Reviewerly/exHarmony huggingface dataset:
Three–Way Classification: Ambiguity and Abstention in Machine Learning

Ambiguity, that is the lack of information to produce a specific classification, is an important issue in decision–making and supervised classification. In case of ambiguity, human–decision makers can resort to abstaining from making precise classifications (especially when error-related costs are high), but this behaviour has been scarcely addressed, and applied, in machine learning algorithms. This contribution grounds on previous works in the areas of three–way decisions, cautious classification and orthopairs, and proposes a set of techniques we developed to address this form of ambiguity, by providing both a general–purpose technique to create three–way algorithms from probabilistic ones, and also more specific techniques which could be applied to popular machine learning frameworks. We also evaluate the proposed idea, by performing a set of experiments where we compare classical classification algorithms with the corresponding three–way generalizations, in order to study the trade–off between classification accuracy and abstention: the results are promising.
Interest point detection method based on multi‐scale Gabor filters

In this study, a novel interest point detection algorithm which combines image intensity variation and edge contour information is proposed. Firstly, the Canny edge contour detector is used to extract the edge map. Secondly, the imaginary parts of multi-scale Gabor filters are applied to smooth input image and then the normalised information entropies at various scales can be acquired. Finally, multiplication of different normalised information entropies will be served as a new measure for interest point that can also be used for interest point detection. This method has two advantages: on the one hand, detection accuracy is greatly improved because combination information is adopted to extract interest points including contour shape information, grey variation of edge pixels and their neighbours; on the other hand, non-interest points can be well inhabited due to multi-scale product in detector is served as an interest point measure. Also, desirable noise robustness and time efficiency are validated through experiments. Compared with four other state-of-art methods, the proposed method shows excellent performance in terms of geometric transformations and localisation accuracy of repeated interest points.
Fall Detection Using Accelerometer on the User’s Wrist and Artificial Neural Networks

Falls can occur during daily activities, being one of the primary sources of morbidity and mortality in the elderly population. Real-time detection of falls and their communication to an emergency center can enable rapid medical care, increasing the sense of security for the elderly and reducing some of the negative consequences of falls. This work presents an artificial neural network based algorithm for fall detection using a three-axis accelerometer wrist-wearable device. Samples were filtered and discretized in time domain allowing a multilayer perceptron neural network to classify the events. The training process was performed using 792 signals acquired by 22 volunteers. Validation results point-out that the proposed solution obtains a sensitivity of 98.10%, a specificity of 98.10% and an accuracy of 98.10%, concluding that the neural network generalization capacity allows the fall detection without the necessity of complex feature extraction algorithms.
Virtual Reality Simulator for Electric Powered Wheelchairs Using a Joystick

For some people with severe physical impairments, the main assistive device to improve their independence in activities of daily living and to enhance overall well-being is an electric powered wheelchair (EPW). However, a large number of wheelchair users find nearly impossible to drive with conventional EPW interfaces, so there is a necessity to offer users EPW training. In this work the SimCadRoM is presented. It is a virtual reality simulator for EPW safe driving learning purposes, testing of driving skills and performance, and testing of input interfaces. At this research stage, some tests were conducted to validate this version of the SimCadRoM as a reliable simulator capable of providing a training experience close to reality. The SimCadRoM was, in general, well accepted by the volunteers and proved to be a system that simulates, very realistically, the usability, kinematics, and dynamics of an EPW in a virtual environment (VE) using a standard EPW’s joystick as input interface. The level of sense of presence in the VE generated by the simulator revealed a great potential for training and transferring skills to a real environment (RE).
A Comparison of Human Crafted Features and Machine Crafted Features on White Blood Cells Classification

Leukocytes can be divided into five types and each type can be classified according to the characteristics of their morphological structure, cytoplasm, and nucleus. It is essential to classify leukocytes precisely because each type of leukocytes has a distinctive characteristic to provide defense against anomaly in our body. Unfortunately, doctors usually use manual verification to classify leukocytes one by one. This method is time-consuming and susceptible to error. To solve this problem, we used machine-crafted features method and human-crafted features method. In this study, we compare both methods and verify the results by comparing their quantitative measurement and computational time. The machine-crafted features method gave better result compared human-crafted features method, but the drawback machine crafted feature method is computational challenging compared to human-crafted feature method.
Ariadne

Data provenance is a powerful tool for debugging large-scale analytics on batch processing systems. This paper presents Ariadne, a system for capturing and querying provenance from Vertex-Centric graph processing systems. While the size of provenance from map-reduce-style workflows is often a fraction of the input data size, graph algorithms iterate over the input graph many times, producing provenance much larger than the input graph. And though current provenance tracing procedures support explicit debugging scenarios, like crash-culprit determination, developers are increasingly interested in the behavior of analytics when a crash or exception does not occur. To address this challenge, Ariadne offers developers a concise declarative query language to capture and query graph analytics provenance. Exploiting the formal semantics of this datalog-based language, we identify useful query classes that can run while an analytic computes. Experiments with various analytics and real-world datasets show the overhead of online querying is 1.3x over the baseline vs. 8x for the traditional approach. These experiments also illustrate how Ariadne's query language supports execution monitoring and performance optimization for graph analytics.
Implementation of Blockchain Data Obfuscation

Data protection is no longer a luxury. With so much of sensitive, personal, financial transaction events which involves money or online payment for buying or selling of goods, data gets transmitted online. Hence, security and privacy have become the need of everyone who participates in those events. Blockchain is our use case for securing the data from any major data breaches and keeping data safe enough. Thus, blockchain data obfuscation or data masking refers to the process in which essential data is masked and stored in the form of the blocks, where each block is connected to preceding blocks in the form of a chain. The blockchain application allows greater transparency, as the blockchain is a type of distributed book and it also provides enhanced security and traceability of data, for example, when the items\goods are recorded on a blockchain, we can backtrack items to their point of origin. Once the data is recorded on the blockchain then a customer can trust the accuracy of blockchain data, as the data cannot be manipulated. The advantage of blockchain is that it will reduce the cost of business, by removing middlemen or third parties.
Image Denoising via Generative Adversarial Networks with Detail Loss

Image denoising is a challenging task which aims to remove additional noise and preserve all useful information. Many existing image denoising algorithms focus on improving the typical object measure, peak signal-to-noise ratio (PSNR), and take the mean square error (MSE) as their loss function to train their networks. Although these algorithms can effectively improve the PSNR on the benchmark dataset, their denoised images often lose some important details or become over-smooth in some texture-rich regions. In order to solve this problem, we introduce Generative Adversarial Networks (GAN) and perceptual loss from single image super-resolution (SISR) field into our image denoising work. The GAN and perceptual loss can help our network to better focus on the recovering of details during denoising. To understand easily, we use the term Detail Loss to represent the whole loss which includes the MSE and the perceptual loss. Besides, we propose a new convolutional neural network which achieves state-of-the-art result on PSNR. Our experimental results show that our method outperforms the current state-of-the-art methods on preserving the details during denoising. Compared with the current state-of-the-art methods, the denoised images by our method are clearer, sharper and more realistic on details.
Revisiting Online Personal Search Metrics with the User in Mind

Traditional online quality metrics are based on search and browsing signals, such as position and time of the click. Such metrics typically model all users' behavior in exactly the same manner. Modeling individuals' behavior in Web search may be challenging as the user's historical behavior may not always be available (e.g., if the user is not signed into a given service). However, in personal search, individual users issue queries over their personal corpus (e.g. emails, files, etc.) while they are logged into the service. This brings an opportunity to calibrate online quality metrics with respect to an individual's search habits. With this goal in mind, the current paper focuses on a user-centric evaluation framework for personal search by taking into account variability of search and browsing behavior across individuals. The main idea is to calibrate each interaction of a user with respect to their historical behavior and search habits. To formalize this, a characterization of online metrics is proposed according to the relevance signal of interest and how the signal contributes to the computation of the gain in a metric. The proposed framework introduces a variant of online metrics called pMetrics (short for personalized metrics) that are based on the average search habits of users for the relevance signal of interest. Through extensive online experiments on a large population of GMail search users, we show that pMetrics are effective in terms of their sensitivity, robustness, and stability compared to their standard variants as well as baselines with different normalization factors.
Product Market Competition With Crypto Tokens and Smart Contracts

This paper models benefits of quoting output price in units of crypto token under duopolistic product market competition with switching costs. Pricing output in tokens provides a firm with a de facto second-mover advantage, raising its equilibrium profit. In addition, the firm can further increase its equilibrium profit by committing via a smart contract to the number of tokens sold. By focusing on ``utility" tokens used at the product market competition stage, the paper highlights potential benefits of issuing crypto tokens that extend beyond the advantages of issuing ``security'' tokens at the venture financing stage.
Towards Further Formal Foundation of Web Security: Expression of Temporal Logic in Alloy and Its Application to a Security Model With Cache

Security analysis of a web system is complicated, and thus analysis using formal methods to describe system specification mathematically has attracted attention.Some previous studies have adopted formal methods, but their models cannot express parallel communication completely.This limitation gives rise to problems where web functions, such as a cache that stores contents, cannot be defined and attacks that forge contents cannot be analyzed.These problems are present in the Alloy-based implementations of current models that do not have the ability to express temporal logic.Therefore, we design implementation and evaluation of temporal logic in Alloy to express time series and parallel computation for web security analysis.In doing so, state transitions in the web can be expressed by fitting them in our proposed syntax.As concrete applications, we describe a web security model that includes caches and show that our proposed syntax can analyze state-of-the-art attacks, such as unauthorized access to users' account pages via caches.The source code of our proposed model in Alloy is publicly available.
Bidirectional LSTM for Continuously Predicting QoE in HTTP Adaptive Streaming

Continuous monitoring of quality of experience (QoE) has increasingly become an important mechanism in quantifying the user's satisfaction over video streaming services. Such mechanism requires approaches with the ability to model the complex temporal dependencies and time-varying characteristics of the QoE. Long-Short Term Memory (LSTM) related approaches carried out by recent research efforts have shown highly potential results since they can leverage past events occurring during a streaming session. However, the unidirectional LSTM structure only consider forward dependencies, leading to high possibility of missing out useful information. In this paper, a novel model for continuous QoE prediction is proposed to consider not only forward dependencies but also backward dependencies. The proposal utilizes a Bidirectional Long Short Term-Memory (BLSTM) model to process inputs obtained from perceptual video quality algorithms, rebuffering, and memory-related temporal data. Comparisons with other state-of-the-art models indicate that the proposed model achieves very promising performance in terms of accuracy.
Nanosecond Indexing of Graph Data With Hash Maps and VLists

We introduce a wait-free, multi-reader, single-writer, "kill -9" durable, indexing structure for in-memory social graph databases. This structure requires no communication from the readers back to the writer, allowing for trivial read scalability and isolation. We support online updates without compromising availability or read performance. Our structure supports looking up small subgraphs in 80 nanoseconds and a materialization rate of 12 nanoseconds per edge. Storage takes 7 bytes per edge per index and supports almost 1 million online writes per second.
Understanding Context in Children's Use of Smartwatches for Everyday Science Reflections

Although much has been written about the benefits of situated learning, its technology implementations have remained bounded in space and time in the form of scheduled sessions or designated spaces. Relatively little research has investigated how learning can be situated in the messy world outside of formal learning contexts. We propose that wearables like smartwatches, because of their intrinsic attributes such as persistence and ease-of-access, present an apt opportunity to explore how such 'messy' situated learning can happen. This paper investigates the notion of context in a 3-week study whereby 18 elementary school students used a custom smartwatch app called ScienceStories to record reflections related to specific science topics throughout the course of their everyday lives.
Implementing CAN bus security by TOUCAN

Modern vehicles embed a lot of software that turns them into Cyper-Physical Systems (CPS). Electronic Control Units (ECUs) communicate through the CAN bus protocol, which was not designed to be secure. This paper presents a proof-of-concept of TOUCAN, a new security protocol designed to secure CAN bus communications following the AUTOSAR standard. The presentation introduces design, implementation and performance of TOUCAN on a test-bed composed by two inexpensive boards that can be demonstrated to exchange secure TOUCAN frames.
Combined wavelet and Gabor convolution neural networks

Handwriting recognition is a very active research in the machine learning community. In this paper, we tackled two important applications: handwritten digit recognition and Signature verification using convolution neural network (CNN). Signature is one of the most popular personal attributes for authentication. It is basic, shabby and adequate to individuals, official associations and courts. This paper focuses on offline signature verification (SV). It is a kind of a classification problem, which classifies the signature as genuine, or forgery. We use CNN in two types of datasets: the MNIST database, and UTSIG database. In order to obtain better accuracy, we propose to preprocess the data in the wavelet domain and in the Gabor filter combining the outputs of both CNN. This combination resulted in higher recognition accuracy compared to other techniques.
Polarimetric Camera Calibration Using an LCD Monitor

It is crucial for polarimetric imaging to accurately calibrate the polarizer angles and the camera response function (CRF) of a polarizing camera. When this polarizing camera is used in a setting of multiview geometric imaging, it is often required to calibrate its intrinsic and extrinsic parameters as well, for which Zhang's calibration method is the most widely used with either a physical checker board, or more conveniently a virtual checker pattern displayed on a monitor. In this paper, we propose to jointly calibrate the polarizer angles and the inverse CRF (ICRF) using a slightly adapted checker pattern displayed on a liquid crystal display (LCD) monitor. Thanks to the lighting principles and the industry standards of the LCD monitors, the polarimetric and radiometric calibration can be significantly simplified, when assisted by the extrinsic parameters estimated from the checker pattern. We present a simple linear method for polarizer angle calibration and a convex method for radiometric calibration, both of which can be jointly refined in a process similar to bundle adjustment. Experiments have verified the feasibility and accuracy of the proposed calibration method.
Form Feedback on the Web: A Comparison of Popup Alerts and In-Form Error Messages

The literature on form feedback mostly focuses on in-form error messages, yet popup alert messages are still prevalent on the web. This study, therefore, set out to identify the differences between popup alerts and in-form messages. A study involving n = 100 participants was designed. The results show only a small significant difference between popup alerts and in-form messages in terms of task completion time. Subjective evaluations of the two methods did not reveal any significant preference for either method.
Understanding retrospection: Blended joint attention and the multimodal construction of film flashbacks

The analysis of film discourse from a multimodal and cognitive perspective has shown in recent years that such an approach to the study of cinema is a very fruitful one. Among the various cinematic techniques that may be analysed as pieces of multimodal discourse, the flashback seems to be particularly appealing because, while being very rich and versatile, it is also a fixed device and common enough in film as to be studied in a systematic way. Given those characteristics – formal variety alongside stability – a relevant question would be: how do spectators make sense of film retrospections? To address this question, this paper suggests an examination of the multimodal cues offered by flashbacks in three different films – Ordinary People (1980), Big Fish (2003) and The Help (2011) – and analyses the cognitive processes that those cues activate and which make the comprehension of the flashback possible. What lies at the basis of the flashback scenes proposed is a joint-attention triangle formed by the viewer and the camera, who look together, first at the character in the present and then at the events taking place in the past. Ultimately, such scenes can only be understood in terms of blended joint attention, and they also reveal the importance of other cognitive processes at work, namely time compression, viewpoint integration, and identity and analogy connections.
Hardness of 3D Motion Planning Under Obstacle Uncertainty

We consider the problem of motion planning in the presence of uncertain obstacles, modeled as polytopes with Gaussian-distributed faces (PGDF). A number of practical algorithms exist for motion planning in the presence of known obstacles by constructing a graph in configuration space, then efficiently searching the graph to find a collision-free path. We show that such a class of algorithms is unlikely to be efficient in the domain with uncertain obstacles. In particular, we show that safe 3D motion planning among PGDF obstacles is \(NP-\)hard with respect to the number of obstacles, and remains \(NP-\)hard after being restricted to a graph. Our reduction is based on a path encoding of \(3-\)SAT and uses the risk of collision with an obstacle to encode the variable assignment. This implies that, unlike in the known case, planning under uncertainty is hard, even when given a graph containing the solution.
Clothing Classification using the Convolutional Neural Network Inception Model

Convolutional neural network (CNN) is a type of artificial neural network commonly used in analyzing images. Images of clothes are great factors in driving the clothing industry today and trends found in the current fashion styles drive the online clothing economy. This study attempts to create a clothing classification tool with the use of convolutional neural networks. A method for creating an automated clothing classifier was presented, trained from 5600 clothing images in 7 classes using the Inception architecture. The trained model obtained an estimated accuracy of 95%. The model identified different clothing categories with an accuracy of 96.2% in coherence with the estimated accuracy result. It also had a recall of 0.981 and a precision of 1. Future works include improvement in recognition capability by enabling future classifiers to recognize and identify full apparel in a single image.
A case study for block-based linked data generation: Recipes as jigsaw puzzles

This article is a proof-of-concept case study to evaluate the functionality of a block metaphor–based linked data generator. In this work, we chose to produce linked data repository of recipes, which provide a medium for people to share their regional and healthy recipes with the masses. However, the same approach can also be adapted easily to other domains. Therefore, the applicability of our approach extends well beyond the food domain that we are considering in this article. As a medium for information sharing and understanding between heterogeneous systems, ontologies will play an important role in the realisation of the Internet of things (IoT) vision. Therefore, an ontology-based recipe repository would also be one of the basic blocks of a smart kitchen environment. However, building ontologies is a challenging task, especially for users who are not conversant in the ontology building languages. This article proposes an approach that can be used even by non-experts and facilitates the sharing and searching of recipe data. In our case, we exploit the features of the block paradigm to publish recipes in Linked Data format. In this way, users do not have to know the OWL (Web Ontology Language) syntax and the text input is kept minimal. As far as we know, this article is the first study that produces linked data using Blockly in the literature. We also conducted a user-based evaluation of the proposed approach using the System Usability Scale (SUS) questionnaire.