Miracle-dz/UniFAR-FTUs huggingface dataset:
Raising the Barcode Scanner: Technology and Productivity in the Retail Sector

Barcodes and barcode scanners transformed the grocery industry in the 1970s. I use store-level data from the 1972, 1977, and 1982 Census of Retail Trade, matched to data on store scanner installations, to estimate scanners' effect on labor productivity. I find that scanners increased a store's labor productivity, on average, by approximately 4.5 percent in the first few years. The effect was larger in stores carrying more packaged products, consistent with the presence of network externalities. Short-run gains were small relative to fixed costs, suggesting that the impediment to widespread adoption of the new technology was profitability, not coordination problems. (JEL J24, L24, L81, O33)(This abstract was borrowed from another version of this item.)
Air Quality Status During Pandemic Covid19 in Urban and Sub-Urban Area in Malaysia

This paper discussed the API status in the urban and sub-urban cities in Malaysia before, during and after Movement Control Order (MCO) was implemented in year the 2020. The lockdown implemented worldwide including Malaysia has come as a respite to the environment, especially in air pollutant levels. 2 cities have been chosen in this study that is Kangar (suburban) and Cheras (urban). The Air Pollutant Index (API) data recorded on an hourly basis throughout different series of national lockdown phases was obtained from the Malaysian Air Pollutant Index website. The findings show that the API status quality in sub-urban does not change much compare to the urban area. But it can be seen that the API reduces during MCO implementation in the sub-urban and urban areas. This is because, during the MCO, there are several restrictions on mass movement and gatherings, and the closure of educational institutions, government and private agencies (except for essential services). This MCO restriction reduced the traffic density, industrial activities, and other social activities.
Phytochemical screening of Malaysian Dipterocarpaceae species from Kuala Keniam, Taman Negara Pahang

The diverse family of Dipterocarpaceae is well-known for its timber as well as the phytochemical constituents which possess medicinal properties. In this preliminary study, 10 Dipterocarpaceae species namely Anisoptera laevis, Dipterocarpus crinitus, Dipterocarpus gracilis, Hopea nervosa, Hopea pubescens, Shorea hopeifolia, Shorea ovalis, Shorea parviflora, Vatica bella and Vatica maingayi were collected from Kuala Keniam, Taman Negara Pahang, Malaysia. The selected parts of the species were macerated in methanol and phytochemically screened to determine the presence of alkaloids, flavonoids, glycosides, saponins, tannins and terpenoids. The results revealed that all crude methanolic extracts were found to contain tannins and terpenoids. Additionally, the crude extracts from the stem bark of D. gracilis and the leaves of H. pubescens were observed to contain alkaloids, flavonoids, reducing sugar, saponins, tannins as well as terpenoids. Further studies on all species are recommended in search for the bioactive chemical constituents that might be beneficial in the development of drug discovery.
Recent advances in wearable exoskeletons for human strength augmentation

The decline in muscular strength and control due to age or stroke-related side-effect has afflicted many individuals with neuromotor disorders because it affects essential motor functions to perform everyday activities and restrains their functional independence. In this regard, a myriad of wearable exoskeletons and functional components have been developed to deliver mechanical force for assisting the movements of different human body parts. However, many of the reported wearable exoskeletons suffer from several critical drawbacks that limit functional usage and practicality despite the significant technological advance of general wearable exoskeletons. Here, this review offers a comprehensive summary of the recent advances of wearable exoskeletons and their constituting functional components. In addition, we discuss the essential challenges that need to be tackled to enhance the functional practicality of the next-generation wearable exoskeletons in assisting the strength and control of individuals with neuromotor disorders.
Profile Analysis of Elementary School Students’ Smart Device Usage

Smart devices have a variety of affordances to foster meaningful learning in elementary school. For the design of smart learning environments, more research is needed to understand students’ smart device usage and their perception of learning with smart devices. In order to capture smart device usage profiles among elementary school students in South Korea, this study carried out Latent Profile Analysis with three constructs: information search, communication, and study. Participants (n=253), who ranged from the fourth to the sixth grade students, were classified into three profiles of smart device usage: low-activity, communication, and high-activity groups. The smart device usage profiles varied depending on smartphone usage experience, and the profiles were significantly related with smart device addiction, not with smart device usage ability. Perceptions of smart education were also significantly associated with the profiles. The high-activity group showed more positive attitudes toward smart education than the others, but no significant difference was found in regard to negative attitudes. Based on the findings, this study discussed implications for the use of smart devices in elementary school.
MODELO QUE RELACIONA EL CAPITAL INTELECTUAL Y LA INNOVACIÓN TECNOLÓGICA EN EMPRESAS DE MANUFACTURA MEXICANAS MODEL RELATING INTELLECTUAL CAPITAL AND TECHNOLOGICAL INNOVATION IN MEXICAN MANUFACTURINGCOMPANIES

In the last forty years the Mexican manufacturing companies have remained stagnant due to their technological shortcomings, they are based more on the mechanical work instead their Intellectual Capital (IC) and they dont have no clear the relationship with the Technological Innovation process (IT). The objective of this research was to find the relationship between the two variables under the assumption that it is high, by analyzing different models of both variables, determining the dimensions and indicators that were used at the instrument applied at manufacturing companies, found a positive correlation of 0.76. With this we propose a model that relates the variables and dimensions of IC and IT.
Enhancing Financial Performance Management of Public Education in the Light of Balanced Scorecard (BSC)

This study aimed at designing a suggested model for enhancing the management of financial performance for public education in the light of a balanced scoreboard. It also aimed at investigating the requirements of implementing the balanced scoreboard in public education and identifying the barriers that hinder its implementation. To achieve the aim of the study, the analytic descriptive method was used. It included reviewing literature and bibliographical survey using the internet and databases on what was written on Balanced Scoreboard (BSC), its dimensions and effect on enhancing the performance especially at the educational institutions in addition to the survey of the field which included the study instrument, the interview, and manipulating its data and analyzing it. of financial follow-up, encouraging the leaders in the educational institutions to exchange their successful experiences in the area of mechanisms and styles of enhancing the management of the financial performance, and developing managerial systems that support the activation of expenditure rationalization and raising its efficiency of achieving the financial dimension in BSC.
Inferential Tasks as an Evaluation Technique for Visualization

Designing suitable tasks for visualization evaluation remains challenging. Traditional evaluation techniques commonly rely on 'low-level' or 'open-ended' tasks to assess the efficacy of a proposed visualization, however, nontrivial trade-offs exist between the two. Low-level tasks allow for robust quantitative evaluations, but are not indicative of the complex usage of a visualization. Open-ended tasks, while excellent for insight-based evaluations, are typically unstructured and require time-consuming interviews. Bridging this gap, we propose inferential tasks: a complementary task category based on inferential learning in psychology. Inferential tasks produce quantitative evaluation data in which users are prompted to form and validate their own findings with a visualization. We demonstrate the use of inferential tasks through a validation experiment on two well-known visualization tools.
How Does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval

Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval. This assumption however is often not correct: some or even all of the feedback documents may be irrelevant. Indeed, the effectiveness of PRF methods may well depend on the quality of the feedback signal and thus on the effectiveness of the first-stage ranker. This aspect however has received little attention before. In this paper we control the quality of the feedback signal and measure its impact on a range of PRF methods, including traditional bag-of-words methods (Rocchio), and dense vector-based methods (learnt and not learnt). Our results show the important role the quality of the feedback signal plays on the effectiveness of PRF methods. Importantly, and surprisingly, our analysis reveals that not all PRF methods are the same when dealing with feedback signals of varying quality. These findings are critical to gain a better understanding of the PRF methods and of which and when they should be used, depending on the feedback signal quality, and set the basis for future research in this area.
Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation

News recommendation for anonymous readers is a useful but challenging task for many news portals, where interactions between readers and articles are limited within a temporary login session. Previous works tend to formulate session-based recommendation as a next item prediction task, while they neglect the implicit feedback from user behaviors, which indicates what users really like or dislike. Hence, we propose a comprehensive framework to model user behaviors through positive feedback (i.e., the articles they spend more time on) and negative feedback (i.e., the articles they choose to skip without clicking in). Moreover, the framework implicitly models the user using their session start time, and the article using its initial publishing time, in what we call neutral feedback. Empirical evaluation on three real-world news datasets shows the framework's promising performance of more accurate, diverse and even unexpectedness recommendations than other state-of-the-art session-based recommendation approaches.
Efficient Antenna Optimization Using a Hybrid of Evolutionary Programing and Particle Swarm Optimization

In this paper, we present a hybrid of Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) algorithms for numerically efficient global optimization of antenna arrays and metasurfaces. The hybrid EP-PSO algorithm uses an evolutionary optimization approach that incorporates swarm directions in the standard self-adaptive EP algorithm. As examples, we have applied this hybrid technique to two antenna problems: the side-lobe-level reduction of a non-uniform spaced (aperiodic) linear array and the beam shaping of a printed antenna loaded with a partially reflective metasurface. Detailed comparisons between the proposed hybrid EP-PSO technique and EP-only and PSO-only techniques are given, demonstrating the efficiency of this hybrid technique in the complex antenna design problems.
Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning

Precision robotic manipulation tasks (insertion, screwing, precisely pick, precisely place) are required in many scenarios. Previous methods achieved good performance on such manipulation tasks. However, such methods typically require tedious calibration or expen-sive sensors. 3D/RGB-D cameras and torque/force sensors add to the cost of the robotic application and may not always be economical. In this work, we aim to solve these but using only weak-calibrated and low-cost webcams. We propose Binocular Alignment Learning (BAL), which could automatically learn the eye-hand coordination and points alignment capabilities to solve the four tasks. Our work focuses on working with unknown eye-hand coordination and proposes diﬀerent ways of performing eye-in-hand camera calibration automatically. The algorithm was trained in simulation and used a practical pipeline to achieve sim2real and test it on the real robot. Our method achieves a competi-tively good result with minimal cost on the four tasks.
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization

Neural abstractive summarization models are prone to generate summaries that are factually inconsistent with their source documents. Previous work has introduced the task of recognizing such factual inconsistency as a downstream application of natural language inference (NLI). However, state-of-the-art NLI models perform poorly in this context due to their inability to generalize to the target task. In this work, we show that NLI models can be effective for this task when the training data is augmented with high-quality task-oriented examples. We introduce Falsesum, a data generation pipeline leveraging a controllable text generation model to perturb human-annotated summaries, introducing varying types of factual inconsistencies. Unlike previously introduced document-level NLI datasets, our generated dataset contains examples that are diverse and inconsistent yet plausible. We show that models trained on a Falsesum-augmented NLI dataset improve the state-of-the-art performance across four benchmarks for detecting factual inconsistency in summarization.
A Survey of Risk-Aware Multi-Armed Bandits

In several applications such as clinical trials and financial portfolio optimization, the expected value (or the average reward) does not satisfactorily capture the merits of a drug or a portfolio. In such applications, risk plays a crucial role, and a risk-aware performance measure is preferable, so as to capture losses in the case of adverse events. This survey aims to consolidate and summarise the existing research on risk measures, specifically in the context of multi-armed bandits. We review various risk measures of interest, and comment on their properties. Next, we review existing concentration inequalities for various risk measures. Then, we proceed to defining risk-aware bandit problems, We consider algorithms for the regret minimization setting, where the exploration-exploitation tradeoff manifests, as well as the best arm identification setting, which is a pure exploration problem—both in the context of risk-sensitive measures. We conclude by commenting on persisting challenges and fertile areas for future research.
Diverse Video Generation from a Single Video

GANs are able to perform generation and manipulation tasks, trained on a single video. However, these single video GANs require unreasonable amount of time to train on a single video, rendering them almost impractical. In this paper we question the necessity of a GAN for generation from a single video, and introduce a non-parametric baseline for a variety of generation and manipulation tasks. We revive classical space-time patches-nearest-neighbors approaches and adapt them to a scalable unconditional generative model, without any learning. This simple baseline surprisingly outperforms single-video GANs in visual quality and realism (confirmed by quantitative and qualitative evaluations), and is disproportionately faster (runtime reduced from several days to seconds). Our approach is easily scaled to Full-HD videos. We also use the same framework to demonstrate video analogies and spatio-temporal retargeting. These observations show that classical approaches significantly outperform heavy deep learning machinery for these tasks. This sets a new baseline for single-video generation and manipulation tasks, and no less important -- makes diverse generation from a single video practically possible for the first time.
Coronal and Chromospheric Emission in A-type Stars

Cool stars on the main sequence generate X-rays from coronal activity, powered by a convective dynamo. With increasing temperature, the convective envelope becomes smaller and X-ray emission fainter. We present Chandra/HRC-I observations of four single stars with early A spectral types. Only the coolest star of this sample, τ 3 Eri (T eff ≈ 8, 000 K), is detected with log(LX/Lbol)=−7.6 while the three hotter stars (T eff ≥ 8, 000 K), namely δ Leo, β Leo, and ι Cen, remain undetected with upper limits log(LX/Lbol)<−8.4 . The drop in X-ray emission thus occurs in a narrow range of effective temperatures around ∼8100 K and matches the drop of activity in the C iii and O vi transition region lines.
Gravitational waves from neutrino mass genesis

The discovery of gravitational waves opens new opportunities to test BSM physics. In particular, the production of a stochastic background of primordial gravitational waves could provide a signature of the generation of the right-right Majorana neutrino mass term necessary, within type-I seesaw mechanism, to explain lightness of neutrinos and their mixing parameters. I will discuss the possibility that such a generation occurs during a strong first order phase transition within Majoron models [1]. As well known, this can indeed produce a stochastic background of gravitational waves. The scale of the phase transition can or cannot coincide with the seesaw scale. In the latter case a low scale phase transition, occurring in the pre-recombination era, might be tested at very low frequencies (10−9–10−6 Hz). Even though the signal can hardly reproduce the NANOGrac putative signal such new physics at low scale might help ameliorating the tensions in the CDM cosmological model (e.g., the Hubble tension). I will also discuss how a phase transition might be responsible for the generation of dark matter in the form of dark neutrinos coupling to the seesaw neutrinos via Higgs induced right handed-right handed neutrino mixing [2].
Emotion-Centric Requirements Change Handling in Software Engineering

Background: Requirements Changes (RCs) -- the additions/modifications/deletions of functional/non-functional requirements in software products -- are challenging for software practitioners to handle. Handling some changes may significantly impact the emotions of the practitioners. Objective: We wanted to know the key challenges that make RC handling difficult, how these impact the emotions of software practitioners, what influences their RC handling, and how RC handling can be made less emotionally challenging. Method: We followed a mixed-methods approach. We conducted two survey studies, with 40 participants and 201 participants respectively. The presentation of key quantitative data was followed by descriptive statistical analysis, and the qualitative data was analysed using Strauss-Corbinian Grounded Theory, and Socio-Technical Grounded Theory analysis techniques. Findings:We found (1) several key factors that make RC handling an emotional challenge, (2) varying emotions that practitioners feel when it is challenging to handle RCs, (3) how stakeholders, including practitioners themselves, peers, managers and customers, influence the RC handling and how practitioners feel due to the stakeholder influence, and (4) practices that can be used to better handle RCs. Conclusion: Some challenges are technical and some are social which also belong to aspects of agile practice, emotional intelligence, and cognitive intelligence. Therefore, to better handle RCs with positive emotions in socio-technical environments, agility, emotional intelligence, and cognitive intelligence need to cooperate with each other.
S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization

Camera relocalization is the key component of simultaneous localization and mapping (SLAM) systems. This paper proposes a learning-based approach, named Sparse Spatial Scene Embedding with Graph Neural Networks (S3E-GNN), as an end-to-end framework for efficient and robust camera relocalization. S3E-GNN consists of two modules. In the encoding module, a trained S3E network encodes RGB images into embedding codes to implicitly represent spatial and semantic embedding code. With embedding codes and the associated poses obtained from a SLAM system, each image is represented as a graph node in a pose graph. In the GNN query module, the pose graph is transformed to form a embedding-aggregated reference graph for camera relocalization. We collect various scene datasets in the challenging environments to perform experiments. Our results demonstrate that S3E-GNN method outperforms the traditional Bag-of-words (BoW) for camera relocalization due to learning-based embedding and GNN powered scene matching mechanism.
NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension

NER has been traditionally formulated as a sequence labeling task. However, there has been recent trend in posing NER as a machine reading comprehension task (Wang et al., 2020; Mengge et al., 2020), where entity name (or other information) is considered as a question, text as the context and entity value in text as answer snippet. These works consider MRC based on a single question (entity) at a time. We propose posing NER as a multi-question MRC task, where multiple questions (one question per entity) are considered at the same time for a single text. We propose a novel BERT-based multi-question MRC (NER-MQMRC) architecture for this formulation. NER-MQMRC architecture considers all entities as input to BERT for learning token embeddings with self-attention and leverages BERT-based entity representation for further improving these token embeddings for NER task. Evaluation on three NER datasets show that our proposed architecture leads to average 2.5 times faster training and 2.3 times faster inference as compared to NER-SQMRC framework based models by considering all entities together in a single pass. Further, we show that our model performance does not degrade compared to single-question based MRC (NER-SQMRC) (Devlin et al., 2019) leading to F1 gain of +0.41%, +0.32% and +0.27% for AE-Pub, Ecommerce5PT and Twitter datasets respectively. We propose this architecture primarily to solve large scale e-commerce attribute (or entity) extraction from unstructured text of a magnitude of 50k+ attributes to be extracted on a scalable production environment with high performance and optimised training and inference runtimes.