Reviewerly/exHarmony huggingface dataset:
Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems-data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.
TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems

We present a data-driven, end-to-end approach to transaction-based dialog systems that performs at near-human levels in terms of verbal response quality and factual grounding accuracy.We show that two essential components of the system produce these results: a sufficiently large and diverse, in-domain labeled dataset, and a neural network-based, pretrained model that generates both verbal responses and API call predictions.In terms of data, we introduce TicketTalk, a movie ticketing dialog dataset with 23,789 annotated conversations.The movie ticketing conversations range from completely open-ended and unrestricted to more structured, both in terms of their knowledge base, discourse features, and number of turns.In qualitative human evaluations, model-generated responses trained on just 10,000 TicketTalk dialogs were rated to "make sense" 86.5% of the time, almost the same as human responses in the same contexts.Our simple, API-focused annotation schema results in a much easier labeling task making it faster and more cost effective.It is also the key component for being able to predict API calls accurately.We handle factual grounding by incorporating API calls in the training data, allowing our model to learn which actions to take and when.Trained on the same 10,000-dialog set, the model's API call predictions were rated to be correct 93.9% of the time in our evaluations, surpassing the ratings for the corresponding human labels.We show how API prediction and response generation scores improve as the dataset size incrementally increases from 5000 to 21,000 dialogs.Our analysis also clearly illustrates the benefits of pre-training.To facilitate future work on transaction-based dialog systems, we have published the TicketTalk dataset at https:// git.io/JL8an.
Constrained Labeled Data Generation for Low-Resource Named Entity Recognition

Named Entity Recognition (NER) in lowresource languages has been a long-standing challenge in NLP.Recent work has shown great progress in two directions: developing cross-lingual features/models to transfer knowledge to low-resource languages, and translating source-language training data into low-resource target-language training data by projecting annotations with cheap resources.We focus on the second direction in this study.Existing methods suffer from the low quality of the resulting annotated data in the target language; for example, they cannot handle word order and lexical ambiguity well.To handle these limitations we propose a novel approach that uses the projected annotation to generate pseudo supervised data with a transformer language model and a constrained beam search.This allows us to generate more diverse, higher quality, as well as higher quantities of annotated data in the target language.Experiments demonstrate that, when combining our method with available cross-lingual features, it achieves state-of-the-art or competitive performance on NER in a low-resource setting, especially for languages that are distant from our source language, English.
General Policies, Representations, and Planning Width

It has been observed that in many of the benchmark planning domains, atomic goals can be reached with a simple polynomial exploration procedure, called IW, that runs in time exponential in the problem width. Such problems have indeed a bounded width: a width that does not grow with the number of problem variables and is often no greater than two. Yet, while the notion of width has become part of the state- of-the-art planning algorithms like BFWS, there is still no good explanation for why so many benchmark domains have bounded width. In this work, we address this question by relating bounded width and serialized width to ideas of generalized planning, where general policies aim to solve multiple instances of a planning problem all at once. We show that bounded width is a property of planning domains that admit optimal general policies in terms of features that are explicitly or implicitly represented in the domain encoding. The results are extended to the larger class of domains with bounded serialized width where the general policies do not have to be optimal. The study leads also to a new simple, meaningful, and expressive language for specifying domain serializations in the form of policy sketches which can be used for encoding domain control knowledge by hand or for learning it from traces. The use of sketches and the meaning of the theoretical results are all illustrated through a number of examples.
Contextualized Rewriting for Text Summarization

Extractive summarization suffers from irrelevance, redundancy and incoherence. Existing work shows that abstractive rewriting for extractive summaries can improve the conciseness and readability. These rewriting systems consider extracted summaries as the only input, which is relatively focused but can lose important background knowledge. In this paper, we investigate contextualized rewriting, which ingests the entire original document. We formalize contextualized rewriting as a seq2seq problem with group alignments, introducing group tag as a solution to model the alignments, identifying extracted summaries through content-based addressing. Results show that our approach significantly outperforms non-contextualized rewriting systems without requiring reinforcement learning, achieving strong improvements on ROUGE scores upon multiple extractive summarizers.
Multilingual Text Summarization using Deep Learning

Along with the extreme expansion of big data and the vast development of the internet, making documentation of the huge internet information is the first interest for people.These online textual data led to information overload and redundancy.Multi-document summarization is one of the solutions to such an issue, used to extract the main ideas of the documents and put them into a short summary.Summarizing documents should not affect the major concepts and the meaning of the original text.This paper proposes a new method for multi-document summarization.The basic idea of the proposed method relied on six different features to be extracted of each sentence in the studied collection, these features must be language.A set of the feature vectors is introduced to Convolutional Neural Networks (CNNs) for classification as either summary or non-summary sentences.A graph of summary sentences was generated and assigned scores by the TextRank algorithm.The implemented system was evaluated on both English and Arabic versions of the dataset of the TAC-2011 MultiLing Pilot by using ROUGE metrics.The proposed method achieved an average F-measure 0.46079, 0.20664 using ROUGE-1 and ROUGE-2 respectively, for English documents, and achieved an average F-measure 0.45624, 0.30725 for Arabic documents.
A Multi-Level Attention Model for Evidence-Based Fact Checking

Evidence-based fact checking aims to verify the truthfulness of a claim against evidence extracted from textual sources.Learning a representation that effectively captures relations between a claim and evidence can be challenging.Recent state-of-the-art approaches have developed increasingly sophisticated models based on graph structures.We present a simple model that can be trained on sequence structures.Our model enables inter-sentence attentions at different levels and can benefit from joint training.Results on a large-scale dataset for Fact Extraction and VERification (FEVER) show that our model outperforms the graphbased approaches and yields 1.09% and 1.42% improvements in label accuracy and FEVER score, respectively, over the best published model. 1
Immersive Telepresence in Remote Education

We present a low-cost telepresence system with application in remote education. It follows an Augmented Distributed Reality approach, where the remote student, wearing a Head Mounted Display, is immersed in a mixed reality scene composed of a real-time 360 video stream of the classroom, a pass-through view of his/her own hands, and additional augmented information obtained via Artificial Intelligence. The system can help students in quarantine or similar situations to improve their learning experience, particularly for hands-on sessions with high interaction between physically present students and their teacher.
How do information technology and knowledge management affect SMEs’ responsiveness to the coronavirus crisis?

All organizations have been affected by the coronavirus pandemic in different ways. Small and medium-sized enterprises (SMEs) are more vulnerable to changes due to their limited resources. However, the capabilities of information technologies and processes of knowledge management can assist these enterprises to survive and respond appropriately to changes. Thus, this study aims to assess the extent to which information technology capabilities influence the responsiveness of SMEs to challenges that have emerged during the coronavirus crisis. It also investigates the degree to which knowledge management affects such a relationship in the context of Saudi Arabia. The study includes developing a survey as a data collection method. The responses from 136 SMEs were used to make an analysis and, consequently, draw a conclusion. It has been found that IT capabilities positively influence SMEs’ responsiveness to changes brought by coronavirus, through supporting work flexibility and providing a wide range of options in the supply chain, processes, and sales. It further found that knowledge management mediates the relationship between IT capabilities and SMEs’ responsiveness.
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

In this paper, we investigate few-shot joint learning for dialogue language understanding.Most existing few-shot models learn a single task each time with only a few examples.However, dialogue language understanding contains two closely related tasks, i.e., intent detection and slot filling, and often benefits from jointly learning the two tasks.This calls for new few-shot learning techniques that are able to capture task relations from only a few examples and jointly learn multiple tasks.To achieve this, we propose a similarity-based few-shot learning scheme, named Contrastive Prototype Merging network (ConProm), that learns to bridge metric spaces of intent and slot on data-rich domains, and then adapt the bridged metric space to specific few-shot domain.Experiments on two public datasets, Snips and FewJoint, show that our model significantly outperforms the strong baselines in one and five shots settings.
Emergency remote teaching in Greece during the first period of the 2020 Covid-19 pandemic

During the first period of the covid-19 pandemic, schools in Greece suspended the face-to-face operation for about three months, and teachers were called in an emergency to adjust the teaching methods remotely. The educational community was called to deal with this emergency with the primary goal to maintain students’ contact with the educational process. The urgent use of distance communication and teaching forms was a project with many difficulties for teachers, students, and parents. In all this transition process, the practice and learning communities that teachers themselves organized were crucial. The Greek eTwinning community immediately implemented three MOOC seminars (Mass Open Online Courses), which were designed to assist them in the use and pedagogical utilization of distance education tools. More than 30,000 teachers participated in these seminars. In this paper, we present the results of a survey conducted on a sample of 1080 Greek teachers, members of the eTwinning community, and we describe how they dealt with remotely teaching. The research refers to tools used, the distance communication content, and the difficulties encountered to implement the whole project.
Hierarchical Context-aware Network for Dense Video Event Captioning

Dense video event captioning aims to generate a sequence of descriptive captions for each event in a long untrimmed video.Video-level context provides important information and facilities the model to generate consistent and less redundant captions between events.In this paper, we introduce a novel Hierarchical Context-aware Network for dense video event captioning (HCN) to capture context from various aspects.In detail, the model leverages local and global context with different mechanisms to jointly learn to generate coherent captions.The local context module performs full interaction between neighbor frames and the global context module selectively attends to previous or future events.According to our extensive experiment on both Youcook2 and Activitynet Captioning datasets, the videolevel HCN model outperforms the event-level context-agnostic model by a large margin.The code is available at https://github.com/KirkGuo/HCN.
Security of COFB against Chosen Ciphertext Attacks

COFB is a lightweight Authenticated Encryption with Associated Data (AEAD) mode based on block ciphers. It was proposed in CHES 2017 and is the basis for GIFT-COFB, a finalist in the NIST lightweight standardization project. It comes with provable security results that guarantee its security up to the birthday bound in the nonce-respecting model. However, the designers offer multiple versions of the analysis with different details and the implications of attacks against the scheme are not discussed deeply. In this article, we look at a group of possible forgery and privacy attacks against COFB. We show that the security for both forgery and privacy is bounded by the number of forgery attempts. We show the existence of forgery and privacy attacks with success probability qd/2n/2, given qd forgery attempts. In particular, we show an attack with 2n/2 attempts using only a single known-plaintext encryption query against COFB. While these attacks do not contradict the claims made by the designers of GIFT-COFB, they show its limitations in terms of the number of forgery attempts. They also show that, while COFB generates a 128-bit tag, it behaves in a very similar manner to an AEAD scheme with 64-bit tag. As a result of independent interest, our analysis provides a contradiction to the main theorem of Journal of Cryptology volume 33, pages 703–741 (2020), which includes an improved security proof of COFB compared to the CHES 2017 version. Finally, we discuss the term nqd/2n/2 that appears in the security proof of GIFT-COFB and CHES 2017, showing why there is a security gap between the provable results and the attacks. We emphasize that the results in this article do not threaten the security of GIFT-COFB in the scope of the NIST lightweight cryptography requirements or the claims made by the designers in the specification document of the design.
Proactive Privacy-preserving Learning for Retrieval

Deep Neural Networks (DNNs) have recently achieved remarkable performance in image retrieval, yet posing great threats to data privacy. On the one hand, one may misuse a deployed DNNs based system to look up data without consent. On the other hand, organizations or individuals would legally or illegally collect data to train high-performance models outside the scope of legitimate purposes. Unfortunately, less effort has been made to safeguard data privacy against malicious uses of DNNs. In this paper, we propose a data-centric Proactive Privacy-preserving Learning (PPL) algorithm for hashing based retrieval, which achieves the protection purpose by employing a generator to transfer the original data into the adversarial data with quasi-imperceptible perturbations before releasing them. When the data source is infiltrated, the adversarial data can confuse menacing retrieval models to make erroneous predictions. Given that the prior knowledge of malicious models is not available, a surrogate retrieval model is instead introduced acting as a fooling target. The framework is trained by a two-player game conducted between the generator and the surrogate model. More specifically, the generator is updated to enlarge the gap between the adversarial data and the original data, aiming to lower the search accuracy of the surrogate model. On the contrary, the surrogate model is trained with the opposing objective that is to maintain the search performance. As a result, an effective and robust adversarial generator is encouraged. Furthermore, to facilitate an effective optimization, a Gradient Reversal Layer (GRL) module is inserted to connect two models, enabling the two-player game in a one-step learning. Extensive experiments on three widely-used realistic datasets prove the effectiveness of the proposed method.
Programmability of covariant quantum channels

A programmable quantum processor uses the states of a program register to specify one element of a set of quantum channels which is applied to an input register. It is well-known that such a device is impossible with a finite-dimensional program register for any set that contains infinitely many unitary quantum channels (Nielsen and Chuang's <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">No-Programming Theorem</mml:mtext></mml:mrow></mml:math>), meaning that a universal programmable quantum processor does not exist. The situation changes if the system has symmetries. Indeed, here we consider group-covariant channels. If the group acts irreducibly on the channel input, these channels can be implemented exactly by a programmable quantum processor with finite program dimension (via <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">teleportation simulation</mml:mtext></mml:mrow></mml:math>, which uses the Choi-Jamiolkowski state of the channel as a program). Moreover, by leveraging the representation theory of the symmetry group action, we show how to remove redundancy in the program and prove that the resulting program register has minimum Hilbert space dimension. Furthermore, we provide upper and lower bounds on the program register dimension of a processor implementing all group-covariant channels approximately.
Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking

This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system.This paper proposes an effective continual learning for the task-oriented dialogue system with iterative network pruning, expanding and masking (TPEM), which preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks.Specifically, TPEM (i) leverages network pruning to keep the knowledge for old tasks, (ii) adopts network expanding to create free weights for new tasks, and (iii) introduces task-specific network masking to alleviate the negative impact of fixed weights of old tasks on new tasks.We conduct extensive experiments on seven different tasks from three benchmark datasets and show empirically that TPEM leads to significantly improved results over the strong competitors.For reproducibility, we submit the code and data at:
Simple or Complex? Learning to Predict Readability of Bengali Texts

Determining the readability of a text is the first step to its simplification. In this paper, we present a readability analysis tool capable of analyzing text written in the Bengali language to provide in-depth information on its readability and complexity. Despite being the 7th most spoken language in the world with 230 million native speakers, Bengali suffers from a lack of fundamental resources for natural language processing. Readability related research of the Bengali language so far can be considered to be narrow and sometimes faulty due to the lack of resources. Therefore, we correctly adopt document-level readability formulas traditionally used for U.S. based education system to the Bengali language with a proper age-to-age comparison. Due to the unavailability of large-scale human-annotated corpora, we further divide the document-level task into sentence-level and experiment with neural architectures, which will serve as a baseline for the future works of Bengali readability prediction. During the process, we present several human-annotated corpora and dictionaries such as a document-level dataset comprising 618 documents with 12 different grade levels, a large-scale sentence-level dataset comprising more than 96K sentences with simple and complex labels, a consonant conjunct count algorithm and a corpus of 341 words to validate the effectiveness of the algorithm, a list of 3,396 easy words, and an updated pronunciation dictionary with more than 67K words. These resources can be useful for several other tasks of this low-resource language.
A Bidirectional Multi-paragraph Reading Model for Zero-shot Entity Linking

Recently, a zero-shot entity linking task is introduced to challenge the generalization ability of entity linking models. In this task, mentions must be linked to unseen entities and only the textual information is available. In order to make full use of the documents, previous work has proposed a BERT-based model which can only take fixed length of text as input. However, the key information for entity linking may exist in nearly everywhere of the documents thus the proposed model cannot capture them all. To leverage more textual information and enhance text understanding capability, we propose a bidirectional multi-paragraph reading model for the zero-shot entity linking task. Firstly, the model treats the mention context as a query and matches it with multiple paragraphs of the entity description documents. Then, the mention-aware entity representation obtained from the first step is used as a query to match multiple paragraphs in the document containing the mention through an entity-mention attention mechanism. In particular, a new pre-training strategy is employed to strengthen the representative ability. Experimental results show that our bidirectional model can capture long-range context dependencies and outperform the baseline model by 3-4% in terms of accuracy.