Reviewerly/exHarmony huggingface dataset:
UNIVERSITY TEACHERS’ PERCEPTIONS OF STUDENT DIFFICULTIES IN ONLINE TEACHING AND LEARNING

Given the current insecurities facing the Indonesian education system during the Covid-19 outbreak, gaining thorough perceptions of students’ difficulties in online teaching and learning is crucial. This study aimed to disclose university teachers’ perceptions of students’ difficulties in online teaching and learning. One survey questionnaire was distributed to obtain data from a total of 95 university teachers to provide insights on students’ difficulties in online teaching and learning. Data were descriptive statistically analyzed using the SPSS software version 21 by calculating frequencies and percentages. The result of descriptive statistical analysis showed that: (a) lack of access to appropriate technologies such as computer, notebook, or smartphones could not join the online teaching and learning; (b) unfamiliarity in using technology has caused students frustrated and expressed inability to study in online teaching and learning; and (c) students were difficult to stay with online class assignments. In conclusion, students who lacked access and were unfamiliar with using technological devices were unable to participate in online teaching and learning. They even found it difficult to stay with online class assignments. The findings indicate the need for university teachers to put students at the center of their teaching and learning by taking into mind the students’ access to and familiarity in using personal technological devices and internet before conducting online teaching and learning. Keywords: Online Teaching and Learning, University Teachers, Student Difficulties DOI： https://doi.org/10.35741/issn.0258-2724.58.1.5
Calibration alternatives to logistic regression and their potential for transferring the statistical dispersion of discriminatory power into uncertainties in probabilities of default

The transformation of credit scores into probabilities of default plays an important role in credit risk estimation. The linear logistic regression has developed into a standard calibration approach in the banking sector. With the advent of machine learning techniques in the discriminatory phase of credit risk models, however, the standard calibration approach is currently under scrutiny again. In particular, the assumptions behind the linear logistic regression provide critics with a target. The previous literature has converted the calibration problem into a regression task without any loss of generality. In this paper, we draw on recent academic results in order to suggest two new single-parameter families of differentiable functions as candidates for this regression. The derivation of these two families of differentiable functions is based on the maximum entropy principle, and thus it relies on a minimum number of assumptions. We compare the performance of four calibration approaches on a realworld data set and find that one of the new single-parameter families outperforms the linear logistic regression. Further, we develop an approach to quantify that part of the general estimation error of probabilities of default which stems from the statistical dispersion of the discriminatory power.
Pruning during training by network efficacy modeling

Deep neural networks (DNNs) are costly to train. Pruning, an approach to alleviate model complexity by zeroing out or pruning DNN elements, has shown promise in reducing training costs for DNNs with little to no efficacy at a given task. This paper presents a novel method to perform early pruning of DNN elements (e.g., neurons or convolutional filters) during the training process while minimizing losses to model performance. To achieve this, we model the efficacy of DNN elements in a Bayesian manner conditioned upon efficacy data collected during the training and prune DNN elements with low predictive efficacy after training completion. Empirical evaluations show that the proposed Bayesian early pruning improves the computational efficiency of DNN training while better preserving model performance compared to other tested pruning approaches.
High-quality rainy image generation method for autonomous driving based on few-shot learning

Rainy image generation aims to transfer images from standard domains such as daytime into rainy domains. Related researches can be divided into unsupervised methods and supervised methods according to use of semantic label constraints. The generalization ability of unsupervised methods is highly related to the domain gap between rain-free images and rain-effect images, which is difficult to keep the layout consistency due to the lack of semantic constraints on the paired data of the target domain. In supervised generative models, the scarcity of paired datasets has a serious impact on the performance of generative results. Moreover, most of the existing rainy paired datasets are synthesized by simply merging and the rain simulated by noise, which can be very different from the images shot in natural condition. So, in order to improve the realism of rainy image generation, we proposed a realistic paired rainy dataset (PRD) in autonomous driving scenes to explore the real rain representations and fusion mechanism with clear images. Besides, aiming at lack of paired samples in autonomous driving scenarios, we are committed to the study of the few-shot learning in generative models. An incremental hybrid training strategy is proposed to make full use of a few datasets. Through extensive experiments, we verify the effectiveness of our proposed method, which achieves more realistic results on limited labeled data. In the future, the dataset can be applied in many other tasks of autonomous driving.
Software Protection under Copyright Law

Software plays a crucial role in most parts of today’s society. It is now an indispensable feature of the world of commerce, finance, industry and manufacture, education and research, medicine, government, entertainment, law and generally daily life. Furthermore, software stands at the heart of all disruptive technologies, such as artificial intelligence, automation and robotics, Internet of Things, biometric and digital identification, and virtual reality, to name but a few.
Study on ecological aesthetic wisdom in Hunan ancestral hall architecture based on BIM technology

On the one hand, ecological aesthetics requires buildings to provide comfortable indoor space environment for human beings; On the other hand, it has a small demand for the environment. In terms of material selection and energy use, it adheres to the principles of reducing use, reusing, recycling and replacing non-renewable resources with renewable resources. In view of the lack of efficient design technology in traditional architecture from the perspective of modernization, the study adopted BIM technology as the main design technology, and designed a design scheme to transform traditional drawings into three-dimensional architectural models according to the need to take into account the aesthetic concept and architectural functionality in the design process of ancestral halls in Hunan. This scheme can overcome the problem of complicated and inefficient design procedures in the process of traditional architectural aesthetic design from the perspective of modern architectural design, combine architectural functionality with traditional aesthetic concepts, and provide a way for the ecological and efficient design of traditional buildings.
Cooperation between Coherent Control and Noises in Quantum Metrology

Abstract In this article, the cooperation between coherent control and noises in open spin systems is studied, aiming to demonstrate that such cooperation can provide new possibilities for parametrization in quantum metrology. The cooperative scheme proposed here outperforms the standard scheme, with higher precision achieved. More specifically, the effect of cooperative interaction between coherent control and noises in conventional single‐spin systems described by Lindblad master equations is illustrated, with the magnitude of a magnetic field taken as the parameter to be estimated and encoded in the noisy dynamics besides the Hamiltonian. The scenarios of both spontaneous emission and dephasing noise have been analyzed, where the associated quantum Fisher information has been given. Furthermore, it has been demonstrated that in the realm where quantum metrology is mostly applied in practice, the precision limit under the cooperative scheme in the presence of noises can surpass the ultimate precision limit under the unitary dynamics. On the other hand, multiple‐spin systems have also been considered. It is shown that the coupling between different spins can help realize non‐local parametrization under the cooperative scheme, with the ground state made entangled. This thus leads to the improvement of precision limit, which has been proved and visualized in this article.
Impact of Dimensionality Reduction on Outlier Detection: an Empirical Study

Outlier detection is a fundamental data analytics technique often used for many security applications. Numerous outlier detection techniques exist, and in most cases are used to directly identify outliers without any interaction. Typically the underlying data used is often high dimensional and complex. Even though outliers may be identified, since humans can easily grasp low dimensional spaces, it is difficult for a security expert to understand/visualize why a particular event or record has been identified as an outlier. In this paper we study the extent to which outlier detection techniques work in smaller dimensions and how well dimensional reduction techniques still enable accurate detection of outliers. This can help us to understand the extent to which data can be visualized while still retaining the intrinsic outlyingness of the outliers.
Performance of Broad Learning System on Movie Review Dataset Compared with Traditional Method LSTM

In recent years, a broad learning system that attempts to replace deep structure learning methods has been proposed. In text classification, the traditional method LSTM is used in most cases. two methods of breadth learning are used in the paper for the classification task of movie review data, namely augmented nodes and simultaneously augmented and mapped nodes, and the training results are compared with those of LSTM. Previously, broad learning has achieved good results on some light datasets, but the movie review dataset is a larger dataset in comparison, with the passage of time, broad learning seems to fade out of the limelight, and few scholars are seen to use it anymore. In this paper, we will experimentally explore whether broad learning can outperform the traditional method LSTM on movie review datasets, and find out the advantages and disadvantages of bbroad learning, and explore the reasons for the decreasing hotness of breadth learning.
Study of a two-level variable model optimization algorithm based on differential privacy

Traditional ADMM methods suffer from slow convergence and low recognition when faced with group-structured data. This paper therefore uses the parallel alternating directional multiplier method (PADMM) algorithm based on a two-layer penalty variable model with cross-features to solve the model, and uses an optimisation algorithm with normalised weight decay Nadam to improve the convergence speed of the model. Considering the risk of privacy leakage when the data is iterated, a moderate amount of noise is added to the model solving process by perturbing the output of the algorithm to achieve privacy protection of the data. Experiments show that for the same privacy budget, the DP-Nadam with weight attenuation is more accurate than the DP-Nadam in terms of model accuracy as the number of training rounds increases, and the loss is also less than that of the traditional DP-Nadam.
Quantum metrology in a lossless Mach–Zehnder interferometer using entangled photon inputs for a sequence of non-adaptive and adaptive measurements

Using multi-photon entangled input states, we estimate the phase uncertainty in a noiseless Mach-Zehnder interferometer (MZI) using photon-counting detection. We assume a flat prior uncertainty and use Bayesian inference to construct a posterior uncertainty. By minimizing the posterior variance to get the optimal input states, we first devise an estimation and measurement strategy that yields the lowest phase uncertainty for a single measurement. N00N and Gaussian states are determined to be optimal in certain regimes. We then generalize to a sequence of repeated measurements, using non-adaptive and fully adaptive measurements. N00N and Gaussian input states are close to optimal in these cases as well, and optimal analytical formulae are developed. Using these formulae as inputs, a general scaling formula is obtained, which shows how many shots it would take on average to reduce phase uncertainty to a target level. Finally, these theoretical results are compared with a Monte Carlo simulation using frequentist inference. In both methods of inference, the local non-adaptive method is shown to be the most effective practical method to reduce phase uncertainty.
Spatio-Temporal Video Denoising Based on Attention Mechanism

The demands of high-quality videos captured by camera become bigger due to the rapid development of pattern recognition and artificial intelligence. Video denoising is the key technology to obtain clear videos. However, the research on video denoising is far from enough now. In this paper, we propose a video denoising method based on convolutional neural network architecture to reduce the noise from the sensor system. We improve the loss function of noise estimation by imposing adaptive penalty on under-estimation error of noise level which makes our method perform robustly. Furthermore, we make use of multi-level features to guide the spatial denoising, where multilayer semantic information of the image is regarded as the perceptual loss. Instead of relying on Optical Flow solving the characterization of inter-frame information, we utilize U-Net-like structure to handle motion implicitly. It is less computationally expensive and avoids distortions caused by inaccurate flow and object occlusion. In order to locate temporal features and suppress useless information, the attention mechanism is introduced to the skip connections of the U-Net-like structure. Experimental results demonstrate that the proposed algorithm outputs more convincing results in both peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) indexes when processing Gaussian noise, synthetic real noise, and real noise compared with selected approaches.
Analysis of Open Set Deep Neural Network Variants towards Classification of Known and Unknown Signals

In environments crowded with electromagnetic activity, cognitive radios (CR) must have the ability to differentiate between different signals, so as to understand their origins if needed, and decide whether they are friendly or unfriendly signals. This is extremely important in the case of military and intelligence applications, but is beginning to become important in the commercial world as well, given the recent discussion around 5G and ORAN. In this paper, we develop and compare three open and three closed-set signal classification models based on various Machine Learning (ML) architectures. Results show that open-set networks are able to perform very close to their closed-set counterparts while also being able to classify unknown signals that they have not been trained on.
Convolutional neural network based decoders for surface codes

Abstract The decoding of error syndromes of surface codes with classical algorithms may slow down quantum computation. To overcome this problem it is possible to implement decoding algorithms based on artificial neural networks. This work reports a study of decoders based on convolutional neural networks, tested on different code distances and noise models. The results show that decoders based on convolutional neural networks have good performance and can adapt to different noise models. Moreover, explainable machine learning techniques have been applied to the neural network of the decoder to better understand the behaviour and errors of the algorithm, in order to produce a more robust and performing algorithm.
Conditional Cross Correlation Network for Video Question Answering

Video question answering (VideoQA) is the process that aims at responding to questions expressed in natural language, according to the semantic content of a given video. VideoQA is a highly challenging task and demands a comprehensive understanding of the video document, including the recognition of the various objects, actions and activities involved together with the spatial, temporal and causal relations between them. To tackle the challenge of VideoQA, most methods propose efficient techniques to fuse the representations between visual and textual modalities. In this paper, we introduce a novel framework based on a conditional cross-correlation network that learns multimodal contextualization with reduced computational and memory requirements. At the core of our approach, we consider a cross-correlation module designed to learn reciprocally constrained visual/textual features combined with a lightweight transformer that fuses the intermodal contextualization between visual and textual modalities. We test the vulnerability of the composing elements of our pipeline using black box attacks. To this purpose, we automatically generate semantic-preserving rephrased questions. The ablation study conducted confirms the importance of each module in the framework. The experimental evaluation, carried out on the MSVD-QA benchmark, validates the proposed methodology with average accuracy scores of 43.58%. When compared with state-of-the-art methods the proposed method yields gains in accuracy of more than 4%and achieves a 43.58% accuracy rate on the MSVD-QA data set.
Robust Zero-Shot Intent Detection via Contrastive Transfer Learning

Intent detector is a central component of any task-oriented conversational system. The goal of the intent detector is to identify the user’s goal by classifying natural language utterances. In recent years, research has focused on supervised intent detection models. Supervised learning approaches cannot accommodate unseen intents, which may emerge after the system has been deployed — the more practically relevant setting, known as zero-shot intent detection. The existing zero-shot learning approaches split a dataset into seen and unseen intents for training and evaluations without taking the sensitivity of the data collection process into account. That is, humans tend to use repeated vocabulary and compose sentences with similar compositional structures. We argue that the source-to-target relationship learning objective of zero-shot approaches under typical data split procedure renders the zero-shot models prone to misclassifications when target intents are divergent from source intents. To this end, we propose INTEND, a zero-shot INTENt Detection methodology that leverages contrastive transfer learning and employs a zero-shot learning paradigm in its true sense. First, in contrast to partitioning the training and testing sets from the same dataset, we demonstrate that selecting training and testing sets from two different datasets, allows for rigorous zero-shot intent detection evaluations. Second, our employed contrastive learning goal encourages the system to focus on learning a generic similarity function, rather than on commonly encountered patterns in the training set. We conduct extensive experimental evaluations using four public intent detection datasets for up to 150 unseen classes. Our experimental results show that INTEND consistently outperforms state-of-the-art zero-shot techniques by a substantial margin. Furthermore, our approach achieves significantly better performance than few-shot intent detection models.
Real-time signal acquisition based on optical excitable response by pre-designing single-tone preamble waveform

Real-time acquisition of target signals is preferred for mobile communication systems. However, under the requirement of ultra-low latency for next-generation communication, traditional acquisition methods need to temporally locate the target signal from a large amount of raw data with correlation-based computing, introducing extra latency. We propose a real-time signal acquisition method based on an optical excitable response (OER) by pre-designing a single-tone preamble waveform. The preamble waveform is designed to be within the amplitude and bandwidth of the target signal, so no extra transceiver is required. The OER generates a corresponding pulse to the preamble waveform in the analog domain, which simultaneously triggers an analog-to-digital converter (ADC) to acquire target signals. The dependence of OER pulse on the preamble waveform parameter is studied, leading to a pre-design of the preamble waveform for an optimal OER. In the experiment, we demonstrate a millimeter-wave (26.5-GHz) transceiver system with target signals of orthogonal frequency division multiplexing (OFDM) format. Experimental results show that the response time is less than 4 ns, which is far lower than the ms-level response time of traditional all-digital time-synchronous acquisition methods.
Multivariate factor assessment for lithogenic and anthropogenic distribution of macro and trace elements in water from Pčinja River Basin, North Macedonia

In this work the distribution of chemical elements in surface water from the Pčinja River Basin, North Macedonia, was assessed. For this purpose, along the course of the Pčinja River, in accordance with a previously specified sampling network with 10 locations from the rivers and 4 locations from the Glažnja and Lipkovsko Lakes, the samples of surface water were collected in the period from June to July 2017. Apart from six locations on the river Pčinja, two samples were collected from the Tabanovska River and one sample each from the tributaries of Kumanovska River, Konjarska River and Kriva River. In all of the collected samples 22 macro and trace elements (Аg, Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, P, Ni, Pb, Sr, V and Zn) were analyzed by inductively coupled plasma - atomic emission spectrometry (ICP-AES). A multivariate factor analysis was applied to analyze relations between the number of variables. The method includes the processing of information from a large number of original variables and their processing into a smaller set (factor) with minimal loss of information from the original variables. From the gathered results spatial distribution maps of factors for the separate groups were prepared as well as distribution maps for the analyzed elements. It was found that the distribution of most elements follows the lithology of the study area, except for some elements (Cu, P) whose increased concentrations are as a result of urban and agricultural and industrial activities.
Adsorption of porcine pulmonin can reduce the toxicity of gold nanorods and reduce lung injury

It is well known that porcine lung phospholipid injection (Curosurf) can effectively inhibit lung inflammation and reduce inflammatory cell damage to lung tissue, but whether it can reduce nanoparticles damage to lung remains to be seen. Poractant was co-incubated with gold nanorods as the model particle in this study to adsorb poractant on the surface of gold nanorods. Elisa kit was used to determine the adsorption degree of human lung surfactant associated protein poractant on the surface of gold nanorods. Gold nanorods adsorbed human lung surfactants associated proteins were evaluated further in vitro and in vivo . The toxicity of gold nanorods after absorption of porcine lung phospholipid was studied using human normal lung epithelial cells BEAS-2B; TNF- and IL-1 in serum were studied after absorption of porcine lung phospholipid using acute lung injury as a model. The results of the experiments show that adsorption of porcine lung phospholipids can reduce the toxicity of gold nanorods and the lung damage caused by gold nanorods.