Reviewerly/exHarmony huggingface dataset:
A Qualitative Study on the Issues, Development Directions, and Roles of Career Education : Perspectives from Career Guidance Teachers of Secondary Education

Secondary career education in Korea has a history of about 10 years after the establishment of the career guidance teacher as a qualification of secondary school teacher in 2011 and the enactment of the 「Career Education Act」 in 2015. Until now, career guidance teachers in secondary education have pointed out various issues with career education in Korea and raised their voices on the direction of improvement. This study attempted to comprehensively explore the current problems of career education, the direction of development, and the roles of members. Sixteen regional secondary career education experts’ opinions were collected through e-mail. The research team analyzed and organized various themes according to the categories of problem recognition, development direction, and member roles through thematic analysis, a qualitative analysis method. As for problem recognition, various problems of career education goals, career guidance teachers, school and social systems, parents, and students themselves were found. Convergence competencies, career flexibility, personality, and entrepreneurship were suggested as the direction of development of the educational content, and education using Edutech and AR/VR were indicated as the direction of development of education methods. In terms of member roles, various roles were presented by dividing them into the schools, career guidance teachers, universities, and local governments. This study is meaningful in that it sought the direction of the linkage of schools-universities-local governments for regional development and career education of local students.
Translation readability of the verb phrase with modality in the TikTok privacy policy

This qualitative descriptive research aims to discuss the types of verb phrases with modality based on Palmer (1990) and Alwi (1992) theory, translation techniques based on theory introduced by Molina and Albir (2002), and translation readability based on theory developed by Nababan (2012) in the TikTok Privacy Policy. The source of data in this study is verb phrases with modality obtained from the webpage on the TikTok website containing the Privacy Policy of the TikTok platform in English and Indonesian versions. The translation readability was discussed through the Focus Group Discussion (FGD). The data were analyzed using the data analysis method by Spradley, consisting of domain, taxonomic, componential, and cultural theme analysis. The linguistic data is the verb phrase with epistemic, dynamic, and deontic modality in the Privacy Policy on the TikTok website. In this research, 100 data of verb phrases with modality were obtained and categorized into three types of modalities; 50 data belong to the deontic modality, 24 data to the dynamic modality, and 18 data to the epistemic modality. Verb phrases with two modalities are also found; 6 data belong to the epistemic and deontic modalities, and 2 data belong to the epistemic and dynamic modalities. Eight translation techniques are adopted, including established equivalent, literal translation, linguistic amplification, transposition, the combination of established equivalent and transposition, the combination of literal translation and transposition, the combination of established equivalent and reduction, and the combination of linguistic amplification and established equivalent. The most used technique is the established equivalent with 62 data or 62% of the total data. The overall readability translation score of verb phrases with modality in the privacy policy has quite high readability, with a score of 2.76.
The use of collaborative strategic reading (CSR) in English reading comprehension class: A study among undergraduate students in Aceh

Some students consider that reading comprehension is still one of the most challenging subjects in English. Others find topics covered while reading English books to be uninteresting. Teachers attempt to address these issues by utilizing a variety of strategies that boost students' reading comprehension abilities. Collaborative Strategic Reading (CSR) is one strategy used to help solve these issues. This study investigated the use of CRS in reading comprehension among undergraduate students. The study was carried out at English Language Education Department of Universitas Islam Negeri (UIN) Ar-Raniry Banda Aceh. This study used quantitative research method namely pre-experimental research design in which the class chosen was only one class consisting of 32 students and given the pre-test, teaching presentation, and post-test. The treatment was the CSR as the method during four times meetings in reading comprehension class. The mean score of the pre-test was 62.1 while, the mean score of the post-test was 77.3. The study concluded that the CRS strategy had a postive impact to the students reading comprehension as seen from the increase of test scores. The use of CSR is therefore useful in teaching reading for students at the university level.
Pengembangan Media Pembelajaran Interaktif Matematika Berbantuan Adobe Flash Profesional untuk Meningkatkan Minat dan Hasil Belajar Peserta Didik SMP di Kabupaten Bandung

This research was conducted because of the low interest and results of students' mathematics learning. In addition, there is no learning media that is interesting and produces student learning. The purpose of this research is to describe the characteristics of professional interactive mathematics learning media assisted by Adobe Flash to increase the interest and learning outcomes of junior high school students. The research method uses the Rzz&amp;D method and experiments with the Dick and Carrey research design. The subjects of this study were class VII junior high school students. The instruments used were validation sheets, student response questionnaires to the media, learning interest questionnaires, and student learning achievement tests. The media corresponded to the problem-based learning model and the discovery learning model. good characteristics to increase interest and learning outcomes and have valid, practical, and effective media qualities.
Multimedia Sebagai Penunjang Pembelajaran Listening Bahasa Inggris Di STIT Nurul Islam Mojokerto

The purpose of this training is to improve the skills of professional academics at STIT Nurul Islam Mojokerto in order to provide and implement various technologies (computers, software, or media) that can support the success and success of learning to listen to English for students in class. To achieve the aims and objectives that have been planned, in general the method used is the practice of using Text to Speech computer technology as an effort to produce good quality English audio listening media.&#x0D;
Current Directions of Methodological Support for Teachers in the Implementation of Online Learning for Schoolchildren

The authors analyzed scientific and methodological publications on the methods of organizing online learning to identify the current directions in methodological support. These directions included the tools that provide digital communication, multimedia presentation, exchange of methodological materials, video hosting sites, and online tests. A survey of 58 teachers from the Novosibirsk and Sverdlovsk regions revealed their ideas about methodological support for online teaching. The research showed almost no correlation between the responses and the respondents’ gender, age, profile training, teaching experience, online teaching experience, etc. The respondents needed methodological support in mastering multifunctional communication tools, e.g., Microsoft Teams or Discord; the forms of presentation of multimedia graphic content as charts, infographics, intelligence maps, etc.; cloud services for the exchange of methodological materials; special forms to create online tests, e.g., Google Forms, WEBanketa, Menti.com. Most teachers demonstrated a good understanding of the procedure for using materials downloaded from video hosting sites as part of online lessons. The results of the study are relevant for Russian schools and determine the directions of additional training for teachers as providers of digital academic environment.
Henry David Thoreau’s Walden

A collection of eight critical essays on Thoreau's Walden arranged in chronological order of publication.
<i>Dithen</i>: A Computation-as-a-Service Cloud Platform for Large-Scale Multimedia Processing

We present Dithen, a novel computation-as-a-service (CaaS) cloud platform specifically tailored to the parallel execution of large-scale multimedia tasks.Dithen handles the upload/download of both multimedia data and executable items, the assignment of compute units to multimedia workloads, and the reactive control of the available compute units to minimize the cloud infrastructure cost under deadline-abiding execution.Dithen combines three key properties: (i) the reactive assignment of individual multimedia tasks to available computing units according to availability and predetermined time-to-completion constraints; (ii) optimal resource estimation based on Kalmanfilter estimates; (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of units servicing workloads.The deployment of Dithen over Amazon EC2 spot instances is shown to be capable of processing more than 80,000 video transcoding, face detection and image processing tasks (equivalent to the processing of more than 116 GB of compressed data) for less than $1 in billing cost from EC2.Moreover, the proposed AIMD-based control mechanism, in conjunction with the Kalman estimates, is shown to provide for more than 27% reduction in EC2 spot instance cost against methods based on reactive resource estimation.Finally, Dithen is shown to offer a 38% to 500% reduction of the billing cost against the current state-of-the-art in CaaS platforms on Amazon EC2 (Amazon Lambda and Amazon Autoscale).A baseline version of Dithen is currently available at http://www.dithen.com
Photon pair production by STIRAP in ultrastrongly coupled matter-radiation systems

Artificial atoms (AAs) offer the possibility to design physical systems implementing new regimes of ultrastrong coupling (USC) between radiation and matter [C. Ciuti et al., Phys. Rev. B 72, 115303 (2005)], where previously unexplored non-perturbative physics emerges. While experiments so far provided only spectroscopic evidence of USC, we propose the dynamical detection of virtual photon pairs in the dressed eigenstates, which is a smoking gun of the very existence of USC in nature. We show how to coherently amplify this channel to reach 100% efficiency by operating advanced control similar to stimulated Raman adiabatic passage (STIRAP) [N.V. Vitanov et al., Rev. Mod. Phys. 89, 015006 (2017)].
Image Based Brain Segmentation: From Multi-Atlas Fusion to Deep Learning

This review aims to identify the development of the algorithms for brain tissue and structure segmentation in MRI images.Starting from the results of the Grand Challenges on brain tissue and structure segmentation held in Medical Image Computing and Computer-Assisted Intervention (MICCAI), this review analyses the development of the algorithms and discusses the tendency from multi-atlas label fusion to deep learning. The intrinsic characteristics of the winners' algorithms on the Grand Challenges from the year 2012 to 2018 are analyzed and the results are compared carefully.Although deep learning has got higher rankings in the challenge, it has not yet met the expectations in terms of accuracy. More effective and specialized work should be done in the future.
Convolutional Recurrent Predictor: Implicit Representation for Multi-Target Filtering and Tracking

Defining a multi-target motion model, an important step of tracking algorithms, is a challenging task due to various factors, from its theoretical formulation to its computational complexity.Using fixed models (as in several generative Bayesian algorithms, such as Kalman filters) can fail to accurately predict sophisticated target motions.On the other hand, sequential learning of the motion model (for example, using recurrent neural networks) can be computationally complex and difficult due to the variable unknown number of targets.In this paper, we propose a multi-target filtering and tracking algorithm which learns the motion model, simultaneously for all targets.It does so from an implicitly represented state map and performing spatio-temporal data prediction.To this end, the multi-target state is modelled over a continuous hypothetical target space, using random finite sets and Gaussian mixture probability hypothesis density formulations.The prediction step is recursively performed using a deep convolutional recurrent neural network with a long short-term memory architecture, which is trained as a regression block, on the fly, over probability density difference maps.Our approach is evaluated over widely used pedestrian tracking benchmarks, remarkably outperforming state-of-the-art multi-target filtering algorithms, while giving competitive results when compared with other tracking approaches: The proposed approach generates an average 40.40 and 62.29 optimal subpattern assignment (OSPA) errors on MOT15 and MOT16/17 datasets, respectively, while producing 62.0%, 70.0% and 66.9% multi-object tracking accuracy (MOTA) on MOT16/17, PNNL Parking Lot and PETS09 pedestrian tracking datasets, respectively, when publicly available detectors are used.
A Dual Attention Network with Semantic Embedding for Few-shot Learning

Despite recent success of deep neural networks, it remains challenging to efficiently learn new visual concepts from limited training data. To address this problem, a prevailing strategy is to build a meta-learner that learns prior knowledge on learning from a small set of annotated data. However, most of existing meta-learning approaches rely on a global representation of images and a meta-learner with complex model structures, which are sensitive to background clutter and difficult to interpret. We propose a novel meta-learning method for few-shot classification based on two simple attention mechanisms: one is a spatial attention to localize relevant object regions and the other is a task attention to select similar training data for label prediction. We implement our method via a dual-attention network and design a semantic-aware meta-learning loss to train the meta-learner network in an end-to-end manner. We validate our model on three few-shot image classification datasets with extensive ablative study, and our approach shows competitive performances over these datasets with fewer parameters. For facilitating the future research, code and data split are available: https://github.com/tonysy/STANet-PyTorch
Communication-Efficient Stochastic Gradient MCMC for Neural Networks

Learning probability distributions on the weights of neural networks has recently proven beneficial in many applications. Bayesian methods such as Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) offer an elegant framework to reason about model uncertainty in neural networks. However, these advantages usually come with a high computational cost. We propose accelerating SG-MCMC under the masterworker framework: workers asynchronously and in parallel share responsibility for gradient computations, while the master collects the final samples. To reduce communication overhead, two protocols (downpour and elastic) are developed to allow periodic interaction between the master and workers. We provide a theoretical analysis on the finite-time estimation consistency of posterior expectations, and establish connections to sample thinning. Our experiments on various neural networks demonstrate that the proposed algorithms can greatly reduce training time while achieving comparable (or better) test accuracy/log-likelihood levels, relative to traditional SG-MCMC. When applied to reinforcement learning, it naturally provides exploration for asynchronous policy optimization, with encouraging performance improvement.
Repetitive motion compensation for real time intraoperative video processing

In this paper, we present a motion compensation algorithm dedicated to video processing during neurosurgery. After craniotomy, the brain surface undergoes a repetitive motion due to the cardiac pulsation. This motion as well as potential video camera motion prevent accurate video analysis. We propose a dedicated motion model where the brain deformation is described using a linear basis learned from a few initial frames of the video. As opposed to other works using linear basis for the flow, the camera motion is explicitly accounted in the transformation model. Despite the nonlinear nature of our model, all the motion parameters are robustly estimated all at once, using only one singular value decomposition (SVD), making our procedure computationally efficient. A Lagrangian specification of the flow field ensures the stability of the method. Experiments on in vivo data are presented to evaluate the capacity of the method to cope with occlusion or camera motion. The method we propose satisfies the intraoperative constraints: it is robust to surgical tools occlusions, it works in real time, and it is able to handle large camera viewpoint changes.
Learning Algorithm for Boltzmann Machines With Additive Weight and Bias Noise

This brief presents analytical results on the effect of additive weight/bias noise on a Boltzmann machine (BM), in which the unit output is in {-1, 1} instead of {0, 1}. With such noise, it is found that the state distribution is yet another Boltzmann distribution but the temperature factor is elevated. Thus, the desired gradient ascent learning algorithm is derived, and the corresponding learning procedure is developed. This learning procedure is compared with the learning procedure applied to train a BM with noise. It is found that these two procedures are identical. Therefore, the learning algorithm for noise-free BMs is suitable for implementing as an online learning algorithm for an analog circuit-implemented BM, even if the variances of the additive weight noise and bias noise are unknown.
Patient-Specific Registration of Pre-operative and Post-recurrence Brain Tumor MRI Scans

Registering brain magnetic resonance imaging (MRI) scans containing pathologies is challenging primarily due to large deformations caused by the pathologies, leading to missing correspondences between scans. However, the registration task is important and directly related to personalized medicine, as registering between baseline pre-operative and post-recurrence scans may allow the evaluation of tumor infiltration and recurrence. While many registration methods exist, most of them do not specifically account for pathologies. Here, we propose a framework for the registration of longitudinal image-pairs of individual patients diagnosed with glioblastoma. Specifically, we present a combined image registration/reconstruction approach, which makes use of a patient-specific principal component analysis (PCA) model of image appearance to register baseline pre-operative and post-recurrence brain tumor scans. Our approach uses the post-recurrence scan to construct a patient-specific model, which then guides the registration of the pre-operative scan. Quantitative and qualitative evaluations of our framework on 10 patient image-pairs indicate that it provides excellent registration performance without requiring (1) any human intervention or (2) prior knowledge of tumor location, growth or appearance.
Lost in Machine Translation: A Method to Reduce Meaning Loss

Reuben Cohn-Gordon, Noah Goodman. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Identification of Unentangled–Entangled Border in the Luttinger Liquid Phase

Quantum discord and entanglement are both criteria for distinguishing quantum correlations in a quantum system. We studied the effect of the transverse magnetic field on the quantum discord of the one-dimensional spin-1/2 XX model. This study focused on the pair of spins at different distances. We show that quantum discord is finite for all studied spin pairs in the Luttinger liquid phase. In addition, relying on our calculations, we show that the derivatives of quantum discord can be used to identify the border between entangled and separable regions in the Luttinger liquid phase.
On the Effects of Distance-decreasing Attacks on Cryptographically Protected GNSS Signals

The security of global navigation satellite systems draws attention increasingly, and authentication mechanisms for civilian services seem very effective in thwarting malicious behavior. For example, the Galileo E1 Open Service introduces navigation message authentication. Authentication, as well as encryption at navigation message or spreading code level, can prevent spoofing attacks, but do not preclude replay attacks. In this work, we consider a type of strong replay attacks, distance-decreasing attacks, against cryptographically protected GNSS signals. Distance-decreasing attack enhance an attacker’s capability of allowing it to mislead the victim receiver that the GNSS signals arrive earlier than true signals. We analyze the instantiation and the effects of the distance-decreasing attacks on unprotected GNSS signals, on navigation message authenticated signals, and on spreading-code encrypted signals. We discuss different strategies that the attacker can adopt to introduce the least bit errors to the re-transmitted signals and avoid being detected at the victim receiver. We provide evaluation results of distance-decreasing attacks on unprotected signals and authenticated navigation message signals, based on different strategies and configurations, and we sketch countermeasures to the different strategies.
SCF-PEPCKS: Secure Channel Free Public Key Encryption With Privacy-Conserving Keyword Search

Searchable encryption is a novel cryptographic primitive that enables a data storage server to retrieve the data ciphertexts without the knowledge of either what is searched for or the contents of the searched data ciphertexts. As a useful extension of the public key encryption with keyword search (PEKS) invented by Boneh et al., secure channel free PEKS (SCF-PEKS) eliminates the limitation that the trapdoors of search keywords should be conveyed to the data storage server through secret communication channels. Nevertheless, the current framework of SCF-PEKS is subjected to the security flaw arising from the keyword guessing (KG) attack and thus fails to provide privacy protection for keyword search. In this paper, we put forward an enhanced SCF-PEKS framework called secure channel free public key encryption with privacy-conserving keyword search (SCF-PEPCKS). Our framework effectively remedies the security weakness in the SCF-PEKS framework and offers resistance against the existing known KG attacks. We develop a concrete SCF-PEPCKS scheme without using random oracles. The security proofs demonstrate that it offers the keyword ciphertext/trapdoor privacy against both the malicious data storage server and the outside attacker. The comparisons and the experimental results indicate that the presented SCF-PEPCKS scheme is secure and practicable.