Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN MODUL IPS BERBASIS MULTIMEDIA INTERAKTIF UNTUK SISWA KELAS IV SEKOLAH DASAR

IPS is a science that examines the various disciplines of social sciences and humanities as well as basic human activities are packed scientifically in order to provide insight and deep understanding to the learners. Learning social studies should be presented as attractive as possible so that students can understand the assessment contained in pembelajajaran IPS with ease. Thus, it is necessary resulting IPS-based learning modules interactive multimedia for the fourth grade students of elementary school. The research method is the development of research or development research. This research included the study to develop and produce new products in a learning system that will be applied to the learners. The results of the study showed a mean value of 3.40 validation of multimedia devices valid category. The trial results obtained practicalities response result of fourth grade students during the lesson is very practical with an average of 3.67 in the category of very appropriate. Results of teachers' response to the practicalities of IPS module in a practical category with an average of 3.38. The observation of the activities of learners in one learning activity in the category of high, with an average gain of 71.15. The observation of the activity at the time of deployment (disseminate) showed that the activity of students in the high category with the acquisition of 68.33. Thus, we can conclude a good interactive multimedia learning modules used in learning activities.Key Words: development, IPS module, interactive multimedia
Building a Corpus for the Zaza-Gorani Language Family.

Thanks to the growth of local communities and various news websites along with the increasing accessibility of the Web, some of the endangered and less-resourced languages have a chance to revive in the information era. Therefore, the Web is considered a huge resource that can be used to extract language corpora which enable researchers to carry out various studies in linguistics and language technology. The Zaza–Gorani language family is a linguistic subgroup of the Northwestern Iranian languages for which there is no significant corpus available. Motivated to create one, in this paper we present our endeavour to collect a corpus in Zazaki and Gorani languages containing over 1.6M and 194k word tokens, respectively. This corpus is publicly available.
Natural Language Inference with Mixed Effects

There is growing evidence that the prevalence of disagreement in the raw annotations used to construct natural language inference datasets makes the common practice of aggregating those annotations to a single label problematic. We propose a generic method that allows one to skip the aggregation step and train on the raw annotations directly without subjecting the model to unwanted noise that can arise from annotator response biases. We demonstrate that this method, which generalizes the notion of a mixed effects model by incorporating annotator random effects into any existing neural model, improves performance over models that do not incorporate such effects.
Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning

This paper illustrates Huawei’s submission to the WMT20 low-resource parallel corpus filtering shared task. Our approach focuses on developing a proxy task learner on top of a transformer-based multilingual pre-trained language model to boost the filtering capability for noisy parallel corpora. Such a supervised task also helps us to iterate much more quickly than using an existing neural machine translation system to perform the same task. After performing empirical analyses of the finetuning task, we benchmark our approach by comparing the results with past years’ state-of-theart records. This paper wraps up with a discussion of limitations and future work. The scripts for this study will be made publicly available.
NICT Kyoto Submission for the WMT’20 Quality Estimation Task: Intermediate Training for Domain and Task Adaptation

This paper describes the NICT Kyoto submission for the WMT’20 Quality Estimation (QE) shared task. We participated in Task 2: Word and Sentence-level Post-editing Effort, which involved Wikipedia data and two translation directions, namely English-to-German and English-to-Chinese. Our approach is based on multi-task fine-tuned cross-lingual language models (XLM), initially pre-trained and further domain-adapted through intermediate training using the translation language model (TLM) approach complemented with a novel self-supervised learning task which aim is to model errors inherent to machine translation outputs. Results obtained on both word and sentence-level QE show that the proposed intermediate training method is complementary to language model domain adaptation and outperforms the fine-tuning only approach.
Review of Place Recognition Approaches: Traditional and Deep Learning Methods

Place recognition is an important topic in many vision-based applications. For this purpose, many traditional and artificial intelligence-based methods have been proposed. Despite years of expertise accrued in this area, it still remains a challenging problem due to the various ways in which the appearance of places in the real world can take place. Recently, rapid improvements have been made in image processing and recognition using deep neural networks. In particular, convolutional neural frameworks (CNN) are widely used in object detection, image classification, as well as in place recognition. The advantage of CNN-based place recognition is that CNN methods can automatically learn image patterns using sample images without any pre-processing and can handle appearance variations better than traditional methods. In this paper, we review and discuss traditional and deep learning-based place recognition algorithms, as well as existing datasets that can be used for performance measurement.
Pengembangan perangkat pembelajaran berbasis blended learning pada mata kuliah pengantar pendidikan

The purpose of this research is; 1) Developing learning tools based on blended learning; 2) Knowing the feasibility of learning tools based on blended learning, and 3) Knowing student responses to learning tools in the Pengantar Pendidikan course. The type of research is Research and Development. Descriptive research results show that 1) the development of blended learning based learning devices in the Pengantar Pendidikan courses is done through 5 stages, a) analyzing product requirements b) product development RPS, RTM, and module, c) expert validation carried out by 3 experts and revised d) small scale trials conducted on 5 to test the readability of the instrument, and e) limited testing carried out on 33 students, 2) learning tools stated feasible based on expert validation 3) Student responses based on the results of student questionnaires are in a good category to support learning activities.
NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder

In order to provide an explanation of machine learning models, causality detection attracts lots of attention in the artificial intelligence research community. In this paper, we explore the cause-effect detection in financial news and propose an approach, which combines the BIO scheme with the Viterbi decoder for addressing this challenge. Our approach is ranked the first in the official run of cause-effect detection (Task 2) of the FinCausal-2020 shared task. We not only report the implementation details and ablation analysis in this paper, but also publish our code for academic usage.
Contextual Embeddings for Arabic-English Code-Switched Data

Globalization has caused the rise of the code-switching phenomenon among multilingual societies. In Arab countries, code-switching between Arabic and English has become frequent, especially through social media platforms. Consequently, research in Natural Language Processing (NLP) systems increased to tackle such a phenomenon. One of the significant challenges of developing code-switched NLP systems is the lack of data itself. In this paper, we propose an open source trained bilingual contextual word embedding models of FLAIR, BERT, and ELECTRA. We also propose a novel contextual word embedding model called KERMIT, which can efficiently map Arabic and English words inside one vector space in terms of data usage. We applied intrinsic and extrinsic evaluation methods to compare the performance of the models. Our results show that FLAIR and FastText achieve the highest results in the sentiment analysis task. However, KERMIT is the best-achieving model on the intrinsic evaluation and named entity recognition. Also, it outperforms the other transformer-based models on question answering task.
The Oversight of Content Moderation by AI: Impact Assessments and Their Limitations

In a world in which artificial intelligence (AI) systems are increasingly shaping our environment, as well as our access to and exclusion from opportunities and resources, it is essential to ensure some form of AI oversight. Such oversight will help to maintain the rule of law, to protect individual rights, and to ensure the protection of core democratic values. Nevertheless, achieving AI oversight is challenging due to the dynamic and opaque nature of such systems. Recently, in an attempt to increase oversight and accountability for AI systems, the proposed US Algorithmic Accountability Act introduced a mandatory impact assessment for private entities that deploy automated decision-making systems. Impact assessment as a means to enhance oversight was likewise recently adopted under the EU's General Data Protection Regulation. Taken together, these initiatives mark the latest development in AI oversight policy.In this paper, we question the merits of impact assessment as a tool for promoting oversight of AI systems. Using the case of AI systems of content moderation, we highlight the strengths and weaknesses of this oversight tool and propose how to improve it. Additionally, we argue that even an improved impact assessment does not fit equally with the over-sight challenge raised by different systems of AI. Especially, impact assessments might be insufficient to oversee AI systems that are deployed to achieve purposes that could be classified as public, such as making our online public sphere safer. Meaningful oversight of AI systems that impose costs on society as a whole, like AI systems of content moderation, cannot be pursued by mechanisms of self-assessments alone. Therefore, as we suggest in this paper, such systems should be additionally subjected to objective mechanisms of external oversight.
Overt speakers in syntax

This paper focuses on a type of direct address in Sason Arabic that fails to behave like a regular vocative phrase. In particular, unlike regular vocative phrases, this form of address spells out both the speaker and the addressee, and it is conditioned by the speaker’s expression of affection towards the addressee. A DP and its adjacent invariable clitic (i.e., a frozen 3rd person singular form of a possessive pronoun) are the constituents of this form of direct address, and either of them can equally spell out the speaker or the addressee; hence, the label mutable direct address (MDA) for this construction. Coordination, constituency, pluralization and substitution tests indicate that the derivation of MDAs does not involve the left periphery of DPs (as vocative phrases do) but the left periphery of clauses. In this respect, we adopt the cartographic representation of speech act phrases (SAP) (e.g., as in Haegeman &amp;amp; Hill 2013) and argue that MDAs instantiate a possible variation within SAP structures. That is, while a regular SAP field is split (over speaker and hearer heads), the SAP underlying MDAs remerges the two heads, so the speech act functional features are bundled and associated with a single SA head. Thus, MDAs provide new evidence for the extent of derivational variations that may arise on the basis of the feature inventory currently accepted for SAP domains.
But the computer say me the time is up: the shaping of oral turns mediated <i>with</i> and <i>through</i> the screen

Research on mode in computer-mediated communication and language learning has primarily focused on mode-as-channel of communication such as audio- or videoconferencing. However, increasingly sophisticated technological tools now facilitate communication in multiple ways so that learners can convey and respond to peers and screen-based resources, both orally, visually and/or through touch with the screen. By highlighting learner-to-learner turns as well as screen related turns, this paper looks at how learners’ oral meaning making is shaped through both verbal and non-verbal resources. This can provide a better understanding of how the interplay between modes and resources on interfaces might be harnessed to increase learners’ oral turns and identify potential interface-related difficulties learners might face. Audio recordings of six dyads are analysed using discourse analysis, with notions from conversational analysis, alongside interface screenshots. Results reveal that screen-based resources become: 1) embedded or modified in oral turns; 2) resources to initiate and support oral turns; 3) diverse topics of talk. It was also found that learners orientate towards some resources as 4) agentive turn-takers (e.g. pop-ups, to initiate an action). Such multimodal experience reveal how peer-to-peer talk can occasionally resemble a multi-party encounter whereby some resources can act as participants in the interaction. It is proposed that a fuller understanding of this interplay can help teachers and designers optimize computer-mediating communicative language learning tasks.
High-Resolution Image Data Collection Scheme for Road Sensing Using Wide-Angle Cameras on General-Use Vehicle Criteria to Include/Exclude Collected Images for Super Resolution

Abstract Fisheye cameras or wide-angle cameras used on automobiles will have various future applications as “distributed sensors in daily transport” -- objects on roadside are frequently observed multiple times by multiple vehicles. Although the object regions are often captured with relatively low image quality (low resolution and large blur) resulting from the character of the lens, it is possible to enhance their quality by post-processing using super resolution (SR) technology. Here, it is required to decide which images to use as inputs for SR: a greater number of lower-quality images or fewer higher-quality images. We evaluated and discussed the input image quality necessary to obtain most effective SR results, especially focusing on degree of image blur. As a conclusion, we found a criterion that was related to the blur level of the initial input image of SR. Then, we considered its potential use as a requisite in observing road environments.
Anaphoric Zero Pronoun Identification: A Multilingual Approach

Pro-drop languages such as Arabic, Chinese, Italian or Japanese allow morphologically null but referential arguments in certain syntactic positions, called anaphoric zero-pronouns. Much NLP work on anaphoric zero-pronouns (AZP) is based on gold mentions, but models for their identification are a fundamental prerequisite for their resolution in real-life applications. Such identification requires complex language understanding and knowledge of real-world entities. Transfer learning models, such as BERT, have recently shown to learn surface, syntactic, and semantic information,which can be very useful in recognizing AZPs. We propose a BERT-based multilingual model for AZP identification from predicted zero pronoun positions, and evaluate it on the Arabic and Chinese portions of OntoNotes 5.0. As far as we know, this is the first neural network model of AZP identification for Arabic; and our approach outperforms the stateof-the-art for Chinese. Experiment results suggest that BERT implicitly encode information about AZPs through their surrounding context.
GINN: Fast GPU-TEE Based Integrity for Neural Network Training

Machine learning models based on Deep Neural Networks (DNNs) are increasingly being deployed in a wide range of applications ranging from self-driving cars to Covid-19 diagnostics. The computational power necessary to learn a DNN is non-trivial. So, as a result, cloud environments with dedicated hardware support emerged as important infrastructure. However, outsourcing computation to the cloud raises security, privacy, and integrity challenges. To address these challenges, previous works tried to leverage homomorphic encryption, secure multi-party computation, and trusted execution environments (TEE). Yet, none of these approaches can scale up to support realistic DNN model training workloads with deep architectures and millions of training examples without sustaining a significant performance hit. In this work, we focus on the setting where the integrity of the outsourced Deep Learning (DL) model training is ensured by TEE. We choose the TEE based approach because it has been shown to be more efficient compared to the pure cryptographic solutions, and the availability of TEEs on cloud environments. To mitigate the loss in performance, we combine random verification of selected computation steps with careful adjustments of DNN used for training. Our experimental results show that the proposed approach may achieve 2X to 20X performance improvement compared to the pure TEE based solution while guaranteeing the integrity of the computation with high probability (e.g., 0.999) against the state-of-the-art DNN backdoor attacks.
Behavior-Based Quality Discrimination

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Truncated singular value decomposition in ripped photo recovery

Singular value decomposition (SVD) is one of the most useful matrix decompositions in linear algebra. Here, a novel application of SVD in recovering ripped photos was exploited. Recovery was done by applying truncated SVD iteratively. Performance was evaluated using the Frobenius norm. Results from a few experimental photos were decent.
An Unsupervised Deep Learning Approach for Real-World Image Denoising

Designing an unsupervised image denoising approach in practical applications is a challenging task due to the complicated data acquisition process. In the real-world case, the noise distribution is so complex that the simplified additive white Gaussian (AWGN) assumption rarely holds, which significantly deteriorates the Gaussian denoisers' performance. To address this problem, we apply a deep neural network that maps the noisy image into a latent space in which the AWGN assumption holds, and thus any existing Gaussian denoiser is applicable. More specifically, the proposed neural network consists of the encoder-decoder structure and approximates the likelihood term in the Bayesian framework. Together with a Gaussian denoiser, the neural network can be trained with the input image itself and does not require any pre-training in other datasets. Extensive experiments on real-world noisy image datasets have shown that the combination of neural networks and Gaussian denoisers improves the performance of the original Gaussian denoisers by a large margin. In particular, the neural network+BM3D method significantly outperforms other unsupervised denoising approaches and is competitive with supervised networks such as DnCNN, FFDNet, and CBDNet.
Automotive in "The Stack": a Cross-sectional View of the Field from Earth, through Platforms and Nonhuman Users to Anti-Users

"The Stack" is the world vision developed by the American digital philosopher Benjamin H. Bratton, in his namesake ponderous essay. It is an abstraction describing a layered taxonomy based on the increasingly dominant ubiquity and pervasiveness of computation. Although "The Stack" is more a philosophical abstraction than an engineering model, it is very useful in understanding a specific technological field as a whole. Since a car is more and more a computational object, a high-speed computing platform on wheels, it is interesting to apply such a vision to the automotive field, starting from its rooting into the planet from which it draws the energetic and mineral resources, passing through the integration in clouds of services, delivered by platforms, and the "autonomization" of vehicles, up to a new form of user, that can even be "nonhuman" (see for instance the case of the autonomous vehicle). Drawing hints and visions from philosophical dissertations, technical and marketing news, white papers and academic research, this article scans the automotive field and more in general mobility and smart cities, going up the six interconnected layers of The Stack (Earth – Cloud – City – Address – Interface – User). In this frame, pushed towards a future sometimes utopian, dystopian scenarios also arise. Furthermore, vulnerabilities give place to that special kind of user that normally is called "hacker", but in the context of The Stack we call the "Anti-User", the category of users that we introduce.
Adversarially Robust Federated Learning for Neural Networks

In federated learning, data is distributed among local clients which collaboratively train a prediction model using secure aggregation. To preserve the privacy of the clients, the federated learning paradigm requires each client to maintain a private local training data set, and only uploads its summarized model updates to the server. In this work, we show that this paradigm could lead to a vulnerable model, which collapses in performance when the corrupted data samples (under adversarial manipulations) are used for prediction after model deployment. To improve model robustness, we first decompose the aggregation error of the central server into bias and variance, and then, propose a robust federated learning framework, named Fed_BVA, that performs on-device adversarial training using the bias-variance oriented adversarial examples supplied by the server via asymmetrical communications. The experiments are conducted on multiple benchmark data sets using several prevalent neural network models, and the empirical results show that our framework is robust against white-box and black-box adversarial corruptions under both IID and non-IID settings.
Application of Convolutional Neural Network in Handwritten Chinese Character Recognition

The handwritten Chinese character data set HWDB, designs and trains a convolutional neural network to recognize handwritten Chinese characters. On this basis, this article tested and studied the effects of activation function, loss function, and learning rate on the performance of convolutional neural networks, and further analyzed the reasons for their effects, and accumulated for the subsequent application of convolutional neural networks in other aspects. The valuable experimental experience and theoretical basis were laid. This paper is based on the relevant technical process, analyzed the experimental effect, summarized the influence of important parameters, and summarized the design ideas of neural network for different image types, laying a foundation for further research in the future.
Handwriting Personality Recognition with Machine Learning: A Comparative Study

Predicting someone’s personality is certainly becoming more and more important in the modern world, a face, voice, handwriting, and many other aspects could be personality traits indicators. Investigating Deep Learning algorithms application under this scope could be very benefit and would provide a solid background for future researches. This paper aims to presents a comparative study of different approaches and methods using Deep Learning techniques in handwriting personality recognition.