Reviewerly/exHarmony huggingface dataset:
Error-corrected quantum sensing

Quantum metrology has many important applications in science and technology, ranging from frequency spectroscopy to gravitational wave detection. Quantum mechanics imposes a fundamental limit on measurement precision, called the Heisenberg limit, which can be achieved for noiseless quantum systems, but is not achievable in general for systems subject to noise. Here we study how measurement precision can be enhanced through quantum error correction, a general method for protecting a quantum system from the damaging effects of noise. We find a necessary and sufficient condition for achieving the Heisenberg limit using quantum probes subject to Markovian noise, assuming that noiseless ancilla systems are available, and that fast, accurate quantum processing can be performed. When the sufficient condition is satisfied, the quantum error-correcting code achieving the best possible precision can be found by solving a semidefinite program. We also show that noiseless ancilla are not needed when the signal Hamiltonian and the error operators commute. Finally we provide two explicit, archetypal examples of quantum sensors: qubits undergoing dephasing and a lossy bosonic mode.
Design and Implementation of Smart Security System for Human Safety

Security has become an important issue everywhere. When it comes to human safety, the proposed system can be applied to make it smarter, safer and automated. Using two sensors Flex and PIR the system was made both manually and automatically activated. Based on the sensor the system turns on and tracks victim's location. Then the location is sent it to some selected numbers as well as saved in the server. This work includes data collection by using sensors. To transmit to a centralized server and upload to the website Raspberry pi and GSM is used. Through this system people of all ages can be benefitted. The system can be upgraded and improved by further additions of features. When a human will bend his fixed finger after facing any kind of danger, the resister value of flex will be changed and system will be on. At that moment not only SMS including location will be sent to the fixed numbers but also it will be uploaded and saved as still images to the website.
Software Requirement Specification Based on a Gray Box for Embedded Systems: A Case Study of a Mobile Phone Camera Sensor Controller

One of the most widely used models for specifying functional requirements is a use case model. The viewpoint of the use case model that views a system as a black box focuses on descriptions of external interactions between the system and related environments. However, for embedded systems that do not disclose most implementation logics outside the system, black box-based use case models may experience the drawback that considerable information that must be defined for system developments is omitted. To solve this shortcoming, several studies have been proposed on the use of kind of white box technique in which the dynamic behaviors of embedded systems are defined first using a state diagram and the results are reflected in the requirement specifications. However, white box-based modeling has not been widely adopted by developers due to tasks that require a lot of time in the requirement analysis phase in the initial phase of the software development life cycle. This study proposes a gray box-based requirement specification method as a trade-off between two contradictory elements (the amount of information required to develop an embedded system and the cost of the effort required during the requirement analysis phase) in terms of the two approaches, the black and the white box-based models. The proposed method suggests that an appropriate depth level of embedded system modeling is required to define the requirements. This study also proposes a mechanism that automatically generates an application programming interface for each component based on the created model. The proposed method was applied to the development of a camera sensor controller in a mobile phone, and the case results proved the feasibility of the method through discussion of the application results.
PointNet Evaluation for On-Road Object Detection Using a Multi-resolution Conditioning

On-road object detection is one of the main topics in the development of autonomous vehicles. Factors related to the diversity of classes, pose changes, occlusions, and low resolution make object detection challenging. Most of the object detection techniques which have been based on RGB images, have limitations because of the influence of environmental lighting conditions. Consequently, other sources of information have become interesting for undertaking this task. This paper proposes an on-road object detection method, which uses 3D information acquired by a LiDAR HD sensor. We evaluate a neural network architecture based on PointNet for multi-resolution 3D objects. To carry this out, a multi-resolution conditioning stage is proposed in order to optimize the performance of the PointNet architecture applied over LiDAR data. Both the training and evaluation processes are performed by using the KITTI dataset. Our approach uses low computational cost algorithms, which are based on occupancy grid maps for on-road object segmentation. The experiments show that the proposed method achieves better results than PointNet evaluated on a single resolution.
Exact Learning of Primitive Formal Systems Defining Labeled Ordered Tree Languages via Queries

A formal graph system (FGS) is a logic programming system that directly manipulates graphs by dealing with graph patterns instead of terms of first-order predicate logic.In this paper, based on an FGS, we introduce a primitive formal ordered tree system (pFOTS) as a formal system defining labeled ordered tree languages.A pFOTS program is a finite set of graph rewriting rules.A logic program is well-known to be suitable to represent background knowledge.The query learning model is an established mathematical model of learning via queries in computational learning theory.In this learning model, we show the exact learnability of a pFOTS program consisting of one graph rewriting rule and background knowledge defined by a pFOTS program using a polynomial number of queries.
Automatic Affective Image Captioning System using Emotion Estimation

Image captioning has been actively studied these days, however, most of the systems output captions of factual expression. In this paper, an automatic affective image captioning system using emotion estimation is proposed. The proposed system consists of four parts: a base caption generation part composed by the conventional CNN (VGG16), a scene estimation part, an emotion estimation part, and a figurative expression generation part. When a human exists in an image, the emotion is estimated from his/her facial expression and simile is used. When a human does not exist in an image, personification of metaphor is used. Evaluation experiments have been carried out using three kinds of evaluation indexes; BLUE, METEOR, and CIDEr. The experimental results indicate the effectiveness of the proposed system to generate affective captions.
A Comparative Study on Unsupervised Domain Adaptation for Coffee Crop Mapping

In this work, we investigate the application of existing unsupervised domain adaptation (UDA) approaches to the task of transferring knowledge between crop regions having different coffee patterns. Given a geographical region with fully mapped coffee plantations, we observe that this knowledge can be used to train a classifier and to map a new county with no need of samples indicated in the target region. Experimental results show that transferring knowledge via UDA strategies performs better than just applying a classifier trained in a region to predict coffee crops in a new one. However, UDA methods may lead to negative transfer, which may indicate that domains are excessively dissimilar, rendering transfer strategies ineffective. We observe a meaningful complementary contribution between coffee crop data; and a visual behavior suggests the existence of clusters of samples that are more likely to be drawn from a specific data.
Crafting an Active Print Collection for the Academic Library: A Case Study from the Claremont Colleges Library

In 2016, The Claremont Colleges Library began to imagine what it might look like to provide access to our collections in ways that are informed by how our community uses those resources. From the beginning, this effort was a response to difficulties helping researchers connect our digital and physical resources, unsatisfactory storage space, and opportunities arising out of renovations and master planning. Through this work we aim to ensure the organization and presentation of our collections support how our researchers need to work with them and be inspired by them. To that end, we have engaged colleagues in other academic libraries on these questions. We have also convened task forces and conducted an in-depth, qualitative study to understand the different yet complementary engagements scholars have when browsing and searching print and electronic resources. In this case study, we will review provocative conversations at Claremont and beyond about the place of print in the digital age and discuss what we are learning from our local investigations of research behaviors. Building on this foundation, we will share plans for a fledgling program at Claremont that we expect will resonate with and be vital to the future of many academic libraries. The “Active Print” program includes a variety of approaches to enhancing the traditional stewardship role of the library by engaging our users in re-envisioning the organization and presentation of our physical collections.
Evaluating AdaBoost for Plagiarism Detection

Plagiarism is a current problem with serious consequences. Recently, many research efforts have addressed plagiarism detection task. This problem is more difficult, when some obfuscation strategy is used. This work proposes and evaluates the adoption of AdaBoost for classifying suspicious text passages as plagiarism or not. We also present a simple post-processing heuristic for improving results granularity. Comparative analysis with other classification methods were conducted and experimental results have shown that Adaboost reached a F-measure of 0.93 and the best granularity score using the proposed post-processing heuristic. The assessment of three distinct obfuscation strategies pointed out that Adaboost is able to detected all of them, but it had problems in differentiating among them, which could to be related to a poor feature selection.
Mobile device interaction using projector metaphor

Projection Augmented Reality (AR), which uses projectors to augment virtual information and objects in physical space, is one of the AR fields that have been actively researched. There have been many attempts to use mobile devices to implement the interactions available in these AR environments; however, due to mobile devices' limited computational power, they were somewhat difficult to use in real applications. We have solved this problem with the latest off-the-shelf devices and support development kit and have proven, through experimentation, that our solution can be used in actual projection AR applications.
Evaluation of Scale-Aware Realignments of Hierarchical Image Segmentation

A hierarchical image segmentation is a set of image segmentations at different detail levels. However, objects may appear at different scales due to their size or to their distance from the camera. One possible solution to cope with that is to realign the hierarchy such that every region containing an object is at the same level (or scale). In this work, we explore the use of regression to predict the best scale value for given region, which is then used to realign the entire hierarchy. Experimental results are presented for two different segmentation methods; along with an analysis of the adoption of different combination of mid-level features to describe regions.
Spatial and Temporal Feature-Based Reduced Reference Quality Assessment for Rate-Varying Videos in Wireless Networks

For the impact of the bitrate change of video streaming services according to the available bandwidth on user satisfaction, in this paper, we propose a spatial and temporal feature-based reduced reference (RR) quality assessment for rate-varying videos in wireless networks called STRQAW. First, simulating the orientation selectivity mechanism of the human visual system (HVS), the histogram of the orientation selectivity-based visual pattern in each frame is extracted as the spatial feature. The histogram similarity between the rate-varying video and the original video is computed as the spatial metric. Second, we extract the temporal variation of the DCT coefficients of the consecutive frame differences as the temporal feature. The temporal variation similarity between the rate-varying video and the original video is calculated as the temporal metric. Finally, we take into account the recency effect and assess the overall quality by combining the temporal and spatial metric. The experimental results using the Laboratory for Image and Video Engineering (LIVE) mobile video quality assessment (VQA) database show that STRQAW is consistent with the subjective assessment results, which means it reflects human subjective feelings well and it provides an evaluation for adjusting compression-coding rates in real time. STRQAW can be used to guide video application providers and network operators working towards satisfying end-user experiences.
One Graduate—Two Majors: Employers’ Demands, Students’ Interests

The article discloses a study on the identification of multidisciplinary master degree majors, which interest potential employers and students in Russia. Results of the initial phase of the research project conducted at Tomsk Polytechnic University are presented. The study focuses on collecting raw data from two categories of respondents: potential employers (industry representatives) and potential master students (4th year bachelor students of engineering majors, TPU). The respondents underline the demand for double major master programs and rate reasons for studying two majors. The study proposes particular pairs of master programs that should be harmonized on the latter stages of the project. The importance of the research lies at the root of communicating modern issues and requirements of industry and society with the responses provided by universities, i.e. the quality and relevance of students’ professional training.
Building a Crowdsourcing based Disabled Pedestrian Level of Service routing application using Computer Vision and Machine Learning

The availability of global and scalable tools to assess disabled pedestrian level of service (DPLoS) is a real need, yet still a challenge in today’s world. This is due to the lack of tools that can ease the measurement of a level of service adapted to disabled people, and also to the limitation concerns about the availability of information regarding the existing level of service, especially in real time. This paper describes preliminary results to progress on those needs. It also includes a design for a navigation tool that can help a disabled person move around a city by suggesting the most adapted routes according to the person’s disabilities. The main topics are how to use advanced computer vision technologies, and how to benefit from the prevalence of handheld devices. Our approach intends to show how crowdsourcing techniques can improve data quality by gathering and combining up-to-date data with valuable field observations.
Sparse DP Quantization Algorithm

We formulate the design of an optimal quantizer as an optimization problem that finds the quantization indices that minimize quantization error. As a solution of the optimization problem, an approach based on dynamic programming, which is called DP quantization, is proposed. It is observed that quantized signals do not always contain all kinds of signal values which can be represented with given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to quantizer design. Signal values with zero frequency do not impact quantization error, so there is the potential to reduce the complexity of the optimal quantizer by not computing signal values that have zero frequency. However, conventional methods for DP quantization were not designed to consider amplitude sparseness, and so fail to reduce complexity. The proposed algorithm offers a reduced complexity optimal quantizer that minimizes quantization error while addressing amplitude sparseness. Experimental results show that the proposed algorithm can achieve complexity reduction over conventional DP quantization by 82.9 to 84.2% on average.
Aiding the Transition of Students From School Into Technical University

This chapter is about different aspects of creating university-based professionally orienting environment. Issues of students' professional self-determination in transition from secondary education to high school are considered. The author suggests to arrange resource center as a training and research innovative complex for solution of youth's problems with early professional orientation, their motivation, for recruitment and selection of the most prepared for further study. As a result of the center's usability there is a developing trend towards form and direction diversity in scientific, educational, and industrial integration, growing university penetration into secondary school, and high school scientific research's impact into industry.
Context-Aware that Performs Adaptation in Mobile Learning

Mobile computing is recognized as the new emerging technology to enhance teaching approaches and provide new learning strategies that exploit learners’ contextual information. The context aware in mobile learning could load dynamically information’s form learners and exploit that information’s to provide a personalized user experience according to it context. The main challenge in context-aware M-learning system is context ontologies, it represents the most part for context modeling. In this paper, we propose an ontology-based approach for modeling context-aware in an adaptive mobile learning environment.
First Steps in the Use of a Game Engine for Historical Roads and Paths Research

Game engines are developing fast and are used in several scientific disciplines. In the domain of cultural heritage, they have been applied mostly for dynamic visualization. On the other hand, GISs are employed to address research questions with a spatial component. In an ideal situation, the visualization power and analytical strength of the two technologies should be combined in one system. With this in mind, the analytical<br />potential of a game engine was investigated based on a comparison with GIS analysis of historical routes. The outcome demonstrates the suitability of the game engine in offering extra analytical possibilities. This analytical capacity encourages further script developments in building more historically accurate models.
Usability Assessments of Mobile Applications as a Function of Geographic Location

Researchers recently demonstrated that subjective usability assessments of common products do not vary across geographic locations. That study did not directly address mobile applications, which are some of the most ubiquitous and geographically diverse systems in use in the United States today. To address this shortcoming, this article examined whether or not geographic location impacts the perceived usability of mobile applications by having 2,590 participants from different regions of the United States rate the usability of several mobile applications using the System Usability Scale. There was a lack of evidence to support statistically significant differences in usability scores across geographic locations for 95% of the mobile applications evaluated. This suggests that system usability assessments for mobile products do not differ across US locations. These findings further reinforce the idea that participants from any locale can be recruited for usability tests as long as all other critical demographic criteria are met.
Deterministic two-photon controlled phase gate by exploiting nonlinear pi-phase shift in photonic molecule generations

To resolve the intrinsic trade-off between the deterministic nature and scalability of controlled phase gate implementation in the linear optics regime, we propose a novel deterministic two-photon controlled phase gate scheme in chiral quantum nanophotonics. The essential ingredient here is that, a non-linear π-phase shift is imprinted during the photonic molecule generations. On one hand, the gate operates in a deterministic way without the assistance of probabilistic ancilla qubit measurement. On the other hand, the gate implementation is of low complexity to be highly scalable. To date, the chiral coupling is readily underpinned by the advent of directional emission technique. Potential implementation platforms include QD, superconducting qubit, Rydberg atom, or N-V center that is coupled to a PhC waveguide, transmission line, or optical fiber.
Digital creativity: learning by story driven digital production

Purpose The goal to stimulate perspective taking and inference making on social phenomena, such as gender roles in society, has proven to be difficult to achieve in general and in particular for primary school students. Thus, the purpose of this paper is to develop creative models and concepts for learning that provide guidance addressing these challenges. Design/methodology/approach A case study methodology, including classroom observations, teacher interviews and analysis of videos created by students, was applied within a large-scale action research project related to cross-border collaboration for educational purposes supported by information and communication technologies among Danish, Norwegian and Swedish schools. Findings This study reports on how teachers organized group work for their sixth grades students to reimagine and videoing fairy tales endings of Cinderella in order to explore and learn about gender roles in society in a cross-border setting. The personal, emotional and social negotiations of working with peers and giving feedback to students in other schools from other countries enhanced their learning. Results suggest that adding the framework of boundary object-driven design helps to improve the process by its focus on a shared understanding, common practice and sense-making. Originality/value The study incorporates the framework on boundary objects as a “mental design device” into a story-driven digital production project, suggesting that creativity in combination with a specific yet open task for student group work enhances learning in social science.
Collaborative lifelogging through the integration of machine and human computation

This article outlines a new class of computing system, which we refer to as collaborative lifelogging (CLL). CLL advances state of the art in lifelogging and self-tracking systems by supporting social group collaboration on sensor data capture through the formation of on-the-fly self-tracking groups and the contextualization of data through human and machine-based computation.