Reviewerly/exHarmony huggingface dataset:
PREDICTING SOCIAL NETWORK ADDICTION USING VARIANT SIGMOID TRANSFER FEED-FORWARD NEURAL NETWORKS (FNN-SNA)

Researchers have reflected on personal traits that may predict Social Networking Sites (SNS) addiction. However, most of the researchers involved in the findings of personality traits predictor for social networking addiction either postulate or based their conclusions on analytical tools. Moreso, a review of the literature reveals that the prediction of social networking addiction using classifiers have not been well researched. We examined the prediction of SNS addiction from a well-structured questionnaire consisting of sixteen (16) personality traits. The questionnaire was administered on the google form with a response rate of 95% out of the 102-sample size. Additionally, a three (3) variant sigmoid transfer feed- forward neural networks was developed for the prediction of SNS addiction. Result indicated that pertinence (β = 0.251, p 0.01) was the most powerful predictor of social networking addiction in general and less obscurity addiction (β = 0.244, p 0.01). Experimental results also showed that the developed classifier correctly predict SNS addiction with 98% accuracy compared to similar classifiers.&#x0D; &#x0D; &#x0D;
Quantum codes from cyclic codes over F<sub>q</sub> + vF<sub>q</sub> + v<sup>2</sup>F<sub>q</sub> + v<sup>3</sup>F<sub>q</sub> + v<sup>4</sup>F<sub>q</sub>

We give a construction of quantum codes over ${\mathbb{F}_q}$ from cyclic codes over a finite non-chain ring ${\mathbb{F}_q} + v{\mathbb{F}_q} + {v^2}{\mathbb{F}_q} + {v^3}{\mathbb{F}_q} + {v^4}{\mathbb{F}_q}$, where q=p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sup> , p is an odd prime, 4|p−1 and v <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> =v.
Formatting Data

In this chapter, you will learn: how to format and parse dates and numbers, how to use the printf-style formatting in Java, and how to create a class that uses a custom formatter.
An Interactive Automatic Violin Fingering Recommendation Interface

Successful execution and musical communication require an effective fingering combination for each note the violinist plays. Fingering annotations on the musical score thus serve as important performance reminders for violinists of all abilities, particularly for beginners. However, fingering information is wholly or partially absent from most violin sheet music and existing fingering generation models require extensive preprocessing with low user-accessibility. Here, we showcase a simple graphical user interface (GUI) that automatically annotates violin fingerings on sheet music saved in the widely-adopted MusicXML format and exports the annotated score into a PDF or CSV file. Our fingering recommendation system is based on a deep learning model that further allows customised user inputs and provides three different fingering modes to best suit the unique background, training goals, and abilities of each violinist.
Deep hash image retrieval based on supervised semantic preservation

In recent years, with the rapid increase of image data in the Internet, the requirements of image data storage and retrieval are increasingly high. Tra-ditional image retrieval is based on high-dimensional features of the image to carry out similarity (Euclidean distance or cosine distance) calculation for retrieval. Storage and retrieval cost is high, although there are some methods to achieve hash retrieval, but the accuracy is generally not very high. In this paper, a deep hashing coding method based on supervised learning is proposed. This method uses deep neural network to obtain approximate binary codes. After quantifying these approximate binary codes, it can simply and quickly search similar images from massive image data, thus realizing large-scale image retrieval technology. We are in general MNIST dataset, CIFAR-10 dataset, SUN397 dataset and large-scale visual dataset ILSVCR2012 commonly used evaluation test data sets, such as the experimental results showed that the proposed method can achieve good retrieval accuracy, the MAP value of CIFAR-10 compared with existing methods improved a lot, to prove the effectiveness of the proposed method.
Intention Estimation Based Adaptive Unscented Kalman Filter for Online Neural Decoding

The commonly used fixed discrete Kalman filters (DKF) in neural decoders do not generalize well to the actual relationship between neuronal firing rates and movement intention. This is due to the underlying assumption that the neural activity is linearly related to the output state. They also face the issues of requiring large amount of training datasets to achieve a robust model and a degradation of decoding performance over time. In this paper, an adaptive adjustment is made to the conventional unscented Kalman filter (UKF) via intention estimation. This is done by incorporating a history of newly collected state parameters to develop a new set of model parameters. At each time point, a comparative weighted sum of old and new model parameters using matrix squared sums is used to update the neural decoding model parameters. The effectiveness of the resulting adaptive unscented Kalman filter (AUKF) is compared against the discrete Kalman filter and unscented Kalman filter-based algorithms. The results show that the proposed new algorithm provides higher decoding accuracy and stability while requiring less training data.
FPGA Board Implementation of Two Hamiltonian Conservative Chaotic Systems

This work presents, for the first time in the literature, the implementation of two Hamiltonian conservative chaotic systems on an FPGA board, using two different numerical methods to solve the difference equations: the improved Euler and the fourth order Runge-Kutta. The chaotic oscillators that are implemented are two cases of four-dimensional Hamiltonian conservative chaotic systems, which are simulated by MATLAB software obtaining their four different time series. Subsequently, the two cases are digitally implemented on an FPGA board using the MATLAB/Simulink tool in conjunction with the Vivado software for the digital design of the modules in VHDL code. Finally, both the Matlab simulation and the digital emulation are compared, and the FPGA board logical resources used are presented. The original contribution of this work is the design of a digital platform capable of displaying the digital emulation of two cases of Hamiltonian conservative chaotic systems in an analog way, with the flexibility to change their initial conditions and parameters using software.
Single Image Dehazing Based on Bright Channels Prior Compensation

Image taken in hazy environment usually have low contrast, low visibility, and blurred details. To solve this problem, we proposed a method to remove haze from a single image. Estimation of atmospheric light and transmission are the key steps of image dehazing. Due to the dark channel prior fails in large areas of sky or dense fog, a transmission estimation approach based on bright channel prior compensation is put forward, which can effectively avoid the problem of color distortion of dehazed image, and the estimated transmission is more accurate after the guide image filtering. At the same time, when the image is processed in the dark channel, the atmospheric light is the global atmospheric light, that is, the atmosphere of each pixel is the same. This approach proposes a method of getting the final atmospheric light via local atmospheric light estimation and global atmospheric light estimation, which solves the problem of atmospheric light is not the same for each pixel and the brightness of the restored image is low. Experiments show that the method in this paper can effectively remove haze, enhance image contrast and details. Compared with other algorithms on real-world images, our algorithm shows favorable performance for haze removal.
An Algorithm of Image Encryption Based on Bessel Self-feedback Chaotic Neural Network

Image encryption protects image information by scrambling digital image data to make the image unrecognizable. One of the digital image characteristics is a large amount of data, and the other is high redundancy. Therefore, based on the linear self-feedback, the introduction of Bessel function as an item since the feedback of chaotic neural network puts forward the Bessel since the nonlinear feedback chaos neural network model. The dynamic characteristics of chaotic neurons are analyzed by inverse bifurcation diagram and Lyapunov exponential evolution diagram, and the model was applied to image encryption.
Video materials in the educational process: Experience of approbation and use (The case of natural science disciplines)

Modern society, like a modern education, is no longer possible to imagine without technological visualization devices and various interactive materials. A few years ago, it was often not possible to widely use not only dynamic images, but even audio materials. Now the educational process is developing in step with technical progress and it is difficult to imagine a lecture or a seminar without educational means of visualizing information. However, the time for static presentations is coming to an end and it is being replaced by the use of educational video materials of various types and directions. The study presents a fairly detailed typology of currently existing video materials that can be used in the educational process. An example of the implementation of an interactive video clip in educational work with geography students is also given. The author pays special attention to the involvement of students in the process of designing, creating and evaluating creative video projects. Practical benefits of such a pedagogical technology are analyzed, both for the development of students' professional skills and for their creative growth. In addition, the article uses the QR codes of the student project, which makes it possible to view the video material presented in the study.
Information Support of the “Instructor-simulatorcosmonaut” System in the Process of Preparation for a Manned Flight

The scheme of information systems to support cosmonauts in the process of training on manned space vehicle simulators is considered. The contents of information available to the instructor and the crew in the process of training on simulators are described.
An Investigation of the Factors Influencing Chinese Readers’ Perception of Sentence Boundaries in Mandarin

Sentence boundaries are used parsimoniously inMandarin Chinese and they often do not occur until the end of a block of clauses and so express the completeness of a meaning or an idea. The concern on perceiving sentence boundaries by Chinese readers is similar to the issue of how Chinese readers segment words boundaries which has been investigated extensively. The former topic has, however, seldomly been examined.Nonetheless, our hypothesis is that native Chinese depends on unspoken principles for judging the degree of completeness of meaning within a block of clauses. In order to clarify these issues, we conducted re-punctuation experiments. Annotation data was also collected from the stimuli texts which were annotated with linguistic information at different levels. The logistic regression method was then applied to test the effects of independent variables on the response variable. It turns out that the syntactic information seems not to affect the response variable. We propose that the sentence boundaries in Chinese aremost probably semantically and textually conceptualized rather than syntactically defined.
Hybrid Classroom – The Experience оf the 107th Khan Krum Primary School in Sofia, Bulgaria

The COVID-19 pandemic imposed a quick introduction of distance learning for many students. In Bulgaria, the Ministry of Education and Science obliged schools to work synchronously with their pupils using video conference rooms. After the easing of the containment measures, regular classroom education was restored. However, the problems with quarantined students persisted. School principals had to solve new organizational challenges. The hardest and most frequent issue was the split of a given class into students present in school, and students learning online. The article introduces an organizational model of a hybrid classroom where teaching is carried out in a physical and an online classroom at the same time. The authors describe their experience with specific hardware and software used for the first time in Bulgaria for educational purposes. They also show their experience with the organization of traineeships for university students as primary school IT teachers using a hybrid classroom.
Building Blocks of a Flip-Chip Integrated Superconducting Quantum  Processor

We have integrated single and coupled superconducting transmon qubits into flip-chip modules. Each module consists of two chips - one quantum chip and one control chip - that are bump-bonded together. We demonstrate time-averaged coherence times exceeding $90\,\mu s$, single-qubit gate fidelities exceeding $99.9\%$, and two-qubit gate fidelities above $98.6\%$. We also present device design methods and discuss the sensitivity of device parameters to variation in interchip spacing. Notably, the additional flip-chip fabrication steps do not degrade the qubit performance compared to our baseline state-of-the-art in single-chip, planar circuits. This integration technique can be extended to the realisation of quantum processors accommodating hundreds of qubits in one module as it offers adequate input/output wiring access to all qubits and couplers.
Label noise detection under the Noise at Random model with ensemble  filters

Label noise detection has been widely studied in Machine Learning because of its importance in improving training data quality. Satisfactory noise detection has been achieved by adopting ensembles of classifiers. In this approach, an instance is assigned as mislabeled if a high proportion of members in the pool misclassifies it. Previous authors have empirically evaluated this approach; nevertheless, they mostly assumed that label noise is generated completely at random in a dataset. This is a strong assumption since other types of label noise are feasible in practice and can influence noise detection results. This work investigates the performance of ensemble noise detection under two different noise models: the Noisy at Random (NAR), in which the probability of label noise depends on the instance class, in comparison to the Noisy Completely at Random model, in which the probability of label noise is entirely independent. In this setting, we investigate the effect of class distribution on noise detection performance since it changes the total noise level observed in a dataset under the NAR assumption. Further, an evaluation of the ensemble vote threshold is conducted to contrast with the most common approaches in the literature. In many performed experiments, choosing a noise generation model over another can lead to different results when considering aspects such as class imbalance and noise level ratio among different classes.
D3Net: A Speaker-Listener Architecture for Semi-supervised Dense  Captioning and Visual Grounding in RGB-D Scans

Recent studies on dense captioning and visual grounding in 3D have achieved impressive results. Despite developments in both areas, the limited amount of available 3D vision-language data causes overfitting issues for 3D visual grounding and 3D dense captioning methods. Also, how to discriminatively describe objects in complex 3D environments is not fully studied yet. To address these challenges, we present D3Net, an end-to-end neural speaker-listener architecture that can detect, describe and discriminate. Our D3Net unifies dense captioning and visual grounding in 3D in a self-critical manner. This self-critical property of D3Net also introduces discriminability during object caption generation and enables semi-supervised training on ScanNet data with partially annotated descriptions. Our method outperforms SOTA methods in both tasks on the ScanRefer dataset, surpassing the SOTA 3D dense captioning method by a significant margin (23.56% CiDEr@0.5IoU improvement).
Report-Guided Automatic Lesion Annotation for Deep Learning-Based  Prostate Cancer Detection in bpMRI

Deep learning-based diagnostic performance increases with more annotated data, but manual annotation is a bottleneck in most fields. Experts evaluate diagnostic images during clinical routine, and write their findings in reports. Automatic annotation based on clinical reports could overcome the manual labelling bottleneck. We hypothesise that dense annotations for detection tasks can be generated using model predictions, guided by sparse information from these reports. To demonstrate efficacy, we generated clinically significant prostate cancer (csPCa) annotations, guided by the number of clinically significant findings in the radiology reports. We included 7,756 prostate MRI examinations, of which 3,050 were manually annotated and 4,706 were automatically annotated. We evaluated the automatic annotation quality on the manually annotated subset: our score extraction correctly identified the number of csPCa lesions for $99.3\%$ of the reports and our csPCa segmentation model correctly localised $83.8 \pm 1.1\%$ of the lesions. We evaluated prostate cancer detection performance on 300 exams from an external centre with histopathology-confirmed ground truth. Augmenting the training set with automatically labelled exams improved patient-based diagnostic area under the receiver operating characteristic curve from $88.1\pm 1.1\%$ to $89.8\pm 1.0\%$ ($P = 1.2 \cdot 10^{-4}$) and improved lesion-based sensitivity at one false positive per case from $79.2 \pm 2.8\%$ to $85.4 \pm 1.9\%$ ($P<10^{-4}$), with $mean \pm std.$ over 15 independent runs. This improved performance demonstrates the feasibility of our report-guided automatic annotations. Source code is made publicly available at https://github.com/DIAGNijmegen/Report-Guided-Annotation. Best csPCa detection algorithm is made available at https://grand-challenge.org/algorithms/bpmri-cspca-detection-report-guided-annotations/.
Guided-Generative Network for noise detection in Monte-Carlo rendering

Estimating the features to be extracted from an image for classification tasks are sometimes difficult, especially if images are related to a particular kind of noise. The aim of this paper is to propose a neural network architecture named Guided-Generative Network (GGN) to extract refined information that allows to correctly quantify the noise present in a sliding window of images. GNN tends to find the desired features to address such a problem in order to emit a detection criterion of this noise. The proposed GGN is applied on photorealistic images which are rendered by Monte-Carlo methods by evaluating a large number of samples per pixel. An insufficient number of samples per pixel tends to result in residual noise which is very noticeable to humans. This noise can be reduced by increasing the number of samples, as proven by Monte-Carlo theory, but this involves considerable computational time. Finding the right number of samples needed for human observers to perceive no noise is still an open problem. The results obtained show that GGN can correctly solve the problem without prior knowledge of the noise while being competitive with existing methods.
Learning Transferable Motor Skills with Hierarchical Latent Mixture  Policies

For robots operating in the real world, it is desirable to learn reusable behaviours that can effectively be transferred and adapted to numerous tasks and scenarios. We propose an approach to learn abstract motor skills from data using a hierarchical mixture latent variable model. In contrast to existing work, our method exploits a three-level hierarchy of both discrete and continuous latent variables, to capture a set of high-level behaviours while allowing for variance in how they are executed. We demonstrate in manipulation domains that the method can effectively cluster offline data into distinct, executable behaviours, while retaining the flexibility of a continuous latent variable model. The resulting skills can be transferred and fine-tuned on new tasks, unseen objects, and from state to vision-based policies, yielding better sample efficiency and asymptotic performance compared to existing skill- and imitation-based methods. We further analyse how and when the skills are most beneficial: they encourage directed exploration to cover large regions of the state space relevant to the task, making them most effective in challenging sparse-reward settings.
Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks

Labelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are diverse valid beliefs about what the correct data labels should be. So far, dataset creators have acknowledged annotator subjectivity, but not actively managed it in the annotation process. This has led to partly-subjective datasets that fail to serve a clear downstream use. To address this issue, we propose two contrasting paradigms for data annotation. The descriptive paradigm encourages annotator subjectivity, whereas the prescriptive paradigm discourages it. Descriptive annotation allows for the surveying and modelling of different beliefs, whereas prescriptive annotation enables the training of models that consistently apply one belief. We discuss benefits and challenges in implementing both paradigms, and argue that dataset creators should explicitly aim for one or the other to facilitate the intended use of their dataset. Lastly, we design an annotation experiment to illustrate the contrast between the two paradigms.
Optimisation of Scalable Ion-Cavity Interfaces for Quantum Photonic  Networks

In the design optimisation of ion-cavity interfaces for quantum networking applications, difficulties occur due to the many competing figures of merit and highly interdependent design constraints, many of which present `soft-limits', amenable to improvement at the cost of engineering time. In this work we present a systematic approach to this problem which offers a means to identify efficient and robust operating regimes, and to elucidate the trade-offs involved in the design process, allowing engineering efforts to be focused on the most sensitive and critical parameters. We show that in many relevant cases it is possible to approximately separate the geometric aspects of the cooperativity from those associated with the atomic system and the mirror surfaces themselves, greatly simplifying the optimisation procedure. Although our approach to optimisation can be applied to most operating regimes, here we consider cavities suitable for typical ion trapping experiments, and with substantial transverse misalignment of the mirrors. We find that cavities with mirror misalignments of many micrometres can still offer very high photon extraction efficiencies, offering an appealing route to the scalable production of ion-cavity interfaces for large scale quantum networks.
Tools and Techniques Adapted for Teaching Software Engineering Topics Remotely during the COVID-19 Pandemic

To stop the spread of the COVID-19 virus, educational institutions abruptly switched from in-person to online, remote mode of teaching without giving educators the necessary tools and training.In this paper, we focus on the Software Engineering Education & Training (SEET) courses at the university levels and address questions like: What tools and techniques did they adapt to handle the modality transition challenges?What lessons they learned and what would they do differently the next time?What are the students' perspective on these, etc.? We interviewed 16 SEET educators from different countries around the world; followed by surveys of more than 300 educator and student participants.Our empirical study found some common themes of challenges, as well as suggestions on tools and techniques to overcome them.