Reviewerly/exHarmony huggingface dataset:
The Way We Type Reveals Our Native Language

Knowing some characteristics of an unknown user is useful information for security and commercial purposes. One of the acquired characteristics is the user’s native language, and its recognition can be achieved with data derived from the text he/she types, since text is the most widespread means of communication between Internet users. Keystroke dynamics, which leverages data derived from how user types, ensures that no sensitive data are leaked. In this work, data from the daily typing of users of five different native languages are collected, keystroke dynamics features are extracted, the most suitable ones are selected using a feature selection algorithm, well-known machine learning models and a boosting algorithm are used, and a rate of correct prediction that exceeds 90% is achieved. Knowing a user’s native language can help strengthen authentication systems, make better use of online services, and protect unsuspecting users from falling victim to fraud.
La ricezione della linguistica strutturale in Italia

The reception of structuralism by Italian linguists can be divided into three periods. In the first of them (roughly, from the 1930s to the end of the 1950s), structural linguistics, while by no means ignored, is essentially rejected (with some exceptions, such as G. Contini or L. Heilmann). The second period roughly coincides with the 1960s, when structural linguistics reaches the height of its success, thanks, primarily, to the work of scholars such as T. De Mauro, G. Lepschy and L. Rosiello. The success of structural linguistics in Italy began to decline from the 1970s onwards: various linguists turned to other conceptual frameworks, such as generative grammar. Some possible reasons for this decline are investigated here.
Vision-Based Virtual Simulation Platform for Planetary Rovers

In order to evaluate the feasibility of planetary exploration missions, it is imperative to construct planetary terrain environments on the ground. Nevertheless, the implementation of this method is characterized by time-consuming and challenging factors. By contrast, resorting to simulation approaches represents a cost-effective and high-efficiency alternative, which can facilitate the simulation validation of planetary rover exploration missions in an effective manner. In this paper, a modular planet rover simulation platform is proposed; by building and overlaying multiple feature layers corresponding to the surface of the planet, we realize the simulation of high-resolution fine terrain and through adjusting the terrain parameters to meet the needs of different simulated terrain. The experimental results show that we have built a scene to satisfy the requirements of visual effects and physically realistic characteristics of simulation. Using an improved PatchMatch stereomethod to recover images captured real time by a planetary rover navigation camera in a virtual environment provided more complete three-dimensional terrain data for subsequent simulation validation of local path planning. Finally, a simulation environment that combines high-fidelity visual effects and kinematic characteristics supports visualizing the simulation platform: we propose a path planning method using global planning combined with local obstacle avoidance, and we obtain the optimal path that satisfies the kinematic constraints.
Aligning Large Language Models for Clinical Tasks

Large Language Models (LLMs) have demonstrated remarkable adaptability, showcasing their capacity to excel in tasks for which they were not explicitly trained. However, despite their impressive natural language processing (NLP) capabilities, effective alignment of LLMs remains a crucial challenge when deploying them for specific clinical applications. The ability to generate responses with factually accurate content and to engage in non-trivial reasoning steps are crucial for the LLMs to be eligible for applications in clinical medicine. Employing a combination of techniques including instruction-tuning and in-prompt strategies like few-shot and chain-of-thought prompting has significantly enhanced the performance of LLMs. Our proposed alignment strategy for medical question-answering, known as 'expand-guess-refine', offers a parameter and data-efficient solution. A preliminary analysis of this method demonstrated outstanding performance, achieving a score of 70.63% on a subset of questions sourced from the USMLE dataset.
Uncertainty in AI: Evaluating Deep Neural Networks on Out-of-Distribution Images

As AI models are increasingly deployed in critical applications, ensuring the consistent performance of models when exposed to unusual situations such as out-of-distribution (OOD) or perturbed data, is important. Therefore, this paper investigates the uncertainty of various deep neural networks, including ResNet-50, VGG16, DenseNet121, AlexNet, and GoogleNet, when dealing with such data. Our approach includes three experiments. First, we used the pretrained models to classify OOD images generated via DALL-E to assess their performance. Second, we built an ensemble from the models' predictions using probabilistic averaging for consensus due to its advantages over plurality or majority voting. The ensemble's uncertainty was quantified using average probabilities, variance, and entropy metrics. Our results showed that while ResNet-50 was the most accurate single model for OOD images, the ensemble performed even better, correctly classifying all images. Third, we tested model robustness by adding perturbations (filters, rotations, etc.) to new epistemic images from DALL-E or real-world captures. ResNet-50 was chosen for this being the best performing model. While it classified 4 out of 5 unperturbed images correctly, it misclassified all of them post-perturbation, indicating a significant vulnerability. These misclassifications, which are clear to human observers, highlight AI models' limitations. Using saliency maps, we identified regions of the images that the model considered important for their decisions.
Augmenting Chest X-ray Datasets with Non-Expert Annotations

The advancement of machine learning algorithms in medical image analysis requires the expansion of training datasets. A popular and cost-effective approach is automated annotation extraction from free-text medical reports, primarily due to the high costs associated with expert clinicians annotating chest X-ray images. However, it has been shown that the resulting datasets are susceptible to biases and shortcuts. Another strategy to increase the size of a dataset is crowdsourcing, a widely adopted practice in general computer vision with some success in medical image analysis. In a similar vein to crowdsourcing, we enhance two publicly available chest X-ray datasets by incorporating non-expert annotations. However, instead of using diagnostic labels, we annotate shortcuts in the form of tubes. We collect 3.5k chest drain annotations for CXR14, and 1k annotations for 4 different tube types in PadChest. We train a chest drain detector with the non-expert annotations that generalizes well to expert labels. Moreover, we compare our annotations to those provided by experts and show "moderate" to "almost perfect" agreement. Finally, we present a pathology agreement study to raise awareness about ground truth annotations. We make our annotations and code available.
The Integration of Stand Alone Photovoltaic Systems in the Development of the Residential Buildings in Al Burullus Graduates Villages

This paper suggests a technical methodology to regenerate the Egyptian Hinterlands of the Graduates’ villages at Al-Burullus Lake into sustainable neighborhoods. The aim of the research is to develop self-sufficient economic housing prototype. The study defines energy potentials and examines the future feasibility for renewable energy integration into rural communities. It aims to develop reliable Stand-Alone Photovoltaic (SAPV) system to residential buildings. The study uses simulation software to develop the reliable SAPV system as well as predicting the yearly energy output. The simulation of the SAPV system configuration is presented using the PVSYST tools software. The simulation defines the total amount of electrical energy generated by PV array supplied to the determined load by users, the various types of power losses, the optimal size of SAPV system configuration, and the total energy flow through the whole year - The study recommends the implementation stages of PV systems in developing countries and addresses their impact on research, education, community, industry, and other governmental sectors in Egypt.
Optimizing the Size of Subword Vocabularies in Dialect Classification

Pre-trained models usually come with a pre-defined tokenization and little flexibility as to what subword tokens can be used in downstream tasks. This problem concerns especially multilingual NLP and low-resource languages, which are typically processed using cross-lingual transfer. In this paper, we aim to find out if the right granularity of tokenization is helpful for a text classification task, namely dialect classification. Aiming at generalizations beyond the studied cases, we look for the optimal granularity in four dialect datasets, two with relatively consistent writing (one Arabic and one Indo-Aryan set) and two with considerably inconsistent writing (one Arabic and one Swiss German set). To gain more control over subword tokenization and ensure direct comparability in the experimental settings, we train a CNN classifier from scratch comparing two subword tokenization methods (Unigram model and BPE). For reference, we compare the results obtained in our analysis to the state of the art achieved by fine-tuning pre-trained models. We show that models trained from scratch with an optimal tokenization level perform better than fine-tuned classifiers in the case of highly inconsistent writing. In the case of relatively consistent writing, fine-tuned models remain better regardless of the tokenization level.
Empathy Identification Systems are not Accurately Accounting for Context

Understanding empathy in text dialogue data is a difficult, yet critical, skill for effective human-machine interaction. In this work, we ask whether systems are making meaningful progress on this challenge. We consider a simple model that checks if an input utterance is similar to a small set of empathetic examples. Crucially, the model does not look at what the utterance is a response to, i.e., the dialogue context. This model performs comparably to other work on standard benchmarks and even outperforms state-of-the-art models for empathetic rationale extraction by 16.7 points on T-F1 and 4.3 on IOU-F1. This indicates that current systems rely on the surface form of the response, rather than whether it is suitable in context. To confirm this, we create examples with dialogue contexts that change the interpretation of the response and show that current systems continue to label utterances as empathetic. We discuss the implications of our findings, including improvements for empathetic benchmarks and how our model can be an informative baseline.
CylE: Cylinder Embeddings for Multi-hop Reasoning over Knowledge Graphs

Recent geometric-based approaches have been shown to efficiently model complex logical queries (including the intersection operation) over Knowledge Graphs based on the natural representation of Venn diagram. Existing geometric-based models (using points, boxes embeddings), however, cannot handle the logical negation operation. Further, those using cones embeddings are limited to representing queries by two-dimensional shapes, which reduced their effectiveness in capturing entities query relations for correct answers. To overcome this challenge, we propose unbounded cylinder embeddings (namely CylE), which is a novel geometric-based model based on three-dimensional shapes. Our approach can handle a complete set of basic first-order logic operations (conjunctions, disjunctions and negations). CylE considers queries as Cartesian products of unbounded sector-cylinders and consider a set of nearest boxes corresponds to the set of answer entities. Precisely, the conjunctions can be represented via the intersections of unbounded sector-cylinders. Transforming queries to Disjunctive Normal Form can handle queries with disjunctions. The negations can be represented by considering the closure of complement for an arbitrary unbounded sector-cylinder. Empirical results show that the performance of multi-hop reasoning task using CylE significantly increases over state-of-the-art geometric-based query embedding models for queries without negation. For queries with negation operations, though the performance is on a par with the best performing geometric-based model, CylE significantly outperforms a recent distribution-based model.
Exploiting Summarization Data to Help Text Simplification

One of the major problems with text simplification is the lack of high-quality data. The sources of simplification datasets are limited to Wikipedia and Newsela, restricting further development of this field. In this paper, we analyzed the similarity between text summarization and text simplification and exploited summarization data to help simplify. First, we proposed an alignment algorithm to extract sentence pairs from summarization datasets. Then, we designed four attributes to characterize the degree of simplification and proposed a method to filter suitable pairs. We named these pairs Sum4Simp (S4S). Next, we conducted human evaluations to show that S4S is high-quality and compared it with a real simplification dataset. Finally, we conducted experiments to illustrate that the S4S can improve the performance of several mainstream simplification models, especially in low-resource scenarios.
TextWorldExpress: Simulating Text Games at One Million Steps Per Second

Text-based games offer a challenging test bed to evaluate virtual agents at language understanding, multi-step problem-solving, and common-sense reasoning. However, speed is a major limitation of current text-based games, capping at 300 steps per second, mainly due to the use of legacy tooling. In this work we present TextWorldExpress, a high-performance simulator that includes implementations of three common text game benchmarks that increases simulation throughput by approximately three orders of magnitude, reaching over one million steps per second on common desktop hardware. This significantly reduces experiment runtime, enabling billion-step-scale experiments in about one day.
Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement

Conversational Question Answering (ConvQA) models aim at answering a question with its relevant paragraph and previous question-answer pairs that occurred during conversation multiple times. To apply such models to a real-world scenario, some existing work uses predicted answers, instead of unavailable ground-truth answers, as the conversation history for inference. However, since these models usually predict wrong answers, using all the predictions without filtering significantly hampers the model performance. To address this problem, we propose to filter out inaccurate answers in the conversation history based on their estimated confidences and uncertainties from the ConvQA model, without making any architectural changes. Moreover, to make the confidence and uncertainty values more reliable, we propose to further calibrate them, thereby smoothing the model predictions. We validate our models, Answer Selection-based realistic Conversation Question Answering, on two standard ConvQA datasets, and the results show that our models significantly outperform relevant baselines. Code is available at: https://github.com/starsuzi/AS-ConvQA.
Assessing the Effectiveness of Government Communication on Public Policy in Ghana: The Case of Planting for Food and Jobs Policy

This paper examined the effectiveness of government communication on public policy in Ghana using the Planting for Food and Jobs (PFJ) policy as a case. Data was obtained from 412 inhabitants from the Western Region who responded to a set of questionnaires and a structured interview guide. Means, standard deviation, Mann-Whitney U-test, and Kruskal Wallis test were employed for data analysis. The study established that though respondents perceived the PFJ policy as highly relevant to job creation and food production, the overall effect of the policy on job creation was low. No significant differences existed in the views of male and female respondents regarding the perceived effect of the policy on job creation and food production. Government communication strategies to promote awareness, public dialogue, shared understanding, and public support towards the policy were all perceived as moderately effective. However, government communication activities to explain the policy details to the public, mobilize citizens and communities to support the policy, and promote positive behavior change for the policy were perceived as lowly effective by the respondents despite the differences in their ethnic backgrounds. Government press releases and press conferences on the progress of the policy were lowly effective. Though government communication at the implementation stage of the policy was effective, it was generally ineffective during the formulation, monitoring, and evaluation stages of the policy. Measures suggested to sustain the policy include increased commitments from state authorities, increased participation by all stakeholders, improved communication on the policy, grassroots ownership, and de-politicization of the policy.
Exploratory study of existing research on software requirements prioritization: A systematic literature review

Abstract Requirements prioritization is giving priority to software requirements on the basis of its importance. The current research paper addresses the importance and limitations of existing research on requirements prioritization. A lot of requirements prioritization techniques are presented by authors, but these techniques have certain limitations. Not all techniques are either applied or suitable to prioritize all types of requirements. Similarly, in these techniques, only few are scalable for large size requirements. The objective of the current research is to identify all possible software requirements prioritization techniques and to show the suitability of these techniques to prioritize different types and sizes of requirements. To achieve research objectives, a systematic literature review (SLR) is carried out. In conducting SLR, planned protocol is designed, which include all the necessary steps necessary for SLR process. As a result of SLR, 60 primary studies were selected for data extraction as a result of SLR. Outcome of SLR shows that not all techniques are scalable to prioritize any size requirements. Also, there are few techniques found that are either applied on all types of requirements or not suitable to prioritize all types of requirements as these techniques are specifically designed for particular types of requirements.
ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution

Large-scale, high-quality corpora are critical for advancing research in coreference resolution. However, existing datasets vary in their definition of coreferences and have been collected via complex and lengthy guidelines that are curated for linguistic experts. These concerns have sparked a growing interest among researchers to curate a unified set of guidelines suitable for annotators with various backgrounds. In this work, we develop a crowdsourcing-friendly coreference annotation methodology, ezCoref, consisting of an annotation tool and an interactive tutorial. We use ezCoref to re-annotate 240 passages from seven existing English coreference datasets (spanning fiction, news, and multiple other domains) while teaching annotators only cases that are treated similarly across these datasets. Surprisingly, we find that reasonable quality annotations were already achievable (90% agreement between the crowd and expert annotations) even without extensive training. On carefully analyzing the remaining disagreements, we identify the presence of linguistic cases that our annotators unanimously agree upon but lack unified treatments (e.g., generic pronouns, appositives) in existing datasets. We propose the research community should revisit these phenomena when curating future unified annotation guidelines.
Clinical Text Anonymization, its Influence on Downstream NLP Tasks and the Risk of Re-Identification

While text-based medical applications have become increasingly prominent, access to clinicaldata remains a major concern. To resolve this issue, further de-identification and anonymization of the data are required. This might, however, alter the contextual information within the clinical texts and therefore influence the learning and performance of possible language models. This paper systematically analyses the potential effects of various anonymization techniques on the performance of state-of-the-art machine learning models based on several datasets corresponding to five different NLP tasks. On this basis, we derive insightful findings and recommendations concerning text anonymization with regard to the performance of machine learning models. In addition, we present a simple re-identification attack applied to the anonymized text data, which can break the anonymization.
Infusing Context and Knowledge Awareness in Multi-turn Dialog Understanding

In multi-turn dialog understanding, semantic frames are constructed by detecting intents and slots within each user utterance.However, recent works lack the capability of modeling multi-turn dynamics within a dialog in natural language understanding (NLU), instead leaving them for updating dialog states only.Moreover, humans usually associate relevant background knowledge with the current dialog contexts to better illustrate slot semantics revealed from word connotations, where previous works have explored such possibility mostly in knowledge-grounded response generation.In this paper, we propose to amend the research gap by equipping a BERT-based NLU framework with knowledge and context awareness.We first encode dialog contexts with a unidirectional context-aware transformer encoder and select relevant inter-word knowledge with the current word and previous history based on a knowledge attention mechanism. Experimental results in two complicated multi-turn dialog datasets have demonstrated significant improvements of our proposed framework. Attention visualization also demonstrates how our modules leverage knowledge across the utterance.
Cloud computing security in health cyber physical systems

The health industry as one of the Critical Infrastructures has rapidly grown in adopting the use of new ubiquitous technologies in advancing its critical services. The industry because of its high need and usage of new technologies is encountering a myriad of challenges including cyber-attacks. Among the technologies adopted by the health industry is the use of cloud computing services for managing Electronic Medical Records (EMR) on the cloud environment. Today’s medical devices use sensors that collect data from the diverse environments such as the human heart and such data is sent to the cloud for better analysis, monitoring, or management. However, attackers exploit every single development to advance health or medical services like all the other cyber systems. The scope of this paper is to investigate how cloud computing services adopted in Health Cyber Physical Systems can be secured from the prevalent cybersecurity challenges. The study followed a systematic literature review, where data was collected from online databases with set search constraints such as language, date of publication and specific keywords. This paper presents how cloud computing integrated with the medical cyber physical systems can impeccably securely manage the health systems. More specifically the collection, monitoring, and management of real-time data and as a result of this integration, different cyber-attacks are escalating.