Reviewerly/exHarmony huggingface dataset:
Using the smartphone camera as a sensor for safety applications

Our goal is to study the use of ubiquitous smartphones for designing ITS applications that would improve safety, mobility and efficiency of ground transportation systems making use of their sensing, analysis, and communication capabilities. In this paper we design an implementation that makes use of the optical sensor available in smartphones for alerting users when not maintaining an established safe distance with the vehicle ahead. For proper functioning of the proposed application, the minimum requirement is the use of a device equipped with at least a back camera, that is properly mounted on the dashboard with the camera facing ahead. Thus, taking advantage of this effortless and easy setup, we capture images of the road ahead and of any vehicles in front. These images are then processed to calculate the distance from the lens of the camera to the back of the vehicle travelling ahead. A warning message is displayed on screen if the vehicle ahead and the one following it are too close to each other, thus aiding drivers in the prevention of possible accidents. This first version of the application was tested in static scenarios that did not involve any sort of motion, to validate our approach and assess its feasibility. The results obtained make us optimistic towards its possible acceptance and wide use.
A Multi-Task Velocity-Based Redundancy Resolution Strategy for Unmanned Aerial Manipulators

Unmanned aerial manipulators (UAMs) provide a remarkable level of dexterity on performing complex aerial manipulation tasks thanks to their high number of degrees of freedom (DOFs). By taking advantage of this redundancy, in this paper, a multi-task online redundancy resolution strategy is proposed to accomplish multiple UAM tasks while satisfying additional constraints such as joint angle limit and obstacle avoidance. The proposed approach is computationally inexpensive to run online, allowing UAM to dynamically react to unforeseen events by modifying its configuration during the execution of the primary task. In addition, the proposed approach incorporates the maximization of the manipulability measure into the objective function of the redundancy resolution framework. Moreover, in order to ensure the motion smoothness and satisfy the robot physical constraints the task relaxation approach for aerial manipulation is presented. Extensive simulation studies in ROS-Gazebo environment are performed to validate the performance of the proposed approach.
Co‐evolved genetic programs for stock market trading

Summary The profitability of trading rules evolved by three different optimised genetic programs, namely a single population genetic program (GP), a co‐operative co‐evolved GP, and a competitive co‐evolved GP is compared. Profitability is determined by trading thirteen listed shares on the Johannesburg Stock Exchange (JSE) over a period of April 2003 to June 2008. An empirical study presented here shows that GPs can generate profitable trading rules across a variety of industries and market conditions. The results show that the co‐operative co‐evolved GP generates trading rules perform significantly worse than a single population GP and a competitively co‐evolved GP. The results also show that a competitive co‐evolved GP and the single population GP produce similar trading rules. The profits returned by the evolved trading rules are compared to the profit returned by the buy‐and‐hold trading strategy. The evolved trading rules significantly outperform the buy‐and‐hold strategy when the market trends downwards. No significant difference is identified among the buy‐and‐hold strategy, the competitive co‐evolved GP, and single population GP when the market trends upwards.
Empirical study of customer communication problem in agile requirements engineering

As Agile principles and values become an integral part of the soft-ware development culture, development processes experience significant changes. Requirements engineering, an individual phase occurring at the beginning of the traditional development, is distributed between various activities according to agile. However, how customer communication related problems are solved within the context of agile requirements engineering (RE)? Empirical study of that problem is done using 2 methods: systematic literature review and semi-structured interviews. Problems related to customer communication in agile RE are revealed and composed into patterns. Patterns are to be supplemented with the solutions in the further research.
Predicting Housing Market Trends Using Twitter Data

In this study, we try to predict the Dutch housing market trends using text mining and machine learning as an application of data science methods in finance. Our main goal is to predict the short term upward or downward trend of the average house price in the Dutch market by using text data collected from Twitter. Twitter is widely used as well and has been proven to be a helpful source of data. However, Twitter, text mining (tokenization, bag-of-words, n-grams, weighted term frequencies) and machine learning (classification algorithms) have not been combined yet in order to predict the housing market trends in short term. In this study, tweets including predefined search words are collected relying on domain knowledge, and the corresponding text is grouped by month as documents. Then words and word sequences are transformed into numerical values. These values served as attributes to predict whether the housing market moves up or down, i.e. we approached this as a binomial classification problem relating text data of a month with (up or down) trends for the following month. Our main results reveal there is a correlation between the (weighted) frequency of words and short term housing trends, in other words, we were able to make accurate predictions of trends in short term using multiple machine learning and text mining techniques combined.
Defects caused by Sources other than Processing

We provide two examples of defects that caused real yield degradation on wafers, where the defects were not created during processing of the wafers. One is a case of a common non-process step unexpectedly causing a defect. The second is related to queue time, and shows that even in a modern highly controlled environment not all risks are always fully accounted for. We describe the mechanism behind the defects and provide specific solutions to the issues found. We also comment on the conditions that are most likely to make wafers susceptible to these defects and some guidelines that follow from these observations.
Digital learning technologies in the University education system

Abstract Digital educational resources developed by the authors for information and methodological support of subject disciplines in the system of university education are considered. The digital learning technologies and the capabilities of the electronic resource “Book” are analyzed.
Degrees of Intimacy in SiSSy Systems "How to Join a Team"

In this paper, we describe a notion of participation that allows a system to join an existing system of systems ("team" in this paper) gradually, from a very superficial interaction that does not require much adjustment to a much more detailed sharing of the semantics between the system and the team. The point is to provide a very low initial entry cost for the system, so it can be encouraged to cooperate and expand its participation because the more it shares, the more services it receives. The key is to concentrate on the notion of communication for cooperation, which leads to deep consideration of the semantics of representation languages, for system structure and behavior, and for interaction among systems and teams and their goals. This in turn provides a ready scale by which systems can manage their participation in, and deepen their engagement with, other teams of cooperating systems.
Towards a Virtual Memory Palace

Mnemonics is a powerful tool to assist memorization of a large amount of complex information. Amongst the different techniques, memory palace, also known as the method of Loci, presents significant results in enhancing memory performance for massive lists of numbers, objects and even texts. Substantial research has been conducted to determine both the validity of applying this complex technique in education, as well as that of applying VR to ease the process of learning this technique. Experiments have shown auspicious results suggesting that a virtual environment helps with spatial memory enhancements. However, previous studies have not investigated factors such as training participants over several stages of learning or including interactivity in the virtual environment. The objective of this pilot project is to assess such effects systematically and suggest several guidelines for improving future studies on virtual memory palace.
Anticipation of Human Actions With Pose-Based Fine-Grained Representations

Anticipating an action that is about to happen allows us to be more efficient in interacting with our environment. However, prediction is a challenging task in computer vision, because videos are only partially available when a decision is to be made. Complicating the issue is that it is not always clear which of the visible activities in the scene are relevant to the action, and which ones are not. We suggest that the key to recognizing an action lies with the human actors, and that it is therefore necessary for the prediction process to attend to persons in a scene. In our work, we extract fine-grained features on visible human actors and predict the future via an L2-regression in feature space. This allows the regressed future feature to focus on the actor. Using this, the future action is classified. More specifically, the fine-grained extraction is guided by a pose prediction system that models current and future human poses in the scene. We run qualitative and quantitative experiments on the Charades dataset, and initial results show that our system improves action prediction.
Relaxed Dubins Problems through Three Points

In this paper, we study the Relaxed 3-Point Dubins Problem (R3PDP), which consists of steering a Dubins vehicle through three consecutive waypoints with prescribed heading orientation angle at the initial waypoint. From a geometric point of view, we show that the shortest path must lie in a sufficient family of 12 candidates, and a formula in terms of the parameters of the R3PDP is established for all the 12 candidates. Analyzing the formula indicates that the shortest path of the R3PDP is determined by the zeros of some nonlinear equations. We propose some efficient algorithms to find the zeros of those nonlinear equations so that any R3PDP can be efficiently solved. Finally, some numerical examples are simulated, illustrating the developments of the paper.
Object Classification Based on Unsupervised Learned Multi-Modal Features For Overcoming Sensor Failures

For autonomous driving applications it is critical to know which type of road users and road side infrastructure are present to plan driving manoeuvres accordingly. Therefore autonomous cars are equipped with different sensor modalities to robustly perceive its environment. However, for classification modules based on machine learning techniques it is challenging to overcome unseen sensor noise. This work presents an object classification module operating on unsupervised learned multi-modal features with the ability to overcome gradual or total sensor failure. A two stage approach composed of an unsupervised feature training and a uni-modal and multimodal classifiers training is presented. We propose a simple but effective decision module switching between uni-modal and multi-modal classifiers based on the closeness in the feature space to the training data. Evaluations on the ModelNet 40 data set show that the proposed approach has a 14% accuracy gain compared to a late fusion approach operating on a noisy point cloud data and a 6% accuracy gain when operating on noisy image data.
Utilizing Data Mining Techniques to Predicting IT Occupation of Persons with Disabilities in Thailand

The purpose of this research was to study techniques in predicting IT occupation of persons with disabilities in Thailand by using appropriate data mining technique. The sample consisted of 130 records of individuals with physical, visual, hearing, mental health, intellectual and learning impairments at the Universal Foundation of Persons with Disabilities (Thailand) including 28 features of attributes to construct the competency tradition model with 5 levels of data types. The efficiency of data classification by using techniques including Decision Tree, Naïve Bayesian and Neural Networks. The results showed that the accuracy of three techniques: Decision Tree method, Naïve Bayesian method and Neural Networks was 82.03%, 86.59% and 66.10% respectively. The study revealed that the most accuracy in data classification was the Naïve Bayesian method because the data in competency model often comprise of irrelevant feature or widely range data, thus, the appropriate feature selection before classification approach is suited to optimize the data classification.
SDWP: A New Data Placement Strategy for Distributed Big Data Warehouses in Hadoop

Horizontal partitioning techniques have been used for many purposes in big data processing, such as load balancing, skipping unnecessary data loads, and guiding the physical design of a data warehouse. In big data warehouses, the most expensive operation of an OLAP query is the star join, which requires many Spark stages. In this paper, we propose a new data placement strategy in the Apache Hadoop environment called "Smart Data Warehouse Placement (SDWP)", which allows performing star join operation in only one Spark stage. We investigate the problem of partitioning and load balancing in a cluster of homogeneous nodes. We take into account the characteristics of the cluster and the size of the data warehouse. With our approach, almost all operations of an OLAP query are executed in parallel during the first Spark stage, without a shuffle phase. Our experiments show that our proposed method enhances OLAP query performances in terms of execution time.
Evaluation and Validation of Semi-Supervised Ant-inspired Sentence-Level Sentiment Prediction Clustering

Exact algorithmic clustering approaches are not affordable for many real-world applications, requiring innovative, approximation methods. Among them evolutionary techniques and semi-supervised learning approaches have led to improved performance on several real world applications. In this paper we combine these two approaches to design a semi-supervised clustering algorithm to predict sentiments in sentence-level product reviews. We evaluate and validate the technique to sentence-level sentiment analysis problem and show that compared to baseline techniques, multi-class logistic regression and lexicon based approaches, our technique outperforms by 20%.
Shadows Can Change the Shape Appearances of Real and Virtual Objects

The human visual system can estimate the shape of an object casting a shadow. The principle has been widely studied, and utilized in 3D scanners. However, estimating the shape of an object on which a shadow is casted (“screen object”) was rarely investigated. In this study, we show that the casted shadow distorts the perceived shape of a screen object in a class of virtual and physical scenes. In addition, we utilized the principle to create variations of Cafe-wall illusion. The principles can be utilized to control the perceived shapes without changing the structure of the screen object.
Transfer Learning for Nanophotonics

We will discuss some new overlap between nanophotonics and artificial intelligence (AI). The first part focuses on the transfer learning method for nanophotonics design. The second part focuses on the potential application of 3D nanofabrication technology in deep learning circuits.
Interoperability in Pervasive Health: A Systematic Review

Smart components highly integrated and miniaturized facilitate the development of wearable devices to support home monitoring of patients with chronic diseases and that should be interoperable with existing electronic health records. Objective: This study aimed to systematize current evidence of how interoperability is considered during the development of new applications to gather patients’ information in their home environments. Methods: A systematic review was performed based on a search of the literature. Results: A total of 37 articles were retrieved from the 4141 articles that result from the initial search. Conclusion: From the 4141 initial references only 81 references explicitly mentioned interoperability issues and, within these 81 references, only eight reported end-to-end solutions that can be integrated and usable in care service provision.
Distributed Hayabusa

Network administrators usually collect and store logs generated by servers, networks, and security appliances so that when network trouble and/or security incidents occur, they can identify the source of the problem by investigating the contents of the logs. The size of the system needed to store and search the log messages tends to increase as the size of the managed network becomes large. A fast log storage and search system called Hayabusa was previously proposed that optimizes a time-dimensional search operation. In this paper, we propose a simple distributed system that adds scalability to the existing Hayabusa system. The evaluation results show that the Distributed Hayabusa system consisting of 10 servers (with multiple worker processes on each server) is 36 times faster than a standalone Hayabusa system. The time required to perform a full-text search over 14.4 billion data records is only about 7 s, which is sufficiently low for the daily operations of administrators managing a very-large-scale network.
Coretet: A 21<sup>st</sup> Century Virtual Reality Musical Instrument for Solo and Networked Ensemble Performance

Coretet is a virtual reality instrument that explores the translation of performance gesture and mechanic from traditional bowed string instruments into an inherently non-physical implementation. Built using the Unreal Engine 4 and Pure Data, Coretet offers musicians a flexible and articulate musical instrument to play as well as a networked performance environment capable of supporting and presenting a traditional four-member string quartet. This paper discusses the technical implementation of Coretet and explores the musical and performative possibilities through the translation of physical instrument design into virtual reality.
OKAPI: In Support of Application Correctness in Smart Home Environments

Typical Internet of Things (IoT) and smart home environments are composed of smart devices that are controlled and orchestrated by applications developed and run in the cloud. Correctness is important for these applications, since they control the home's physical security (i.e. door locks) and systems (i.e. HVAC). Unfortunately, many smart home applications and systems exhibit poor security characteristics and insufficient system support. Instead they force application developers to reason about a combination of complicated scenarios-asynchronous events and distributed devices. This paper demonstrates that existing cloud-based smart home platforms provide insufficient support for applications to correctly deal with concurrency and data consistency issues. These weaknesses expose platform vulnerabilities that affect system correctness and security (e.g. a smart lock erroneously unlocked). To address this, we present OKAPI, an application-level API that provides strict atomicity and event ordering. We evaluate our work using the Samsung SmartThings smart home devices, hub, and cloud infrastructure. In addition to identifying shortfalls of cloud-based smart home platforms, we propose design guidelines to make application developers oblivious of smart home platforms' consistency and concurrency intricacies.
Using Event Representations to Generate Robot Semantics

Most semantic models employed in human-robot interactions concern how a robot can understand commands, but in this article the aim is to present a framework that allows dialogic interaction. The key idea is to use events as the fundamental structures for the semantic representations of a robot. Events are modeled in terms of conceptual spaces and mappings between spaces. It is shown how the semantics of major word classes can be described with the aid of conceptual spaces in a way that is amenable to computer implementations. An event is represented by two vectors, one force vector representing an action and one result vector representing the effect of the action. The two-vector model is then extended by the thematic roles so an event is built up from an agent, an action, a patient, and a result. It is shown how the components of an event can be put together to semantic structures that represent the meanings of sentences. It is argued that a semantic framework based on events can generate a general representational framework for human-robot communication. An implementation of the framework involving communication with an iCub will be described.
Towards an Understanding of Value Creation in Agile Software Development

Recently, studies involving the creation of business value in Agile Software Development (ASD) have been growing substantially. However, the concept of value creation in ASD has not yet been clearly defined. Besides, the literature does not define practices that can create business value for ASD. Identifying these practices can change the mindset of agile teams, since surveys indicate that, from the point of view of the agile team, the creation of value is poorly understood. Thus, this study carried out a Systematic Literature Review to identify how value creation is defined in ASD, and how practices can improve this value creation. Despite the lack of studies on the subject, we identified practices and its positive and negative influence on value creation.
Evaluation of Information Widgets for a Virtual Reality Serious Game

As K-12 humanities teachers increasingly use virtual reality programs as pedagogical tools, creators of virtual reality serious games should better understand the way that the design of 3D user interfaces influence learning and user engagement. This study explores user preferences towards selection technique, level of transparency, and positioning of 3D information widgets in an open-world virtual reality environment. Using a recently constructed virtual heritage game, Little Manila Recreated, this article adds to a growing body of user studies that attempt to establish best practices for the use of 3D information widgets in VR history projects designed for K-12 classrooms.
Academic Paper Recommendation Based on Clustering and Pattern Matching

With the rapid growth of the scholarly literature, finding relevant and influential articles is becoming increasingly important. Research shows that a scholar’s past works represent his latent interests. However, its effectiveness in recommending scholarly papers has not been well explored in the existing studies. In this paper, we propose an academic paper recommendation model, called CPM, which mainly mines researcher’s published works for improving scholarly navigation. Firstly, a scholar’s papers are divided into different interest points by clustering technologies. Then, scholar’s information needs are represented in terms of pattern equivalence classes. Finally, matching degree and preference degree are integrated to rank the candidate papers. Experimental results on real datasets demonstrate that CPM outperforms (5.6% in terms of NDCG@5 and 8.1% in terms of MRR) the baseline method.