Reviewerly/exHarmony huggingface dataset:
The Problem of Alienation as a Destructive Factor in Development of Russian Universities

One of the main tasks facing domestic universities is to train graduates who are able to work professionally in a transforming economy. The solution of this problem is impossible without fundamental changes in management practices both in relation to universities (implemented in the state educational policy) and within them. At the same time, it is necessary to take into account not only the tasks caused by the changing situation in the country and abroad, but also the negative trends in the development of the domestic education sector, characteristic of the last three decades. The required changes can actually be implemented if management efforts are combined by both the Ministry of Science and Higher Education of the Russian Federation and university management. The most important task of university management is to overcome such negative consequences as the transformation of the motives and values of key subjects of the educational process (primarily teachers and students), the problem of alienation affecting these social communities, changing the nature and forms of communication between them, etc. The article substantiates the relevance of such changes, provides the main ways to overcome the problem of alienation, based on the use of management concepts adequate to the current situation (concepts of value-oriented management of the university and productive management of its strategic assets).
Automatic evaluation of answer scripts using computational models

Answer script evaluation is an important part of assessing students&apos; performance. Typically, an answer script evaluation is done manually that sometimes can be biased, time-consuming and inconsistent. The goal of this work is to introduce a system that computationally evaluates the subjective answers and electronic circuits from the examinee and hence reduces the time and effort of human intervention, as well as makes the evaluation procedure impartial. Our experiment consists of keyword extraction from the answer script, evaluating the order of words and sentences, and measuring various similarities between the input answers and stored correct answer models. Automatic evaluation of answer scripts has been found very useful in our experiments, and often the assigned marks are the same as manually scored marks. Since not all the answers in the exam can be described with words, we have focused on the electronic components and circuits, and also created a model that reads the hand-drawn circuits, and updates those to their digital representation, for evaluation of the related circuit diagrams for the question. The results from the evaluation have an accuracy of 93% are encouraging and consistent with respect to human evaluation.
Taking the Measure of the Earthquake That Destroyed Tokyo: A Century on, We're Good at Detecting Earthquakes, Not So Good at Protecting Against Them

At 11:58 am on Saturday, 1 September 1923, the Kanto region of Japan started to shake. The earthquake began with a yiolent horizontal back-and-forth, motion followed by two vertical jolts, another horizontal shock even stronger than the first. The intensity of the tremor damaged the seismometer that took the reading at top, interrupting the waveform in the north-south direction. The earthquake went on for somewhere between 4 and 10 minutes.
Quick Text Classification with Privacy Protection based on Secure Multiparty Computing

Abstract: I intend and complete activity a insured Naive Bayes sign to resolve the issue of private theme request. Alice, on one side, is holding a text, and Impact, on the other, is holding a classifier. Bob won't have advanced by any means, and Alice will just know how the calculation answered her text at the show's decision. Reaction depends on Secure Multiparty Computation (SMC). Utilizing Rust variant, it is basic and get to classify unstructured text. I can determine if a SMS is marketing mail or hot dog in inferior 340 milliseconds when Impact's model's declaration remark breadth covers all dispute (n = 5200) and Alice's SMS has m = 160 unigrams. This plan is adjustable and maybe secondhand in miscellaneous position place the Earnest Bayes classifier maybe secondhand. For n = 369 and m = 8, the sane number of marketing mail SMSs in the educational group, plan just takes 21 milliseconds
On the Syntax of Unusual Subjects: Exceptional Case Marking Constructions

This study aims to explain the mixed properties of the ECM subject in Korean and Mongolian in terms of the Relator Phrase (RP) analysis proposed by den Dikken (2007, 2017a, 2017b). The embedded subject in the Korean ECM is known to exhibit both higher/lower and A/A
High utility sequence mining using cuckoo search algorithm

High utility sequence mining is a hot topic in the field of finding knowledge in the databases. It aims at discovering patterns of high importance which can be used to optimise businesses, provide better user experience, click stream analysis and much more. Key problems while mining high utility sequences is that one of the effective pruning strategies called downward closure property used while mining HUI (high utility itemsets) fails here, and also since the number of subsequences can be very large, there is an issue of combinatorial explosion. Several algorithms have been proposed to efficiently mine such patterns but they can be unsatisfying in terms of memory usage and runtime. In this paper, mining such patterns have been proposed with the help of an evolutionary technique called Cuckoo Search. Instead of scanning the entire database and generating each subsequence and calculating its utility which can be a very costly operation, focus is on randomly generating a subset of sequences of good value which act as initial population, from which new sequences are generated which have higher utilities.
Enhanced phishing URL detection using hybrid feature-based machine learning method

The rapid growth of the internet has led to increased usage of e-commerce, but it has also attracted cybercriminals who seek to steal personal information through various means, including phishing schemes. In these schemes, individuals are tricked into disclosing sensitive information through fake URLs. Due to the semantics-based attack strategy used in phishing, it can be difficult to distinguish between legitimate and phishing URLs, taking advantage of computer users&apos; vulnerabilities. Although software companies offer anti-phishing systems that utilize blacklists, heuristics, visuals, and machine learning, they cannot prevent all phishing attempts. To address this issue, this research proposes five classification methods that use hybrid features, including natural language processing (NLP) and principal component analysis (PCA). The study finds that the Random Forest algorithm and XGboost utilizing NLP and word vector features outperform their competitors, achieving a 99.5% accuracy rate in classifying phishing URLs. These findings are significant as they demonstrate the potential of machine learning techniques, such as NLP and PCA, in improving the accuracy of anti-phishing systems. By incorporating these techniques into the classification process, it is possible to enhance the ability of such systems to differentiate between legitimate and phishing URLs. This can help in preventing cybercriminals from stealing sensitive information and provide a safer online experience for users.
“Superhuman Speed”: How Autonomous Drones Beat the Best Human Racers

It's flying so fast that following it with my camera is hopeless, so I give up and watch in disbelief. The shrieking whine from the four motors of the racing quadrotor Dopplers up and down as the drone twists, turns, and backflips its way through the square plastic gates of the course at a speed that is literally super-human. I'm cowering behind a safety net, inside a hangar at an airfield just outside of Zurich, along with the drone's creators from the Robotics and Perception Group at the University of Zurich.
Electroencephalography Signal Analysis for Virtual Reality Sickness: Head-mounted Display and Screen-based

The use of virtual reality (VR) technology is growing in the current era of the COVID-19 pandemic. However, the use of VR is causing problems for its users. Some symptoms, such as nausea, headache, and eye strain, are felt after using VR. These symptoms are called VR sickness. This study used electroencephalography (EEG) to record participants' brain activity changes when experiencing VR sickness. Participants are given VR impressions via screens and head-mounted displays (HMD). In addition, the subject also filled out a simulator sickness questionnaire (SSQ) before and after being given a VR. Brain waves in the alpha frequency range (8 Hz-13 Hz) and low beta frequency (13 Hz-21 Hz) were analyzed through the power spectral density (PSD). From the SSQ results, participants who saw VR through the screen experienced increased nausea symptoms. On the other hand, participants who saw VR through HMD experienced an increase in nausea and oculomotor symptoms (p<0.05). Based on power spectral analysis, changes in alpha wave PSD were obtained in the frontal, central, and parietal brain regions. There was also a shift in the value of the alpha peak frequency from before and after the participants were given VR. The average value of the alpha peak frequency shifts to a significant value. Therefore, it is concluded that the VR sickness from HMD viewing is more significant than through screens in the form of PSD results.
SFDA: Chinese Diabetic Text Classification Based on Sentence Feature Level Data Augmentation

Many type 2 diabetes patients and high-risk groups has an increasing demand for specialized information on diabetes. However, the long-tail problem often generate difficulties in model training and reduced classification accuracy. In this paper, we propose enhancing senmantic feature approach to solve the long-tail problem in Chinese diabetes text classification and detailed practice is as followes: we enrich the tail classes knowledge by enhancing semantic features module and then use the attention aggregation module to improve the semantic representation by fusing these semantic features. As for the enhancing semantic feature module, we proposed two strategies: using different dropouts while pre-trained language model is same and using different pre-trained language model. As for the attention aggregation module, its purpose is to better fusing the semantic features obtained previously. After processing by these two modules, we send the final feature vector into the classifier. The final accuracy of 89.1% was obtained for the classification of Chinese diabetes in the NCAA2023 assessment.
How Python Swallowed the World: Lessons from Compiling Top Programming Languages

Programmers seek languages that let them solve particular problems in concise, elegant ways and communicate those solutions to other programmers. For the last 10 years, IEEE Spectrum has been trying to help with that search with its annual interactive rankings of the Top Programming Languages, the latest of which is now available on our website by scanning the QR code below.
A User Intent Recognition Model for Medical Queries Based on Attentional Interaction and Focal Loss Boost

Pre-trained language models such as BERT and RoBERTa have obtained new state-of-the-art results in the user intent recognition task. Nevertheless, in the medical field, the models frequently neglect to make full use of label information and seldom take the difficulty of intent recognition for each query sentence into account. In this paper, a new user intent recognition model based on Text-Label Attention Interaction and Focal Loss Boost named TAI-FLB is proposed to identify user intents from medical query sentences. The model focuses on incorporating a text-to-label attention interaction mechanism based on label embedding to exploit the information from labels. Moreover, during training process, the loss contribution of difficult samples with unclear intention is increased to shift model focus towards difficult samples in medical query statements. Experimental evaluation was performed on two publicly available datasets KUAKE and CMID. The results demonstrated that the proposed TAI-FLB model outperformed other baseline methods and demonstrated its effectiveness.
Ultrasound based breast cancer recognition using Mask RCNN

As breast cancer is the leading cause of death in women globally, effective treatment is dependent on early identification.. This paper presents a design for the Computer-Aided Diagnostic (CAD) system of Ultrasound based Breast Cancer Recognition using mask regional convolutional neural network (Mask RCNN). The dataset used in the proposed model consists of three classes benign, malignant, and normal. The paper includes a detailed description of the dataset used, implementation of the proposed model, and performance evaluation of the model. To get better efficacy, the dataset is pre-processed. The results of the experiment show that the proposed method detects breast cancer in all three classes, i.e., benign, malignant, and normal with an average accuracy of 97.93%. The annotated dataset used in this paper can also be used for future research in this area. Overall, the proposed method provides a promising solution for early breast cancer detection and may help improve patient outcomes.
Ballot Tabulation Using Deep Learning

Currently deployed election systems that scan and process hand-marked ballots are not sophisticated enough to handle marks insufficiently filled in (e.g., partially filled-in), improper marks (e.g., using check marks or crosses instead of filling in bubbles), or marks outside of bubbles, other than setting a threshold to detect whether the pixels inside bubbles are dark and dense enough to be counted as a vote. The current works along this line are still largely limited by their degree of automation and require substantial manpower for annotation and adjudication. In this study, we propose a highly automated deep learning (DL) mark segmentation model-based ballot tabulation assistant able to accurately identify legitimate ballot marks. For comparison purposes, a highly customized traditional computer vision (T-CV) mark segmentation-based method has also been developed to compare with the DL-based tabulator, with a detailed discussion included. Our experiments conducted on two real election datasets achieved the highest accuracy of 99.984% on ballot tabulation. In order to further enhance our DL model’s capability of detecting the marks that are underrepresented in training datasets, e.g., insufficiently or improperly filled marks, we propose a Siamese network architecture that enables our DL model to exploit the contrasting features between a hand-marked ballot image and its corresponding blank template image to detect marks. Without the need for extra data collection, by incorporating this novel network architecture, our DL model-based tabulation method not only achieved a higher accuracy score but also substantially reduced the overall false negative rate.
Mapping the Origins of Heaviness between 1970 and 1995

Metal music has been undergoing a remarkable sonic development. Pioneering releases of the early 1970s by Black Sabbath already contained all essential ingredients of metal’s sonic signature. The growing need for heavier sounds was afforded by a rapidly advancing recording technology, alongside the exploration of production techniques and aesthetics. This chapter traces significant developments in metal music production from the 1970s to the 1990s by looking at key artists, albums and audio professionals to outline how heaviness in recorded form developed in the genre. Many of the analysed engineering practices were adopted, improved and have become standard in contemporary metal production. In this process, production was brought to the fore, making it an increasingly fundamental element of the music, even an art form in itself. This development led to what has become metal’s standard hyper-real aesthetic, which will most likely be pushed further in the ongoing and genre-defining quest for greater heaviness.
ARFL: Adaptive and Robust Federated Learning

Federated Learning (FL) is a machine learning technique that enables multiple local clients holding individual datasets to collaboratively train a model, without exchanging the clients' datasets. Conventional FL approaches often assign a fixed workload (local epoch) and step size (learning rate) to the clients during the client-side local model training and utilize all collaborating trained models' parameters evenly during the server-side global model aggregation. Consequently, they frequently experience problems with data heterogeneity and high communication costs. In this paper, we propose a novel FL approach to mitigate the above problems. On the client side, we propose an adaptive model update approach that optimally allocates a needful number of local epochs and dynamically adjusts the learning rate to train the local model and regularizes the conventional objective function by adding a proximal term to it. On the server side, we propose a robust model aggregation strategy that potentially supplants the local outlier updates (models' weights) prior to the aggregation. We provide the theoretical convergence results and perform extensive experiments on different data setups over the MNIST, CIFAR-10, and Shakespeare datasets, which manifest that our FL scheme surpasses the baselines in terms of communication speedup, test-set performance, and global convergence.
Novel IBVS System Design for Cable-Driven Hyper-Redundant Manipulator With MLESAC-Based Feature Vector Optimization

The visual servoing control extends the potential application of cable-driven hyper-redundant manipulators (CDHRM) to automatically execute tasks by sensing the visual information of environment. However, the high proportion of feature point mismatching and visual occlusion may occur in the complex and narrow environment that CDHRM works in, which make the traditional image-based visual servoing (IBVS) system hard to work or even cause instability. In this article, a novel IBVS system is proposed for CDHRM with eye-in-hand configuration, where the ability of mismatching resistance is improved by optimizing the vector of feature points, while the stability and tracking performance are still guaranteed. The maximum likelihood estimation random sample consensus (MLESAC) algorithm is designed to estimate the inlier model that resists mismatching and extracts well-matched feature points (namely the inliers) for the first captured image. Since the inliers may become mismatched in the newly captured image, the inliers and inlier model are both updated according to the feature quality weights. As a result, the newly mismatched feature points are excluded so that the weighted feature vector with less mismatching and higher quality can be generated. Subsequently, the weighted IBVS control law based on this vector is designed to achieve the mismatching resistance as well as guarantee the asymptotic stability and tracking performance of system. Comparative experiments are implemented for the proposed MLESAC algorithm and novel IBVS system, and the results verify that our method has better adaptability to the environment when applied in CDHRM, even with partial visual occlusion.
Multi-objective Optimization for Multi-Robot Path Planning on Warehouse Environments

Today, robots can be found in almost any field. Examples include robots for transporting materials in hospitals and warehouses, surveillance, intelligent laboratories and space exploration. Whatever the reason for moving the robot and whatever its location, all robot applications anywhere require path calculation. In this paper, we address the problem of collision-free path planning in multirobot environments, known as Free Multi-Robot Path Planning (MPP). In this paper we propose a novel approach to solve the MPP problem using multi-objective optimization, for which we define two functions that has to be minimized. In experimentation, it is compared with previous approaches to the problem, improving them in some scenarios. Finally, new lines of research are proposed to improve this path calculation problem using multi-objective optimization and to address new and more complex problems in warehouse environments.
Predicting Errors in Google Translations of Online Health Information

Machine translation technology is having increasing applications in health and medical settings that involve communications and interactions between people from diverse language, cultural background. Machine translation tools offer low-cost, and accessible solutions to help close the gap in cross-lingual health communications. The risks of machine translation need to be effectively controlled and properly managed to boost the confidence in this developing technology among health professionals. This study integrates the methodological benefits of machine learning in machine translation quality evaluation, and more importantly, the prediction of clinically relevant machine translation errors based on the study of linguistic features of the English source texts.
Agency and Control

While graphic online content may be upsetting, people who regularly work with such content – like journalists, content moderators at social media companies, and human rights investigators – have developed various strategies for minimizing the risk of psychosocial harm when engaging with such material. Many of these strategies can be adapted by the general public to limit the risk of harm and maximize the potential for positive outcomes from their online engagement. Such strategies include tactics aimed at increasing our control over when and how users view graphic online material, varying how users engage with upsetting media, treating such content like “toxic waste” and limiting exposure of the user, their loved ones, and their households, and striking the balance between looking at and engaging with such content, and knowing when it may be better to look away.
SERE: Exploring Feature Self-Relation for Self-Supervised Transformer

Learning representations with self-supervision for convolutional networks (CNN) has been validated to be effective for vision tasks. As an alternative to CNN, vision transformers (ViT) have strong representation ability with spatial self-attention and channel-level feedforward networks. Recent works reveal that self-supervised learning helps unleash the great potential of ViT. Still, most works follow self-supervised strategies designed for CNN, e.g., instance-level discrimination of samples, but they ignore the properties of ViT. We observe that relational modeling on spatial and channel dimensions distinguishes ViT from other networks. To enforce this property, we explore the feature <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SE</b> lf- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RE</b> lation (SERE) for training self-supervised ViT. Specifically, instead of conducting self-supervised learning solely on feature embeddings from multiple views, we utilize the feature self-relations, i.e., spatial/channel self-relations, for self-supervised learning. Self-relation based learning further enhances the relation modeling ability of ViT, resulting in stronger representations that stably improve performance on multiple downstream tasks.