Reviewerly/exHarmony huggingface dataset:
Practical Security of the Continuous-Variable Quantum Key Distribution with Locally-Generated Local Oscillators

Continuous-variable quantum key distribution (CVQKD) with the local local oscillator (LLO) is confronted with new security problems due to the reference pulses transmitted together with quantum signals over the insecure quantum channel.In this paper, we propose a method of phase attack on reference pulses of the LLO-CVQKD with time-multiplexing.Under this phase attack, the phase drifts of reference pulses are manipulated by eavesdroppers, and then the phase compensation error is increased.Consequently, the secret key rate is reduced due to the imperfect phase compensation for quantum signals.Based on the noise model of imperfect phase compensation, the practical security of LLO-CVQKD under phase attack is analyzed.The simulation results show that the practical security is reduced due to the phase attack, yet it is still tight when system parameters are estimated by training signals.
Optical hash function based on the interaction between multiple scattering media and coherent radiation

An approach for constructing optical hash function has been proposed based on the interaction between multiple scattering media and coherent radiation. Unlike the traditional Hash function via mathematical transformations or complex logic operations, the proposed method employs a multiple scattering media and Sobel filters for data scrambling and features extraction. An arbitrary length input data can be compressed into a fixed length (256-bit) Hash value after a cascade iterative processing. Its safety relies on the unpredicted and non-duplicated disorder multiple scattering media, in other word, there is tremendous difficulty of knowing the multiple scatting media with a specific internal state or efficiently simulating the light interaction effect between the multiple scattering media. Simulation results are presented to demonstrate the avalanche effect and collision resistance performance of the proposed designing strategy of the optical Hash function.
Interaction Design Strategy Based on the Consideration of Subconsciousness and Behavioral Habits

The concept, connotation, and feature representation of user subconsciousness and behavioral habits are discussed in this paper. The vital significance and guiding role of the user's subconsciousness and behavioral habits in the interaction design are pointed out. Based on Maslow's hierarchy of needs, the extraction and utilization approaches of subconsciousness and behavioral habits in the interaction design are outlined. The user's subconsciousness and behavioral habits are transformed into necessary elements in the interaction design to innovate and explore new interaction design methods and enrich interaction design content.
Extending OGC Standards for Supporting Big-Earth Data Retrieval and Analytics

With the fast evolution and spread of remote sensing technologies, Earth data gets gathered in unprecedented rates, and despite the increasing capabilities of computing and telecommunication infrastructures, put heavy stress on traditional approaches for their description, discovery, access and processing. Size vastly increases not only by the quantity and density of sensor arrays but also by the finer granularity of their sensing capacities. Furthermore, data being gathered from a wide range of sensor setups, require rich descriptive metadata in order to provide sufficient background information for their discovery and processing, metadata in turn becoming big on their own too. All those factors result on large combinations of data and metadata that need to be processed in combination before the data consumer gets a targeted result, often requiring substantial computational time or data access time even on large infrastructures. In this paper, building on existing OGC standards we present extensions that can provide them with declarative processing and asynchronous results delivery capabilities suitable for the new era.
Electronic speckle pattern interferometry: A powerful but underutilized experimental technique

Electronic speckle pattern interferometry is a cost effective technique for imaging the deflection shapes of harmonically vibrating objects. The images produced by the interferometer show the vibrating object superimposed with fringes representing contours of equal out of plane amplitude. These images can be used to determine deflection shapes, find and characterize resonances, and ensure the proper placement of sensors. Constructing the interferometer requires only minimal optical expertise, and with slight modifications the interferometer can be made to image static displacement, transient vibrations, fluid flow and thermal effects.
Developing Speaking Course Book for English Department Students

The study aims at describing the development process, the quality, and the practicality of Speaking Course Book entitled Speaking for Daily Activities for English Department students of the private university in West Surabaya. It employs Design and Development Research (DDR) and uses Richey and Klein’s model of DDR collaborated with Richard’s model of material development. The development processes of the book support Richey and Klein’s model of DDR, Richards’ model of development material and material development for speaking skills proposed by Bao. The mean score of items in evaluation checklist is 3.80 meaning that the quality of the book is very good, and it fully matches the characteristics of a good speaking course book. The appraisal forms show that both the lecturer and the students could use the book very easily. Also, the mean score of items in observation checklist is 3.65 meaning the practicality of the book is very good. Penelitian ini bertujuan untuk mendeskripsikan proses pengembangan, kualitas, dan kepraktisan Speaking Course Book untuk mahasiswa Jurusan Bahasa Inggris sebuah universitas swasta di Surabaya Barat, yaitu Speaking for Daily Activities. Penelitian ini merupakan Penelitian Pengembangan yang menggunakan kolaborasi antara model Richey and Klein dalam Penelitian Pengembangan dan model pengembangan materi dari Richard. Proses pengembangan buku ajar mendukung model Penelitian Pengembangan dari Richey and Klein, model pengembangan materi dari Richards, dan pengembangan materi untuk keterampilan berbicara oleh Bao. Skor rata-rata item dalam daftar evaluasi adalah 3,80, yang berarti kualitas buku sangat bagus dan sangat sesuai dengan karakteristik buku ajar berbicara yang baik. Kepraktisan buku sangat baik. Hal ini bisa dilihat dari form isian yang menunjukkan bahwa dosen dan mahasiswa dapat menggunakan buku tersebut dengan sangat mudah. Selain itu, skor rata-rata item dalam daftar pengamatan adalah 3,65 yang berarti sangat bagus.
A Proposal of Entertainment and Performance Learning Assistant System for Seniors

The population of seniors has rapidly increased all over the world. It may cause large negative impacts to the societies if they have unhealthy lives. According to World Health Organization (WHO), the physical health determines the Quality of Life (QoL) of a human. However, a lot of seniors in Indonesia, for example, are in unhealthy conditions, stay in homes, and do never have exercises. In this paper, we propose an Entertainment and Performance Learning Assistant System (EPLAS) for assisting self-activities of seniors using the projection mapping. By showing dynamic movies, EPLAS helps seniors enjoying entertainments such as travels or games and learning performances such as exercises or hobbies. For evaluations, we implemented a simple system for EPLAS, and asked 20 seniors in Indonesia to have three exercises on chairs and watch one 360-degree travel video using it. The questionnaire results found that 80% of them agreed with the positive impacts on health conditions, while video contents and the background music need to be improved.
Segmentation and Simulation Analysis of Sports Video Images Based on Cellular Neural Network

In this paper, sports video images are taken as the main research object, with the video processing technology, to design and perform a set of methods including video controlling, panorama synthesis, static background suppression, moving object tracking and quantification functions for the sports analysis system. Aiming at the video sequence with relatively static background, this paper also proposes a video moving object segmentation algorithm based on cellular neural network, to complete the program design and development based on MATLAB experimental software platform, and to further improve traditional algorithm by analyzing the experimental results. Finally, the experimental results show that the algorithm shows performance advantage in segmentation performance and application.
Live 6DoF Video Production with Stereo Camera

We propose a light-weight 6DoF video production pipeline which uses only one stereo camera as input. The subject can move freely in any direction (lateral and depth) as the stereo camera follows to keep the subject within the frame. DeepKeying, our own proprietary keying method based on deep learning, helps with ease of live shooting with no need for green screen. The live video stream is processed in real time to provide 6DoF video experience. Our method enables a sense of immersion at production quality.
Truth Discovery of Multi-Source Text Data

Most existing truth discovery approaches are designed for structured data, and cannot meet the strong need to extract trustworthy information from raw text data for its unique characteristics such as multifactorial property of text answers (i.e., an answer may contain multiple key factors) and the diversity of word usages (i.e., different words may have the same semantic meaning). As for text answers, there are no absolute correctness or errors, most answers may be partially correct, which is quite different from the situation of traditional truth discovery. To solve these challenges, we propose an optimization-based text truth discovery model which jointly groups keywords extracted from the answers of the specific question into a set of multiple factors. Then, we select the subset of multiple factors as identified truth set for each question by parallel ant colony synchronization optimization algorithm. After that, the answers to each question can be ranked based on the similarities between factors answer provided and identified truth factors. The experiment results on real dataset show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art approaches.
The effects of high variability training on voice identity learning

High-variability training has been shown to benefit the learning of phonetic contrasts and new face identities. Here, we investigated whether high-variability training also aids voice identity learning. In Experiment 1, we contrasted high variability training (including stimuli extracted from several different recording sessions and speaking styles) with low variability training (including stimuli extracted from one recording session of read sentences; i.e., one speaking style). Listeners learned to recognise 4 voice identities (2 through high-variability training, 2 through low-variability training) and were subsequently tested on an old/new recognition task using novel read sentences. We found no high-variability training advantage in Experiment 1 – instead we found a high-variability disadvantage. However, in this experiment there was full overlap of speaking style in the low variability voices across the two task phases. We therefore ran a second experiment (Experiment 2), in which test stimuli were sourced from a previously unheard speaking style: The manipulation of high versus low-variability training was otherwise achieved in the same way as before (many speaking styles versus one speaking style). In this experiment, we observed a high-variability advantage. Findings are discussed in the context of the mechanisms thought to underpin advantages for high-variability training.
Determinants of Women Entrepreneurs’ Performance in Ethiopia (Evidence from Hawassa City Administration)

This study is conducted to assess the determinants of women entrepreneurs' performance: the case of Hawassa City Administration, Ethiopia.The study employed an explanatory research design with quantitative and qualitative research approaches.The required data were collected from 278 selected women entrepreneurs by adopting a multistage sampling technique.The data collected through questionnaire were analyzed using SPSS software version 21.Inferential statistics such as correlation and multiple linear regression were applied.The data collected through key informant interview was analyzed qualitatively using narration for triangulation.The findings of the study showed that educated women had better entrepreneurship performance since they can easily use technologies and can get management experience easily.In the same way, women entrepreneurs experience to increase their profitability.Likewise, having sufficient start up capital for business, access to credit to expand an existing business, having sufficient working capital, and having had collateral to get credit are significant factors that affect women entrepreneurs' performance.Equally, having high production capacity due to available capital, delivering a product that meets customers need with a fair price, having convenient display room and selling premises, and having good market linkages are significant factors that affect women entrepreneurs' performance.Furthermore, having the desire for achievement, independence, wealth and having self-discovery and job satisfaction are significant determinants of women entrepreneurs' performance.Therefore, the researcher recommends Hawassa city administration in collaboration with other NGO who were working with women should give continuous training to enable them to be more productive.Likewise, the financial institutions in the city should arrange long-term financial credit for women entrepreneurs.
A 3-layer RDH method in encrypted domain for medical information security

Digitisation of sensitive images demands a lossless security mechanism and a sophisticated privacy preservation technique. Sensitive imagery, e.g., medical, forensic, military images, etc., needs special care during transmission as a little distortion can lead to catastrophic diagnosis mistake. With immense advancements, popularity, and success of service-oriented architecture (SOA), providing safe and secure online medical facility is one hard challenge for both research community and the industry. This paper proposes a 3-layer embedding mechanism enabled reversible data hiding (RDH) scheme with additional electronic patient record (EPR) hiding technique for encrypted medical images. LSB modification and LSB substitution technique are used for the embedding and EPR hiding. The experiments carried out on the medical test images on three levels of embedding and the experimental results show great potential in terms of security, embedding capacity, and recovered image quality.
BYOD for learning and teaching in Greek Schools: Challenges and constraints according to teachers’ point of view

The benefits of mobile devices, such as laptops, tablets and smartphones, has led a number of schools to define Bring Your Own Device (BYOD) policies in order to allow teachers and students to bring their own devices to school for teaching and learning. This paper presents the results of a survey conducted among teachers of primary and secondary Greek schools, on their opinion and concerns regarding the adoption of BYOD model in the educational process. BYOD seems to be a promising technology potentially adding long-term value in teaching and learning, when designed in a secure and efficient manner. On the other hand, it requires adequate infrastructure and support and to address the security concerns raised.
Certificateless batch verification protocol to ensure data integrity in multi-cloud using lattices

Cloud storage provides flexible On-demand data storage services to users at anytime and anywhere. However, this model of data storage introduces number of new security threats in Cloud due to loss of control over the outsourced data. One of the security challenge in cloud is data integrity protection. In this work, we introduced certificateless batch verification protocol using lattices, which supports integrity checking of multiple data files in cloud environment. Here, third party auditor (TPA) check the integrity of the multiple files stored on cloud servers at a time. By utilising, random masking technique it preserves the privacy against TPA and in case of failure it identifies the invalid proofs using Fibonacci search. The security analysis proves the storage correctness and privacy against the TPA of the proposed method. The experimental results shows that the proposed method decreases computation cost over the TPA approximately 10% compared to individual verification.
R&amp;D for the expansion of the Tokyo regional analysis center using Google Cloud Platform

The Tokyo regional analysis center is a computing center for the ATLAS experiment at the Large Hadron Collider and one of the Worldwide LHC Computing Grid Tier 2 sites. The batch system is managed by HTCondor. For the future expansion of the center, R&D project using Google Cloud Platform (GCP), which is one of commercial cloud resources, was launched. The system is a hybrid system that worker nodes of HTCondor are deployed on Google Cloud Platform while job manager nodes and file storages are placed on on-premises resources. To manage worker nodes on GCP, GCP Condor Pool Manager (GCPM) has been developed. The system with GCPM worked in the ATLAS production system. In this paper, details of the R&D system are introduced and experiences on Google Cloud Platform are reported.
Application of High-Speed Digital Image Correlation to Taylor Impact Testing

Taylor impact studies were conducted using high-speed 3D digital image correlation (DIC). Taylor impact experiments are a well-established means of assessing material behavior at high strains and strain rates. Classically, the dynamic strength was determined from the final deformed shape of the sample. However, digital image correlation has been identified as a promising technique to provide time-resolved, full-field characterization of the dynamic failure behavior. This enables a better understanding of the deformation history and can provide a more robust validation of computational models, especially for complex, anisotropic problems. DIC involves applying a speckle pattern to the sample surface and tracking the deformation using two synchronized cameras. This allows for the determination of displacement, velocity, and strain field histories. In instances where the deformation is multiaxial, especially for non-planar samples, stereo DIC provides one of the only practical means of collecting full-field 3D data.
Raspberry Pi Based Robot Application using QR Code: QR-Robot

Today, QR codes are widely used and they take part in every aspect of life. It has developed in many areas ranging from sales materials in the shopping markets to the pharmaceutical industry, from agricultural materials to information processing for promotion. For the fact that QR codes are easy to create and use, a wide variety of information can be written to QR codes. In this study, according to the information in the QR codes, a mobile robot moved and reached a certain target. The Raspberry Pi minicomputer and a camera were used on the designed robot. First, the robot finds the QR code on the image taken from the camera, then the information on the QR code is resolved to give the robot the necessary command directives. In the study, SimpleCV framework was used for image processing and zbarlight library was used to solve QR codes.
An Empirical Study on the Relationship between Open Source Software Success and Test Effort

Open source software is gaining popularity and significance in recent years. Similarly, testability is a residual software property which the developers and users no longer ignore. This is because if the testability of software is high, the test effort and development cost decrease proportionally. This is the case because test effort is highly affected by testability. In this paper, we investigate the relationship between test effort and success of the open source software by employing source codes and easily accessible parameters from the repository. Moreover, the proposed methods are empirically examined by analyzing 17 open source software projects. According to the empirical results, a meaningful positive correlation is obtained between test effort and success. This implies the importance of preparing test scenarios and codes to obtain successful open source software project.
A Color Image Enhancement Method by Using the Hue Component

In this paper, we propose a new enhancement method for color images by using hue component. In color image processing, hue preserving is required. Certainly, the hue should not be processed, however it is possible to use for color image processing. However, the hue component is hardly used in the color image processing so far. In this paper, we clarify a new image enhancement method using hue component. In the method, high frequency component of hue is used for the unsharp masking for intensity processing. This method is effective for sharpening the blurred color image area. Although the proposed method is very simple processing, the emphasis effect is high, and this fact is clarified through image processing examples.
Unconstrained Monotonic Neural Networks.

Monotonic neural networks have recently been proposed as a way to define invertible transformations. These transformations can be combined into powerful autoregressive flows that have been shown to be universal approximators of continuous probability distributions. Architectures that ensure monotonicity typically enforce constraints on weights and activation functions, which enables invertibility but leads to a cap on the expressiveness of the resulting transformations. In this work, we propose the Unconstrained Monotonic Neural Network (UMNN) architecture based on the insight that a function is monotonic as long as its derivative is strictly positive. In particular, this latter condition can be enforced with a free-form neural network whose only constraint is the positiveness of its output. We evaluate our new invertible building block within a new autoregressive flow (UMNN-MAF) and demonstrate its effectiveness on density estimation experiments. We also illustrate the ability of UMNNs to improve variational inference.
Spatio-Temporal Feature Extraction and Distance Metric Learning for Unconstrained Action Recognition

In this work, we proposed a framework for zero-shot action recognition with spatio-temporal feature (ST-features) in order to address the problem of unconstrained action recognition. It is more challenging than the constrained action recognition problem, since a model has to recognize actions which do not appear in the training step. The proposed framework consists of two models: 1) ST-feature extraction model and 2) verification model. The ST-feature extraction model extracts discriminative ST-features from a given video clip. With these features, the verification model computes the similarity between them to examine class-identity whether their classes are identical or not. The experimental results show that the proposed framework can outperform other action recognition methods under the unconstrained condition.