Reviewerly/exHarmony huggingface dataset:
Social capital: Evaluating its roles in competitiveness and ensuring human development

This contribution identifies the features of social capital (SC) development and its relationship with competitiveness based on a two-tier analysis: (1) the relationship of SC with key indicators of competitiveness and human development at the macroeconomic level; (2) the impact of social capital components on financial and non-financial indicators of enterprise competitiveness.The peculiarities of social capital development are revealed using the statistical data of 171 countries, in particular, the value of the social capital sub-index as a constituent of the global sustainability competitiveness index from 2014 to 2020.The differences in social capital development are determined via a cluster analysis performed by means of the software STATISTICA 10.0.As a result, it was uncovered that world countries are highly heterogeneous at the level of social capital development, and the differences, which narrowed slightly in 2017-2019, showed an increase again in 2020.The identified cluster countries, where SC development is the highest, are also among the leaders in terms of competitiveness and human development.Regarding the impact of social capital on competitiveness growth at an enterprise, we found out that it is influenced mainly by the development of relational and cognitive social capital.These findings were obtained as a result of our own survey conducted on a representative sample of enterprise owners and managerial staff in December 2020 -March 2021.
Discovering Language Model Behaviors with Model-Written Evaluations

Ethan Perez, Sam Ringer, Kamile Lukosiute, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, Andy Jones, Anna Chen, Benjamin Mann, Brian Israel, Bryan Seethor, Cameron McKinnon, Christopher Olah, Da Yan, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Guro Khundadze, Jackson Kernion, James Landis, Jamie Kerr, Jared Mueller, Jeeyoon Hyun, Joshua Landau, Kamal Ndousse, Landon Goldberg, Liane Lovitt, Martin Lucas, Michael Sellitto, Miranda Zhang, Neerav Kingsland, Nelson Elhage, Nicholas Joseph, Noemi Mercado, Nova DasSarma, Oliver Rausch, Robin Larson, Sam McCandlish, Scott Johnston, Shauna Kravec, Sheer El Showk, Tamera Lanham, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Jack Clark, Samuel R. Bowman, Amanda Askell, Roger Grosse, Danny Hernandez, Deep Ganguli, Evan Hubinger, Nicholas Schiefer, Jared Kaplan. Findings of the Association for Computational Linguistics: ACL 2023. 2023.
Subscriptions and external links help drive resentful users to alternative and extremist YouTube channels

Do online platforms facilitate the consumption of potentially harmful content? Using paired behavioral and survey data provided by participants recruited from a representative sample in 2020 ( n = 1181), we show that exposure to alternative and extremist channel videos on YouTube is heavily concentrated among a small group of people with high prior levels of gender and racial resentment. These viewers often subscribe to these channels (prompting recommendations to their videos) and follow external links to them. In contrast, nonsubscribers rarely see or follow recommendations to videos from these channels. Our findings suggest that YouTube’s algorithms were not sending people down “rabbit holes” during our observation window in 2020, possibly due to changes that the company made to its recommender system in 2019. However, the platform continues to play a key role in facilitating exposure to content from alternative and extremist channels among dedicated audiences.
Sources of Hallucination by Large Language Models on Inference Tasks

Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization. We present a series of behavioral studies on several LLM families (LLaMA, GPT-3.5, and PaLM) which probe their behavior using controlled experiments. We establish two biases originating from pretraining which predict much of their behavior, and show that these are major sources of hallucination in generative LLMs. First, memorization at the level of sentences: we show that, regardless of the premise, models falsely label NLI test samples as entailing when the hypothesis is attested in training data, and that entities are used as “indices’ to access the memorized data. Second, statistical patterns of usage learned at the level of corpora: we further show a similar effect when the premise predicate is less frequent than that of the hypothesis in the training data, a bias following from previous studies. We demonstrate that LLMs perform significantly worse on NLI test samples which do not conform to these biases than those which do, and we offer these as valuable controls for future LLM evaluation.
On the Difference between the Lexicon and Computation (Regarding Slavic Yers)

This article discusses three concerns regarding Becker and Gouskova’s (2016) analysis of Russian yers that relies on cluster-based yer vocalization and two sublexicons (morphemes with and without yers), to which lexically specific constraints refer. First, it misses the basic generalization about Slavic yers expressed by the established analysis (Lower): yer vocalization is triggered by five different mechanisms, instead of one mechanism under Lower. It is further shown that the major objection against Lower disappears when the existence of final empty nuclei is recognized. Second, Becker and Gouskova confound generalizations about the lexical distribution of yers in morphemes and the computational mechanism that decides which yers appear on the surface. They argue that Lower was established before relevant cluster-based generalizations were discovered, hence misses out on relevant empirical material that invalidates its central idea, that clusters are irrelevant for yer vocalization. However, the phenomena their argument is based on do not concern yer vocalization (computation): they are lexical in kind and therefore confirm the irrelevance of clusters for yer vocalization, supporting Lower. Third, although generalizations about yer-deletion-created clusters are central for Becker and Gouskova’s analysis, they are irrelevant for learners (children or adults). The authors’ experimental evidence precisely shows that speakers are happy to lexicalize and compute sequences (such as yerCC) that are absent from the lexicon. The gaps at hand are thus accidental, rather than systematic.
Detecting insertion, substitution, and deletion errors in radiology reports using neural sequence-to-sequence models

Background: Errors in grammar, spelling, and usage in radiology reports are common. To automatically detect inappropriate insertions, deletions, and substitutions of words in radiology reports, we proposed using a neural sequence-to-sequence (seq2seq) model. Methods: Head CT and chest radiograph reports from Mount Sinai Hospital (MSH) (n=61,722 and 818,978, respectively), Mount Sinai Queens (MSQ) (n=30,145 and 194,309, respectively) and MIMIC-III (n=32,259 and 54,685) were converted into sentences. Insertions, substitutions, and deletions of words were randomly introduced. Seq2seq models were trained using corrupted sentences as input to predict original uncorrupted sentences. Three models were trained using head CTs from MSH, chest radiographs from MSH, and head CTs from all three collections. Model performance was assessed across different sites and modalities. A sample of original, uncorrupted sentences were manually reviewed for any error in syntax, usage, or spelling to estimate real-world proofreading performance of the algorithm. Results: Seq2seq detected 90.3% and 88.2% of corrupted sentences with 97.7% and 98.8% specificity in same-site, same-modality test sets for head CTs and chest radiographs, respectively. Manual review of original, uncorrupted same-site same-modality head CT sentences demonstrated seq2seq positive predictive value (PPV) 0.393 (157/400; 95% CI, 0.346–0.441) and negative predictive value (NPV) 0.986 (789/800; 95% CI, 0.976–0.992) for detecting sentences containing real-world errors, with estimated sensitivity of 0.389 (95% CI, 0.267–0.542) and specificity 0.986 (95% CI, 0.985–0.987) over n=86,211 uncorrupted training examples. Conclusions: Seq2seq models can be highly effective at detecting erroneous insertions, deletions, and substitutions of words in radiology reports. To achieve high performance, these models require site- and modality-specific training examples. Incorporating additional targeted training data could further improve performance in detecting real-world errors in reports.
Energy Optimization Heuristics for Budget-Constrained Workflow in Heterogeneous Computing System

With the rapid development of commercialized computation, the heterogeneous computing system (HCS) has evolved into a new method of service provisioning based on utility computing models, in which the users consume services and resources based on their quality of service requirements. In certain models using the pay-as-you-go concept, the users are charged for accessed services based on their usage. In addition, the commercialized HCS provider also assumes the responsibility to reduce the energy consumption to protect the environment. This paper considers a basic model known as directed acyclic graphs (DAG), which is designed for workflow applications, and investigates heuristics that allows the scheduling of various tasks of a workflow into the dynamic voltage and frequency scaling enabled HCS. The proposed approaches, which are Minimum-Cost-Up-to-Budget (MCUB) and Maximum-Cost-Down-to-Budget (MCDB), could not only satisfy budget constrains but could also optimize overall energy consumption. The approaches along with their variants are implemented and evaluated using four types of basic DAGs. From the experimental results, we conclude that MCDB outperforms MCUB in energy optimization and makespan criterion while meeting budget constraints faced by users.
Impact of Import and Export on GDP of Egypt: Application of ARDL Model

This paper examines the applications of Autoregressive Distributed Lag (ARDL) model in the analysis of Egypt’s import and export data concerning the consequential impacts on the country’s Gross Domestic Product (GDP) between 1980 to 2010 years. Given that exports and imports significantly impact the economic status of Egypt through the balance of payment, it is therefore vital for policymakers to adequately comprehend and understand the factors affecting the revenue of the country’s economy. The study provides a wide range of elasticity estimates made from the data collected in the field. Moreover, this paper seeks to examine the current estimated import and export demand function for Egypt by employing the ARDL approach using the Eviews program. The results from ARDL approach analysis agrees with the hypothesis that Egypt has a long run relationship with export, import demand, economic growth prices of exports and imports, and volatility of an actual effective exchange rate in the market. Therefore, the imports and exports in Egypt are affected by the country’s GDP. Hence it is growth driven.
Sparse Bayesian mass mapping with uncertainties: peak statistics and feature locations

Weak lensing convergence maps - upon which higher order statistics can be calculated - can be recovered from observations of the shear field by solving the lensing inverse problem. For typical surveys this inverse problem is ill-posed (often seriously) leading to substantial uncertainty on the recovered convergence maps. In this paper we propose novel methods for quantifying the Bayesian uncertainty in the location of recovered features and the uncertainty in the cumulative peak statistic - the peak count as a function of signal to noise ratio (SNR). We adopt the sparse hierarchical Bayesian mass-mapping framework developed in previous work, which provides robust reconstructions and principled statistical interpretation of reconstructed convergence maps without the need to assume or impose Gaussianity. We demonstrate our uncertainty quantification techniques on both Bolshoi N-body (cluster scale) and Buzzard V-1.6 (large scale structure) N-body simulations. For the first time, this methodology allows one to recover approximate Bayesian upper and lower limits on the cumulative peak statistic at well defined confidence levels.
Continuation of periodic solutions of various types of delay differential equations using asymptotic numerical method and harmonic balance method

This article presents an extension of the asymptotic numerical method combined with the harmonic balance method to the continuation of periodic orbits of delay differential equations. The equations can be forced or autonomous and possibly of neutral type. The approach developed in this paper requires the system of equations to be written in a quadratic formalism which is detailed. The method is applied to various systems, from Van der Pol and Duffing oscillators to toy models of clarinet and saxophone. The harmonic balance method is ascertained from a comparison to standards time-integration solvers. Bifurcation diagrams are drawn which are sometimes intricate, showing the robustness of this method.
Evaluating the Coordination of Agents in Multi-agent Reinforcement Learning

The present study provides an in-depth analysis of inter-agent coordination through a complete exploration of agent behavioral dimensions. We evaluate the behavioral dimensions in a multi-agent predator-prey pursuit task where predator agent coordination necessarily exists due to a shared goal. We explore two conditions, one that is void of explicit coordination (fixed-strategy), and one that has the potential for explicit coordination (learning agents). This comprehensive evaluation of multi-agent behavioral dimensions provides theoretical evidence for true inter-agent coordination by a learning algorithm and the behavioral dimensions that agents coordinate in a cooperative task.
Paraxial focal length measurement method with a simple apparatus

In this paper, we realize an accurate and flexible measurement method for paraxial focal length with a simple apparatus. The proposed method utilizes the established measurement principle which is based on the differentiation of the Gaussian optical formula and geometrical relationship of camera-lens system to obtain the focal length of a lens. Some image processing methods are employed to experimentally realize this measurement principle with high accuracy and practicability. Image sharpness evaluation function is utilized to ensure the system conforms to the Gaussian optical formula. System calibration is adopted to compensate for the errors caused by the simple apparatus. In addition, aberration is considered to obtain a paraxial focal length of a lens. Experimental results indicate that repeatability error of measurement system is less than 0.11% for most lenses.
Very Low Bitrate Semantic Compression of Airplane Cockpit Screen Content

This paper addresses the problem of encoding the video generated by the screen of an airplane cockpit. As other computer screens, cockpit screens consist of computer-generated graphics often atop a natural background. Existing screen content coding schemes fail notably in preserving the readability of textual information at the low bitrates required in avionic applications. We propose a screen coding scheme where textual information is encoded according to the relative semantics rather than in the pixel domain. The encoder localizes textual information, and the semantics of each character are extracted with a convolutional neural network and predictively encoded. Text is then removed via inpainting, and the residual background video is compressed with a standard codec and transmitted to the receiver together with the text semantics. At the decoder side, text is synthesized using the decoded semantics and superimposed over the decoded residual video recovering the original frame. Our proposed scheme offers two key advantages over a semantics-unaware scheme that encodes text in the pixel domain. First, the text readability at the decoder is not compromised by compression artifacts, whereas the relative bitrate is negligible. Second, removal of high-frequency transform coefficients associated with the inpainted text drastically reduces the bitrate of the residual video. Experiments with real cockpit video sequences show BD-rate gains up to 82% and 69% over a reference H.265/HEVC encoder and its screen content coding extension. Moreover, our scheme achieves quasi-errorless character recognition already at very low bitrates, whereas even HEVC-SCC needs at least three or four times more bitrate to achieve a comparable error rate.
USING GIS AND PHOTOGRAMMETRY FOR ASSESSING SOLAR PHOTOVOLTAIC POTENTIAL ON FLAT ROOFS IN URBAN AREA CASE OF THE CITY OF BEN GUERIR / MOROCCO

Abstract. Renewable energy sources are at the forefront of political discussions around the world because of the scarcity of fossil fuels and climate change caused by the accumulation of greenhouse gases. By 2030, Morocco will cover 52% of these energy needs through renewable energies, in order to preserve the environment (COP 22). This paper aims to estimate the potential of photovoltaic solar energy from flat roofs in the city of Ben Guerir, Morocco using remote sensing and GIS data. To achieve this goal, vector orthophoto resulting from the photogrammetric restitution acquired in 2015 were used to generate a 3D model (DSM). The annual solar irradiation is calculated by the analyser of the solar tool. Each roof is calculated based on algorithms for the most common solar panel technologies (mono-si and poly-si). The applicability of this methodology has been demonstrated in the urban area of Benguerir, Morocco, and can be widespread in any other region of the world. The results obtained for a total roofing surface of 135 Ha, i.e. more than 345 Gwh of electricity annually generate. For an average roof of 60 m2 that could supply 5 to 6 households; A planned investment between 118,218 and 167,296 DH, and an annual maintenance charge of 2%. This study may be an initial assessment of solar potential in the city, which can be used to support the management decision regarding investment in the urban solar system.
Fighting Substance Dependency Combining AAT Therapy and Virtual Reality with Game Design Elements

Smoking poses a significant health risk and is still the main cause of premature mortality today. The Approach Avoidance Task (AAT) developed by Rinck and Becker aims to develop a substance dependence therapy that can reach the digital society. In this paper, a demonstrator that transfers the AAT procedure into virtual reality (VR) is presented. This demonstrator was used to carry out an evaluation with twenty participants who were asked to use the program and evaluate it by means of questionnaires and interviews. In addition, the reaction times (RTs) of the test persons were recorded and evaluated. The results show that the transfer of the AAT procedure to VR is possible and promising. Above all, the use of three-dimensional scenarios and objects, with which one interacts during the training, were well received and increased the immersion as well as the felt embodiment. The use of game design elements has also proved helpful and has had a positive influence on user motivation.
A fast and memory-efficient two-pass connected-component labeling algorithm for binary images

Connected-component labeling is an important process in image analysis and pattern recognition. It aims to deduct the connected components by giving a unique label value for each individual component. Many algorithms have been proposed, but they still face several problems such as slow execution time, falling in the pipeline, requiring a huge amount of memory with high resolution, being noisy, and giving irregular images. In this work, a fast and memory- efficient connected-component labeling algorithm for binary images is proposed. The proposed algorithm is based on a new run-base tracing method with a new resolving process to find the final equivalent label values. A set of experiments were conducted on different types of binary images. The proposed algorithm showed high performance compared to the other algorithms.
Building the first comprehensive machine-readable Turkish sign language resource: methods, challenges and solutions

This article describes the procedures employed during the development of the first comprehensive machine-readable Turkish Sign Language (TiD) resource: a bilingual lexical database and a parallel corpus between Turkish and TiD. In addition to sign language specific annotations (such as non-manual markers, classifiers and buoys) following the recently introduced TiD knowledge representation (Eryiğit et al. 2016), the parallel corpus contains also annotations of dependency relations, which makes it the first parallel treebank between a sign language and an auditory-vocal language.
Improved PHARM for JPEG Steganalysis: Making PHARM More Efficient and Effective

The PHase-Aware pRojection Model (PHARM) feature set, built as phase-aware histograms of quantized projections obtained by convolving residuals with random matrices, achieves competitive detection performance against modern adaptive JPEG steganography while having significant computational cost. In this paper, we propose three improvements to the original PHARM, making it more efficient and effective. First, we reduce the maximum projection matrix size to decrease the computational complexity of convolution and better capture steganographic embedding changes. Second, we select more than one phase pair per projection to compute phase-aware histograms, thus correspondingly reducing the number of projections for each residual. Third, the transposition symmetry is also taken into consideration to make our features more robust while preserving the feature dimensionality. The numerous experiments are given to demonstrate the efficiency and effectiveness of our improved PHARM.
'My guardian did not approve!'

column Share on 'My guardian did not approve!': stories from fieldwork in Saudi Arabia Authors: Soud Nassir University of Technology Sydney University of Technology SydneyView Profile , Adel Al-Dawood University of Minnesota University of MinnesotaView Profile , Elham Alghamdi Dalhousie University Dalhousie UniversityView Profile , Eman Alyami Dalhousie University Dalhousie UniversityView Profile Authors Info & Claims InteractionsVolume 26Issue 3May - June 2019 pp 44–49https://doi.org/10.1145/3318145Published:23 April 2019Publication History 4citation999DownloadsMetricsTotal Citations4Total Downloads999Last 12 Months233Last 6 weeks32 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Strategies for Inclusion in the Design of Pervasive Computing for Health and Wellbeing

Participatory design has become a popular method in pervasive computing for health and wellbeing. Involving end users as participants in the design of a technology helps to ensure feasibility, acceptability, and usability—key factors that promote the uptake of pervasive computing for improving health. These factors are particularly important for emerging technologies, as we are still learning how they fit into users’ lives. Pervasive computing researchers have a unique opportunity to include many voices in influencing the design and implementation of emerging technologies. Approaching the conceptualization of pervasive systems, applications,