Reviewerly/exHarmony huggingface dataset:
Automated amortised resource analysis for term rewrite systems

In this paper we establish an automated amortised resource analysis for term rewrite systems. The method is presented in an inference system akin to a type-and-effect system and gives rise to polynomial bounds on the innermost runtime complexity of the analysed term rewrite system. Our analysis does not restrict the input rewrite system in any way so that rewrite systems may serve as abstractions of first-order, eagerly evaluated functional programs over user-defined inductive data-types. This facilitates integration in a general framework for resource analysis of programs. In particular, we have implemented the method and integrated it into our analysis tool . Furthermore, we have coupled the established analysis with a complexity reflecting transformation from pure OCaml programs. This extends the provided analysis to a fully automated resource analysis of realistic functional programs.
Shearography Technique on Inspection of Advanced Aircraft Composite Material

Abstract The A320 aft fixed fairing is an advanced composite structure fabricated from laminate carbon woven and combines with honeycomb core which is externally mounted on the flap track fairing as a wing part of Airbus aircraft. These flaps are movable control surfaces to widen up the surface of the wing in order to increase the lifting. This paper discussed on the inspection of A320 aft fixed fairing by using shearography NDT technique. Shearography technique reveals the stress-affected zone due to additional loading that can be realized by the laser speckle correlation on the inspected composite panel. This A320 aft fixed fairing is identified as a scrap composite panel which having different sizes and orientations of defect. The results showed that the changes of shearing direction in laser shearography technique are an effective means to reveal the defects propagation in the composite component. The shearing at Y-axes direction is able to detect the cracks at horizontal orientation while shearing at X-axis has successfully detects the cracks at vertical orientation. Therefore, shearography could be utilized as an alternative inspection for other NDT method for inspection of composite material.
Yours Truly? Survey on Accessibility of Our Personal Data in the Connected World

Due to the advancement of embedded sensing and wireless communication technologies, in recent times, there is a sharp rise in the number of smart personal and home (multi-user) specific devices. Sensors embedded in these devices collect significant amount of personal data including raw sensor data and derived data, such as, motion, location, heart rate, oxygen saturation, step counts. This user generated data can be helpful to understand their behavior, activity, health, and quality of life and can help healthcare providers (if shared with users' consent), to get a better insight about patients and their everyday life. The lack of control over these personal data and their flow makes the user vulnerable to external attacks and data abuse. Also, most of the devices do not allow the users to access and share their self-produced raw sensor data. In this study, we investigate the accessibility of the user generated data for a wide range of currently available devices. We also present an overview of the type of data that popular home and wearable devices collect from their users. Finally, we propose a more accessible data platform where users can store and manage their data with other stakeholders in an acces.s-controlled manner.
Design and Diversity?

This pictorial engages with the concept of 'diversity' as it has been taken up in the current technology industry by companies such as Google, Facebook, and Twitter. Drawing on methods of design fiction, we use a visual parody of a fictional social media corporation to explore possible consequences of diversity efforts-both within industrial workplace settings and the platforms they produce. By outwardly foregrounding diversity through explicitly absurd presentations, our investigation uses humor and farce to draw attention to how such efforts further entrench the very forms of institutional racism, ableism, and sexism they are meant to disrupt; for example, by creating additional work for the people they are designed to include. We offer these visual explorations not to promote or discourage diversity efforts, but to help design researchers imagine what such efforts become when labor and unintended consequences go unaccounted.
Elastic, geo-distributed RAFT

Raft is a protocol to maintain strong consistency across data replicas in cloud. It is widely used, especially by workloads that span geographically distributed sites. As these workloads grow, Raft's costs should grow, as least proportionally. However, auto scaling approaches for Raft inflate costs by provisioning at all sites when one site exhausts its local resources. This paper presents Geo-Raft, a scale-out mechanism that enables precise auto scaling for Raft. Geo-Raft extends Raft with the following abstractions: (1) secretaries which takes log processing for the leader and (2) observers which process read requests for followers. These abstractions are stateless, allowing for elastic auto scaling, even on unreliable spot instances. Geo-Raft provably preserves strong consistency guarantees provided by Raft. We implemented and evaluated Geo-Raft with multiple auto scaling techniques on Amazon EC2. Geo-Raft scales in resource footprint increments 5-7X smaller than Multi-Raft, the state of the art. Using spot instances, Geo-Raft reduces costs by 84.5% compared to Multi-Raft. Geo-Raft improves goodput of 95th-percentile SLO by 9X.Geo-Raft operates key-value services for 6 months without losing data or crash.
PPAP: Perspective Projection Augment Platform with Pan–Tilt Actuation for Improved Spatial Perception

In this paper, we propose PPAP, an augmented reality platform with an actuated projector for dynamic user-perspective projection. In PPAP, a stationary camera is used jointly with a pan-tilt motorized projector-camera unit. With the servo control of the steerable pan-tilt system, the system is able to continuously orient itself to match the user's view center of the projection-mapped surface. This provides users with greatly widened viewing angles in the augmented scene, when compared to the stationary projection. Through user studies, in which users judged the size and distance of a projected virtual object, we verified that the perspective projection with the actuated projector helps users better understand the spatial relationship of the virtual object in the augmented scene in terms of depth perception.
DRONE: A Tool to Detect and Repair Directive Defects in Java APIs Documentation

Application programming interfaces (APIs) documentation is the official reference of the APIs. Defects in API documentation pose serious hurdles to their comprehension and usage. In this paper, we present DRONE, a tool that can automatically detect the directive defects in APIs documents and recommend repair solutions to fix them. Particularly, DRONE focuses on four defect types related to parameter usage constraints. To achieve this, DRONE leverages techniques from static program analysis, natural language processing and logic reasoning. The implementation is based on the Eclipse-plugin architecture, which provides an integrated user interface. Extensive experiments demonstrate the efficacy of the tool.
Decisional Second-Preimage Resistance: When Does SPR Imply PRE?

There is a well-known gap between second-preimage resistance and preimage resistance for length-preserving hash functions. This paper introduces a simple concept that fills this gap. One consequence of this concept is that tight reductions can remove interactivity for multi-target length-preserving preimage problems, such as the problems that appear in analyzing hash-based signature systems. Previous reduction techniques applied to only a negligible fraction of all length-preserving hash functions, presumably excluding all off-the-shelf hash functions.
Real-time camera pose estimation based on volleyball court view

The use of technology in sports has increased in recent years.One of the most influential of these technologies is referee support systems.Team sports such as volleyball require accurate and robust tracking systems that do not affect either the players or the court.This paper introduces the application of intrinsic and extrinsic camera calibration in a 12-camera volleyball referee system.Intrinsic parameters are calculated by using the classic pinhole model and Zhang's method.To perform extrinsic calibration in real time, the volleyball court is treated as a global calibration artifact.Calibration keypoints are defined as courtline intersections.In addition, a new keypoint detection algorithm is proposed.It enables achievement of an accurate camera pose in regard to the court.With all 12 cameras calibrated in a common coordinate system, a dynamic camera stereo pair creation is possible.Therefore, with known ball 2D image coordinates, the 3D real ball coordinates can be reconstructed and the ball trajectory can be estimated.The performance of the proposed method is tested on a synthetic data set, including 3Ds Max rendering and real data scenarios.The mean camera pose error calculated for data biased with keypoint detection errors is approximately equal to 0.013% of the measurement volume.For the real data experiment with a human hand phantom, it is possible to determine the presence of the human phantom on the basis of the ball reflection attitude.
Exploring Methods for Predicting Important Utterances Contributing to Meeting Summarization

Meeting minutes are useful, but creating meeting summaries are a time consuming task. Aiming at supporting such task, this paper proposes prediction models for important utterances that should be included in the meeting summary by using multimodal and multiparty features. We will tackle this issue from two approaches: Handcrafted feature models and deep neural network models. The best handcrafted feature model achieved 0.707 in F-measure, and the best deep-learning based verbal and nonverbal model (V-NV model) achieved 0.827 in F-measure. Based on the V-NV model, we implemented a meeting browser, and conducted a user study. The results showed that the proposed meeting browser better contributes to the understanding of the content of the discussion and the participant roles in the discussion than the conventional text-based browser.
A Lower Bound of Fast Algebraic Immunity of a Class of 1-Resilient Boolean Functions

Boolean functions should possess high fast algebraic immunity when used in stream ciphers in order to stand up to fast algebraic attacks. However, in previous research, the fast algebraic immunity of Boolean functions was usually calculated by the computer. In 2017, Tang, Carlet, and Tang first mathematically proved that every function belonging to a class of 1-resilient Boolean functions has the fast algebraic immunity no less than n - 6. Inspired by the Tang's method, we also demonstrate that the fast algebraic immunity of another class of the 1-resilient Boolean functions is no less than n - 6. Meanwhile, we also prove some combinator facts originated from the Tu-Deng Conjecture.
Distance Metrics in Open-Set Classification of Text Documents by Local Outlier Factor and Doc2Vec

In this paper, we investigate the influence of distance metrics on the results of open-set subject classification of text documents. We utilize the Local Outlier Factor (LOF) algorithm to extend a closed-set classifier (i.e. multilayer perceptron) with an additional class that identifies outliers. The analyzed text documents are represented by averaged word embeddings calculated using the fastText method on training data. Conducting the experiment on two different text corpora we show how the distance metric chosen for LOF (Euclidean or cosine) and a transformation of the feature space (vector representation of documents) both influence the open-set classification results. The general conclusion seems to be that the cosine distance outperforms the Euclidean distance in terms of performance of open-set classification of text documents.
SMT-based Planning for Robots in Smart Factories

Smart factories are on the verge of becoming the new industrial paradigm, wherein optimization permeates all aspects of production, from concept generation to sales. To fully pursue this paradigm, flexibility in the production means as well as in their timely organization is of paramount importance. AI planning can play a major role in this transition, but the scenarios encountered in practice might be challenging for current tools. We explore the use of SMT at the core of planning techniques to deal with real-world scenarios in the emerging smart factory paradigm. We present special-purpose and general-purpose algorithms, based on current automated reasoning technology and designed to tackle complex application domains. We evaluate their effectiveness and respective merits on a logistic scenario, also extending the comparison to other state-of-the-art task planners.
Human Action Recognition for Social Robots

Social assistive robotics is at the forefront of the effort for ensuring independent living and inclusion, especially for the elderly and vulnerable members of the population. We consider that to be proactive is a core requirement of such robotic systems. One key aspect of being proactive is to understand what the user is doing. We explored the task of human action recognition in the context of a social robotic platform based on a framework that we proposed in earlier work. We implemented a processing pipeline specific to this task then collected a dataset using a Pepper robot. The proposed domain adaptation method to increase performance of the method showed promising results.
CCEvovis: A Clone Evolution Visualization System for Software Maintenance

Understanding the evolution of code clones is important in software maintenance. With the information about how code clones evolve, both developers and researchers can understand the impacts of code clones and build a more robust code clone management system. So far, many studies have investigated the evolution of code clones to better understand the effects of code clones. However, only a few systems have been presented to support managing code clones based on the information about how code clone evolves. To mitigate this problem, in this paper, we present CCEvovis, a system that visualizes the evolved code clones across multiple versions of a program. CCEvovis highlights and visualizes the clone change to support software maintenance. CCEvovis is available at: https://github.com/hirotaka0616/CCEvovis.
A Reverse Turing Test for Detecting Machine-Made Texts

As AI technologies rapidly advance, the artifacts created by machines will become prevalent. As recent incidents by the Deepfake illustrate, then, being able to differentiate man-made vs. machine-made artifacts, especially in social media space, becomes more important. In this preliminary work, in this regard, we formulate such a classification task as the Reverse Turing Test (RTT) and investigate on the contemporary status to be able to classify man-made vs. machine-made texts. Studying real-life machine-made texts in three domains of financial earning reports, research articles, and chatbot dialogues, we found that the classification of man-made vs. machine-made texts can be done at least as accurate as 0.84 in F1 score. We also found some differences between man-made and machine-made in sentiment, readability, and textual features, which can help differentiate them.
A Novel Distribution Analysis for SMOTE Oversampling Method in Handling Class Imbalance

Class Imbalance problems are often encountered in many applications. Such problems occur whenever a class is under-represented, has a few data points, compared to other classes. However, this minority class is usually a significant one. One approach for handling imbalance is to generate new minority class instances to balance the data distribution. The Synthetic Minority Oversampling TEchnique (SMOTE) is one of the dominant oversampling methods in the literature. SMOTE generates data using linear interpolation between minority class data point and one its K-nearest neighbors. In this paper, we present a theoretical and an experimental analysis of the SMOTE method. We explore the accuracy of how faithful SMOTE method emulates the underlying density. To our knowledge, this is the first mathematical analysis of the SMOTE method. Moreover, we study the impacts of the different factors on generation accuracy, such as the dimension of data, the number of examples, and the considered number of neighbors K on both artificial, and real datasets.
A Study on Machine Vision Techniques for the Inspection of Health Personnels’ Protective Suits for the Treatment of Patients in Extreme Isolation

The examination of Personal Protective Equipment (PPE) to assure the complete integrity of health personnel in contact with infected patients is one of the most necessary tasks when treating patients affected by infectious diseases, such as Ebola. This work focuses on the study of machine vision techniques for the detection of possible defects on the PPE that could arise after contact with the aforementioned pathological patients. A preliminary study on the use of image classification algorithms to identify blood stains on PPE subsequent to the treatment of the infected patient is presented. To produce training data for these algorithms, a synthetic dataset was generated from a simulated model of a PPE suit with blood stains. Furthermore, the study proceeded with the utilization of images of the PPE with a physical emulation of blood stains, taken by a real prototype. The dataset reveals a great imbalance between positive and negative samples; therefore, all the selected classification algorithms are able to manage this kind of data. Classifiers range from Logistic Regression and Support Vector Machines, to bagging and boosting techniques such as Random Forest, Adaptive Boosting, Gradient Boosting and eXtreme Gradient Boosting. All these algorithms were evaluated on accuracy, precision, recall and F 1 score; and additionally, execution times were considered. The obtained results report promising outcomes of all the classifiers, and, in particular Logistic Regression resulted to be the most suitable classification algorithm in terms of F 1 score and execution time, considering both datasets.
A root cause analysis method for preventing erratic behavior in software development: PEBA

Measures taken to prevent faults from being introduced or going undetected can secure development of highly reliable software systems. One such measure is analyzing root causes of recurring faults and preventing them from appearing again. Previous methods developed for this purpose have been reactive in nature and relied heavily on fault reporting mechanisms of ogranizations. Additionally, previous efforts lack a defined mechanism for innovating corrective actions. In this study, we strive to complement the existing methods by introducing a proactive and qualitative method that does not rely on fault data. During the course of the research, in addition to an extensive literature search, an empirical field study is conducted with representatives of companies active in safety-critical and business-critical domains. Our proposed method relies on identifying mismatches between development practices and development context in order to predict erratic behaviors. Corrective actions in this method are innovated by resolving these mismatches. The use of the method is demonstrated in two safety-critical projects. Evaluation of the proposed method is done by two experts with respect to proactivity, resource-intensity, and effectiveness.
How are Mobile Makerspaces Utilized in Schools?

To explore diverse means to apply digital fabrication in formal education, this poster presents an overview of the literature regarding the use of mobile makerspaces in K-12 school contexts. Among the reviewed literature, mobile makerspace activities were integrated with school curriculums, especially in STEM fields, and teachers were highly involved in planning and implementing the activities. We noticed that technology experts support the activities as well as teachers' professional development by providing technical assistance. Our findings contribute to uncover the current practices of mobile makerspaces and call for in-depth scientific investigation.
A Lucas Critique to the Difficulty Adjustment Algorithm of the Bitcoin System

We develop an economic model of the Proof-of-Work blockchain system. We demonstrate that, when the hash supply is highly elastic, the Bitcoin difficulty adjustment algorithm (DAA) fails to stabilize the block arrival rate. In contrast, the DAA of another cryptocurrency, Bitcoin Cash, is stable under a weak condition. We also provide empirical evidence and simulation results supporting the model’s prediction. The current Bitcoin system might collapse once a sharp price reduction lowers the mining reward. While this crisis can be prevented through upgrading, miners may disagree because a large fraction of miners profit from the instability of DAAs.
Personalising Security Education: Factors Influencing Individual Awareness and Compliance

Security education and awareness are frequently overlooked for users in both workplace and personal contexts, and even where some level of provision is offered it is rarely done in a manner that is matched specifically to the needs of the audience. However, by personalising the provision, and making the presentation and messaging more appropriate to the individuals receiving it, there is a greater chance of achieving understanding, engagement, and resultant compliance. This paper examines the gap that exists between the typical and desirable provision of security education. It highlights baseline areas of security literacy that ought to be applicable to all users, but then illustrates how variations in individuals’ understanding of threshold concepts could complicate the task of delivering the related education. It is proposed that security education should be more tailored, recognising factors such as the user’s role, prior knowledge, learning style, and current perception of security, in order to deliver a more personalised security education plan that is framed towards individual circumstances and can be delivered in a manner that suits their needs.
A Requirements Catalog of Mobile Personal Health Records for Prenatal Care

Mobile Personal Health Records (mPHRs) for prenatal care are mobile health applications that allow pregnant women manage, access and share their health data with healthcare providers. In this paper, a requirements catalog was developed, according to standards, guidelines and relevant literature. The catalog covers requirements about Sustainability, Internationalization (i18n), Operability, Reliability, Functional suitability and Performance efficiency. The usefulness of this catalog consists in conducting audit evaluations of prenatal care mPHRs, in addition to helping stakeholders and developers identify potential requirements for their mPHRs.