Reviewerly/exHarmony huggingface dataset:
Accumulative Poisoning Attacks on Real-time Data

Collecting training data from untrusted sources exposes machine learning services to poisoning adversaries, who maliciously manipulate training data to degrade the model accuracy. When trained on offline datasets, poisoning adversaries have to inject the poisoned data in advance before training, and the order of feeding these poisoned batches into the model is stochastic. In contrast, practical systems are more usually trained/fine-tuned on sequentially captured real-time data, in which case poisoning adversaries could dynamically poison each data batch according to the current model state. In this paper, we focus on the real-time settings and propose a new attacking strategy, which affiliates an accumulative phase with poisoning attacks to secretly (i.e., without affecting accuracy) magnify the destructive effect of a (poisoned) trigger batch. By mimicking online learning and federated learning on MNIST and CIFAR-10, we show that model accuracy significantly drops by a single update step on the trigger batch after the accumulative phase. Our work validates that a well-designed but straightforward attacking strategy can dramatically amplify the poisoning effects, with no need to explore complex techniques.
Mobile-Based Interactive Learning Multimedia to Improving Problem Solving Ability in Vocational Secondary School

This research aims to solve some common problems faced by learners, namely difficulties in understanding materials with abstract ideas and the lack of problem-solving skills. Mobile-based interactive learning multimedia as an innovation in educational setting is a self-learning media that the learners can use both in school and at home to help them understand the materials easily, minimize learning difficulties, and increase their problem-solving skills. This is a Research and Development (R&amp;D) with the APPED model. The subjects of this research were the 10th grade students of the Vocational Program of Computer and Network Technology at SMK Negeri 1 Tulung Klaten (State Vocational School 1 of Tulung, Klaten). The data analysis technique was done through the descriptive quantitative. The results showed that the mobile-based interactive multimedia learning was feasible with the Very Good category. The mobile-learning-based interactive multimedia learning was effectively used with a percentage of 77.85% and the difference in significance, based on the Wilcoxon Test on the pretest posttest of 0.000 it indicates that there is a difference in the increase in problem-solving skills while learning using the mobile-based interactive multimedia learning.
Comparing Cross Correlation-Based Similarities

The real-valued Jaccard and coincidence indices, in addition to their conceptual and computational simplicity, have been verified to be able to provide promising results in tasks such as template matching, tending to yield peaks that are sharper and narrower than those typically obtained by standard cross-correlation, while also attenuating substantially secondary matchings. In this work, the multiset-based correlations based on the real-valued multiset Jaccard and coincidence indices are compared from the perspective of template matching, with encouraging results which have implications for pattern recognition, deep learning, and scientific modeling in general. The multiset-based correlation methods, and especially the coincidence index, presented remarkable performance characterized by sharper and narrower peaks while secondary peaks were attenuated, which was maintained even in presence of intense levels of noise. In particular, the two methods derived from the coincidence index led to particularly interesting results. The cross correlation, however, presented the best robustness to symmetric additive noise, which suggested a new combination of the considered approaches. After a preliminary investigation of the relative performance of the multiset approaches, as well as the classic cross-correlation, a systematic comparison framework is proposed and applied for the study of the aforementioned methods. Several results are reported, including the confirmation, at least for the considered type of data, of the coincidence correlation as providing enhanced performance regarding detection of narrow, sharp peaks while secondary matches are duly attenuated. The combined method also resulted promising for dealing with signals in presence of intense additive noise.
Using Coulomb Stress Changes and Seismic Spectrum Intensities Evaluation of the Seismic Hazard Potential of Meishan Earthquake in Central Taiwan

The earthquake (M L 5.2) which occurred at Chushan town, Nantou on Nov.11, 2017 resulted many aftershocks around the hypocenter of the mainshock.The coulomb stress changes (∆CFSs) by the mainshock demonstrated by most of the aftershocks located within the portions with ∆CFSs greater than 0.1 bar, revealing the aftershocks may be induced by the mainshock.After investigating the ∆CFSs at different depths, the results showed most of ∆CFSs with positive values under depth 15km were transferred to the southeast and southwest regions.About ten days later, there were many earthquakes (M L 5.2~3) with depth below 15km clustered exactly in the southeast region.The southwest region near the Meishan fault, caused large earthquake with M L 7.0 before 110 years ago.This means the Meishan fault may be triggered by the successive earthquakes near the fault.Based on the seismic spectrum intensities, the seismic hazards by the potential Meishan earthquake are estimated.The maximum seismic intensities will occur at Chushan township and the segment of freeway 3 connecting Nantou city and Chushan township may be damaged seriously.The landslides may happen again at the junctions of Nantou and Yunlin counties, where great landslides happened when Chi-Chi earthquake (M L 7.3) occurred in 1999.
Modified Poisson compositing technique on skewed grid

&lt;abstract&gt;&lt;p&gt;Image compositing is the process of seamlessly inserting a portion of a source image into a target image to create a new desirable image. This work describes an image composition approach based on numerical differentiation utilizing the Laplacian operator. The suggested procedure uses the red-black strategy to speed up computations by using two separate relaxation factors for red and black nodes, as well as two accelerated parameters on a skewed grid. The Skewed Modified Two-Parameter Overrelaxation (SkMTOR) approach is a modification of the existing MTOR method. The SkMTOR has been used to solve numerous linear equations in the past, but its applicability in image processing has never been investigated. Several examples were used to test the suggested method in solving the Poisson equation for image composition. The results demonstrated that the image composition was successfully constructed using all six methods considered in this study. The six methods evaluated yielded identical images based on the similarity measurement results. In terms of computing speed, the skewed variants perform much quicker than their corresponding regular grid variants, with the SkMTOR showing the best performance.&lt;/p&gt;&lt;/abstract&gt;
SINGLE STAGE DEEP TRANSFER LEARNING MODEL FOR APPAREL DETECTION AND CLASSIFICATION FOR E-COMMERCE

Although many computer vision based object detection techniques are evolved in the past decade but suffers from inconsistent detection accuracy especially for multi-class classification problems. In this paper proposed an approach using Single Stage Deep Transfer Learning model (SS-DTLM) for multi-class apparel detection using customized YoloV3 algorithm by adapting 3-level Spatial pyramid pooling (SPP), a multi scale image feature extractor for faster and reasonable apparel detection and classification. This approach produced a reasonable Mean Average Precision (mAP), reliable object detection and classification. Our model trained and tested on Open Images Dataset (OIDV4) with 6 object classes and Custom built Apparel Dataset with 5 object classes of apparels. Finally Experimental Results are compared with base line Yolov3 and Yolov3-Tiny algorithms. Further this paper also emphasized various color spaces of the detected image using SS-DTLM by applying K-Means clustering algorithm for further analysis.
CAMGAN: Combining attention mechanism generative adversarial networks for cartoon face style transfer

In this paper, we presents an apporch for real-world human face close-up images cartoonization. We use generative adversarial network combined with an attention mechanism to convert real-world face pictures and cartoon-style images as unpaired data sets. At present, the image-to-image translation model has been able to successfully transfer style and content. However, some problems still exist in the task of cartoonizing human faces:Hunman face has many details, and the content of the image is easy to lose details after the image is translated. the quality of the image generated by the model is defective. The model in this paper uses the generative adversarial network combined with the attention mechanism, and proposes a new generative adversarial network combined with the attention mechanism to deal with these problems. The channel attention mechanism is embedded between the upper and lower sampling layers of the generator network, to avoid increasing the complexity of the model while conveying the complete details of the underlying information. After comparing the experimental results of FID, PSNR, MSE three indicators and the size of the model parameters, the new model network proposed in this paper avoids the complexity of the model while achieving a good balance in the conversion task of style and content.
Pre-define Rotation Amplitudes Object Rotation in Handheld Augmented Reality

Interaction is one of the important topics to be discussed since it includes the interface where the end-user communicates with the augmented reality (AR) system. In handheld AR interface, the traditional interaction techniques are not suitable for some AR applications due to the different attributes of handheld devices that always refer to smartphones and tablets. Currently interaction techniques in handheld AR are known as touch-based technique, mid-air gesture-based technique and device-based technique that can led to a wide discussion in related research areas. However, this paper will focus to discover the device-based interaction technique because it has proven in the previous studies to be more suitable and robust in several aspects. A novel device-based 3D object rotation technique is proposed to solve the current problem in performing 3DOF rotation of 3D object. The goal is to produce a precise and faster 3D object rotation. Therefore, the determination of the rotation amplitudes per second is required before the fully implementation. This paper discusses the implementation in depth and provides a guideline for those who works in related to device-based interaction.
Convex Aggregation for Opinion Summarization

Recent advances in text autoencoders have significantly improved the quality of the latent space, which enables models to generate grammatical and consistent text from aggregated latent vectors. As a successful application of this property, unsupervised opinion summarization models generate a summary by decoding the aggregated latent vectors of inputs. More specifically, they perform the aggregation via simple average. However, little is known about how the vector aggregation step affects the generation quality. In this study, we revisit the commonly used simple average approach by examining the latent space and generated summaries. We found that text autoencoders tend to generate overly generic summaries from simply averaged latent vectors due to an unexpected L2-norm shrinkage in the aggregated latent vectors, which we refer to as summary vector degeneration. To overcome this issue, we develop a framework Coop, which searches input combinations for the latent vector aggregation using input-output word overlap. Experimental results show that Coop successfully alleviates the summary vector degeneration issue and establishes new state-of-the-art performance on two opinion summarization benchmarks. Code is available at https://github.com/megagonlabs/coop.
Deploying a Web App on the Cloud

This chapter presents one way to deploy a web app. First, it summarizes an integrated development environment (IDE) useful for developing, testing, and debugging Python web frameworks. Subsequently, it explains how to organize the file structure before deploying a web app and presents a practical example of improving web app deployment.
Digital Currencies and Community Empowerment in Austria: Gesell’s Concept of Effective Demand as a Basis for Local Digital Currencies

This paper presents a basic framework of a novel complementary crypto currency and a case study on its acceptance by Austrian people. The currency aims to mitigate the impact of economic crises on local communities by stimulating a constant money circulation within them. We therefore evaluated the concept of effective demand proposed by Silvio Gesell as possible underlying framework. Based on these findings a local crypto currency named circle coin was developed and used in a social survey. We find that circle coin had a much higher potential for general acceptance compared to regular crypto currencies. Furthermore, governmental support for a crypto currency was found to have no impact and the support of the local economy was the single most significant attribute which predicted the possible increased acceptance of circle coin. Given that participants indicated a readiness to use the new currency for everyday payments and refused to use others beyond a short try-out, this is vital information if the implementation of a local digital complementary currency is aspired.
Development of Interactive Media to Improve Early Childhood Financial Literacy

This study aims to describe the process of developing interactive media.In particular, this study aims to produce its to improve early childhood financial literacy.This study uses the ADDIE development model consisting of 5 main stages : (1) Analysis, (2) Design, (3) Develop and (4) Implement and (5) Evaluate.Data analysis techniques through stages; First, analysis of validity through the average score of the developed media aspects.Second, if each category is valid, then proceed to the analysis of the effectiveness of the product through the analysis of media practicality questionnaire data by teachers and early childhood.Validation by media and materials experts to determine the success of media design.Based on the results of the validity test questionnaire for interactive media development, it can be said that the media is categorized as valid with a validity percentage of 84%, this interactive media as whole has a good validity.
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction

State-of-the-art NLP models can adopt shallow heuristics that limit their generalization capability (McCoy et al., 2019).Such heuristics include lexical overlap with the training set in Named-Entity Recognition (Taillé et al., 2020a) and Event or Type heuristics in Relation Extraction (Rosenman et al., 2020).In the more realistic end-to-end RE setting, we can expect yet another heuristic: the mere retention of training relation triples.In this paper we propose several experiments confirming that retention of known facts is a key factor of performance on standard benchmarks.Furthermore, one experiment suggests that a pipeline model able to use intermediate type representations is less prone to over-rely on retention.
Effects of Social Media on Security-Agenda Setting in Nairobi City County, Kenya

Social Media (SM), its reach, impact, and potential in a globalized world are no longer contested; it has affected people’s lives, regarding its use and misuse. Groups of gangsters, terrorist associations, non-state actors with bad intentions and rebellious including Gaza, Al-Shabaab, routinely utilize social media websites like Facebook, Twitter Instagram, and WhatsApp to disseminate propaganda, recruit and inspire their sympathizers as well as instill fear in the members of the public and claim their terrorist attacks. This study aimed to identify the effects of social media on security agenda setting to introducing new technologies for use by the security agents to enhance and improve security. It was informed by Agenda-setting Theory and Technological determinism to help see how technology has affected human activity and thought. The study was conducted in Nairobi City County on social media users and security agents. The researcher used a descriptive research plan. The validity test was conducted through a pilot study and specialists’ judgment and reliability through test-retest strategy. The data was gathered using semi-organized surveys and analyzed both quantitatively and qualitatively. Quantitative information was examined through descriptive statistics which incorporates; frequencies, percentages, mean, standard deviation among others. Qualitative information was be analyzed using themes. The findings show that social media has a significant influence on security agenda setting in Nairobi City County. Different social media sites, such as Facebook, Twitter, LinkedIn, Instagram, and WhatsApp play a significant role in the spread of information and fight against crime in Nairobi County. They help in mobilization, data gathering, and analysis. Their contribution is affirmed by inferential analysis outcome, which shows that both dependent and independent variables are positively and significantly related. The study recommends the DCI’s office, through the ICT ministry, County government, and National government to reduce chances of youth recruitment by criminal organizations via social media by using different platforms, such as Facebook to learn much about gang affiliations and identify whether their comments or pictures shared are meant to attract or convince unsuspecting people. A similar approach can be applied by law enforcers within Nairobi County to reduce cases of youth recruitment through social media by criminal organizations. The government is also recommended to prevent such social media mobilization that spreads false information by enforcing criminal sanctions and hefty penalties for any offenses or suspected spread of information that can trigger insecurity within the scope area.
A Discourse on Innovation of English Teaching in China from the Perspective of Artificial Intelligence

With the progression of economic globalisation, China’s expectations about the quality of English teaching in the country have increased, and because of the restrictive drawbacks of orthodox English teaching models, they cannot meet these requirements. The emergence of artificial intelligence (AI) technology can create new opportunities for the optimisation of English teaching. As such, this study aims to systematically review and analyse the current research on the innovations afforded by AI for China’s English teaching model, especially the application of AI in unquiesced English teaching. The descriptive–analytical approach is used to study and analyse the literature. The research results highlight evidence of success cases, illustrating that the implementation of AI in English teaching in China can provide new ideas and create new spaces for the innovation of teaching models.&#x0D; Keywords: artificial intelligence; English teaching in China; innovation in English teaching&#x0D; &#x0D;
Hyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization

A new nonconvex smooth rank approximation model is proposed to deal with HSI mixed noise in this paper. The low-rank matrix with Laplace function regularization is used to approximate the nuclear norm, and its performance is superior to the nuclear norm regularization. A new phase congruency lp norm model is proposed to constrain the spatial structure information of hyperspectral images, to solve the phenomenon of “artificial artifact” in the process of hyperspectral image denoising. This model not only makes use of the low-rank characteristic of the hyperspectral image accurately, but also combines the structural information of all bands and the local information of the neighborhood, and then based on the Alternating Direction Method of Multipliers (ADMM), an optimization method for solving the model is proposed. The results of simulation and real data experiments show that the proposed method is more effective than the competcing state-of-the-art denoising methods.
Addressing Slot-Value Changes in Task-oriented Dialogue Systems Through Dialogue Domain Adaptation

Recent task-oriented dialogue systems learn a model from annotated dialogues, and such dialogues are in turn collected and annotated so that they are consistent with certain domain knowledge.However, in real scenarios, domain knowledge is subject to frequent changes, and initial training dialogues may soon become obsolete, resulting in a significant decrease of the model performance.In this paper, we investigate the relationship between training dialogues and domain knowledge, and propose dialogue domain adaptation, a methodology aiming at adapting initial training dialogues to changes intervened in the domain knowledge.We focus on slot-value changes (e.g., when new slotvalues are available to describe domain entities) and define an experimental setting for dialogue domain adaptation.First, we show that current state-of-the-art models for dialogue state tracking are still poorly robust to slot-value changes of the domain knowledge.Then, we compare different domain adaptation strategies, showing that simple techniques are effective to reduce the gap between training dialogues and domain knowledge.
Usability and User Experience Test on the Muezzin and Khatib Scheduling Mobile Application Using SUS and User Experience Questionnaire (Case Study: Al Abroor Mosque)

One of the Dewan Kemakmuran Masjid (committee to serve public service in a mosque) main task is to provide prayer facilities in a mosque, such as managing schedules for muezzin and imam salat. Most of the time, the plan is executed manually. Moreover, preparing the schedule is not easy. It needs cautious and excessive effort as DKM members should be careful not to make an unbalanced schedule for each muezzin/ khatib. It is also an error-prone process since putting the unavailable person in the schedule slot is possible. To overcome the condition, we build a mobile application for managing schedules for muezzin and imam salat. The application has been tested with usability testing and a user experience questionnaire. The results from both testing tools yield excellent results; the application could implement and comfortably used for managing schedules. From the result, it can be inferred that Al Aboor meets the criteria for a good mobile application and can be developed further.
Aligning Pretraining for Detection via Object-Level Contrastive Learning

Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning. Such generality for transfer learning, however, sacrifices specificity if we are interested in a certain downstream task. We argue that this could be sub-optimal and thus advocate a design principle which encourages alignment between the self-supervised pretext task and the downstream task. In this paper, we follow this principle with a pretraining method specifically designed for the task of object detection. We attain alignment in the following three aspects: 1) object-level representations are introduced via selective search bounding boxes as object proposals; 2) the pretraining network architecture incorporates the same dedicated modules used in the detection pipeline (e.g. FPN); 3) the pretraining is equipped with object detection properties such as object-level translation invariance and scale invariance. Our method, called Selective Object COntrastive learning (SoCo), achieves state-of-the-art results for transfer performance on COCO detection using a Mask R-CNN framework. Code and models will be made available.