Reviewerly/exHarmony huggingface dataset:
Physically‐based Book Simulation with Freeform Developable Surfaces

Abstract Reading books or articles digitally has become accessible and widespread thanks to the large amount of affordable mobile devices and distribution platforms. However, little effort has been devoted to improving the digital book reading experience, despite studies showing disadvantages of digital text media consumption, such as diminished memory recall and enjoyment, compared to physical books. In addition, a vast amount of physical, printed books of interest exist, many of them rare and not easily physically accessible, such as out‐of‐print art books, first editions, or historical tomes secured in museums. Digital replicas of such books are typically either purely text based, or consist of photographed pages, where much of the essence of leafing through and experiencing the actual artifact is lost. In this work, we devise a method to recreate the experience of reading and interacting with a physical book in a digital 3D environment. Leveraging recent work on static modeling of freeform developable surfaces, which exhibit paper‐like properties, we design a method for dynamic physical simulation of such surfaces, accounting for gravity and handling collisions to simulate pages in a book. We propose a mix of 2D and 3D models, specifically tailored to represent books to achieve a computationally fast simulation, running in real time on mobile devices. Our system enables users to lift, bend and flip book pages by holding them at arbitrary locations and provides a holistic interactive experience of a virtual 3D book.
The Observatory of Anonymity: An Interactive Tool to Understand Re-Identification Risks in 89 countries

Anonymity offers strong guarantees for people to use technology without fear of mass surveillance, identity theft, and data misuse. To anonymize and share data widely, de-identification is the main tool used in academia and industry. Yet mounting evidence suggest that de-identification may not protect people’s privacy in practice. We present The Observatory of Anonymity, an interactive website demonstrating how few pieces of personal data can easily re-identify us. Taking advantage of modern web technologies, it allows participants to explore their correctness score—the likelihood to be correctly and uniquely identified from their demographics only. Trained on census data from 89 countries, it demonstrates the effectiveness of re-identification attacks on deemed-anonymous data. The website further allows analysts to upload their own data samples to train our machine learning models in real time. The Observatory provides a unique tool for individuals, researchers, and practitioners to assess whether current de-identification practices satisfy the anonymization standards of modern data protection laws such as GDPR and CCPA.
Alpha Matte Generation from Single Input for Portrait Matting

In the portrait matting, the goal is to predict an alpha matte that identifies the effect of each pixel on the foreground subject. Traditional approaches and most of the existing works utilized an additional input, e.g., trimap, background image, to predict alpha matte. However, (1) providing additional input is not always practical, and (2) models are too sensitive to these additional inputs. To address these points, in this paper, we introduce an additional input-free approach to perform portrait matting. We divide the task into two subtasks, segmentation and alpha matte prediction. We first generate a coarse segmentation map from the input image and then predict the alpha matte by utilizing the image and segmentation map. Besides, we present a segmentation encoding block to downsample the coarse segmentation map and provide useful feature representation to the residual block, since using a single encoder causes the vanishing of the segmentation information. We tested our model on four different benchmark datasets. The proposed method outperformed the MODNet and MGMatting methods that also take a single input. Besides, we obtained comparable results with BGM-V2 and FBA methods that require additional input.
A Novel Key Point Trajectory Model for Fall Detection from RGB-D Videos

This paper aims to address the problem of fall detection from RGB-D image sequences. Towards this goal, we propose a novel Key Point Trajectory Model which represents a fall action as a series of trajectory descriptors. In the proposed model, 16 key points including 14 skeleton points and 2 centers of body parts are extracted from each pair of RGB and depth images. Then a global trajectory descriptor is constructed on 16 trajectories that are obtained by connecting the key points across several frames in the RGB-D sequence. The trajectory descriptor incorporates the spatial, depth, and temporal context of key points and characterizes the global motion of human over a short period of time. A random forest is employed to learn the classifier of trajectory descriptors, and an integration rule is developed for detecting falls according to the classification results of all trajectory descriptors within a video. Experiments conducted on two fall detection datasets demonstrate that our method achieves better performance in comparison with state-of-the-art methods.
A Non-compensatory Multicriteria Model for Sorting the Influence of PBL Over Professional Skills

In this paper, we describe and apply a new model for evaluating the influence of Project-Based Learning (PBL) active learning methodology over the student’s skills. The proposal is based on the adaption of multicriteria outranking algorithm to study the influence of PBL in the skills that Industrial Engineering and Management students felt they acquired. The authors used the results of a survey filled by 30 students after the PBL of a particular edition. Results shown a positive impact of PBL in skills like Autonomy; Communication (oral and written); Critical thinking; Initiative and creativity; Disseminate of content.
Framework for Securing Automatic Meter Reading Using Blockchain Technology

The Automatic Meter Reader (AMR) for energy consumption is one of the most important issues in smart cities, as meters and electricity companies suffer from insecurity. The Internet of Things (IoT) can be used to achieve effective and reliable AMR in real time. Blockchain is a very advanced technology and technology that can be used to secure transactions such as meter readings and meter control. It is based on the idea of sequencing data blocks in a secure and distributed manner. Ensures the security of the blockchain representing the data in each block (reading the meter for example). The block is created and verified by many devices distributed on a network. Blockchain can be implemented in various ways and environments such as the Ethereum platform. In this paper, we will present a new automated meter reading platform using blockchain technology to meet the complete security requirements of AMR systems. When DoS attack was launched, requests did not affect the data itself, but the response speed of the blockchain network to incoming transactions from the servers was reduced very slightly. In addition, the results show that Blockchain could provide a promising technology that can participate in securing network meters.
Secure Data Access Privacy Preserving Using Cloud Services

Abstract Notwithstanding the huge computational advantages, re-appropriating information to the public internet, furthermore avoiding end-users, quick authority towards the systems which using their information, which inescapable gets up to the minute privacy tasks. Circulated registering allows different pros and unrivalled comfort for the cloud users to obtain the needed permission of cloud gave that the close by establishment limits need not be thought of. While getting to information, there may be a co-employable connection between various customers which assembles sharing and exchanging of data, a grim cycle. The viewpoint on present privacy courses of action is basically on confirmation to catch that the information of an independent can’t be moved nearer illegitimately, anyway there arose an insurance problem when a user interest for information splitting into various customers between cloud specialist. The customer security may be uncovered by tried induction appeal itself, if data permission approval for the users is gained or loosed. In the introduced structure, an assurance saving affirmation show is used to hinder the above security bothers. According to this Method, control of data through shared induction is cultivated by route toward convey baffling permission appeal that offers security to the cloud users. Permission authority relies upon credits so the cloud customers can simply get to their own endorsed data fields. Advanced encryption standard estimation is used to achieve data lack of clarity and data protection. The proposed system oversaw secure assurance saving data access authority is appealing for various customer in cloud consistent limit.
Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computation

The generalization power of deep-learning models is dependent on rich-labelled data. This supervision using large-scaled annotated information is restrictive in most realworld scenarios where data collection and their annotation involve huge cost. Various domain adaptation techniques exist in literature that bridge this distribution discrepancy. However, a majority of these models require the label sets of both the domains to be identical. To tackle a more practical and challenging scenario, we formulate the problem statement from a partial domain adaptation perspective, where the source label set is a super set of the target label set. Driven by the motivation that image styles are private to each domain, in this work, we develop a method that identifies outlier classes exclusively from image content information and train a label classifier exclusively on class-content from source images. Additionally, elimination of negative transfer of samples from classes private to the source domain is achieved by transforming the soft class-level weights into two clusters, 0 (outlier source classes) and 1 (shared classes) by maximizing the between-cluster variance between them.
Identifying groups of determinants in Bayesian model averaging using Dirichlet process clustering

Abstract Model uncertainty is a pervasive problem in regression applications. Bayesian model averaging (BMA) takes model uncertainty into account and identifies robust determinants. However, it requires the specification of suitable model priors. Mixture model priors are appealing because they explicitly account for different groups of covariates as robust determinants. Specific Dirichlet process clustering (DPC) model priors are proposed; their correspondence to the binomial model prior derived and methods to perform the BMA analysis including a DPC postprocessing procedure to identify groups of determinants are outlined. The application of these model priors is demonstrated in a simulation exercise and in an empirical analysis of cross‐country economic growth data. The BMA analysis is performed using the Markov chain Monte Carlo model composition sampler to obtain samples from the posterior of the model specifications. Results are compared with those obtained under a beta‐binomial and a collinearity‐adjusted dilution model prior.
CryptMe: A Cryptographic framework with Steganography for securing data

Data Security has become one of the major concerns in the modern world. As everything is going digital and our data is on the cloud, its security is a major research area. The various suggested method provides better security and more important integrity. However, with more advancement in technology, these proposed methods are getting snooped. This paper proposed an approach of a combination of Steganography, Cryptography, and Transposition cipher technique. In particular, it focuses on the LSB technique in another way i.e., while performing the LSB steganography technique instead of embedding data at LSB of the base image, this method takes MSB of Video Frame and LSB of Stego Image. This combination helps in providing very little distortion and higher quality of the secure image. The value of efficiency of the algorithm would be determined by the PSNR value.
Improved Variable Step Length A* Search Algorithm for Path Planning of Mobile Robots

The use of Mobile Robots in diverse sectors are increasing day by day. Nowadays, mobile robots are widely used in industrial sectors, but real-time path planning and collision free path tracking raises more challenging issues. A good path planning process for these mobile robots is needed in order for them to perform better. In path planning problems, the $A^{\ast}$ algorithm has been widely investigated and applied, but the cost and efficiency of the path is not completely taken into consideration. This algorithm has been tweaked and improved in a number of ways to facilitate path planning. Taking into account the variable-step-length $A^{\ast}$ search algorithm, which can take steps longer than one. This method uses a fixed step length and produces a better path than the $A^{\ast}$ Search algorithm. However, if the step length can be modified in the process as required, a more optimal direction can be obtained. As a result, this paper proposes an improved updated variable-step-length based $A^{\ast}$ algorithm that can update or change step length. This algorithm will efficiently find a path with a shorter length. In order to check the feasibility and efficiency of the proposed system, simulation and practical experiments are carried out. The findings show that the improved $A^{\ast}$ algorithm will increase the planned route’s safety and efficiency while also reducing the robot’s movement time in difficult terrain.
IOT Based System for Empowering Differently Abled Person

Concentrating and coping with variations in the face such as physically, audibly and vocally disabled people are a difficult task through a single interface. There has been a lot of study into each problem and separate solutions have been suggested. The paper aims to develop a single device solution in order to be easy, simple, accurate and economical. The main goal of the device is to make people who are different, feel secure when they are seen, heard and addressed. The device can read by taking a picture to visually impaired people. In addition, the picture is achieved by translating the text to text and speech synthesis to an audio format, reading extracted text that trades papers, books and other materials that are accessible every day. For those that are hard to hear, the input comes in the form of a microphone speech and audio captured will then be transformed to a text which will be shown on the computer screen in the form of a pop-up window for the user. The vocal impairment is supported by the user's feedback as text through a personalized, built in keyboard, which recognizes text, translates text into voice and provides the speech output. The user also helps voice impairments.
Tensor dispersion-based multi-view feature embedding for dimension reduction

With the development of feature extraction technique, one image object can be represented by multiple heterogeneous features from different views that locate in high-dimensional space. Multiple features can reflect various characteristics of the same object; they contain compatible and complementary information among each other, integrating them together used in the special image processing application that can obtain better performance. However, facing these multi-view features, most dimensionality reduction methods fail to completely achieve the desired effect. Therefore, how to construct an uniform low-dimensional embedding subspace, which exploits useful information from multi-view features is still an important and urgent issue to be solved. So, we propose an innovative fusion dimension reduction method named tensor dispersion-based multi-view feature embedding (TDMvFE). TDMvFE reconstructs a feature subspace of each object by utilizing its k nearest neighbors, which preserves the underlying neighborhood structure of the original manifold in the lower dimensional mapping space. The new method fully exploits the channel correlations and spatial complementarities from the multi-view features by tensor dispersion analysis model. Furthermore, the method constructs an optimization model and derives an iterative procedure to generate the unified low dimensional embedding. Various evaluations based on the applications of image classification and retrieval demonstrate the effectiveness of our proposed method on multi-view feature fusion dimension reduction.
Phishing Prevention Techniques: Past, Present and Future

Nowadays, phishing has been considered as one of the most frequent attack in world of cyber-space; during this attack, an attacker aims to gather personal information of the targeted individual by accessing its system with the help of credentials he provided unknowingly. The objective of this attack lies in between the financial or personal gain of the attacker. The information collected during execution can be a username, password, credit/debit card number, pin, validity or CVV number. While dealing with such ambush, it has always been a challenging task to detect a phishing attack with high accuracy. Phishing should be possible from various perspectives such as an attacker can access through messages, calls, texts, commercials, website popups, etc. To shield the user from these attacks, numerous anti-phishing tools have been created over the time. The issue is so wide that there are no such ways to moderate all the vulnerabilities adequately; hence, to resolve this, different methods are been regularly executed. This paper presents an overall classification of an anti-phishing solution based on various categories of phishing mitigation techniques that work on different parameters to provide out the best solution. It also provides further research gap that will be addressed.
Identifying and Applying the Information Technology Competence Framework in an Online Teaching Environment

This study aims to identify the information technology (IT) competence framework to propose the requirements and procedures for use tools and resources in online teaching, and point out barriers to the application of IT in the online teaching environment. This study survey was conducted on sample space (n = 527) and 45 expert opinions. The reliability of the competence framework scale was calculated by using Cronbach's alpha and expert opinions. The experimental results demonstrated that the competencies in the experimental questionnaire were reliable. An overall alpha score for the pilot data was found at high (r = 0.792), which indicates a high reliability of the instrument. This study used the combination of qualitative and quantitative methods. The results will contribute to the theoretical and practical pedagogy of technology in education and online education.
Placement optimization of solar arrays in photovoltaic power generation facility site

In a photovoltaic generation plant, the tilted angle, azimuth angle, and solar arrays’ interval are the main design parameters because they affect the annual amount of received light. Increasing the tilted angle of the solar array and the interval between the front and the back can reduce the shadow ratio, but the number of solar arrays that can be installed decreases, and the total amount of light received drops in the entire facility will decrease. Based on the solar orbit analysis, the amount of solar radiation with shadows on the solar array is determined using the ratio of shadows on the solar arrays. Photovoltaic generation plants will be built on complex shaped sites. It is considered to place the solar arrays at a site in a multiply connected area that contains land that cannot be placed. Because of the weather in the installation area affects the placement of the solar arrays the weighting factors of the amount of solar radiation and the sunshine duration are used. This study proposes a method to determine the placement of the solar arrays that maximizes the amount of received light on a site. This placement problem is formulated as a combinatorial optimization problem and solved by genetic algorithms. The proposed method is able to obtain the optimal value for the solar arrays placement.
A Display Method of Large Underwater Photo-Mosaics Based on Pyramid Model of Tiled Images

In deep seafloor exploration, optical imaging provides short-range, high resolution visual information. In order to obtain a wide range of detailed visual information, it is common to stitch multiple images into a photo-mosaic which could reach tens of billions of pixels in size. Due to hardware and software constraints, it is very difficult even impossible to browse and display such a large image with existing image viewers. In this paper, we propose a display method based on pyramid model and develop a super large image display system of tiled images dedicated to underwater photo-mosaics display. Before the image can be displayed, the first step is to construct and store the multi-resolution hierarchical model of tiles, in which sub-mosaics of the same size are original tiles with the highest resolution. Similar to quadtree coding, each tile is encoded based on its location in the pyramid. Then all tile data are stored in MongoDB database. Each record in MongoDB is a key-value pair structure corresponding to a specific tile, in which key is the encoding of a tile and value is the tiled image data stored in binary stream format. Image display is implemented based on graphical user interface. By mouse operation, the system can display images at different resolutions and browse different part of the image. Based on the pyramid model and key-value storage structure, there are more than 60,000 high-definition tiled images in our application. The full panorama resolution is 16.2 billion pixels, about 45GB in RAM, but only the tiles displayed in the current window need to be loaded. Hence, our system reduces the memory requirement greatly and makes image browsing more smoothly.
A method of query expansion based on topic models and user profile for search in folksonomy

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.