Reviewerly/exHarmony huggingface dataset:
Investigation of Chromatic Aberration and Its Influence on the Processing of Underwater Imagery

The number of researchers utilising imagery for the 3D reconstruction of underwater natural (e.g., reefs) and man-made structures (e.g., shipwrecks) is increasing. Often, the same procedures and software solutions are used for processing the images as in-air without considering additional aberrations that can be caused by the change of the medium from air to water. For instance, several publications mention the presence of chromatic aberration (CA). The aim of this paper is to investigate CA effects in low-cost camera systems (several GoPro cameras) operated in an underwater environment. We found that underwater and in-air distortion profiles differed by more than 1000 times in terms of maximum displacement and in terms of curvature. Moreover, significant CA effects were found in the underwater profiles that did not exist in-air. Furthermore, the paper investigates the effect of adjustment constraints imposed on the underwater self-calibration and the reliability of the interior orientation parameters. The analysis of the precision shows that in-air RMS values are just due to random errors. In contrast, the underwater calibration RMS values are 3x-6x higher than the exterior orientation parameter (EOP) precision, so these values contain both random error and the systematic effects from the CA. The accuracy assessment shows significant differences.
Digital Certificate System for Verification of Educational Certificates using Blockchain

While the number of universities, tertiary education students and number of graduates per year constantly increase, the need to easily verify degree certificates generates new business opportunities. In this paper we project two financial models balancing where the price for the service is balanced between the graduate and the employer as the main stakeholders of that service. Students demand a proof-of-certification at low cost and easy to check, employers also demand quick and trustable verification of degrees when recruiting. As large number of students graduate every year, the problem of fake certificates is a big issue. One can easily get fake certificates in India. Companies hiring thousands of fresher spend large amount of money to get the educational certificates and transcripts verified of applicants. A Digital Certificate using blockchain technology can address this problem. Blockchain is a decentralized distributed digital ledger collectively maintained by a network of computers, called nodes. The data in the blockchain cannot be modified by a person without the consent of everyone else who maintains the records. This makes the data secure.
Towards Using Probabilistic Models to Design Software Systems with Inherent Uncertainty

The adoption of machine learning (ML) components in software systems raises new engineering challenges. In particular, the inherent uncertainty regarding functional suitability and the operation environment makes architecture evaluation and trade-off analysis difficult. We propose a software architecture evaluation method called Modeling Uncertainty During Design (MUDD) that explicitly models the uncertainty associated to ML components and evaluates how it propagates through a system. The method supports reasoning over how architectural patterns can mitigate uncertainty and enables comparison of different architectures focused on the interplay between ML and classical software components. While our approach is domain-agnostic and suitable for any system where uncertainty plays a central role, we demonstrate our approach using as example a perception system for autonomous driving.
Neural Networks, Secure by Construction

We present StarChild and Lazuli, two libraries which leverage refinement types to verify neural networks, implemented in F $$^*$$ and Liquid Haskell. Refinement types are types augmented, or refined, with assertions about values of that type, “integers greater than five”, which are checked by an SMT solver. Crucially, these assertions are written in the language itself. A user of our library can refine the type of neural networks, “neural networks which are robust against adversarial attacks”, and expect F $$^*$$ to handle the verification of this claim for any specific network, without having to change the representation of the network, or even having to learn about SMT solvers. Our initial experiments indicate that our approach could greatly reduce the burden of verifying neural networks. Unfortunately, they also show that SMT solvers do not scale to the sizes required for neural network verification.
Deep Learning Architecture for Group Activity Recognition using Description of Local Motions

Nowadays, the recognition of group activities is a significant problem, specially in video surveillance. It is increasingly important to have vision architectures that automatically allow timely recognition of group activities and predictions about them in order to make decisions. This paper proposes a computer vision architecture able to learn and recognise group activities using the movements of it in the scene. It is based on the Activity Description Vector (ADV), a descriptor able to represent the trajectory information of an image sequence as a collection of the local movements that occur in specific regions of the scene. The proposal evolves this descriptor towards the generation of images able to be the input queue of a two-stream convolutional neural network capable of robustly classifying group activities. Hence, this proposal, besides the use of trajectory analysis that allows a simple high level understanding of complex groups activities, takes advantage of the deep learning characteristics providing a robust architecture for multi-class recognition. The architecture has been evaluated and compared to other approaches using BEHAVE and INRIA dataset sequences obtaining great performance in the recognition of group activities.
The Case of Missed Cancers: Applying AI as a Radiologist’s Safety Net

We investigate the potential contribution of an AI system as a safety net application for radiologists in breast cancer screening. As a safety net, the AI alerts on cases suspected to be malignant which the radiologist did not recommend for a recall. We analyzed held-out data of 2,638 exams enriched with 90 missed cancers. In screening mammography settings, we show that a system alerting on 11 out of every 1,000 cases, could detect up to 10.7% of the radiologists’ missed cancers. Thus, significantly increasing radiologist’s sensitivity to 80.3%, while only slightly decreasing their specificity to 95.3%. Importantly, the safety net demonstrated a significant contribution to their performance even when radiologists utilized both mammography and ultrasound images. In those settings, it would have alerted 8.5 times per 1,000 cases, and detected 11.7% of the radiologists’ missed cancers. In an analysis of the missed cancers by an expert, we found that most of the cancers detected by the AI were visible post-hoc. Finally, we performed a reader study with five radiologists over 120 exams, 10 of which were originally missed cancers. The AI safety net was able to assist 3 out of the 5 radiologists in detecting missed cancers without raising any false alerts.
Siamese Network kullanarak İki Aşamalı Sıralı Kayıplara dayalı Otomatik Hash Kodu Üretimi

Today, large-scale image retrieval methods are used for fast access to increasing visual information. Hashing methods are an image retrieval approach that provides computationally effective and high-speed access. Hash codes created with hand-crafted features extracted from images in the past are now built with deep learning architectures. Convolutional neural network (CNN)'s an impressive performance using optimized features strengthens this trend day by day. However, it contradicts the hash codes consisting of '0' and '1' values and the procedure of updating the parameters and output of CNN architectures. To solve this problem, solutions such as bringing the bits in the CNN output to '0' and '1' values with a threshold value, and converting the output bits to binary values with the help of loss functions in the CNN output are presented in the literature. It is very useful for studies involving loss function solutions for end-to-end training. However, in these studies, loss weights are generally empirical. In order to solve this situation, a framework consisting of two steps is suggested in this study. In the first stage, Euclidean distance is used in the output of a CNN architecture that is trained in a pairwise manner. After reducing the distance between the two images below the specified value, in the second step, the network parameters are transferred, and the output is drawn to binary values with binarization loss. Thus, binary hash codes are obtained automatically for each image without using any additional weight.
Fuzzy-Logic using Unary Bit-Stream Processing

There is a growing attention to the theory of fuzzy-logic and its applications. Efficient hardware design of the fuzzy-inference engine has become necessary for high-performance applications. Considering the facts that fuzzy-logic variables have truth values in the [0, 1] interval and fuzzy controllers include minimum and maximum operations, this work proposes to apply the concept of unary processing to the platform of fuzzy-logic. In unary processing, data in the [0, 1] interval is encoded as bitstream with the value defined by the frequency of 1s. Operations such as minimum and maximum functions can be implemented using simple logic gates. Latency, however, has been an important issue in the unary designs. To mitigate the latency, the proposed design processes right-aligned bit-streams. A one-hot decoder is used for fast detection of the bit-stream with maximum value. Implementing a fuzzy-inference engine with 81 fuzzy-inference rules, the proposed architecture provides 82%, 46%, and 67% saving in the hardware area, power and energy consumption, respectively, and 94% reduction in the number of used LUTs compared to conventional binary implementation.
Classification of Image Steganography in Substitution Technique

The science of communicating secret data using multimedia cover like audio, image, video files, etc. is called steganography. The technique steganography is used for hiding a secret data from unauthorized access or user. Steganography technique can be implemented using many different file formats, or in other words we can use different file formats as cover media but here we would talk about Digital Image-based Steganography for its obvious advantages. Also, we know that rapid and unimaginable developments in digital image processing methods make researcher work even more harder to achieve the ultimate objectives of steganography like robustness, security, resistance to different attacks, and payload capacity. The steganography method also confirms that the methodology or technique also increases the quality of the stego image keeping secure the secret data. The quality measures are PSNR and MSE. Through the following paper, we will study various states of art methods for steganography and compare the same so that it would be a great and ready reference material for the research scholars who are working on Digital Image Steganography.
A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm

Image enhancement is a necessary and indispensable technique for increasing the quality of digital images.The main task is to generate a new intensity value for each pixel in the image using a transformation function after the input image receives the intensity value of each pixel.The proposed transfer function in this study is called the Regional Similarity Transfer Function (RSTF) that considers the density distribution similarity between adjoining pixels.Dragonfly Algorithm (DA) intuitive optimization technique, which is preferred in engineering applications, has been used to optimize the parameter values of the proposed transfer function.Image quality evaluation is performed with six criteria between the improved and original images.Our experimental results show that the intensity distribution between adjoining pixels show an increase in contrast and brightness over the similarity degree.Excessive brightness, blur, and deterioration in the images is resolved with the proposed method.
Better than the best? Answers via model ensemble in density-based clustering

Abstract With the recent growth in data availability and complexity, and the associated outburst of elaborate modelling approaches, model selection tools have become a lifeline, providing objective criteria to deal with this increasingly challenging landscape. In fact, basing predictions and inference on a single model may be limiting if not harmful; ensemble approaches, which combine different models, have been proposed to overcome the selection step, and proven fruitful especially in the supervised learning framework. Conversely, these approaches have been scantily explored in the unsupervised setting. In this work we focus on the model-based clustering formulation, where a plethora of mixture models, with different number of components and parametrizations, is typically estimated. We propose an ensemble clustering approach that circumvents the single best model paradigm, while improving stability and robustness of the partitions. A new density estimator, being a convex linear combination of the density estimates in the ensemble, is introduced and exploited for group assignment. As opposed to the standard case, where clusters are typically associated to the components of the selected mixture model, we define partitions by borrowing the modal, or nonparametric, formulation of the clustering problem, where groups are linked with high-density regions. Staying in the density-based realm we thus show how blending together parametric and nonparametric approaches may be beneficial from a clustering perspective.
Blockchain Technology for Data Sharing in Decentralized Storage System

In Cloud architecture, the storage systems can be centralized and decentralized environments. Centralized storage systems stored data in private cloud, maintained in a single location, can be accessed by one or more user, whereas in decentralized storage system, the data is stored on more than one or multiple servers. Users or companies are being a part of the decentralized cloud storage cloud to host the servers. The data may be stored on any server, i.e., no dedicated server for data storage and which can be accessed easily. In decentralized storage system, the files are stored and protected with the help of blockchain technology. Blockchain is nothing but a chain of blocks (computers or servers), which are connected by using cryptography. Each block contains a set of transactions which has been encrypted and shared in a secured way among multiple servers. Blockchain technology ensures confidentiality and integrity, which can be implemented by using peer-to-peer network. In this paper, we discussed how the technology improves security used by different applications in distributed environment. Further, we proposed adaptive encryption algorithm to improve security and access control.
Analyzing Student’s Learning Interests in the Implementation of Blended Learning Using Data Mining

&lt;p class="0keywords"&gt;Blended Learning combines teaching and learning activities in the classroom and online teaching. In its implementation, this learning method requires a lot of data. One of them is the student's online test score data which can be used as an evaluation of learning. For this reason, in this study, data mining is used to determine the results of online student examinations as well as to determine student interest in learning about the implementation of Blended Learning. Data mining techniques are used in the logs of online learning session results, so that one can assess the online learning system used. By assessing the system, it can be identified which students who have studied hard and those who have not studied in the online exam. The series data used are student test score data on the State Polytechnic of Malang Learning Management System (LMS). The student score dataset is arranged based on variables in the Educational Process Mining (EPM) Dataset of UCI, which are obtained from teacher’s assignments. In addition, data mining classification is used to determine student interest in learning towards blended learning. In the process of data mining, comparative analysis is carried out using the features of the RapidMiner tool to carry out the process of student data for training and data validation. This process uses several algorithms along with student data which is divided into two sets for training and validation. From the results of data mining calculations produce a classification with minimum errors. From the test, the resulting linear regression algorithm has RMSE 0.000 and SE 0.000, while the neural network algorithm has RMSE 0.525 and SE 0.275.&lt;/p&gt;
Towards Resistant Audio Adversarial Examples

Adversarial examples tremendously threaten the availability and integrity of machine learning-based systems. While the feasibility of such attacks has been observed first in the domain of image processing, recent research shows that speech recognition is also susceptible to adversarial attacks. However, reliably bridging the air gap (i.e., making the adversarial examples work when recorded via a microphone) has so far eluded researchers. We find that due to flaws in the generation process, state-of-the-art adversarial example generation methods cause overfitting because of the binning operation in the target speech recognition system (e.g., Mozilla Deepspeech). We devise an approach to mitigate this flaw and find that our method improves generation of adversarial examples with varying offsets. We confirm the significant improvement with our approach by empirical comparison of the edit distance in a realistic over-the-air setting. Our approach states a significant step towards over-the-air attacks. We publish the code and an applicable implementation of our approach.
Visual Analysis of Argumentation in Essays

This paper presents a visual analytics system for exploring, analyzing and comparing argument structures in essay corpora. We provide an overview of the corpus by a list of ArguLines which represent the argument units of each essay by a sequence of glyphs. Each glyph encodes the stance, the depth and the relative position of an argument unit. The overview can be ordered in various ways to reveal patterns and outliers. Subsets of essays can be selected and analyzed in detail using the Argument Unit Occurrence Tree which aggregates the argument structures using hierarchical histograms. This hierarchical view facilitates the estimation of statistics and trends concerning the progression of the argumentation in the essays. It also provides insights into the commonalities and differences between selected subsets. The text view is the necessary textual basis to verify conclusions from the other views and the annotation process. Linking the views and interaction techniques for visual filtering, studying the evolution of stance within a subset of essays and scrutinizing the order of argumentative units enable a deep analysis of essay corpora. Our expert reviews confirmed the utility of the system and revealed detailed and previously unknown information about the argumentation in our sample corpus.
SpheriCol: A Driving Assistance System for Power Wheelchairs Based on Spherical Vision and Range Measurements

This paper presents “SpheriCol”, a new driving assistance system for power wheelchair users. The ROS-based aid system combines spherical images from a twin-fisheye camera and range measurements from on-board exteroceptive sensors, to synthesize different augmented views of the surrounding environment. Experiments with a Quickie Salsa wheelchair show that SpheriCol improves situational awareness and supports user's decision in challenging maneuvers, such as passing through a door or corridor centering.
Perceived Speed, Frustration and Enjoyment of Interactive and Passive Loading Scenarios in Virtual Reality

Long waits and disruptive loading breaks can evoke negative emotions, like frustration. While there is a lot of research on 2D-based loading scenarios, it is unclear how people react to loading screens in an immersive virtual reality (VR) environment. In this paper we conducted a user study to investigate the effects of interactive and passive loading screens on the users’ loading screen experience (LSE) in VR. We measured perceived speed, enjoyment and frustration for long and short waiting times. Results show that interactive loading screens improved participants’ LSE through increasing perceived speed and enjoyment, and decreased their frustration while waiting. Thus, previous findings of 2D-based research were confirmed. Therefore, our research provides a first approach for further investigations of different loading screens in VR.
Object Classification on a High-Resolution Tactile Floor for Human-Robot Collaboration

The growing trend in the manufacturing industry towards human-robot collaboration as a new paradigm has the potential to increase the efficiency of robot-enabled production lines. This development requires safety-oriented sensor systems, frameworks and reliable algorithms in order to avoid hazardous situations. These systems should be able to distinguish between different objects, mobile vehicles and human workers to allow adaptive control strategies of the robots. In this paper, we introduce a tactile flooring system with high spatial resolution to determine the humans in a workplace and to continue the robot's task when mobile units are entering the workspace and no life-threatening situation is expected. The pressure data of the tactile floor is transformed to image data which is used to determine the objects presented. There are several categories to distinguish between specific vehicles, humans and users resulting in a multi-label classification problem with up to three categories presented in one pattern. Our CNN classifies five different objects with an exact match of 75%.
Using unethical data to build a more ethical world

Data scientists use data to train models. Those models calculate probabilities to capture patterns in the data. It’s difficult to build ethical models when the available training data contains racism, sexism, or other stereotypes. Contact center data, including calls, chats, texts, and emails, is no exception. Instead of building a model to automate decision-making processes, we use the unethical findings from our model as an insight. We discuss debiasing options for removing racism from the model but find that removing this bias removes a crucial insight that an analyst deserves to know. By leaving the model with all the biases learned from the training data, we can provide better analytics. Analysts can recommend solutions that start to dismantle the systemic racism present in our society. Debiasing is not always appropriate. Censoring the model makes it harder to identify what can be done to prevent racism in our procedures and society.