Reviewerly/exHarmony huggingface dataset:
Design and implementation of wireless helmet and mechanical wheelchair

Cases of disability have been on record since the beginning of time. There are several types of disabilities, including physical and mental. The negative impact of disability should have minimal effect on a person’s quality of life, and this statement motivated this research. One of the most influential physicists, Stephen Hawking, once said, “Disability need not be an obstacle to success.” This specific study focuses on the physically impaired who are unable to move about or carry out their day-to-day tasks independently. The study presents a mechanical wheelchair system controlled by the user’s head movement. This is particularly most useful to disabled individuals suffering from paralysis or impaired muscle function. The basic design of the proposed system includes a wearable headpiece and the electronic mechanical wheelchair unit. The system is light and easy to use, and these features ensure a smooth and easy transition to a substantially improved quality of life.
Applying Spatio-temporal Analysis in Telecom Fraud Investigation

Recently telecom fraud has become a severe problem worldwide, resulting in billions of dollars each year. To prevent telecom fraud, Taiwan National Police Agency proposes an Anti-money Mule Project to detect and arrest mules responsible for withdrawing victims’ ATM cash from telecom fraud. This study used the Python program to integrate visual data for the following three significant phases: time series, spatial connection, and geographic map data. These phases can analyze 2,238 remittance accounts that were withdrawn transaction records by the money mules. Our research results have demonstrated that Spatio-temporal analysis could help investigators linkage transaction records by the same money mule, narrow down the scope of the investigation, and increase the possibility of finding out the suspect. The Spatio-temporal analysis is successfully applied as a systematic, proactive investigation technique in Taiwan’s telecom fraud investigation.
“You have said too much”: Java-like verbosity anti-patterns in Python codebases

As a popular language for teaching introductory programming, Java can profoundly influence beginner programmers with its coding style and idioms. Despite its many advantages, the paradigmatic coding style in Java is often described as verbose. As a result, when writing code in more concise languages, such programmers tend to emulate the familiar Java coding idioms, thus neglecting to take advantage of the more succinct counterparts in those languages. As a result of such verbosity, not only the overall code quality suffers, but the verbose non-idiomatic patterns also render code hard to understand and maintain. In this paper, we study the incidences of Java-like verbosity as they occur in Python codebases. We present a collection of Java-Like Verbosity Anti-patterns and our pilot study of their presence in representative open-source Python codebases. We discuss our findings as a call for action to computing educators, particularly those who work with introductory students. We need novel pedagogical interventions that encourage budding programmers to write concise idiomatic code in any language.
RPM: Local Differential Privacy based Inventory Forecasting

In the Cost-Per-Mille (CPM) advertising business scenario that adopts the guaranteed delivery model, ad publisher predicts the future available impressions inventory of each advertising placement under the constraints of each target audience based on the historical impressions data, so as to sell advertising resources reasonably. However, historical data threatens user and publisher privacy during the Sharing-Aggregation-Analysis procedure. In addition, internet companies that act as ad publishers are reluctant to release data constrained by laws and regulations, which creates Isolated Data Island. In order to realize privacy-preserving data sharing and publishing and improve data availability as much as possible to build inventory structure, we propose a novel Local Differential Privacy (LDP) method RPM to process user impressions logs and design a privacy-preserving inventory forecasting(PPIF) framework combining clustering and sampling in this work. LDP is a promising privacy standard. There is no need for trusted third parties to participate. Each user perturbs sensitive data locally and uploads the disturbed records. The real data does not leave the local area, providing the user with the privacy preserving effect of indistinguishable individuals. There is a trade-off between privacy protection and data availability. Under availability constraints, LDP is usually used in industry only for frequency and mean statistics operations. There is plenty of room for research in designing foundational LDP technologies to support more data-mining computing tasks such as clustering. The privacy-preserving effect of RPM is demonstrated by rigorous mathematical proof, and the simulation experiments prove that the inventory estimation results obtained by using RPM are more accurate than other existing LDP methods.
Ruolo e opportunità del bibliotecario dei dati per la Scienza aperta

Open Science involves a profound transformation of doing research that has an impact on libraries and data librarians. Through documentation and literature analysis, the article focuses on the profile of the data librarian described as state of the art in Europe and Italy. Despite the delay of national policies, the profile of the data librarian in Italy is growing, acquiring awareness of the importance of the role through pioneering experiences of research support services.
Target Recognition and Tracking Based on Scale Adaptive Kernel Correlation Filter for Dynamic Perspective of Robots

An autonomous target recognition and tracking method based on improved Kernel Correlation Filter (KCF) is proposed to deal with camera jitter and changing environments in dynamic perspective for mobile robots. Firstly, the Speeded Up Robust Features detection algorithm is combined with Random Sample Consensus to recognize targets and mark target areas. And then, KCF is used for rough tracking of the above target areas. Meanwhile, the target re-identification strategy is applied to update the tracking data in real time to solve the problem that the scale of KCF tracking box is too inflexible for accurate tracking. Finally, tests are carried out not only indoors but also outdoors. The experimental results show that the proposed method can obviously enhance the stability and accuracy of dynamic perspective. Its accuracy is higher than SIFT and KAZE when the camera moves rapidly, and it has better real-time performance and tracking accuracy than KCF and MIL.
Emoji can improve university students’ intimate perception with their counselor：evidence from behavior and eye movements

The use of emoji in the network environment is very important for the establishment of intimacy. This also plays an important role for college counselors in the network communication of students. The experiment simulated three different dialogue modes of We-chat. 41 sophomore students were selected using a 2 (validity: positive, negative) *3 (dialogue modes: text-only, emoji-only, text+emoji) within-subjects design, and eye-movement technology was used to examine the effect of different response styles of counselors on students' perceptions of college counselors' intimacy. The results showed that students gazed at the interest area more often and for a longer total time in both positive and negative states with the text+emoji dialogue method. This suggests that the text+emoji dialogue method promotes students' intimate perception with their counselor.
Comparison of BlindLoginV2 and AudioBlindLogin with the common textual password authentication for the blind and visually impaired using smartphones

BlindLoginV2 and AudioBlindLogin were designed as memorable and usable graphical authentication systems for the blind and visually impaired, to reduce their difficulties in authentication as compared to alphanumeric or biometric authentication. The main aim of this paper is to compare the use of these two alternative graphical authentication systems with the traditional textual-based authentication, and to encourage the blind and visually impaired to use smartphone authentication, by overcoming the difficulties of maneuvering the virtual keyboard. It compares BlindLoginV2 and AudioBlindLogin with the 4-character common traditional textual authentication system, in terms of password creation time, mean login time performance and failure rate, and identifies the factors influencing the mean login time performance of BlindLoginV2 (object picture superiority effect) and AudioBlindLogin (auditory picture superiority effect) for the blind and visually impaired. Quantitative testing results show that both proposed systems are more accessible for the visually impaired as compared with the traditional text-based authentication system because the password creation time and mean login time is significantly shorter with lower failure rates. Results also show that the type of authentication method, the visual capability and the age of the user, and the interactions between these three factors significantly influences the mean login time performance.
Study of the mechanical properties of magnetic composites supported on a PU matrix

Magnetic nanoparticles have been used to confer better properties to materials, particularly to polymers. Due the properties of the polymer, such as flexibility and lightness, combined, white nanoparticles provide other properties such as microhardness, corrosion resistance, among others. In this study, three types of nanoparticles were elaborated: magnetite, cobalt ferrite and nickel ferrite, through the coprecipitation method used in the elaboration of skin-like polyurethane (PU) -based composites. Specimens were made at different nanoparticle weights (0.1%, 0.3%, 0.5% and 1.0%). Likewise, the hardness was measured by means of a phase II Model PHT-2500 portable digital hardness tester and the tension tests were carried out on an Autograph Shimadzu universal machine. The stress results were plotted using the Jupyter Notebook for interpretation. Finding some improvements in the materials manufactured using the different ferrites already mentioned.
Flexible Extrinsic Parameter Calibration for Multicameras With Nonoverlapping Field of View

In many measurement applications using multi-cameras, there is often a non-overlapping field of view (nFOV) between multi-cameras. It benefits greatly from having all of the cameras unified in a single coordinate frame by means of common spatial constraints. However, it is very difficult to establish common spatial constraints for accurate and quick calibration of the extrinsic parameters between multi-cameras in the case of long working distance, asymmetric working angle or limited working space. To overcome these issues, we propose a flexible calibration method using a camera rig, which can be easily modeled as a hand-eye calibration problem and solved by only several sets of calibration images. To further improve the calibration accuracy, the camera intrinsic parameters, lens distortion coefficients, and extrinsic parameters are optimized simultaneously, similar to binocular calibration. However, this causes instability of multi-parameters. We extend the epipolar constraint to two images with nFOV and add it into the optimization objective function together with the re-projection error, which ensures the basic consistency of intrinsic parameters before and after optimization, and reduce the fluctuation caused by the number of images involved in calibration. Experiments and application have proved that the proposed new methods are feasible and effective.
SA-LIFT: a similar area learning invariant feature transform network framework

Image stitching adopts image registration and image fusion technology, which combines several images with overlapping areas into a wide-view, high-resolution panoramic image. Image Stitching is widely used in remote sensing image processing, medical diagnosis, military detection and many other fields. When the image is stitched, the feature points extracted by methods based on grayscale, frequency domain and image features are too redundant, and the feature points are not comprehensive enough. In order to solve the above problems, this paper proposes a network framework for extracting feature points of image stitching based on deep learning, which is called SA-LIFT. First, the image is divided into similar and non-similar areas by extracting model. Second, an improved LIFT model is proposed, which uses three convolutional networks to extract image feature points. The experimental results show that the method used in this paper has good effects in improving comprehensiveness and reducing redundancy when extracting the feature points of image stitching.
[Paper] Quality Improvement for Real-time Free Viewpoint Video Using View-dependent Shape Refinement

In this paper, we propose a quality improvement method of a real-time free viewpoint video (FVV) synthesis. Two functions, adaptive silhouette dilation and view-dependent shape refinement, are applied to obtain a 3D model with accurate shapes in the real-time FVV. The adaptive dilation reduces the missing part of a reconstructed 3D model caused by a camera calibration error. In addition, the excessively expanded part of a 3D model caused by the silhouette dilation is corrected by the view-dependent refinement algorithm on a screen rendering. A part of the model outlines becomes transparently displayed to refine the model shape by comparing the camera image used for texture mapping with a background model. In the experiments, the proposed method achieved the best quality compared with conventional real-time FVV methods. Furthermore, the processing was executed in real-time because the computation cost of the proposed functions was extremely small.
A Making Mode is Missing Code: Research Through Design Lacks Discourse Around Programming

Making programmable things, such as prototypes and other interactive physical artefacts, has inherent value for Research through Design inquiries. However, the intersection between programming and Research through Design appears to have received little attention. To investigate this issue, we conducted a literature review examining 51 papers. In nearly every case, the artefact’s program and the act of programming appear to be severely under-documented. It does not seem to matter where the code came from, what kind of responsiveness the behaviour has, how responsive the interaction is, or how the code maps to the perceived behaviour. Analysis of our corpus revealed six themes and three leverage points for supporting designerly research’s engagement with programming. We use these to offer recommendations to deepen and broaden the range of insights that may be articulated by Research through Design that involves interactive artefacts.
Automobile airbag defect detection algorithm based on improved Faster RCNN

The traditional image processing method has a low detection rate for various kinds of automobile airbag surface defects in the production process, which is difficult to meet the actual demand of industrial production. In order to improve the detection rate of automobile airbag surface defects and meet the real-time requirements of industrial detection, this paper proposes an improved Faster RCNN deep learning algorithm. Firstly, the method adopts the E-FPN to enhance the feature extraction ability of the network for multi-scale targets. Then, ROI Align algorithm is introduced instead of ROI Pooling algorithm to improve the detection ability of small targets. Finally, the designed Light Head is used to improve the running speed of the network. The experimental results show that the average precision of the improved Faster RCNN algorithm for automobile airbag defect detection reaches 97.2%, and the detection time is 23.73 milliseconds, which is obviously superior to the original algorithm and has higher detection accuracy and practicability.
Big Data Analysis of Intellectual Property Service Agencies

Intellectual property service agencies mainly provide services such as intellectual property agency or management for enterprises or individuals. There are a large number of intellectual property service agencies in the Beibu Gulf City Cluster, but the research and analysis on them are still relatively few. Based on the Beibu Gulf City Cluster, this paper analyzes the information of the provinces, cities, establishment time, staff size, registered capital and other information of the intellectual property rights service agencies. The first step of big data analysis is to pre-process and store the data of intellectual property service agencies to the data warehouse, and then conduct visual analysis on them through R language. Through visual analysis, enterprises or individuals can have a relatively comprehensive grasp of information related to intellectual property service agencies, and then choose appropriate agencies to provide intellectual property services for them.
Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach

We propose a unified framework to study policy evaluation (PE) and the associated temporal difference (TD) methods for reinforcement learning in continuous time and space. We show that PE is equivalent to maintaining the martingale condition of a process. From this perspective, we find that the mean--square TD error approximates the quadratic variation of the martingale and thus is not a suitable objective for PE. We present two methods to use the martingale characterization for designing PE algorithms. The first one minimizes a ``martingale loss function", whose solution is proved to be the best approximation of the true value function in the mean--square sense. This method interprets the classical gradient Monte-Carlo algorithm. The second method is based on a system of equations called the ``martingale orthogonality conditions" with test functions. Solving these equations in different ways recovers various classical TD algorithms, such as TD($\lambda$), LSTD, and GTD. Different choices of test functions determine in what sense the resulting solutions approximate the true value function. Moreover, we prove that any convergent time-discretized algorithm converges to its continuous-time counterpart as the mesh size goes to zero, and we provide the convergence rate. We demonstrate the theoretical results and corresponding algorithms with numerical experiments and applications.
High resolution spectrally resolved interferometry in the mid-IR

One of the most widespread and reliable techniques for spectral phase characterization of optical elements is spectrally resolved interferometry (SRI) [1] , [2] , which utilizes a two-arm interferometer illuminated by a broadband light source and a spectrometer to record the generated spectral fringes. The investigated optical element is placed in one arm, and its spectral phase can be extracted by using Fourier-transform (FT) based methods [3] , [4] for instance, which are the most accurate, even when low dispersion values are to be determined. One detriment to this evaluation method, that it requires high spectral resolution. Considering that most of the spectrometers in the mid-infrared (MIR) region have typically a few nm spectral resolution, we propose two alternative methods for measurements in this spectral region.
Image Recognition by Quantum Annealing Using Multi-bit Spin Variables

This paper presents an image-recognition technique using quantum annealing. We used four handwritten numbers of 0, 1, 2, and 3 from the MNIST (Modified National Institute of Standards and Technology) digit classification dataset as images to be recognized. We used two models in this study which were basically the same as a simple neural network, one had one fully connected layer and the other had 3x3 Sobel filters in front of that layer. The images were first resized to 1/2 the original in both directions, and pixel data were converted to 196 one-dimension data then output to four outputs through a fully coupled neural network. The optimum values of 196 x 4 weights and 4 bias values of the fully connected layer were obtained by quantum annealing in which the loss function is expressed as Hamiltonian of the Ising model. The weights and biases are multi-valued variables consisting of multiple spin variables in the Ising model. Quantum annealing was simulated using Pyqubo to create Ising models from flexible mathematical expressions. Prediction accuracy when using the Sobel filters reached about 95% and Sobel filter improved it by about 5%.
Research on UAV Detection of Threat Target around oil Pipeline Based on Deep Learning

With the development of UAV, UAV has been applied to various projects with its advantages of low construction cost,low safety risk coefficient and convenient operation.In terms of UAV platform, currently composite wing and multi-rotor UAV are typically adopted, which can realize basic flight route. In terms of image detection, neural network is mainly used to classify and recognize the target in the image. In this paper, the YOLOV4 algorithm is improved to make it more suitable for UAV detection of ground targets.In the ground detection of UAV, most of them are small targets, so clustering method is used to redesign anchor for small targets. Because the features of small targets have more details in the shallow feature layer, the shallow feature is superimposed into the feature extraction layer, and the shallow feature and the deep feature are fused.In the data processing, data enhancement, color dithering, flipping, cutting of the data set for expansion. Through the test of the modified network, the following results are obtained: the overall mAP is improved by 9.3%, the detection mAP for small targets such as people is improved by 23.75%, and the detection mAP for working vehicles is improved by 15.4%. The detection efficiency of small targets is improved, and the speed can meet the real-time requirements, and it can be deployed in the UAV for UAV detection.
ASM: Augmentation-based Semantic Mechanism on Abstractive Summarization

Many transformer-based encoder-decoder models have made significant progress on summary generating tasks. And the availability of pre-trained models further improves its performance with self-supervised objectives on large text corpora. However, most models' architectures and their training criteria pay more attention to the lexical and syntactic structure rather than semantic similarity. In this paper, we augment training data in semantic space and propose Augmentation-based Semantic Mechanism (ASM) for encoder feedback with corresponding criterion to capture global semantic meanings. Notably, we enhance the encoder's comprehension of summaries in semantic space, and facilitate the integration of global semantics and local syntax during generating summaries. By leveraging pre-trained language models, we have driven our results to a new level (45.11 on CNN/DailyMail, 45.35 on XSum in ROUGE-1). Additionally, the human evaluation and further experiments also validate the effectiveness of our proposed method for generating abstractive summaries. Our augmented data and source code for summarization will be made public.