Reviewerly/exHarmony huggingface dataset:
You’re looking for what? Comparing search for familiar, nameable objects to search for unfamiliar, novel objects

Searching for items in one’s environment often includes considerable reliance on semantic knowledge. The present study examines the importance of semantic information in visual and memory search, especially with respect to whether the items reside in long-term or working memory. In Experiment 1, participants engaged in hybrid visual memory search for items that were either highly familiar or novel. Importantly, the relatively large number of targets in this hybrid search task necessitated that targets be stored in some form of long-term memory. We found that search for familiar objects was more efficient than search for novel objects. In Experiment 2, we investigated search for familiar versus novel objects when the number of targets was low enough to be stored in working memory. We also manipulated how often participants in Experiment 2 were required to update their target (every trial vs. every block) in order to control for target templates that were stored in long-term memory as a result of repeated exposure over trials. We found no differences in search efficiency for familiar versus novel objects when templates were stored in working memory. Our results suggest that while semantic information may provide additional individuating features that are useful for object recognition in hybrid search, this information could be irrelevant or even distracting when searching for targets stored in working memory.
CompoundDome

The head-mounted display (HMD) is widely used as a method to experience virtual space. However, HMD has problems in mounting, such as skin touching the equipment used by others, functional issues such as easy to induce VR sickness. In this research, we propose a wearable dome device named "CompoundDome", which enables interaction with the real world by projecting images on the dome. In our system, we used a 600 mm diameter dome, and a projector projects images to the dome to cover the wearer's field of view. With this configuration, the equipment does not touch the skin, and motion sickness can be reduced. HMD also lacks in providing face-to-face communication, because it hides user's face. In addition, the wearer can not see the outside when wearing the HMD. Hence, we applied screen paint to the transparent dome in a mesh form. With this configuration, users can see the image when the image is projected, and they can see the outside of the dome when the image is not projected. Furthermore, users and the surrounding people can make face to face communication by photographing the face with the camera installed in the dome and projecting the face in the virtual space. In this paper, we describe the composition of CompoundDome, in comparison with other virtual space presentation means, and various applications enabled by CompoundDome.
Prospectivity Mapping for Epithermal Deposits of Western Milos Using a Fuzzy Multi Criteria Evaluation Approach Parameterized by Airborne Hyperspectral Remote Sensing Data

A Mineral prospectivity mapping (MPM) approach using a GIS-based weighted linear combination implementation of a Multi-Criteria Evaluation approach utilising a fuzzy Analytical Hierarchy Process to elucidate expert knowledge has been implemented to analyse the spatial distribution of epithermal deposits on the Island of Milos, Greece and model their association with exploration evidence data with the aim of providing insights into the controls on ore deposition. An integrated field and Digital Airborne Imaging Spectrometer (DAIS) hyperspectral and thermal multispectral airborne remote sensing dataset supported by field mapping and laboratory analyses, has been utilised to resolve hydrothermal alteration and parameterise the MPM. This study has highlighted the intimate spatial relationship between topographic highs and locations with high grade silicified alteration at a number of locations. The ability of high spatial resolution multispectral Thermal InfraRed (TIR) remote sensing imagery, integrated with topographic data, to resolve these silicified topographic highs provides an additional tool in the exploration of epithermal deposits. The spatial relationships between silicified lithocaps, high-grade altered rocks, faulting and topographic highs were utilised in the development of the MPM model. A close association between the modelled results and the hydrothermal alteration mapped in the field supports the accuracy of this MPM approach.
Practicality and Effectiveness of E-Book Based LCDS to Foster Students’ Critical Thinking Skills

This study aims to analyze the practicality and effectiveness of E-book based LCDS to foster students' critical thinking skills, especially on static fluid material. The research method used a quasi-experiment with pretest-posttest with control group design. The sampling technique used purposive sampling technique, which was the high school students in Bandar Lampung. The instruments which were used are the e-book implementation observation sheet, students' responses to e-books, and critical thinking skills test questions. Data analysis techniques use descriptive analysis with percentage, N-gain analysis. The results showed that LCDS-based ebooks: 1) practical, as indicated by a) average score of e-book implementation in each learning activity, namely 84.43% with very high criteria and b) positive response of students (84.45%) to the e-book. 2) effective, as shown by significant differences in students' critical thinking skills between the experimental class and the control class. The critical thinking skills of students in the experimental class who were taught using E-book based LCDS are better than the control class.
Design and Development of a Computer Numeric Controlled 3D Printer, Laser Cutter and 2D Plotter all in one machine

This paper proposes the technique used to design and develop a Computer Numeric Controlled 3D Printer, Laser cutter and 2D Plotter all in one machine. The specialty of this machine is that the user will not have to change tool whenever he/she wants to perform any other operation. So this machine can not only 3D print an object it will also be able to laser cut (or engrave through laser) an object and it will also produce 2D drawing for particular object. Frame of the machine and the working of the components will be based on the Cartesian type of 3D printers. It is a commercially viable and cost effective machine. The paper also describes the analysis and calculations performed for the choice of different electrical and structural components of this machine.
An Improved Artificial Bee Colony Algorithm With Fitness-Based Information

Artificial bee colony (ABC) algorithm is widely known for its distinguished exploration ability. However, its exploitation ability is relatively poor. To solve the problem, we propose a novel combinatorial search strategy, whose guided vector can be freely switched between a random vector and the global best vector. It can help improve the exploitation ability of ABC. At the same time, a random vector is beneficial to regulate the enhanced exploitation ability. In addition, both of them can pass information on to a current vector instead of only perturbing a current vector itself. The two guided vectors are chosen with a probability depending on the ratio of fitness information of a current vector to that of the global-best vector. Thus, one of the two guided vectors can be adaptively selected to direct the search. In addition, a mechanism of frequency of perturbation is employed to enhance the scale of information sharing between a current vector and a guided vector for each onlooker bee. Moreover, a modified greedy selection mechanism is designed to choose a child vector inspired by simulated annealing. Furthermore, the search strategy of multiple scouts is also employed in the last stage. Based on all these modifications, an improved ABC (IABC) is proposed. Finally, a few experiments are carried on 58 benchmark problems, including CEC2014 benchmark problems. The computational results exhibit the merit of IABC.
Bernoulli's Chaotic Map-Based 2D ECG Image Steganography

Signal processing technology comprehends fundamental theory and implementations for processing data. The processed data is stored in different formats. The mechanism of electrocardiogram (ECG) steganography hides the secret information in the spatial or transformed domain. Patient information is embedded into the ECG signal without sacrificing the significant ECG signal quality. The chapter contributes to ECG steganography by investigating the Bernoulli's chaotic map for 2D ECG image steganography. The methodology adopted is 1) convert ECG signal into the 2D cover image, 2) the cover image is loaded to steganography encoder, and 3) secret key is shared with the steganography decoder. The proposed ECG steganography technique stores 1.5KB data inside ECG signal of 60 seconds at 360 samples/s, with percentage root mean square difference of less than 1%. This advanced 2D ECG steganography finds applications in real-world use which includes telemedicine or telecardiology.
Active Learning with n-ary Queries for Image Recognition

Active learning algorithms automatically identify the salient and informative samples from large amounts of unlabeled data and tremendously reduce human annotation effort in inducing a machine learning model. In a multi-class classification problem, however, the human oracle has to provide the precise category label of each unlabeled sample to be annotated. In an application with a significantly large (and possibly unknown) number of classes (such as object recognition), providing the exact class label may be time consuming and error prone. In this paper, we propose a novel active learning framework where the annotator merely needs to identify which of the selected n categories a given unlabeled sample belongs to (where n is much smaller than the actual number of classes). We pose the active sample selection as an NP-hard integer quadratic programming problem and exploit the Iterative Truncated Power algorithm to derive an efficient solution. To the best of our knowledge, this is the first research effort to propose a generic n-ary query framework for active sample selection. Our extensive empirical results on six challenging vision datasets (from four different application domains and varied number of classes ranging from 10 to 369) corroborate the potential of the framework in further reducing human annotation effort in real-world active learning applications.
Secondary constructions of RSBFs with good cryptographic properties

Rotation symmetric Boolean functions have been used as components of different cryptosystems. How to construct rotation symmetric Boolean functions with good cryptographic properties is an interesting and significant problem. So far little attempt has been made to provide secondary constructions of rotation symmetric Boolean function. In this paper, based on indirect sum, Maiorana-McFarland functions and concatenation, we propose secondary constructions of rotation symmetric Boolean functions with resiliency, bentness and high algebraic immunity.
Learning Agents with Prioritization and Parameter Noise in Continuous State and Action Space

Among the many variants of RL, an important class of problems is where the state and action spaces are continuous—autonomous robots, autonomous vehicles, optimal control are all examples of such problems that can lend themselves naturally to reinforcement based algorithms, and have continuous state and action spaces. In this paper, we introduce a prioritized form of a combination of state-of-the-art approaches such as Deep Q-learning (DQN) and Deep Deterministic Policy Gradient (DDPG) to outperform the earlier results for continuous state and action space problems. Our experiments also involve the use of parameter noise during training resulting in more robust deep RL models outperforming the earlier results significantly. We believe these results are a valuable addition for continuous state and action space problems.
Practical Evaluation of Masking for NTRUEncrypt on ARM Cortex-M4

To protect against the future threat of large scale quantum computing, cryptographic schemes that are considered appropriately secure against known quantum algorithms have gained in popularity and are currently in the process of standardization by NIST. One of the more promising so-called post-quantum schemes is NTRUEncrypt, which withstood scrutiny from the scientific community for over 20 years. Similar to classical algorithms like AES, implementations of NTRUEncrypt must be protected against physical attacks. While different masking and hiding countermeasures have been proposed in the past, practical power analysis evaluations of masking for NTRUEncrypt are lacking. We therefore provide a practical evaluation of masking applied to index-based multiplication and a modern parameter set using trinary polynomials. With the use of SIMD instructions available in the Cortex-M4 microcontroller, we are able to implement additive masking without any significant performance overhead compared to an unmasked implementation. Our implementation showed no observable first-order leakage using a HW model and two million measurement traces. Successful second-order attacks are demonstrated for our implementation using SIMD instructions, which processes the mask and masked data simultaneously, as well as for a sequential implementation built for comparison. Finally, we show that applying both our low cost masking countermeasure together with a known and equally efficient shuffling scheme can provide a good trade-off achieving a high level of security without a large performance penalty.
An Empirical Study of Image Resolution and Pose on Automatic Face Recognition

Face image resolution and pose are two important factors that severely degrade the recognition ability. This paper presents a comparison of (i) the Wavelet Transform, (ii) the 2DPCA, (iii) the AdaBoost-LDA, and (iv) Fisherfaces based face recognition algorithms. Simulation results on the Multi-PIE database show that the 2DPCA face recognition algorithm can be reliably used for extremely low face image resolution of 15×15 pixels and from frontal (0°) to +35° of pose variation in near-real time. Whereas for high face image resolution of 40×40 pixels and up to 251×231 pixels, the Fisherfaces yields high accuracy across four different pose variation at the cost of much higher computation. Moreover, the recognition rate of the AdaBoost-LDA is unaffected by the image resolution from 251×231 down to 15×15 pixels. In addition, time cost comparison is also shown.
Trækvejret

A flexible wooden device, "Trækvejret" which emulates a slow rate of breathing is placed in a coffee break room. This work examines related works on reflection, triggering reflection, and breathing and biofeedback technologies. We demonstrate how simple technology such as Trækvejret, which does not measure or give feedback about a user's breathing can nonetheless potentially be useful and provoking, encouraging reflection and potentially, behaviour change.
Ties Between Mined Structural Patterns in Program and Their Identifier Names

Identifier names in readable and maintainable source codes are always descriptive. These names are given based on the implicit knowledge of experienced programmers. In this paper, we propose a structural pattern mining method based on support vector machines (SVM) for source codes. We extract 1,000 method names in object-oriented source codes collected from online software repositories and create 1,000 datasets labeled by positive and negative class. The structural features used for the input feature vectors to the SVM learning are designed for representing partial characteristics in the abstract syntax tree (AST) parsed from a source code. Applying this method, we made an F1 score list of the 1,000 method names, which shows the degree of patterning of each name, by using our structural features. From the list, we confirmed structural patterns were strongly associated with specific method names. A qualitative evaluation of method names was also conducted by mapping the structural feature vector of each program example to the two-dimensional plane in the same way as a previous major study. From the evaluation, we confirmed that the contrasting structure among the programs corresponds to the names given to programs. Furthermore, we show examples of visualization of structural patterns using structural features extracted by feature selection.
Research on Key Technologies of Student Electronic Depository System Based on Blockchain

Aiming at the problem that a large number of students' electronic deposit data is easy to forge and destroy, a student electronic deposit application architecture based on blockchain is proposed. There are some features on blockchain, such as decentralized, the non-tamperable of data, the consensus mechanism and data encryption features, and the electronic depository network. All these features provide a secure and credible storage environment, ensures the traceable of the student's electronic deposit information, the trust of the entire network, and the security of the data. At the same time, an improved PBFT consensus algorithm is proposed, which eliminates the certificate elimination phase to reduce network resource consumption, and constructs a decentralized electronic depository platform in combination with the blockchain to realize the authenticity, integrity and reliability of the electronic certificate data. The further development and application of blockchain technology is prospected, hoping to provide inspiration and reference for future related research.
Selecting Open Source Projects for Traceability Case Studies

[Context & Motivation] Once research questions and initial theories have shaped, empirical research typically requires to select cases to study subsumed ideas. Issue trackers of todays open source systems (OSS) are a gold mine for empirical research, not least to study trace links among the included issue artifacts. [Question / problem] The huge amount of available OSS projects complicates the process of finding suitable cases to support the research goals. Further, simply picking a large number of projects on a random basis does not imply generalizability. Therefore the selection process should be carefully designed. [Principle ideas / results] In this paper we propose a method to choose OSS projects to study trace links found in issue tracking systems. Builds upon purposive sampling and cluster analysis, relevant project characteristics are identified whereas irrelevant information is filtered. Every step of the method is demonstrated on a live example. [Contributions] The proposed strategy selects an information-rich, representative and diverse sample of OSS to perform a traceability case study. Our work may be used as practical guide for other researchers to perform project selection tasks.
Is it required to remove borrowing techniques in clearly subtraction operations in elementary school?

Operational learning on chunks, especially subtraction operations conducted in grade II elementary school, is still an important and interesting issue to discuss, and one of the problems in learning to count the number of counts is the application of borrowing techniques. There are still many teachers who are learning this part unreasonably to the students because it is too abstract and tends to provide a forced way and does not offer a contextual strategy to the students. Operational learning on chunks, especially reduction operations conducted in grade II primary school, is still an important and interesting issue to discuss and discuss, and one of the problems in learning to count the number of counts is the application of borrowing techniques. There are still many teachers who are learning this part unreasonably to the students, because it is too abstract and tends to provide a forced way and does not offer a contextual strategy to the students. The problem mainly arises when the number is reduced in the form of hundreds or thousands that contain the number 0. The subtraction algorithm offered by teachers is in fact very difficult to understand by students. As a result, many errors are made by students in the operation of counting numbers like this. This study aims to provide alternative solutions for teachers in completing the reduction of counting operations, especially on the use of borrowing techniques. This research uses a descriptive method where researchers collect data and studies that related to the process of reducing the number of counting done by the students.
The image-to-physical liver registration sparse data challenge

Over the last 25 years, the number of papers written that involve image guidance, liver, and registration has, on average, doubled every 6-8 years. While image guidance has a long history within the neurosurgical theatre, it’s translation to other soft-tissue organs such as the liver has been slower given the inherent difficulty in image-to-physical registration. More specifically, deformations have been recognized to compromise image guidance fidelity in virtually all soft-tissue image guidance applications. As a result, an active area of investigation is the development of sparse-data-driven nonrigid image-to-physical liver registration techniques to compensate for deformation and provide accurate localization for image guided liver surgery. In this work we have leveraged our extensive human-to-phantom registration testing framework based on the work in [1] and Amazon Web Services to create a sparse data challenge for the image guided liver surgery community (https://sparsedatachallenge.org/). Our sparse data challenge will allow research groups from across the world to extensively test their approaches on common data and have quantitative accuracy measurements provided for assessment of fidelity. Welcome to the Sparse Data Challenge for image-to-physical liver registration assessment.
Cooperative differential evolution framework with utility-based adaptive grouping for large-scale optimization

Decomposing the large-scale problem into small-scale subproblems and optimizing them cooperatively are critical steps for solving large-scale optimization problem. This article proposes a cooperative differential evolution with utility-based adaptive grouping. The problem decomposition is adaptively executed by the two mechanisms of circular sliding controller and relation matrix, which consider the variable interactions on the basis of the short-term and long-term utilities, respectively. The circular sliding controller provides baselines for the subproblem optimizer. The size of the sliding window and the sliding speed in the controller are adjusted adaptively so that the variables with higher activeness can be optimized extensively. The relation matrix–based grouping strategy enables interacted variables to be grouped into the same subproblem with higher probabilities. The novelty is that decomposition is conducted as the optimization process without extra computational burden. For subproblem optimization, we use a self-adaptive differential evolution operator that adaptively adjusts the parameters to guide the search to the optimum solutions of the subproblems. Experiments on the benchmarks of CEC2008 and CEC2010, and practical problems show the effectiveness of the proposed algorithm.