Reviewerly/exHarmony huggingface dataset:
Keeping the Data within the Garment

Fashion technology designs typically combine sensing technology and actuators to register and respond to information about the environment and/or the human body. The ways in which designers use and integrate these data into garments, however, varies on a scale from highly theatrical and outward-oriented designs to subtle and inward-oriented applications. This pictorial presents five garment designs created between 2013 and 2020, that occupy the more utilitarian and inward-oriented end of the fashion technology spectrum (Fig 1). We visualize and analyze how these five designs combine sensing and actuation, highlighting the benefits of direct biofeedback and of keeping the personal data within the garment. The pictorial aims to show that striking the right balance between sensing and actuation is pivotal to realizing the physical, functional, social and ethical wearability of fashion technology design.
Automatic Uncertainty Propagation Based on the Unscented Transform

Automatic uncertainty propagation reduces the effort for computation of uncertainty and is thus a useful tool in a variety of applications. Typically, such tools utilize Taylor series approximations (in particular linearization) or Monte Carlo Methods to perform the calculations. In this paper, we propose the use of the Unscented Transform for automatic uncertainty propagation. A comparison between the approaches - realized in a toolbox for the MATLAB environment and illustrated in two application examples - shows that the Unscented Transform overcomes some of the limitations of linearization and Monte Carlo methods, providing reliable estimates of the output expectation and standard deviation in nonlinear problems evaluating a reduced number of sigma points.
NON-WELL-FOUNDED PROOFS FOR THE GRZEGORCZYK MODAL LOGIC

Abstract We present a sequent calculus for the Grzegorczyk modal logic $\mathsf {Grz}$ allowing cyclic and other non-well-founded proofs and obtain the cut-elimination theorem for it by constructing a continuous cut-elimination mapping acting on these proofs. As an application, we establish the Lyndon interpolation property for the logic $\mathsf {Grz}$ proof-theoretically.
Integrating Individual and Aggregate Diversity in Top-<i>N</i> Recommendation

Recommender systems have become one of the main components of web technologies that help people to cope with information overload. Based on the analysis of past user behavior, these systems filter items according to users’ likes and interests. Two of the most important metrics used to analyze the performance of these systems are the accuracy and diversity of the recommendation lists. Whereas all the efforts exerted in the prediction of the user interests aim at maximizing the former, the latter emerges in various forms, such as diversity in the lists across all user recommendation lists, referred to as aggregate diversity, and diversity in the lists of individuals, known as individual diversity. In this paper, we tackle the combination of these three objectives and justify this approach by showing through experiments that handling these objectives in pairs does not yield satisfactory results in the third one. To that end, we develop a mathematical model that is formulated using multiobjective optimization approaches. To cope with the intractability of this nonlinear integer programming model, its special structure is exploited by a decomposition technique. For the solution of the resulting formulation, we propose an iterative framework that is composed of a clique-generating genetic algorithm, a constructive heuristic, and an improvement heuristic. The former is designed to incorporate all objective functions into the generated cliques and specifically impose a certain level of individual diversity, whereas the latter chooses one clique for each user such that the desired aggregate diversity level is fulfilled. We conduct experiments on three data sets and show that the proposed modeling approach successfully handles all objectives according to the needs of the system and that the proposed methodology is capable of yielding good upper bounds.
Digital Bookmark

Recently, people prefer to read books via a combination of formats - E-Books along with printed books. We conducted a scoping survey and a lab-study which informed various inhibiting factors associated with switching between formats to conveniently use multiple formats. To improve the switching experience, we present Digital Bookmark that synchronises the current page location digitally between both printed and e-books. The page number of the printed book is electronically read using a conductive tag and transmitted to the e-book via the internet. The current location on the e-book is converted to the corresponding page number of the printed book and presented on the display of the Digital Bookmark. We present the results of a controlled lab-study to assess the parameters of switching between printed and electronic books. The initial feedback from a local reading group suggests that our Digital Bookmark would encourage multi-format reading and improve their user experience.
Towards an evolutionary-based approach for natural language processing

Tasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community. The increased interest in this area is mainly due to the success of deep learning methods. Genetic Programming (GP), however, was not under the spotlight with respect to NLP tasks. Here, we propose a first proof-of-concept that combines GP with the well established NLP tool word2vec for the next word prediction task. The main idea is that, once words have been moved into a vector space, traditional GP operators can successfully work on vectors, thus producing meaningful words as the output. To assess the suitability of this approach, we perform an experimental evaluation on a set of existing newspaper headlines. Individuals resulting from this (pre-)training phase can be employed as the initial population in other NLP tasks, like sentence generation, which will be the focus of future investigations, possibly employing adversarial co-evolutionary approaches.
Design of Pothole Detector Using Gray Level Co-occurrence Matrix (GLCM) And Neural Network (NN)

Abstract Roads are land transportation infrastructure that covers all parts of the road. Roads with bad conditions will interfere with the achievement of activities to a destination. The situation also includes damage to the road surface in the form of holes. To overcome this, in this Final Project a hole detector was detected in the road using the Gray Level Co-occurrence Matrix (GLCM) and Neural Network (NN). The tool detects holes in the surface of the road using a camera by walking along the road being examined. The camera is used instead of the eye to detect road surface damage. The method used to detect holes is the GLCM. The GLCM method produces several features, namely entropy, contrast, energy, homogeneity, and correlation which will then be processed using a NN to produce a decision whether there is a hole or not. In addition to knowing where the location of the damage is equipped with GPS (Global Positioning System). The results of image feature extraction using the GLCM and road classification using NN can be used in the hole detection process. Testing is done using a car prototype that is monitored through the computer. The percentage of successful hole detection is 86.6% using 10 hidden. When a hole is detected the device manages to take a picture, then sends the hole coordinates to the server.
Useless, not Worthless

We report on the outcomes of a hackathon organised around the themes of absurd musical interfaces, questionable sonic interactions and unworkable music designs. At the core of the project is the intention to explore absurd making as a way to support critical and disruptive design practices. We reflect on how surreal, nonsensical and fragile artefacts can be helpful to stretch and critique conventional ideas of what is useful and appropriate in technology research and development. After introducing both concepts and methods that shaped the event we present a selection of useless interfaces designed by the hackathon's attendees. These musical artefacts, and the considerations around them, are then discussed as a viable means for communicating both design concerns and future visions. We also consider two features identified as playing a crucial role within the event: the discovery of contradictions and the importance of context-based ingredients.
Traffic Signs Recognition in a mobile-based application using TensorFlow and Transfer Learning technics

Nowadays, Machine Learning applications are spreading widely in different science and research fields which gave, in fact, the possibility to enhance the results of all kind of both, automated tasks and further possible application areas. Autonomous smart driving cars presents one of the major fields that uses machine learning technics to push further the automated tasks inside the car systems. Many types of research related to this topic enabled real application to fully automate some parts of the car driving process. Road lane detection, pedestrian and car approximation detection, and fastest road finding using real-time traffic statistics present some of the possible application areas that could use the Machine learning technics to improve autonomous driving cars systems. Traffic signs present an important part of the daily driving routine, therefore, traffic signs recognition for mobile-based application is a great solution that provides a new layer for autonomous car driving systems. In this paper, we propose a powerful tool for traffic signs recognition in a mobile-based application. This tool uses TensorFlow together with transfer learning technic that makes it easier to train our dataset on a pre-trained Model using the convolutional network (ConvNet). The used model is a Single Shot MultiBox Detector (SSD) MobileNet V2 based model which uses one single deep network to train the model on multiple objects per image. This network uses 300x300 annotated input images with multiple objects to provide faster training time and faster detection results compared to other types of neural networks. The annotation is made by providing the coordinates of the rectangle that surrounds the given object together with its label which defines the name of the object. The coordinates are usually given by providing the (x,y) coordinates of the top-left and bottom-right points of the surrounding rectangle. This presents a powerful technic for real-time detection on mobile devices with low computational capabilities. The resulting model of the training is then converted to a TensorFlow Lite quantized model using TensorFlow Lite converter which provides compatibility with mobile devices with low computational capacity. The quantized model showed 4 times faster detection compared to the float model on the mobile device.
Settings-Free Hybrid Metaheuristic General Optimization Methods

Several population-based metaheuristic optimization algorithms have been proposed in the last decades, none of which are able either to outperform all existing algorithms or to solve all optimization problems according to the No Free Lunch (NFL) theorem. Many of these algorithms behave effectively, under a correct setting of the control parameter(s), when solving different engineering problems. The optimization behavior of these algorithms is boosted by applying various strategies, which include the hybridization technique and the use of chaotic maps instead of the pseudo-random number generators (PRNGs). The hybrid algorithms are suitable for a large number of engineering applications in which they behave more effectively than the thoroughbred optimization algorithms. However, they increase the difficulty of correctly setting control parameters, and sometimes they are designed to solve particular problems. This paper presents three hybridizations dubbed HYBPOP, HYBSUBPOP, and HYBIND of up to seven algorithms free of control parameters. Each hybrid proposal uses a different strategy to switch the algorithm charged with generating each new individual. These algorithms are Jaya, sine cosine algorithm (SCA), Rao’s algorithms, teaching-learning-based optimization (TLBO), and chaotic Jaya. The experimental results show that the proposed algorithms perform better than the original algorithms, which implies the optimal use of these algorithms according to the problem to be solved. One more advantage of the hybrid algorithms is that no prior process of control parameter tuning is needed.
Full-text Search Using Database Index

Database management systems usually allow partial text lookups using LIKE clauses and Regular Expressions. However, LIKE clauses and regular Expressions tend to underperform on larger datasets. They lack in performance as databases need to check the table to find whether there is the same word pattern as in regular expressions or as in LIKE statement. But it is difficult to have an efficient search query if we use regular expressions or LIKE Statements. If we use LIKE operator on the un-indexed column it would be extremely difficult to find out the matches because it needs to fully scan the columns. If the column is indexed then matching can be better than fully scan as matching can be performed against index keys. In worst case LIKE operator may have leading wildcards that require every index to be checked. Due to these limitations we have, we need to use another method which is efficient and flexible and there comes the full-text search where indexes are formed by words(from the words in the columns where the full-text search is enabled). Full-text search can be extended by incorporating semantics. By incorporating operators which support semantics [7], the search can be made more fruitful. Our proposed work will incorporate semantics-related operators in full-text search in PostgreSQL.
An Exploratory Analysis Pertaining to Stress Detection in Adolescents

With the rise of social networking era, there is a sudden and great increase in user-generated content. Millions of people share their daily life and status by blogging on social media like Twitter. Therefore, it is a great source to analyze sentiments by simply using the text of social media and simple manner of expression. This paper addresses the issues of mental health such as psychological stress and depression, particularly in adolescents. However, adolescents’ emotions are mostly reflected in their posts such as tweets and Facebook posts, which may include some words usually, not spoken freely in an open environment. This paper took the advantages of extracting the data from Twitter and performed an exploratory analysis to identify the stress in adolescents. Four popular machine learning algorithms, namely Naïve Bayes, random forest, support vector machine, and logistic regression, were employed.
Exploring Location Histories as a Design Material for Reflection with Memory Compass &amp; Memory Tracer

There are a growing number of GPS-based smartphone applications that record a person's location over time. This accumulation of geolocation metadata offers a valuable resource for supporting reflection on past life experiences. Yet, little design research has explored how location histories can be applied as a material in designing such experiences. We propose Memory Compass, an application that offers a novel way to explore your past, and Memory Tracer, a device which periodically surfaces location-based past moments from your life. We reflect on key decisions in our process and early implications for future research.
Video Object-Tracking Using Particle Filtering and Feature Fusion

In this paper, a novel video tracking scheme is proposed using the notion of particle filtering. For each pixel of the frame, two features namely Local Binary Pattern (LBP) and the RGB are fused to generate a new feature. Fusion is carried out in the probabilistic framework and the fusion coefficients are determined based on trial and error. Particle filter based modeling is used to track the object in the feature plane. The proposed scheme has been tested on different frames of different benchmarked data sets and the performance of the proposed scheme is found to be superior than the existing method.
Improvement of Learning Stability of Generative Adversarial Network Using Variational Learning

In this paper, we propose a new network model using variational learning to improve the learning stability of generative adversarial networks (GAN). The proposed method can be easily applied to improve the learning stability of GAN-based models that were developed for various purposes, given that the variational autoencoder (VAE) is used as a secondary network while the basic GAN structure is maintained. When the gradient of the generator vanishes in the learning process of GAN, the proposed method receives gradient information from the decoder of the VAE that maintains gradient stably, so that the learning processes of the generator and discriminator are not halted. The experimental results of the MNIST and the CelebA datasets verify that the proposed method improves the learning stability of the networks by overcoming the vanishing gradient problem of the generator, and maintains the excellent data quality of the conventional GAN-based generative models.
On the Perception and Use of Information from Social Media in Investigative Police Work: Findings from a Scandinavian Study

Abstract The purpose of this article is to report from a qualitative Scandinavian study with the aim of shedding some light on how investigators in the Scandinavian police services perceive the use of information from social media in investigative police work. Based on 12 group interviews and 49 informants from Danish, Norwegian, and Swedish police services, we present three overarching themes mirroring the general perception amongst the interviewed investigators that: (1) information from social media is valuable in almost all types of crime investigation; (2) the use of social media information is fraught with technical pitfalls resulting in a general fear of making mistakes; (3) the legal frameworks governing digital investigative action are vague, leading to a feeling amongst the investigators of working in a grey zone. Overall, the informants express the view that this seemingly unregulated part of investigative work requires a major overhaul.
"This just felt to me like the right thing to do"

Parenting comes with many responsibilities, one of which is making ongoing decisions affecting their child's health. While today's parents have access to an abundance of parenting advice and data-both offline and online-little is known about their lived experience with these resources and how it interacts with other aspects of decision-making like intuition. Drawing on a survey of 65 parents and interviews of 12 parents of children aged 0-5 in the U.S., we provide the following contributions: an analysis of parents' experiences and needs when using different resources to make health and wellbeing decisions for their child; a definition of parents' lived experiences with intuition throughout the decision-making process; and a discussion of tensions and opportunities for designing in this sensitive space. Our findings can inform new design directions for interactive technology-based parenting support, particularly the potential to consider intuition and make parenting information and data more socially oriented.
Topic Analysis of Internet Public Opinion on Natural Disasters Based on Time Division

With the rapid development of the Internet, Internet public opinion has received more and more attention. Natural disasters are sudden and real-time. However, the current stage of Internet public opinion is often divided only by the size of the public opinion, which can't accurately applied to public opinion on natural disaster networks. This paper uses the similarity calculation method to consider the impact of text content semantics on the division of Internet public opinion stage, and proposes a scheme for adaptively dividing the time series based on text similarity. The experiment finally completed the dynamic time series division of Internet public opinion, followed the public opinion hotspots in real time during the development of the incident, studied the evolution of Internet public opinion hotspots in different stages, and provided a reference for the government to monitor the changes in the Internet public opinion hotspots in real-time and formulate a response decision.
Authorship Identification for Arabic Texts Using Logistic Model Tree Classification

Identifying the author of an anonymous text based on a predefined collection of sample texts for candidate authors is called Authorship identification. Due to the huge increase in the volume of digital texts, authorship identification becomes a need. This paper proposed a novel set of features in addition to the known features in the field in order to improve the authorship identification process in Arabic texts. The proposed framework in this paper was evaluated using a dataset that is a part of the Authorship attribution of Ancient Arabic Texts dataset. The used classifier in this research is the Logistic Model Tree classifier, which works fine with small datasets. The experimental results showed an accuracy of 88.73%. Such results support the efficacy of the proposed framework in order to identify the author of an anonymous text.
Privacy Protection in Smart Health

Smart health exploits smart health devices (e.g., fitness trackers, heart rate or glucose monitoring units) and Internet of Things technologies to improve users' health and wellness. By enabling self-monitoring and data sharing among users and healthcare professions, smart health can increase healthy habits, timely treatments, reduce hospital visits/re-admissions and even save lives. While smart health comes with great benefits, it also poses a privacy threat to the re-identification of users and their personal data. This paper presents an approach to protecting users' privacy by generalizing critical data so that they belong to multiple users as a way to anonymize user identity. Unlike existing anonymization techniques, our approach efficiently produces shared data that satisfy user-specified anonymity requirements while keeping the data as informative as possible. The approach is based on an Artificial Intelligence search technique using two proposed heuristics. The paper describes and illustrates the approach with experiments to compare its effectiveness with other techniques. The results show that, given a trade-off of privacy preserving, data retention and computational cost, our approach gives the most effective solution for data sharing as expected.
Introduction of Non-Volatile Computing In Memory (nvCIM) by 3D NAND Flash for Inference Accelerator of Deep Neural Network (DNN) and the Read Disturb Reliability Evaluation : (Invited Paper)

In this paper, we introduce the optimal design methods of 3D NAND nvCIM [1], and then address the read disturb reliability issue. In recent years CIM [2] is widely considered as a promising solution to accelerate the DNN inference hardware. Theoretically, nvCIM can drastically reduce the power consumption by data movement because of no need to move the weights during computation. 3D NAND has the advantage of extremely low Icell (~nA), while the large ON/OFF ratio provides the capability to sum >10'000 cells together to improve the performance bandwidth and energy efficiency. We think that 3D NAND nvCIM has the potential to serve as the inference accelerator for the high-density fully- connected (FC) network which often requires high-bandwidth inputs. The read disturb property is studied. It is suggested that the "on-the-fly" calibration technique can well maintain the inference accuracy for 10-year usage.