Reviewerly/exHarmony huggingface dataset:
THE IMPACT OF SOCIAL MEDIA IN ENGLISH LANGUAGE LEARNING

This qualitative study, using a case study design, aimed to explore the impact of social media in Englishlanguage learning here in Northern Iraq. The 10 members in this examination, having been chosen purposively,included secondary school learners, language educators, and teachers in both private and non-public schools andcolleges, ESL instructors, and a practitioner of Teaching English to Speakers of Other Languages. Results depend onthe information assembled from the participants through written interviews using Strengths-Weaknesses-Opportunities- Threats (SWOT) matrix.Information on this research has uncovered that social media possess several strengths or opportunities in Englishlanguage learning at large. Be that as it may, their couple of traps and dangers have likewise become possibly themost important factor and caused obstruction in language learning. As appeared in this research, these internet-basedsocial media sites could be effective platforms that provide up-to-date information, a significant measure of languageinput, communication with native language speakers, and front line learning. By and by, these social media coulddivert language students to deception, unscholarly and ungrammatical contents, obscenities, and genericcollaborations. This research likewise remembered some useful uses of social media in English language learning.Suggestions for further research opportunities about this topic were also included by the researcher.
Security Issues with IoT for Smart Homes: Blockchain as a Solution

With the increasing use of smart technologies like the Internet of Things, home automation has become the hottest trend these days. It provides all the modern features and convenience that a homeowner can expect. Home appliances can be automated via wireless connectivity and they can be accessible on the go. However, there are so many security concerns with the significant rise of smart home appliances like light bulbs, power switches, door locks, home security, etc., especially because of limited processing power and storage. They are highly vulnerable to security threats. Hence, security has become the primary concern and matter of discussion among researchers in this area. This way, a decentralized database blockchain is gaining a lot of popularity to assure security in this field. This article is aimed to explore the security of smart homes and investigate the adoption of blockchain. This research paper is aimed to explore security measures that manufacturers should consider for IoT systems and the way Blockchain can help solve this problem.
The Design of Filtration System Using Coconut ShellCharcoal for Domestic Wastewater Purification

Water pollution is a sanitation problem that will always demand attention and the sustainable solution. As one of the river'spollutants, blackwater requires good treatment techniques so that it can be discharged into the body of water and does not cause environmentalor health problems. One of the vest purification techniques is the filtration method by using materials that are cheap and easy to obtain, such ascoconut shell charcoal. Charcoal has great potential due to its ablility to remove suspended solids and bind chemical pollutants. This studyaims to apply the concept of blackwater purification using coconut shell charcoal filtration method, as well as to determine the most optimalblackwater waste discharge in reducing turbidity. Observations were made on the turbidity variable (NTU) which was recorded at eachresidence time of 0 to 9 days. The result of regression analysis demonstrates that the thickness of the charcoal filter layer and the discharge ofwastewater have a significant effect on reducing the turbidity level of the treated blackwater. Thus, it can be concluded that coconut shellcharcoal is very potential to be used as a filter in blackwater treatment. Blackwater is best be purified with 30 cm of charcoal filter layer and 9days of residence time.
Privacy-Preserving Multiparty Protocol for Feature Selection Problem.

In this paper, we propose a secure multiparty protocol for the feature selection problem. Let $D$ be the set of data, $F$ the set of features, and $C$ the set of classes, where the feature value $x(F_i)$ and the class $x(C)$ are given for each $x\in D$ and $F_i \in F$. For a triple $(D,F,C)$, the feature selection problem is to find a consistent and minimal subset $F' \subseteq F$, where `consistent' means that, for any $x,y\in D$, $x(C)=y(C)$ if $x(F_i)=y(F_i)$ for $F_i\in F'$, and `minimal' means that any proper subset of $F'$ is no longer consistent. The feature selection problem corresponds to finding a succinct description of $D$, and has various applications in the field of artificial intelligence. In this study, we extend this problem to privacy-preserving computation model for multiple users. We propose the first algorithm for the privacy-preserving feature selection problem based on the fully homomorphic encryption. When parties $A$ and $B$ possess their own personal data $D_A$ and $D_B$, they jointly compute the feature selection problem for the entire data set $D_A\cup D_B$ without revealing their privacy under the \em{semi-honest} model.
Impact Of Modern Technology In Education

Technology is a gift of God. After the gift of life it is perhaps the greatest of God's gifts. It is the mother of civilisations, of arts and of sciences. Technology has certainly changed the way we live.It has impacted different facets of life and redefined living. Undoubtedly, technology plays an important role in every sphere of life. Several manual tasks can be automated, thanks to technology. Also, many complex and critical processes can be carried out with ease and greater efficiency with the help of modern technology. Thanks to the application of technology, living haschanged and it has changed for better. Technology has revolutionised the field of education. Theimportance of technology in schools cannot be ignored. In fact, with the onset of computers in education, it has become easier for teachers to impart knowledge and for students to acquire it.The use of technology has made the process of teaching and learning all the more enjoyable.
Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images

Accurate classification of cancer is directly related to the choice of treatment options and prognosis. Pathological diagnosis is the gold standard for cancer diagnosis. The digitalization of pathological images and breakthroughs in deep learning have made computer-aided diagnosis and prediction about prognosis possible. In this paper, we first briefly describe four deep learning methods commonly used in this field, and then review the latest research progress in cancer classification based on deep learning and histopathological images. Finally, the general problems in this field are summarized, and the possible development direction in the future is suggested.
DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks.

We introduce DeepABM, a framework for agent-based modeling that leverages geometric message passing of graph neural networks for simulating action and interactions over large agent populations. Using DeepABM allows scaling simulations to large agent populations in real-time and running them efficiently on GPU architectures. To demonstrate the effectiveness of DeepABM, we build DeepABM-COVID simulator to provide support for various non-pharmaceutical interventions (quarantine, exposure notification, vaccination, testing) for the COVID-19 pandemic, and can scale to populations of representative size in real-time on a GPU. Specifically, DeepABM-COVID can model 200 million interactions (over 100,000 agents across 180 time-steps) in 90 seconds, and is made available online to help researchers with modeling and analysis of various interventions. We explain various components of the framework and discuss results from one research study to evaluate the impact of delaying the second dose of the COVID-19 vaccine in collaboration with clinical and public health experts. While we simulate COVID-19 spread, the ideas introduced in the paper are generic and can be easily extend to other forms of agent-based simulations. Furthermore, while beyond scope of this document, DeepABM enables inverse agent-based simulations which can be used to learn physical parameters in the (micro) simulations using gradient-based optimization with large-scale real-world (macro) data. We are optimistic that the current work can have interesting implications for bringing ABM and AI communities closer.
Μορφές πραγμάτωσης της πληροφοριακής δομής – διαγλωσσική προσέγγιση

In this paper we compare Greek information structure with that of English, German, Spanish, and Italian, examining structures associated with specific focus types, namely information and contrastive focus, as well as contrastive topic. Our goal is to investigate aspects of Greek information structure in an attempt to answer the question why Greek, despite its comparatively greater degree of word order flexibility, does not have certain patterns which are attested in the other languages. To this end, we systematically study subject information focus structures and VP-fronting. Our examination shows that Greek neither employs p-movement or scrambling for subject information focusing nor has VP fronting for information focus or contrastive topic/focus purposes.
Test time Adaptation through Perturbation Robustness.

Data samples generated by several real world processes are dynamic in nature \textit{i.e.}, their characteristics vary with time. Thus it is not possible to train and tackle all possible distributional shifts between training and inference, using the host of transfer learning methods in literature. In this paper, we tackle this problem of adapting to domain shift at inference time \textit{i.e.}, we do not change the training process, but quickly adapt the model at test-time to handle any domain shift. For this, we propose to enforce consistency of predictions of data sampled in the vicinity of test sample on the image manifold. On a host of test scenarios like dealing with corruptions (CIFAR-10-C and CIFAR-100-C), and domain adaptation (VisDA-C), our method is at par or significantly outperforms previous methods.
PENGEMBANGAN MEDIA PEMBELAJARAN MEMBACA SLOKA MENGGUNAKAN APLIKASI SPARKOL VIDEOSCRIBE

Chanting slokas is one of the media to spread Hindu teachings, as did the sages. However, the problem that is often faced is that it is not easy to sing the sloka well. Many people have difficulty learning to read slokas, including STAH DN Jakarta students. Seeing this problem inspired researchers to conduct research and development of reading sloka media, namely by utilizing the Sparkol VideoScribe application.The research uses research and development methods, namely making audio-visual media, so that students can see the script and hear the strains of the song. The rhythm used is the Sruti rhythm, because the UDG (national sloka reading competition) uses the Sruti rhythm. Videos are also accompanied by up and down rhythms. The limited trial used the experimental method, namely the experimental class was treated with video and the control class was conventionally treated. The result of the research is that video reading sloka based on the VideoScribe application is very effective in helping students learn to read sloka independently. The video is equipped with text and sound, students can also be guided by the rise and fall of the rhythm. The conclusion of this study is that video reading sloka based on the VideoScribe application is very important to develop because it is proven to be able to help students learn to read sloka independently.
An ObsPy library for event detection and seismic attribute calculation: preparing waveforms for automated analysis

We have implemented an extension for the observational seismology obspy software package to provide a streamlined tool tailored to the processing of seismic signals from non-earthquake sources, in particular those from deforming systems such as glaciers and landslides. This seismic attributes library provides functionality to: (1) download and/or pre-process seismic waveform data; (2) detect and catalogue seismic events using multi-component signals from one or more seismometers; and (3) calculate characteristics ('attributes'/'features') of the identified events. The workflow is controlled by three main functions that have been tested for the breadth of data types expected from permanent and campaign-deployed seismic instrumentation. A selected STA/LTA-type (short-term average/long-term average), or other, event detection algorithm can be applied to the waveforms and user-defined functions implemented to calculate any required characteristics of the detected events. The code is written in Python 2/3 and is available on GitHub together with detailed documentation and worked examples.
Visual-Aware Attention Dual-Stream Decoder for Video Captioning

Video captioning is a challenging task that captures different visual parts and describes them in sentences, for it requires visual and linguistic coherence. The attention mechanism in the current video captioning method learns to assign weight to each frame, promoting the decoder dynamically. This may not explicitly model the correlation and the temporal coherence of the visual features extracted in the sequence frames. To generate semantically coherent sentences, we propose a new Visual-aware Attention (VA) model, which concatenates dynamic changes of temporal sequence frames with the words at the previous moment, as the input of attention mechanism to extract sequence features. In addition, the prevalent approaches widely use the Teacher-forcing (TF) learning during training, where the next token is generated conditioned on the previous ground-truth tokens. The semantic information in the previously generated tokens is lost. Therefore, we design a Self-forcing (SF) stream that takes the semantic information in the probability distribution of the previous token as input to enhance the current token. The Dual-stream Decoder (DD) architecture unifies the TF and SF streams, generating sen-tences to promote the annotated captioning for both streams. Meanwhile, with the Dual-stream Decoder utilized, the ex-posure bias problem is alleviated, caused by the discrepancy between the training and testing in the TF learning. The effectiveness of the proposed Visual-aware Attention Dual-stream Decoder (VADD) is demonstrated through the result of ex-perimental studies on Microsoft video description (MSVD) corpus and MSR-Video to text (MSR-VTT) datasets.
Generalization ability of Extreme Learning Machine using different Sample Selection Methods

The recent explosion of data has triggered the need of data reduction for completing the effective data mining task in the process of knowledge discovery in databases (KDD). The process of instance selection (IS) plays a significant role for data reduction by eliminating the redundant, noisy, unreliable and irrelevant instances, which, in-turn reduces the computational resources, and helps to increase the capabilities and generalization abilities of the learning models. . This manuscript expounds the concept and functionalities of seven different instance selection techniques (i.e., ENN, AllKNN, MENN, ENNTh, Mul- tiEdit, NCNEdit, and RNG), and also evaluates their effectiveness by using single layer feed-forward neural network (SLFN), which is trained with extreme learning machine (ELM). Unlike traditional neural network, ELM randomly chooses the weights and biases of hidden layer nodes and analytically determines the weights of output layer node. The generalization ability of ELM is analyzed by using both original and reduced datasets. Experiment results depict that ELM provides better generalization with these IS methods.
Unboxing Audacity: Mixing Rhetorically with Digital Audio Workstations

Gomes argues that learning the predictable set of rhetorical moves allowed by digital audio workstation programs enables soundwriters to be more ethical, creative, and rhetorically effective, which he illustrates throughout with a soundscape he composed of someone sneezing on her way to class. Throughout his chapter, he returns to the concept of preserving headroom, the audio engineer's way of reserving enough space in a sonic mix for a composition to play effectively, without leading to distortion. Gomes teaches us how to understand headroom in terms of amplitude, frequency, and stereophonic space, expanding his readers' and listeners' understanding of these rhetorical tools.
The Future of Machines: Property and Personhood

The use of tools was once believed to be a distinguishing feature of human intelligence which allowed us to deny personhood to animals, which like tools, were property rather than persons. As we get increasingly dependent on our increasingly sophisticated tools, the law will need to consider when (if ever) machines cease to be mere tools and become a part of our person. Could they even increase in sophistication to the point when they may be conferred legal personhood? Or will rapidly advancing machine intelligence first strip us of our personhood? Might the law of property prove to be a bulwark against such an outcome?
Friedrich Gulda - L’enfant terrible

espanolFriedrich Gulda fue uno de los pianistas mas influyentes del siglo XX. Cansado de ciertos convencionalismos del mundo de la musica clasica, decidio experimentar con el jazz. Asi, el estudio intensivo de este genero, asi como una manera de tocar el repertorio pianistico clasico totalmente independiente y personal – con ornamentos improvisados, variaciones con respecto a lo que en la partitura esta escrito, entre otros muchos ejemplos – lo convirtieron en una figura completamente controvertida y criticada. Tanto que algunos le llegaron incluso a apodar “el pianista terrorista”. Esta investigacion tiene como objetivo dar a conocer la trayectoria profesional y musical de Gulda, como ejemplo de musico polifacetico. Descubriremos que el compaginar o conocer musica de diferentes generos, tales como la clasica o el jazz, es una experiencia plenamente enriquecedora y beneficiosa para el desarrollo artistico de los musicos. Asimismo, reflexionaremos sobre como dichos ideales podrian incluirse dentro del ambito educativo, en beneficio de los futuros profesionales de la musica. EnglishFriedrich Gulda was one of the most influential pianists of the 20th century. Tired of certain conventions of the world of classical music, he decided to experiment with jazz. The deep study of this genre, as well as a totally different and personal way of playing the classical piano repertoire - with improvised ornaments, variations with respect to what is written in the score, among many other examples - turned it into a completely controversial and criticized figure. So much so that some even nicknamed him “the terrorist pianist.” This research aims to make known Gulda’s professional and musical career, as an example of a versatile musician. We will discover that combining or knowing music of different genres, such as classical or jazz, is a fully enriching and beneficial experience for the artistic development of musicians. Likewise, we will reflect on how these ideals could be included in the educational field, in order to benefit the future of the music professionals.
Urban sensing for anomalous event detection: Distinguishing between legitimate traffic changes and abnormal traffic variability

© 2019, Springer Nature Switzerland AG. Sensors deployed in different parts of a city continuously record traffic data, such as vehicle flows and pedestrian counts. We define an unexpected change in the traffic counts as an anomalous local event. Reliable discovery of such events is very important in real-world applications such as real-time crash detection or traffic congestion detection. One of the main challenges to detecting anomalous local events is to distinguish them from legitimate global traffic changes, which happen due to seasonal effects, weather and holidays. Existing anomaly detection techniques often raise many false alarms for these legitimate traffic changes, making such techniques less reliable. To address this issue, we introduce an unsupervised anomaly detection system that represents relationships between different locations in a city. Our method uses training data to estimate the traffic count at each sensor location given the traffic counts at the other locations. The estimation error is then used to calculate the anomaly score at any given time and location in the network. We test our method on two real traffic datasets collected in the city of Melbourne, Australia, for detecting anomalous local events. Empirical results show the greater robustness of our method to legitimate global changes in traffic count than four benchmark anomaly detection methods examined in this paper. Data related to this paper are available at: https://vicroadsopendata-vicroadsmaps.opendata.arcgis.com/datasets/147696bb47544a209e0a5e79e165d1b0_0.
LibGuides: Access to library resources – issues and tips: Login problems

Having trouble getting access to an article or database? Find out about known issues and learn some tips and tricks.
An Incremental Learning Scheme with Adaptive Earlystopping for AMI Datastream Processing

Streaming data on power usage delivered through the Advanced Metering Infrastructure (AMI) inherent the concept drift problem, in which the shape of the data changes over time. This phenomenon causes performance degradation in the processing of AMI data using deep learning models. In order to overcome this, updates of deep learning (DL) models using dataflow in real-time should be performed. Although there have been many studies tried to handle the issue so far, the problem with existing methodologies is that they have not fully considered the factors that affect the training efficiency of online learning in an environment where concept drift exists. In this paper, adaptive online learning techniques are proposed to solve the issue. This technique first determines batch size and epoch size by considering data integration latency and unstableness of the current DL model. Then it adjusts the epoch according to concept drift existence of current training input batch. By applying this technique, we were able to effectively reduce the load on learning time series power data using the DL model, while at the same time showing better performance in forecasting with newly incoming data.
Class Incremental Online Streaming Learning.

A wide variety of methods have been developed to enable lifelong learning in conventional deep neural networks. However, to succeed, these methods require a `batch' of samples to be available and visited multiple times during training. While this works well in a static setting, these methods continue to suffer in a more realistic situation where data arrives in \emph{online streaming manner}. We empirically demonstrate that the performance of current approaches degrades if the input is obtained as a stream of data with the following restrictions: $(i)$ each instance comes one at a time and can be seen only once, and $(ii)$ the input data violates the i.i.d assumption, i.e., there can be a class-based correlation. We propose a novel approach (CIOSL) for the class-incremental learning in an \emph{online streaming setting} to address these challenges. The proposed approach leverages implicit and explicit dual weight regularization and experience replay. The implicit regularization is leveraged via the knowledge distillation, while the explicit regularization incorporates a novel approach for parameter regularization by learning the joint distribution of the buffer replay and the current sample. Also, we propose an efficient online memory replay and replacement buffer strategy that significantly boosts the model's performance. Extensive experiments and ablation on challenging datasets show the efficacy of the proposed method.