA) task, by asking users to predict the response accuracy of a VQA agent with and without explanations. We use between-subjects and within-subjects experiments to probe explanation effectiveness in terms of improving user prediction accuracy, confidence, and reliance, among other factors. The results indicate that the explanations help improve human prediction accuracy, especially in trials when the VQA system's answer is inaccurate. Furthermore, we introduce active attention, a novel method for evaluating causal attentional effects through intervention by editing attention maps. User explanation ratings are strongly correlated with human prediction accuracy and suggest the efficacy of these explanations in human-machine AI collaboration tasks.
Privacy Protection Technology in the Age of A.I.

Abstract In the era of artificial intelligence, no user has ever disclosed personal privacy data to a certain party. The current AI software can fully integrate the fragment information of a user on different site platforms, and finally complete the portrait of the user. In order to prevent personal privacy leakage under the condition of AI, this paper studies the technology of personal privacy protection from the perspective of social engineering. It is found that by fully splitting the personal account group, i.e. using multiple mobile phone numbers and account binding status, and avoiding the use of the registration free login function, personal information is split into multiple or even more than ten user information by AI. At this time, these information will be discarded by AI due to incomplete information, so as to achieve the effect of protecting personal information.
Survey: Classification and Reconstruction of Archaeological Artefacts

Cultural heritage is the benefaction of our bygone civilization, including physical and intangible properties that express the ways of living developed by a community. Those properties available convey the culture they followed and pass on the information from generations to generations, help the common people to understand, preserve and educate them to inherit the culture that is feasible and makes life easier. The physical properties might be ruined for various reasons. The deliberate act for preserving those matters from damage is by reconstruction. As the digital era is employing everywhere, technology can be used to document the fragments obtained in the excavation sites to be maintained for research purpose, educating and rebuilding without affecting the original fragments. This paper describes about different techniques used to classify the collected fragments into similar sets based on texture, colour and rearrange them to obtain its original structure and also in identifying the missing parts and reconstruct it accordingly.
False Trajectory Privacy Protection Scheme Based on Location Service

Abstract When the user applies for a location continuous query service, the user location privacy may be revealed due to different types of trajectory identification. A false trajectory privacy protection scheme is proposed, which uses the real-time generation of similar trajectories to protect the user’s personal location privacy. The false location set is established by initial anonymous location selection, similar trajectory location calculation and generated location selection. The false location is used to form a false trajectory similar to the real location trajectory, which reduces the probability of attacker identification and achieves real-time trajectory anonymity based on location service. Finally, the effectiveness of the scheme is verified by performance analysis and experimental results.
Detection of Leukemia in Blood Samples Applying Image Processing Using a Novel Edge Detection Method

In current times, pathologists visually inspect blood cell images under the microscope for the purpose of identifying blood disorders. Identified blood disorders are classified into several blood diseases. Our work aims at studying and designing a model (framework) for the detection of leukemia (blood cancer) and its types using microscopic blood sample images and analyzing them for diagnosis at an earlier stage. Earlier, hematologists used to visually inspect the microscopic images, thereby making the diagnosis process error-prone and time-consuming. However, the use of newly developed automatic image processing systems manages to successfully overcome most of the visual inspection related drawbacks. The early diagnosis of blood cancer will greatly aid in better treatment. In this process, the acquired dataset images are taken as inputs and the images are sent through different image processing techniques such as image enhancement (preprocessing), segmentation, feature extraction and classification. The method proposed is applied to a large number of images of varying quality and is found to provide satisfactory results.
Circulation Desk-less Library Management System Using Radio-Frequency Identification(RFID) Technology and Computer Vision

Basu, Abhinaba Biswas, ArnabLibrary circulation involves lending materials to patrons and checking the material. There were several efforts to simplify the circulation process using bar codes, and then using RFID-based self-check-in checkout system. Despite that, the circulation counters are still a necessary component of a library. Our objective is to provide brief insight into circulation desk-less system in the library using radio-frequency identification (RFID) and computer vision. For that, the system will detect user, track their activities and let the user check-in or check out books and freely move out. It will save time for patrons and increase the efficiency of the circulation process. The system can handle impersonation, restrict borrowing books more than allotted quota and misplacing materials by alerting library staffs instantly. The system can also provide smart inventory management which will instantly detect the location of any material and detect any misplaced item. Authorized library staff can get complete physical stock verification from the system instantly. The system can completely revolutionize the way library works and increase its efficiency manifolds. A small prototype system has been developed and provided satisfactory results.
Dialectical Reality of Public Accountant’s Objectivity and Independence: A Bourdieusian Perspective

This paper aims to reveal the social reality of auditing practices in the battle of dialectical feelings about the boundaries of objectivity and autonomy of independence experienced by Public Accountants in defending their professional rights. The disclosure includes the concept of values / beliefs / doxa of autonomy independence implied in the social reality of oppression of intellectual power. This method of expressing social reality used Pierre Bourdieu's perspective approach which focuses more on social situations in practical areas than social construction in theoretical conversation. So that through this construction abler to touch and express honestly about the reality of self-struggle in the practice of auditing. The results of this study are useful for the theoretical development of the sociological aspects of the struggle of public accountants as an intellectual agents maintaining their autonomy against external pressures so as to encourage the creation of more objective and independent audit practice policies.
Scene-Adaptive Video Frame Interpolation via Meta-Learning

Video frame interpolation is a challenging problem because there are different scenarios for each video depending on the variety of foreground and background motion, frame rate, and occlusion. It is therefore difficult for a single network with fixed parameters to generalize across different videos. Ideally, one could have a different network for each scenario, but this is computationally infeasible for practical applications. In this work, we propose to adapt the model to each video by making use of additional information that is readily available at test time and yet has not been exploited in previous works. We first show the benefits of `test-time adaptation' through simple fine-tuning of a network, then we greatly improve its efficiency by incorporating meta-learning. We obtain significant performance gains with only a single gradient update without any additional parameters. Finally, we show that our meta-learning framework can be easily employed to any video frame interpolation network and can consistently improve its performance on multiple benchmark datasets.
The Effect of Chance Success on Equalization Error in Test Equation Based on Classical Test Theory

The aim of this study was to determine the effect of chance success on test equalization. For this purpose, artificially generated 500 and 1000 sample size data sets were synchronized using linear equalization and equal percentage equalization methods. In the data which were produced as a simulative, a total of four cases were created with no chance success, and three different levels (20%, %25, %33) of chance success and the default chance success were corrected by the correction formula. In the simulated data, four different scenarios have been created that do not include chance success and contain three different success rates (20%, 25%, 33%). Accordingly, the test equalization was performed by using linear equalization and equipercentile equalization methods under two different sample sizes and four different chance success conditions. Weighted mean square error of equating methods was found for each situation, and the method with the lowest weighted mean square error was accepted as the most suitable equating method. At the end of the study, it was found out that; while linear equating is the most suitable method for equating test points with chance success; equipercentile equating is the most suitable method for equating test points without chance success.
Campus Book Delivery at the University of Oklahoma

At the University of Oklahoma, we began campus or home delivery of items from our library as well as those from other libraries. We have added more services over the years in response to requests f...
On (Valiant’s) Polynomial-Size Monotone Formula for Majority

This exposition provides a proof of the existence of polynomial-size monotone formula for Majority. The exposition follows the main principles of Valiant’s proof (J. Algorithms, 1984), but deviates from it in the actual implementation. Specifically, we show that, with high probability, a full ternary tree of depth $$2.71\log _2n$$ computes the majority of n values when each leaf of the tree is assigned at random one of the n values.
Unit Testing Using Jest

In previous chapters, we learned how to create web applications using libraries such as React and Next.js. We now know how to develop an application using these libraries. What next?
Counting Plane Cubic Curves over Finite Fields with a Prescribed Number of Rational Intersection Points

For each integer \in [0,9]$, we count the number of plane cubic curves defined over a finite field $\mathbb{F}_q$ that do not share a common component and intersect in exactly $k \mathbb{F}_q$-rational points. We set this up as a problem about a weight enumerator of a certain projective Reed-Muller code. The main inputs to the proof include counting pairs of cubic curves that do share a common component, counting configurations of points that fail to impose independent conditions on cubics, and a variation of the MacWilliams theorem from coding theory.
Normalizing Flows with Multi-Scale Autoregressive Priors

Flow-based generative models are an important class of exact inference models that admit efficient inference and sampling for image synthesis. Owing to the efficiency constraints on the design of the flow layers, e.g. split coupling flow layers in which approximately half the pixels do not undergo further transformations, they have limited expressiveness for modeling long-range data dependencies compared to autoregressive models that rely on conditional pixel-wise generation. In this work, we improve the representational power of flow-based models by introducing channel-wise dependencies in their latent space through multi-scale autoregressive priors (mAR). Our mAR prior for models with split coupling flow layers (mAR-SCF) can better capture dependencies in complex multimodal data. The resulting model achieves state-of-the-art density estimation results on MNIST, CIFAR-10, and ImageNet. Furthermore, we show that mAR-SCF allows for improved image generation quality, with gains in FID and Inception scores compared to state-of-the-art flow-based models.
GOOGLE PLAY STORE BASED APPLICATIONS FOR SEARCH RANK FRAUD AND MALWARE DETECTION

Google play store contains million of apps and thousands of applications are published on every day. To promote the app and make the users to download is becoming complicated; the published try to increase the visibility of the app on the search console. Also it becomes difficult for the users to identify the malware app and download the good one. Few publishers on the play store try to increase their apps rank with fraud and black hat methods. The publishers increase their ranking by create fake bot reviews; provide fake rating through fake accounts. This misleads the users to download the malware apps from the store. In the proposed model, we try to identify the fair reviews and fake reviews by mining the reviewer profile, their rating patterns. With help of fair play detection we also identify that most of the malware apps use fraud ranking strategies to improve their downloads.
Investigation and Analysis on Informatization of English Education in Higher Vocational Colleges in Information Age

Abstract The rapid development of the information age has promoted the rapid development of the information of higher vocational education. Taking the 13 vocational colleges in Jilin Province as the research object, this paper introduces the present situation of the information of English education, points out their achievements and shortcomings in the construction of information infrastructure, and puts forward the strategies to speed up the information process of the higher vocational English education.
Intelligent Water Drops-Based Image Steganography

The level of impersonation from nature is astonishingly high in the domain of computational intelligence, particularly in the field of evolutionary computation and swarm-based frameworks. Attempts are being made to develop more number of algorithms, which imitate nature and the activities that take place in a specific natural phenomenon. One of the most recent optimization algorithms developed being inspired by the nature is the intelligent water drops (IWD) algorithm, which is based on the phenomenon taking place between the drops of water that flow in a river and the soil present in the riverbed. This paper puts forward the proposal of a novel intelligent water drop-based complex region determination (IWD-CRD) technique for proficiently concealing information within images. Superiority in the performance of the proposed algorithm over the existing ones has been demonstrated in terms of hiding capacity (HC), mean squared error (MSE) and peak signal-to-noise ratio (PSNR) of the stego-images.
Handling Sparsity in Cross-Domain Recommendation Systems: Review

Cross-domain recommendation Systems (CDRS) is a significant research area which has been target of many companies these days. From last few years, there is amount of publications in CDRS domain including recommendation systems which have been rising sharply alongside information retrieval and machine learning. Recommender systems help companies to specifically identify preferences of the user from the collected data from various sources. These data values are then used to identify user preferences also known as recommendations. In cross-domain, we use the data from one domain such as movies to recommend items in other domains, for instance, Books. There are numerous issues which recommender systems suffer together with sparse data, synonymy, data privacy, algorithm scalability, perspective awareness (context) and cold start problems. Data sparsity is a major issue in recommender systems, especially in the presence of novel users or items, or when user drift exists. This paper reviews recent efforts made for CDRS sparsity and user drift which are prevalent in most CDRSs such as user-based, item-based or knowledge transfer. This paper formalizes the CDRS illustrates sparsity related issues which are addressed in prior works and finally proposes for future research trail.
Reference: A Grammatical Cohesion In Seloka Journal Introduction Published by Semarang State University

Good writing is one that is cohesive especially in terms of reference. Cohesive is one of the essential textual components to create the organized and comprehensiveness of the texts. This study aims to investigate cohesion in six journal introduction sections written by post-graduate students at Semarang State University, West Java, Indonesia. This study used discourse analysis as an approach to analyzing grammatical cohesive devices. six journal introduction sections were chosen based on purposive sampling. The journal introduction sections were analyzed using Halliday and Matthiessen (2014) theory that contributing to the cohesion of the text, namely cohesive devices. The results of this study showed that post-graduate students at Semarang State University tend to use demonstrative references more frequently than the other grammatical cohesive devices. From 393 occurrences of grammatical cohesive devices presented in the six journal introduction sections, 322 of them were in the form of demonstrative reference. Through this article, the writer wants to help un English post-graduate students to have a better understanding of making introduction sections in their journal article.
ANALYSIS OF LOGIC AND STRATEGY GAMES AS A TOOL FOR THE TEACHING AND APPROACH TO MATHEMATICAL CONCEPTS

ANALYSIS OF LOGIC AND STRATEGY GAMES AS A TOOL FOR THE TEACHING AND APPROACH TO MATHEMATICAL CONCEPTS
The role of formalism in system requirements (full version)

A major determinant of the quality of software systems is the quality of their requirements, which should be both understandable and precise. Most requirements are written in natural language, good for understandability but lacking in precision. To make requirements precise, researchers have for years advocated the use of mathematics-based notations and methods, known as formal. Many exist, differing in their style, scope and applicability. The present survey discusses some of the main formal approaches and compares them to informal methods. The analysis uses a set of 9 complementary criteria, such as level of abstraction, tool availability, traceability support. It classifies the approaches into five categories: general-purpose, natural-language, graph/automata, other mathematical notations, seamless (programming-language-based). It presents approaches in all of these categories, altogether 22 different ones, including for example SysML, Relax, Eiffel, Event-B, Alloy. The review discusses a number of open questions, including seamlessness, the role of tools and education, and how to make industrial applications benefit more from the contributions of formal approaches. (This is the full version of the survey, including some sections and two appendices which, because of length restrictions, do not appear in the submitted version.)