Reviewerly/exHarmony huggingface dataset:
Recurrent techniques used in UX design: a report from user survey and interviews with professional designers

The job market in UX design has been growing in Brazil over the last decade, occupied by professionals from different backgrounds, mainly from areas of digital product design, interaction and information architecture. The use of methods, techniques and design tools is part of the daily life of these professionals. From in-depth interviews with seven designers living in the city of São Paulo (Brazil), this work sought to identify the main techniques currently adopted by designers who work focused on the UX and whether such procedures are regularly revised, adapted or replaced. As a result, we present a list containing the ten most used techniques, organised in phases corresponding to the stages proposed in the Double Diamond: Discover (In-depth Interview, Online Survey, Benchmark); Define (User Journey, Canvas); Develop (Internal Co-creation, Prototyping, Design Critique); and Deliver (Wizard of Oz, Usability Test).
An Application of the Khumbelo Function on the Camellia Algorithm to Prevent Attacks in IoT Devices

Camellia is an encryption algorithm implemented in many Internet of Things (IoT) devices. However, intruders attack the Camellia cipher using Substitution Box (S-Box) distinguishers. The distinguishers are tables that provide probabilities for guessing the secret keys of the algorithms. Distinguishers are used in the majority of attacks. The most well-known distinguishers are Linear Approximation Table (LAT), Difference-Distribution Table (DDT), and Differential-Linear Connectivity Table (DLCT). This research focuses on preventing these attacks using a novel function called the Khumbelo function to distract the construction of S-Box distinguishers. The Khumbelo function distracted the distinguishers’ constructions by reducing the probability of building the table. The Khumbelo function successfully decreased the attack probability of LAT (from 54.6875 percent to 0 percent), DDT (from 1.5625 percent to 0 percent), and DLCT (from 50.0000 percent to 0 percent). The Khumbelo function is generated using a 4-Byte output S-Box instead of the original 1-Byte output S-Box in Camellia.
Towards privacy-preserving cooperative control via encrypted distributed optimization

Abstract Cooperative control is crucial for the effective operation of dynamical multi-agent systems. Especially for distributed control schemes, it is essential to exchange data between the agents. This becomes a privacy threat if the data are sensitive. Encrypted control has shown the potential to address this risk and ensure confidentiality. However, existing approaches mainly focus on cloud-based control and distributed schemes are restrictive. In this paper, we present a novel privacy-preserving cooperative control scheme based on encrypted distributed optimization. More precisely, we focus on a secure distributed solution of a general consensus problem, which has manifold applications in cooperative control, by means of the alternating direction method of multipliers (ADMM). As a unique feature of our approach, we explicitly take into account the common situation that local decision variables contain copies of quantities associated with neighboring agents and ensure the neighbor’s privacy. We show the effectiveness of our method based on a numerical case study dealing with the formation of mobile robots.
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla

This work presents ‘BanglaNLG,’ a comprehensive benchmark for evaluating natural language generation (NLG) models in Bangla, a widely spoken yet low-resource language. We aggregate six challenging conditional text generation tasks under the BanglaNLG benchmark, introducing a new dataset on dialogue generation in the process. Furthermore, using a clean corpus of 27.5 GB of Bangla data, we pretrain ‘BanglaT5’, a sequence-to-sequence Transformer language model for Bangla. BanglaT5 achieves state-of-the-art performance in all of these tasks, outperforming several multilingual models by up to 9% absolute gain and 32% relative gain. We are making the new dialogue dataset and the BanglaT5 model publicly available at https://github.com/csebuetnlp/BanglaNLG in the hope of advancing future research on Bangla NLG.
Modelling Language Acquisition through Syntactico-Semantic Pattern Finding

Usage-based theories of language acquisition have extensively documented the processes by which children acquire language through communicative interaction. Notably, Tomasello (2003) distinguishes two main cognitive capacities that underlie human language acquisition: intention reading and pattern finding. Intention reading is the process by which children try to continuously reconstruct the intended meaning of their interlocutors. Pattern finding refers to the process that allows them to distil linguistic schemata from multiple communicative interactions. Even though the fields of cognitive science and psycholinguistics have studied these processes in depth, no faithful computational operationalisations of these mechanisms through which children learn language exist to date. The research on which we report in this paper aims to fill part of this void by introducing a computational operationalisation of syntactico-semantic pattern finding. Concretely, we present a methodology for learning grammars based on similarities and differences in the form and meaning of linguistic observations alone. Our methodology is able to learn compositional lexical and item-based constructions of variable extent and degree of abstraction, along with a network of emergent syntactic categories. We evaluate our methodology on the CLEVR benchmark dataset and show that the methodology allows for fast, incremental and effective learning. The constructions and categorial network that result from the learning process are fully transparent and bidirectional, facilitating both language comprehension and production. Theoretically, our model provides computational evidence for the learnability of usage-based constructionist theories of language acquisition. Practically, the techniques that we present facilitate the learning of computationally tractable, usage-based construction grammars, which are applicable for natural language understanding and production tasks.
A Survey on Dynamic Neural Networks for Natural Language Processing

Effectively scaling large Transformer models is a main driver of recent advances in natural language processing. Dynamic neural networks, as an emerging research direction, are capable of scaling up neural networks with sub-linear increases in computation and time by dynamically adjusting their computational path based on the input. Dynamic neural networks could be a promising solution to the growing parameter numbers of pretrained language models, allowing both model pretraining with trillions of parameters and faster inference on mobile devices. In this survey, we summarize the progress of three types of dynamic neural networks in NLP: skimming, mixture of experts, and early exit. We also highlight current challenges in dynamic neural networks and directions for future research.
Long Document Summarization with Top-down and Bottom-up Inference

Text summarization aims to condense long documents and retain key information. Critical to the success of a summarization model is the faithful inference of latent representations of words or tokens in the source documents. Most recent models infer the latent representations with a transformer encoder, which is purely bottom-up and thus does not capture long-distance context well. Also, self-attention-based models face the challenge of quadratic complexity with respect to sequence length. We propose a method to improve summarization models on these two aspects. Our method assumes a hierarchical latent structure of a document where the top-level captures the long range dependency at a coarser time scale and the bottom token level preserves the details. Critically, our method enables token representations to be updated in both a bottom-up and top-down manner. In the bottom-up pass, token representations are inferred with local self-attention to leverage its efficiency. Top-down correction is then applied to allow tokens to capture global context. We demonstrate the effectiveness on a diverse set of summarization datasets, including narrative, conversational, scientific documents and news. Our model achieves state-of-the-art performance on a wide range of long document summarization benchmarks, compared to recent efficient transformers. We show that our model can summarize an entire book and achieve competitive performance using 0.27% parameters and much less training data, compared to a recent GPT-3-based model. These results indicate the general applicability and benefits of the framework.
How Many Data Samples is an Additional Instruction Worth?

Recently introduced instruction-paradigm empowers non-expert users to leverage NLP resources by defining a new task in natural language. Instruction-tuned models have significantly outperformed multitask learning models (without instruction); however they are far from state-of-the-art task-specific models. Conventional approaches to improve model performance via creating datasets with large number of task instances or architectural changes in the model may not be feasible for non-expert users. However, they can write alternate instructions to represent an instruction task. Is Instruction-augmentation helpful? We augment a subset of tasks in the expanded version of NATURAL INSTRUCTIONS with additional instructions and find that it significantly improves model performance (up to 35%), especially in the low-data regime. Our results indicate that an additional instruction can be equivalent to ~200 data samples on average across tasks.
MLASK: Multimodal Summarization of Video-based News Articles

In recent years, the pattern of news consumption has been changing. The most popular multimedia news formats are now multimodal - the reader is often presented not only with a textual article but also with a short, vivid video. To draw the attention of the reader, such video-based articles are usually presented as a short textual summary paired with an image thumbnail.In this paper, we introduce MLASK (MultimodaL Article Summarization Kit) - a new dataset of video-based news articles paired with a textual summary and a cover picture, all obtained by automatically crawling several news websites. We demonstrate how the proposed dataset can be used to model the task of multimodal summarization by training a Transformer-based neural model. We also examine the effects of pre-training when the usage of generative pre-trained language models helps to improve the model performance, but (additional) pre-training on the simpler task of text summarization yields even better results. Our experiments suggest that the benefits of pre-training and using additional modalities in the input are not orthogonal.
Improving Cross-modal Alignment for Text-Guided Image Inpainting

Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. Existing methods are based on a strong vision encoder and a cross-modal fusion model to integrate cross-modal features. However, these methods allocate most of the computation to visual encoding, while light computation on modeling modality interactions. Moreover, they take cross-modal fusion for depth features, which ignores a fine-grained alignment between text and image. Recently, vision-language pre-trained models (VLPM), encapsulating rich cross-modal alignment knowledge, have advanced in most multimodal tasks. In this work, we propose a novel model for TGII by improving cross-modal alignment (CMA). CMA model consists of a VLPM as a vision-language encoder, an image generator and global-local discriminators. To explore cross-modal alignment knowledge for image restoration, we introduce cross-modal alignment distillation and in-sample distribution distillation. In addition, we employ adversarial training to enhance the model to fill the missing region in complicated structures effectively. Experiments are conducted on two popular vision-language datasets. Results show that our model achieves state-of-the-art performance compared with other strong competitors.
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex

Terry Yue Zhuo, Zhuang Li, Yujin Huang, Fatemeh Shiri, Weiqing Wang, Gholamreza Haffari, Yuan-Fang Li. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Efficient Hybrid Generation Framework for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) has attracted broad attention due to its commercial value. Natural Language Generation-based (NLG) approaches dominate the recent advance in ABSA tasks. However, current NLG practices are inefficient because most of them directly employ an autoregressive generation framework that cannot efficiently generate location information and semantic representations of ABSA targets. In this paper, we propose a novel framework, namely Efficient Hybrid Generation (EHG) to revolutionize traditions. Specifically, we leverage an Efficient Hybrid Transformer to generate the location and semantic information of ABSA targets in parallel. Besides, we design a novel global hybrid loss function in combination with bipartite matching to achieve end-to-end model training. Extensive experiments demonstrate that our proposed EHG framework outperforms current state-of-the-art methods in almost all cases and outperforms existing NLG-based methods in terms of inference efficiency.
Faithfulness-Aware Decoding Strategies for Abstractive Summarization

Despite significant progress in understanding and improving faithfulness in abstractive summarization, the question of how decoding strategies affect faithfulness is less studied. We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive summarization. We find a consistent trend where beam search with large beam sizes produces the most faithful summaries while nucleus sampling generates the least faithful ones. We propose two faithfulness-aware generation methods to further improve faithfulness over current generation techniques: (1) ranking candidates generated by beam search using automatic faithfulness metrics and (2) incorporating lookahead heuristics that produce a faithfulness score on the future summary. We show that both generation methods significantly improve faithfulness across two datasets as evaluated by four automatic faithfulness metrics and human evaluation. To reduce computational cost, we demonstrate a simple distillation approach that allows the model to generate faithful summaries with just greedy decoding.
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control

Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables. There currently exists two challenges in the field: 1) Faithfulness: how to generate sentences that are factually correct given the table content; 2) Diversity: how to generate multiple sentences that offer different perspectives on the table. This work proposes LoFT, which utilizes logic forms as fact verifiers and content planners to control LT2T generation. Experimental results on the LogicNLG dataset demonstrate that LoFT is the first model that addresses unfaithfulness and lack of diversity issues simultaneously. Our code is publicly available at https://github.com/Yale-LILY/LoFT.
Blind Omnidirectional Image Quality Assessment: Integrating Local Statistics and Global Semantics

Omnidirectional image quality assessment (OIQA) aims to predict the perceptual quality of omnidirectional images that cover the whole 180×360° viewing range of the visual environment. Here we propose a blind/no-reference OIQA method named Local Statistics and Global Semantics metric (LSGS) that bridges the gap between low-level statistics and high-level semantics of omnidirectional images. Specifically, statistic and semantic features are extracted in separate paths from multiple local viewports and the hallucinated global omnidirectional image, respectively. A quality regression along with a weighting process is then followed that maps the extracted quality-aware features to a perceptual quality prediction. Experimental results demonstrate that the proposed LSGS method offers highly competitive performance against state-of-the-art methods.
Has OpenAI Achieved Artificial General Intelligence in ChatGPT?

In this paper, we present an analysis of ChatGPT, a language model developed by OpenAI, through the lens of Human-Centric Functional Modeling (HCFM). ChatGPT is designed to interact through a chat interface in a conversational manner, with the ability to answer follow-up questions, admit mistakes, challenge incorrect premises, and reject inappropriate requests. Since HCFM is hypothesized to provide a functional model for assessing the existence and magnitude of general problem-solving ability (intelligence), and since according to ChatGPT itself HCFM is the only such functional model in existence, the purpose of the paper is to demonstrate the usefulness of HCFM in determining whether an AI like ChatGPT is an AGI. Using Human-Centric Functional Modeling, we aim to determine whether ChatGPT exhibits narrow problem-solving ability, classifying it as an artificial intelligence (AI), or whether it exhibits general problem-solving ability, classifying it as AGI. We also consider the magnitude of ChatGPT's problem-solving ability within the conceptual space defined by HCFM. Finally, this paper also explores the issue from the perspective of the “collective social brain” hypothesis, which predicts which AI behavior the majority of humans will find to be intelligent, as well as predicting that true machine intelligence lies outside such narrow human definitions of intelligent behavior.
Cyberinformatics tool for in-season crop-specific land cover monitoring: Design, implementation, and applications of iCrop

Cyberinformatics tools have supported decision makings in agriculture through cutting-edge big data, artificial intelligence/machine learning (AI/ML), and high-performance computing technologies. An open and easy-to-use agricultural cyberinformatics tool based on the findable, accessible, interoperable, reusable (FAIR) data principle is essential for the efficient distribution of crop-specific land cover information. This paper introduces iCrop, a new cyberinformatics tool to enable in-season crop type monitoring for the Conterminous United States (CONUS). As a web-based geographic information system (GIS), iCrop not only delivers three sets of new ML-based field-level crop-specific land cover geospatial data, including pre-season crop cover maps, in-season crop cover maps, and Refined Cropland Data Layer (R-CDL), but also provides a suite of mapping and geoprocessing functionalities through the FAIR geospatial data standards, such as Web Map Service (WMS), Web Coverage Service (WCS), and Web Processing Service (WPS). Meanwhile, we outline several use cases to highlight iCrop's applications under various agricultural operation scenarios, its functionality for land use change analysis, and its interoperability with generic web-based and desktop GIS software (e.g., GeoPlatform and QGIS). Our experimental results show that the new cyberinformatics tool can provide timely and unique crop-specific land cover information through the geoprocessing functionalities to facilitate U.S. agricultural information management and decision support. Moreover, this paper can be used as a systematic guidance for the design and implementation of the cyberinformatics tool to disseminate agro-geoinformation based on the FAIR data principle.
Towards Trusted Data Sharing and Exchange in Agro-Food Supply Chains: Design Principles for Agricultural Data Spaces

In the modern agricultural landscape, realizing data’s full potential requires a unified infrastructure where stakeholders collaborate and share their data to gain insights and create business value. The agricultural data ecosystem (ADE) serves as a crucial socio-technical infrastructure, aggregating diverse data from various platforms and, thus, advertising sustainable agriculture and digitalization. Establishing trustworthy data sharing and exchange in agro-food value chains involves socioeconomic and technological elements addressed by the agricultural data space (ADS) and its trust principles. This paper outlines key challenges to data sharing in agro-food chains impeding ADE establishment based on the review of 27 studies in scientific literature. Challenges mainly arise from stakeholders’ mistrust in the data-sharing process, inadequate data access and use policies, and unclear data ownership agreements. In the ADE context, interoperability is a particularly challenging topic for ensuring the long-term sustainability of the system. Considering these challenges and data space principles and building blocks, we propose a set of design principles for ADS design and implementation that aim to mitigate the adverse impact of these challenges and facilitate agricultural data sharing and exchange.
SISTEM PEMANTAUAN PENGGUNAAN PROTOKOL KESEHATAN COVID-19 MENGGUNAKAN METODE HAAR CASCADE DAN NEURAL NETWORK

SISTEM PEMANTAUAN PENGGUNAAN PROTOKOL KESEHATAN COVID-19 MENGGUNAKAN METODE HAAR CASCADE DAN NEURAL NETWORK&#x0D; COVID-19 is the biggest health problem that occurs in the world today, this virus with its rapid spread and strong resistance has claimed many lives. One way to prevent the rapid spread of this virus is to break the chain of its spread, and always complying with the COVID-19 Health protocol. Some examples of the COVID-19 Health Protocol are the use of masks, normal body temperature, always washing hands, and others. However, public awareness of compliance is still very low, so the COVID-19 pandemic has not ended until now. The best solution at this time is to monitor and ensure the use of the COVID-19 Health protocol. However, with the advancement of technology now and minimizing human contact, the author created a MONITORING SYSTEM FOR THE USE OF COVID-19 HEALTH PROTOCOL USING HAAR CASCADE AND NEURAL NETWORK METHODS that use machine learning technology so that there will be no contact between humans. With this tool, the author hopes that it can reduce the spread of COVID-19 and can end it so that all people can return to normal activities without being disturbed by virus disturbances.
The Impact of Using the "Go-Chemist!" App on Students‘ Motivation to Learn Atomic Structure in Chemistry

The purpose of this study is to analyze the effect of using the Go-Chemist! app on student motivation on the topic of atomic structure. In this study, we performed a quasi-experimental design. The sample was 71 tenth-grade students from two intact classes at a public high school in Jakarta, Indonesia. The control group was taught using a printed book, while the experimental group was instructed using the Go-Chemist! application. Student motivation in both groups was then evaluated before and after intervention using the science motivation questionnaire (SMQ). In order to examine the difference and increase in the scores of the two groups, independent and paired sample t-tests were employed. The results reflected that after treatment, students in the experimental group scored higher than students in the control group in terms of motivation. In addition, there was a significant increase in motivation among experimental group students compared to their counterparts. This suggested that the use of the Go-Chemist! is effective in improving students’ motivation in atomic structure. As such, we recommend curriculum developers, policymakers, teachers, and students take advantage of Go-Chemist! app in the teaching and learning of chemistry.