Reviewerly/exHarmony huggingface dataset:
The Accuracy Performance of Semantic Segmentation Network with Different Backbones

With the fast improvement of classification networks, many of these networks are being in use as backbones of semantic segmentation networks to improve the accuracy. Using different classification networks as the backbone of the same semantic segmentation network may show different accuracy performance. This paper selected the sandstone dataset and self-driving cars dataset to compare the accuracy performance differences of VGG-16, ResNet-34, and Inceptionv3 as the backbone of UNet, where the original encoder of the UNet is replaced by a backbone. The three backbone networks are imported from Segmentation Models library, and they have weights trained on ImageNet dataset. The best accuracy performance of the semantic segmentation network on the sandstone dataset is when VGG-16 is used as the backbone, it achieved 76.22% MIoU. On the other hand, the highest accuracy performance of the semantic segmentation network on self-driving cars dataset is 75.47% MIoU, achieved when Inceptionv3 is used as the backbone. However, the accuracy is improved when using all the three backbones with both datasets, compared to the accuracy performance of the UNet without using any backbone network.
Digital Technology Boosting Agricultural Supply-Side Constitutive Revolution in Poor Areas Based on the Intelligent Environment of the Internet of Things

China is a big agricultural country, and China’s agricultural development is related to the development of the country. Especially in poor rural areas, the development of agriculture still stays at low-level manual farming. Therefore, with the help of the developed Internet of Things technology, it is necessary for digital technology to develop and reform the rural areas in poverty-stricken areas. This paper aims to promote the supply-side structural revolution of agriculture in poverty-stricken areas by digital technology based on intelligent environment of Internet of Things. Firstly, the KNN intelligent algorithm is improved to improve the accuracy and reduce the time complexity. Then, based on the data support of Internet of Things technology, a prediction model is built, and the parameters are optimized by particle swarm optimization, and finally the best prediction model with high prediction accuracy, good stability and reduced prediction time complexity is obtained. Taking the source area of the Danjiang River in the South-to-North Water Transfer Project as the research object, the total grain output and soybean production demand in this area from 2010 to 2018 were selected, and the change trend of soybean production and demand in this area from 2019 to 2021 was predicted. The experimental data show that the soybean output in this region has increased year by year, with a cumulative increase of 6,985.4 tons from 2010 to 2018. Soybean output sometimes exceeds demand, and sometimes it is less than demand. There will be an oversupply situation in 2019 and 2021, and an oversupply situation in 2020. Therefore, the prediction model of Internet of Things can effectively predict the supply and demand situation of soybean with an accuracy rate of over 90%, which can provide data support for the decision-making of structural change of agricultural supply side in poverty-stricken areas.
On Evaluating Anonymity of Onion Routing

Anonymous communication networks (ACNs) aim to thwart an adversary, who controls or observes chunks of the communication network, from determining the respective identities of two communicating parties. We focus on low-latency ACNs such as Tor, which target a practical level of anonymity without incurring an unacceptable transmission delay. While several definitions have been proposed to quantify the level of anonymity provided by high-latency, message-centric ACNs (such as mix-nets and DC-nets), this approach is less relevant to Tor, where user–destination pairs communicate over secure overlay circuits. Moreover, existing evaluation methods of traffic analysis attacks on Tor appear somewhat ad hoc and fragmented. We propose a fair evaluation framework for such attacks against onion routing systems by identifying and discussing the crucial components for evaluation, including how to consider various adversarial goals, how to factor in the adversarial ability to collect information relevant to the attack, and how these components combine to suitable metrics to quantify the adversary’s success.
Re-ranking vehicle re-identification with orientation-guide query expansion

Vehicle re-identification, which aims to retrieve information regarding a vehicle from different cameras with non-overlapping views, has recently attracted extensive attention in the field of computer vision owing to the development of smart cities. This task can be regarded as a type of retrieval problem, where re-ranking is important for performance enhancement. In the vehicle re-identification ranking list, images whose orientations are dissimilar to that of the query image must preferably be optimized on priority. However, traditional methods are incompatible with such samples, resulting in unsatisfactory vehicle re-identification performances. Therefore, in this study, we propose a vehicle re-identification re-ranking method with orientation-guide query expansion to optimize the initial ranking list obtained by a re-identification model. In the proposed method, we first find the nearest neighbor image whose orientation is dissimilar to the queried image and then fuse the features of the query and neighbor images to obtain new features for information retrieval. Experiments are performed on two public data sets, VeRi-776 and VehicleID, and the effectiveness of the proposed method is confirmed.
How can generative adversarial networks impact computer generated art? Insights from poetry to melody conversion

Recent advances in deep learning and generative adversarial networks (GANs), in particular, has enabled interesting applications including photorealistic image generation, image translation, and automatic caption generation. This has opened up possibilities for many cross-domain applications in computer generated arts and literature. Although there are existing software-based approaches for generating musical accompaniment of a given poetry, there are no existing implementation using GANs. This work proposes a novel poetry to melody generation conditioned on poem emotion using GANs. A dataset containing pairs of poetry and melody based on three emotion categories is introduced. Furthermore, various GAN architectures including SpecGAN and WaveGAN were explored for automatic melody synthesis for a given class of poetry. Conditional SpecGAN produced the best melodies according to quantitative metrics. Melodies produced by SpecGAN were evaluated by volunteers who deemed the quality to be above average.
MNSSD: A Real-time DNN based Companion Image Data Annotation using MobileNet and Single Shot Multibox Detector

Images, text, and other forms of data collected through the device or sensors that we carry with us, such as a smart phone, camera, and so on, are referred to as companion data. There is considerable interest in automatic assignment of keywords to images in order to index, retrieve and analyze huge amounts of image data. In the past decade, many image annotation methods have been developed, many of which perform well on benchmark datasets. However, very little attention has been given to annotating the companion image data in real time using deep neural network. A new baseline technique for companion image data annotation using deep neural networks is introduced in this study that approaches annotation as a retrieval task. As an object detector, the Single Shot Multibox Detector is utilized, while the MobileNetV2 architecture serves as the classifier. This portable and lightweight architecture may also be utilized in integrated systems (such as smart phones and Raspberry Pis) to conduct real-time image annotation. We achieved 82.37% precision and 74.03% F1 score using the method described in this paper. For image annotation, our baseline model beats the existing best practices. We anticipate that adopting a baseline will provide a solid foundation for comparing and comprehending future annotation methods.
Development of a 3-Axis Force Sensor for an Intelligent Gripper that Safely Grips Unknown Objects

In this study, we designed and manufactured a 3-axis force sensor for an intelligent gripper that safely grips an unknown object. The 3-axis force sensor consists of an Fx force sensor, an Fy force sensor, and an Fz force sensor in one body, and is manufactured by attaching a strain gauge. The characteristics evaluation showed that the rated output error was within 0.2, the nonlinearity error was within 0.05, and the reproducibility error was within 0.06%. Therefore, the 3-axis force sensor designed and manufactured in this study can be used to measure weight and control the force used to grip an unknown object by attaching it to the intelligent gripper.
An anatomy-based iteratively searching convolutional neural network for organ localization in CT images

Organ localization is a common and essential preprocessing operation for many medical image analysis tasks. We propose a novel multi-organ localization method based on an end-to-end 3D convolutional neural network. The proposed algorithm employs a regression network to learn the position relationship between any patch and target organs in a medical computed tomography (CT) image. With this framework, it can iteratively localize the target organs in a coarse-to-fine manner. The main idea behind this method is to embed the anatomy of structures in a deep learning-based approach. For implementation, the proposed network outputs an 8-dimensional vector that contains information about the position, scale, and presence of each target organ. A piecewise loss function and a multi-density sampling strategy help to optimize this network to learn anatomy layout characteristics over the entire CT image. Starting from a random position, this network can accurately locate the target organ with a few iterations. Moreover, a dual-resolution strategy is employed to improve the accuracy affected by varying organ scales, further enhancing the localizing performance for all organs. We evaluate our method on a public data set (LiTS) to locate 11 organs in the thoraco-abdomino-pelvic region. The proposed method outperforms state-of-the-art methods with a mean intersection over union (IOU) of 80.84%, mean wall distance of 3.63 mm, and mean centroid distance of 4.93 mm, constituting excellent accuracy. The improvements on relatively small-size and medium-size organs are noteworthy.
Prediction of Solar Power Using Linear Regression

Solar power has been a significant contributor to energy generation and the economy in recent years. The reason for the significant rise in solar energy usage has been its abundance and cost-free nature. Yet, these qualities cannot support the solar to be used at its full potential. The intermittent nature of the solar has been a significant drawback in its progress. With the advancement in artificial intelligence, solar power forecasting can be done by machine learning methods. Therefore, this paper demonstrates the results obtained by a linear regression model using python for predicting solar energy. After the evaluation process, the best performance achieved was RMSE of 44.371 and R2_value of 0.966 among all the scenarios.
A European approach to COVID-19 apps? A landscape analysis of apps stores in the initial stages of the health crisis

This article explores to what extent a European approach can be identified in the COVID-19 mobile apps landscape that surfaced in the initial stages of the crisis. Based on information collected from the two most prominent app stores (Google Play for Android-powered devices and Apple’s App Store for iOS-powered devices) between April and August 2020, we examine differences between COVID-19 related apps released in European countries and elsewhere in the world. The article focuses on two aspects: the involvement of actors from the public sector in releasing COVID-19 related apps and the measures implemented for personal data protection. The findings suggest that differences exist according to the geographical context in which apps were released and the specific functions they perform. We identify specific trends in Europe that confirm a stronger emphasis on data protection than what happened on a global scale, and to a certain extent a greater involvement of the public sector through health authorities.
A Robust Three-Stage Hybrid Framework for English to Bangla Transliteration

Phonetic typing using the English alphabet has become widely popular nowadays for social media and chat services. As a result, a text containing various English and Bangla words and phrases has become increasingly common. Existing transliteration tools display poor performance for such texts. This paper proposes a robust Three-stage Hybrid Transliteration (THT) framework that can transliterate both English words and phonetic typed Bangla words satisfactorily. This is achieved by adopting a hybrid approach of dictionary-based and rule-based techniques. Experimental results confirm superiority of THT as it significantly outperforms the benchmark transliteration tool.
A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models

Pre-trained language models (PLMs) cannot well recall rich factual knowledge of entities exhibited in large-scale corpora, especially those rare entities.In this paper, we propose to build a simple but effective Pluggable Entity Lookup Table (PELT) on demand by aggregating the entity's output representations of multiple occurrences in the corpora.PELT can be compatibly plugged as inputs to infuse supplemental entity knowledge into PLMs.Compared to previous knowledge-enhanced PLMs, PELT only requires 0.2%∼5% pre-computation with capability of acquiring knowledge from out-ofdomain corpora for domain adaptation scenario.The experiments on knowledge-related tasks demonstrate that our method, PELT, can flexibly and effectively transfer entity knowledge from related corpora into PLMs with different architectures.Our code and models are publicly available at https://github.com/thunlp/PELT.
EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation

Complete Multi-lingual Neural Machine Translation (C-MNMT) achieves superior performance against the conventional MNMT by constructing multi-way aligned corpus, i.e., aligning bilingual training examples from different language pairs when either their source or target sides are identical.However, since exactly identical sentences from different language pairs are scarce, the power of the multi-way aligned corpus is limited by its scale.To handle this problem, this paper proposes "Extract and Generate" (EAG), a two-step approach to construct large-scale and high-quality multi-way aligned corpus from bilingual data.Specifically, we first extract candidate aligned examples by pairing the bilingual examples from different language pairs with highly similar source or target sentences; and then generate the final aligned examples from the candidates with a welltrained generation model.With this two-step pipeline, EAG can construct a large-scale and multi-way aligned corpus whose diversity is almost identical to the original bilingual corpus.Experiments on two publicly available datasets i.e., WMT-5 and OPUS-100, show that the proposed method achieves significant improvements over strong baselines, with +1.1 and +1.4 BLEU points improvements on the two datasets respectively.
Towards Abstractive Grounded Summarization of Podcast Transcripts

Podcasts have shown a recent rise in popularity.Summarization of podcasts is of practical benefit to both content providers and consumers.It helps people quickly decide whether they will listen to a podcast and/or reduces the cognitive load of content providers to write summaries.Nevertheless, podcast summarization faces significant challenges including factual inconsistencies of summaries with respect to the inputs.The problem is exacerbated by speech disfluencies and recognition errors in transcripts of spoken language.In this paper, we explore a novel abstractive summarization method to alleviate these issues.Our approach learns to produce an abstractive summary while grounding summary segments in specific regions of the transcript to allow for full inspection of summary details.We conduct a series of analyses of the proposed approach on a large podcast dataset and show that the approach can achieve promising results.Grounded summaries bring clear benefits in locating the summary and transcript segments that contain inconsistent information, and hence improve summarization quality in terms of automatic and human evaluation.
Classification of Breast Cancer Tumors Using Mammography Images Processing Based on Machine Learning

&#x0D; Abstract— Using intelligent methods to identify and classify a variety of diseases, in particular cancer, has gained tremendous attention today. Tumor classification plays an important role in medical diagnosis. This study's goal was to classify breast cancer (BC) tumors using software-based numerical techniques. To determine whether breast cancer masses are benign or malignant, we used MATLAB version 2020b to build a neural network. In the first step, the features of the training images and their output classes were used to train the network. Optimal weights were obtained after several repetitions, and the network was trained to produce the best result in the test phase after several repetitions.&#x0D; Because of using effective and accurate features, the method suggested here, which was based on an artificial neural network, delivered the diagnostic accuracy, specificity, and sensitivity of 100%, 100%, and 100%, respectively, to discern benign from malignant BC tumors, showing a better performance compared to previously proposed methods. One of the challenges for imaging-based diagnostic techniques in medicine is the difficulty of processing dense tissues. Breast cancer is one of the most common progressive diseases among females. Early diagnosis makes treatment easier and more effective. Using AI-based methods for automated diagnosis purposes can be valuable and have a reduced error rate because accurate diagnosis by manual means is time-consuming and error-prone.
TEACHING IN TIMES OF PANDEMIC, CONVERGING TOWARDS A HYBRID SCENARIO

Hybrid model, online synchronous model, synchronous and blended model: over the last year, teachers have had to adapt to a series of regulatory changes because of the pandemic caused by the SARS-CoV-2 virus, especially in the university environment. In this sense, planning classes has become a suggestion, since in a single semester, different ways of teaching have been employed ranging from 100% synchronous to hybrid. This raises the following question: how have teachers adapted to the changes? Have they changed the way they teach, what are the most commonly used teaching methodologies today? Similarly, has the technological leap been a real problem when changing from the 100% face-to-face model to the different varieties that the health authorities have allowed us to use? For all these reasons, the present study aims to analyze the impact of the application of the different educational models at the university in order to discover the main obstacles teachers have found during this period, and, above all, to study the main methodologies used during this period. To do this, we have carried out a survey among the teachers of the ESIC Business and Marketing School in Barcelona, which has allowed us to collect quantitative and qualitative aspects. As for the conclusions, initially, we can say that there has been a radical change in the way in which classes are being taught. Lectures have almost completely been abandoned and great value is being given to group work and projects. Finally, one of the most outstanding aspects in the conclusions is that teachers emphasize the importance of recycling and methodological innovation in order to continue implementing new teaching methodologies.
Dynamic adaptive security path planning based on A* algorithm

Abstract Aiming at the problems of large memory consumption, long operation time and low security of traditional A * algorithm, a dynamic adaptive improved algorithm based on A * algorithm is proposed. Firstly, according to the heuristic function of the proportion of obstacles in the map, the adaptive dynamic weighting is carried out to shorten the search time. Secondly, the jump point search (JPS) algorithm is integrated to remove a large number of redundant points and improve the running speed of the algorithm. Finally, according to the relative position of the starting point and the target point, the obstacles are expanded dynamically to keep a safe distance between the planned path and the obstacles. In order to verify the effect, the improved algorithm is tested in different sizes and different proportion of obstacles. The results show that the improved algorithm is efficient and safe in global path planning.
Face detection from in-car video for continuous health monitoring

Face detection in videos from smart cars or homes is becoming increasingly important in human-computer interaction, emotion recognition, gender and age identification, driving assistance, and vital sign measurements, as heart rate and respiratory rate is derived from the video. However, face detection suffers from variations in illumination, subject motion, different skin colors, or camera distances. We compare three algorithms for in-car application: Haar cascade classifier (HCC), histogram of oriented gradients (HoG), and a deep neural network (DNN). For evaluation, we consider the freely available “driver monitoring dataset” multimodal database (DMD) and self-collected videos recorded in a research car. We analyze run-time, accuracy, and F1-score. HoG has highest computational time as compared to HCC and DNN with 2.99 frames per second(fps), 7.00 fps, and 18.25 fps, respectively. For DMD, the F1-scores are 91.75%, 95.91%, and 99.48% for HCC, HoG, and DNN respectively, and 88.05%, 83.68%, and 99.66% for our dataset, respectively. All in all, DNN is fastest and most accurate. Moreover, we observed that DNN handles occlusions and varying illumination better than the other approaches. In conclusion, DNN can be applied successfully for in-car face detection as a first step towards real-time continuous health monitoring.
Reduction of retrace error from broadband variable transmission spheres in Fizeau interferometry

We have previously presented broadband variable transmission sphere (BVTS) systems based on the Alvarez lens for use in Fizeau interferometry. A BVTS system may replace multiple conventional transmission spheres with one system composed of two plano-freeform optical elements. In this paper, we discuss retrace error as a source of aberrations in Fizeau interferometry measurements when using BVTS systems. We explore a design optimization method to decrease retrace error by optimizing freeform surfaces to reduce targeted Fringe Zernike wavefront aberrations. Significant reduction in peak-to-valley retrace error is demonstrated.
Magnitude estimation of self-speed under different visual cue conditions in virtual space

In recent years, the concept of the metaverse has spread, and the demand for virtual reality (VR) technology has increased. We focused on human perception in VR spaces, particularly their own locomotive velocity. The amount of visual information in the VR space was changed, and the subjective velocity perception was evaluated using the psychophysical method of magnitude estimation. When walking in a bleak hallway, the velocity was felt slower than when walking in a hallway with many objects. However, the exponent of Stevens&#x2019; power law was not significantly changed by visual cues in the hallways.