Reviewerly/exHarmony huggingface dataset:
The Predictive Value of Tagalog Voice Morphology in Filler-Gap Dependency Formation

Research has shown that when processing filler-gap dependencies (FGDs), comprehenders do not wait until they encounter all of the bottom-up information in the input. Instead, they use various types of linguistic information to predictively posit a gap that would allow the FGD to be resolved. They can use syntactic, lexical, morphological, and prosodic. Here we examine whether Tagalog comprehenders use the language's voice morphology to guide their incremental interpretations. Comprehenders used voice to actively associate the filler with the gap (experiment 1). The way in which they used voice varied by type and varied across FGDs (experiment 2). In an acceptability judgment study (experiment 3), agent voice had high cue validity across the board, while patient voice had variable, FGD-specific cue validity. We argue that comprehenders used FGD-specific cue validities when processing FGDs, but they also engaged with other classes of linguistic information, including (but not limited to) information about the structural similarities and the thematic complexity of the FGDs involved, and the relative frequency of voice in the language.
E-HoA: A Distributed Layered Architecture for Context-aware Autonomous Vehicles

The Embedded Higher-order Agent (E-HoA) architecture presented in this paper addresses the growing need for context-aware ambient systems, such as autonomous vehicles. This multi-process architecture, to be embedded into the control loop of these vehicles, includes a Belief-Desire-Intention agent that can consistently assist the symbolic execution of intentions. It also performs the appropriate conversion of these intentions into real physical vehicle maneuvers based on ROS. The proposed architecture offers gradually 4 levels of reactivity, from arch-reflex to the deep modification of the previously built symbolic execution plan. The presented use-case, the daily delivery of a network of pharmacy offices by an autonomous vehicle taking into account contextual (spatio-temporal) traffic features, shows the efficiency and the modularity of the architecture, as well as the scalability of the reaction levels.
Stackoverflow tag prediction using tag associations and code analysis

Stackoverflow is a platform or forum for people to gain knowledge, get solutions and interact on topics related to different programming concepts. Stackoverflow is one of the biggest Question and Answer forums with more than a million users. There is a wide variety of questions covered on this platform that are segmented with appropriate tags. The user by default enters the tag manually. Due to an enormous number of tags, it becomes difficult to search for the correct tag, many often left unconsidered. An automatic tagging system can be employed that shows likely tags depending on the text entered. The objective dataset chosen by us consists of about 0.5 million such questions, which can be technical or non-technical, collected from various information-dense websites, primarily Stackoverflow. We introduce a tag association scheme (TagAssc) along with code-analysis and strategic tag sampling schemes to get better accuracy scores in predicting tags with limited computing resources.
Converting Neural Networks to Rule Foam

A system of rules can approximate a trained neural classifier after sampling from that classifier. The rules define a generalized probability mixture that then describes the classifier. The size or granularity of the rule if-parts defines a foam-like structure with a few large rule if-part set bubbles in patternclass centers and many smaller if-part sets near class borders. The rule foam's mixture gives a Bayesian posterior over the rules. The posterior describes the relative importance of each rule for each observed input and output. The foam's mixture also gives the conditional variance that measures the uncertainty in its output. So the rule base is statistically interpretable as well as modular and adaptive. A rule foam with 1000 Gaussian rules approximated a 96.85% accurate MNIST neural classifier and had itself 95.66% classification accuracy. Foams can also approximate other foams. Some approximator foams out-performed the target foam that generated their training data. The rule foam's granularity mitigates the rule explosion inherent in the rule-based approximator's graph-covering structure
Securing Document by Digital Signature through RSA and Elliptic Curve Cryptosystems

Electronic document authentication is becoming a major concern due to the huge data passing through computer networks, especially Internet. The asymmetric cryptography, the cryptography based on elliptic curves and more recently digital signature appear as credible alternatives to guarantee the authenticity, non forgery, non reuse, inalterability and irrevocability of electronic documents. In this paper, we evaluated the performance of document security systems by the digital signature. We perform tests that showed elliptic curve cryptography is more advantageous in terms of memory consumption and computing speed over RSA cryptosystem. The simulation result showed that elliptic curve cryptography is a preferred choice over RSA cryptosystem and allows its use in embedded systems equiped with very limited memory and computing power.
User identification across multiple online social networks using cross link attribute and network relationship

An online user creates accounts on numerous social platforms to fulfill his/her needs. The user shares interactive content through posts/tweets and communicates with friends and colleagues. In this paper, we inspect the problem of social accounts matching across social media services by proposing a two-step method. In the first step, the proposed method uses cross-link posts to get the seed users’ accounts. After that, it utilizes the network features of the seed user and finds the association between the network relationships (follower and following) of the same user. By evaluating the experimental results, we found that the accuracy achieved by the proposed technique with the exact username is 98% and while the accuracy reduces to 45% when username across the sites is differed by one character.
A Secure Block Operation based Bit-plane Image Encryption using Chaotic Maps

This paper proposes a scheme for image encryption based on fixed block operations of size $2\times 2$. The proposed scheme encrypts images of any sizes. This technique addresses the problem of executing fixed block based permutations and diffusions in variable-sized images. For fixed block size based operation, the proposed method initially generates eight bit-planes and then combines bit-planes into two groups, where each group has four bit-planes. The combined bit-planes are then divided into blocks of size $2\times 2$. Then the array of two groups of blocks are permuted by using two different Piece-wise Linear Chaotic Map (PWLCM) systems independently. After block permutation, the block diffusion (bit-XOR) is performed by using a key block generated by another PWLCM system. Finally, the diffused blocks are combined to generate a cipher image, which is same size of original image. Apart from that, the proposed algorithm uses the plaintext related keys to resist the ciphertext against the major type of attacks known as “known-plaintext attack (KPA)” and “chosen-plaintext attack (CPA) The computer simulations show the best performance of the algorithm on encryption of images of variable sizes. The security evaluation ensures that the algorithm proposed is resistive to several widely used attacks.
Evaluation of Photovoltaic Solar Power of a Dual-Axis Solar Tracking System

In this research project, a tip-tilting angle of a photovoltaic solar cell was developed to increase generated electrical power output. An active, accurate, and simple dual-axis tracking system was designed by using an Arduino Uno microprocessor. The system consisted of two sections: software and apparatus (hardware). It was modified by using a group of light-dependent resistor sensors, and two DC servo motors were utilized to rotate the solar panel to a location with maximum sunlight. These components were arranged in a mechanical configuration with the gearbox. The three locations of the solar cell were chosen according to the tilt angle values, at zero angles, which included an optimal 33-degree angle for the Baghdad location and a variable angle with the dual-axis tracking system. For maximum value of the extracted solar energy, a photovoltaic solar panel that collects sunlight should be in normal position onto this radiation. Solar trackers relocated the panel toward the path of the Sun to ensure that the collector rotated at an optimal tilt angle. The results showed that the generated power at the dual-axis position was 3.384 watts per hour (W/h), the 33-degree angle yielded 2.237 W/h, and the zero-degree angle yielded 1.09 W/h. The results confirmed that the performance of a dual-axis solar tracking system is active and efficient.
Xnet: Task-specific attentional domain adaptation for satellite-to-aerial scene

Domain shift remains to be challenging in the current unsupervised deep domain adaptation literature. Although many state-of-the-art models have been proposed recently, non-adversarial and adversarial domain adaptation, as two major types of deep domain adaptation frameworks, cannot fully address this concern. In this paper, our proposed deep domain adaptation model attempts to address this challenge by leveraging the desired structural properties of both non-adversarial and adversarial models. Our contributions are two-fold: We solve the domain shift problem via domain-specific deep feature generator; we further adapt the generated features to the classification task by introducing task-specific attention learning. We name the proposed framework as ‘Xnet’, given the X- shape of the proposed deep neural network. The objective is to demonstrate the effectiveness of the proposed model both on traditional digit recognition tasks and the newly proposed satellite-to-aerial scene adaptation task. The full implementation (based on Tensorflow) as well as the trained networks are available at https://github.com/ubcsip/Xnet.
Prevalence and determinants of the dangerous selfie among medical and nursing students: a cross-sectional study from eastern India

Globally, there has been an exponential rise in smartphone use and selfie taking among youth. To make selfies exciting, dangerous selfies are often taken that may lead to catastrophic consequences, including death. This study aims to estimate the prevalence of dangerous selfies and to determine the factors associated with dangerous selfies among medical and nursing students in India.The study was conducted at the All India Institute of Medical Sciences (AIIMS), Bhubaneswar, India, in April-August 2018. The inclusion criteria were students enrolled in the Bachelor of Medicine and Bachelor of Surgery (MBBS) and nursing courses of AIIMS, Bhubaneswar. Students who did not use smartphones were excluded from the study. The interview schedule and Selfitis Behaviour Scale (SBS) were used to collect information on sociodemographic variables, smartphone use and variables related to selfies and dangerous selfies. Forward stepwise logistic regression was undertaken with the probability of entry and removal as 0.05 and 0.10, respectively.Of 633 eligible participants, 595 were included in the study. The mean (SD) age of the participants was 21.2 (1.6) years. More than half (56.8%) of the participants were female, 384 (64.5%) were medical students and 211 (35.5%) were nursing students. Nearly two-thirds of the participants (70.6%) preferred to take selfie. One hundred thirty three (40.3%) of the participants posted selfies on social media daily. The prevalence of dangerous selfies was 8.74% (95% CI: 6.73-11.28). Eight injury episodes while taking selfies were reported by seven (1.2%) participants. Being male (AOR 4.96, 95% CI 2.53-9.74), posting selfies on social media daily (AOR 3.33, 95% CI 1.71-6.47) and an SBS score > 75 (AOR 4.97, 95% CI 1.43-17.28) were independent predictors of dangerous selfies.Nearly one in ten medical and nursing students reported having taken a dangerous selfie, and one in one hundred reported having been injured while attempting to take a selfie. Being male, posting selfies on social media daily and an SBS score > 75 were independent predictors of dangerous selfies. Further research is required to identify the community burden of dangerous selfies and to develop strategies to prevent selfie-related fatalities among youths.
On the Resilience of Ant Algorithms. Experiment with Adapted MMAS on TSP

This paper focuses on the resilience of a nature-inspired class of algorithms. The issues related to resilience fall under a very wide umbrella. The uncertainties that we face in the world require the need of resilient systems in all domains. Software resilience is certainly of critical importance, due to the presence of software applications which are embedded in numerous operational and strategic systems. For Ant Colony Optimization (ACO), one of the most successful heuristic methods inspired by the communication processes in entomology, performance and convergence issues have been intensively studied by the scientific community. Our approach addresses the resilience of MAX–MIN Ant System (MMAS), one of the most efficient ACO algorithms, when studied in relation with Traveling Salesman Problem (TSP). We introduce a set of parameters that allow the management of real-life situations, such as imprecise or missing data and disturbances in the regular computing process. Several metrics are involved, and a statistical analysis is performed. The resilience of the adapted MMAS is analyzed and discussed. A broad outline on future research directions is given in connection with new trends concerning the design of resilient systems.
The Reform of Cultivation Mode of Chinese University English Translation Talents in the Age of Artificial Intelligence

The advent of the era of artificial intelligence will make artificial intelligence steadily integrate into the cultivation mode for translation talents, greatly innovate the concepts and methods of translation teaching in college English in China, and continuously improve the quality of talent cultivation. The paper aims to analyze the connotation and main characteristics of artificial intelligence, and then probe into the current situation of English translation talents cultivation in Chinese universities. Finally, it puts forward the path of reform of the cultivation mode for translation talents from the aspects of the objectives of translation talent cultivation and curriculum system, teaching philosophy and teaching methods, teaching resources and teaching tools as well as the teaching evaluation.
Visual Analytics and Annotation of Pervasive Eye Tracking Video

We propose a new technique for visual analytics and annotation of long-term pervasive eye tracking data for which a combined analysis of gaze and egocentric video is necessary. Our approach enables two important tasks for such data for hour-long videos from individual participants: (1) efficient annotation and (2) direct interpretation of the results. Exemplary time spans can be selected by the user and are then used as a query that initiates a fuzzy search of similar time spans based on gaze and video features. In an iterative refinement loop, the query interface then provides suggestions for the importance of individual features to improve the search results. A multi-layered timeline visualization shows an overview of annotated time spans. We demonstrate the efficiency of our approach for analyzing activities in about seven hours of video in a case study and discuss feedback on our approach from novices and experts performing the annotation task.
Classifier-Agnostic Saliency Map Extraction

Extracting saliency maps, which indicate parts of the image important to classification, requires many tricks to achieve satisfactory performance when using classifier-dependent methods. Instead, we propose classifier-agnostic saliency map extraction. This allows to find all parts of the image that any classifier could use, not just one given in advance. This way we extract much higher quality saliency maps.
Visually Enhanced E-learning Environments Using Deep Cross-Medium Matching

In the past few years, e-learning solutions are gradually replacing the traditional learning environments. The short attention span and lack of focus in many students is one of the factors which requires attention of e-learning course designers. Visually enhanced and dynamic e-learning courses proved to be more effective in keeping the attention of the students. In this paper, a framework for designing visually enhanced e-learning environments using deep cross-medium matching is proposed. The proposed framework uses deep neural networks for matching the textual and visual information together in order to suggest dynamic visual content for the textual e-learning materials. The proposed framework can improve the learning experience of students by providing dynamic visually enhanced e-learning environment.
An Ontology for Collaborative Decision Making

This article focuses on an ontology construction for collaborative decision making. To do this, a state of the art on collaborative decision-making, on ontology engineering and on collaboration engineering has been done. An eight-step ontology development methodology was adopted and implemented to build the ontology. A corpus made up of more than seventy-seven (77) documents was the starting point for the extraction of terms from the ontology and the UML (Unified Modeling Language) language served as a description language of our ontology. This ontology is intended to be the starting point for a facilitation support system in a Collaborative Decision Making process. The aim of the work is to produce a new system according the “Facilitator in the box” paradigm.
Distributed Cognition: Understanding Complex Sociotechnical Informatics.

Distributed cognition theory posits that our cognitive tasks are so tightly coupled to the environment that cognition extends into the environment, beyond the skin and the skull. It uses cognitive concepts to describe information processing across external representations, social networks and across different periods of time. Distributed cognition lends itself to exploring how people interact with technology in the workplace, issues to do with communication and coordination, how people's thinking extends into the environment and sociotechnical system architecture and performance more broadly. We provide an overview of early work that established distributed cognition theory, describe more recent work that facilitates its application, and outline how this theory has been used in health informatics. We present two use cases to show how distributed cognition can be used at the formative and summative stages of a project life cycle. In both cases, key determinants that influence performance of the sociotechnical system and/or the technology are identified. We argue that distributed cognition theory can have descriptive, rhetorical, inferential and application power. For evidence-based health informatics it can lead to design changes and hypotheses that can be tested.
Implementation of a Verbal Compiler: The Need to Develop Audio Language to Keep Pace with Rapid Development becomes a Necessity

This research paper aims to make essential developments in Speech Recognition(SR), the compiler gives the user a choice to choose the type o f output, whether it is textual or conversational (audio). Many large companies have developed such Speech Recognition Systems (SRS), especially the companies producing Smartphones, Computers, and Laptops. If translation is taken as a model application, they have not yet developed the perfect systems. The purpose of this paper is to add facilities to the Speech Recognition (SR) software so that it can deal with spoken languages.
AF-SEG: An Annotation-Free Approach for Image Segmentation by Self-Supervision and Generative Adversarial Network

Traditional segmentation methods are annotation-free but usually produce unsatisfactory results. The latest leading deep learning methods improve the results but require expensive and time-consuming pixel-level manual annotations. In this work, we propose a novel method based on self-supervision and generative adversarial network (GAN), which has high performance and requires no manual annotations. First, we perform traditional segmentation methods to obtain coarse segmentation. Then, we use GAN to generate a synthetic image, on which the image foreground is pixel-to-pixel corresponding to the coarse segmentation. Finally, we train the segmentation model with the data pairs of synthetic images and coarse segmentations. We evaluate our method on two types of segmentation tasks, including red blood cell (RBC) segmentation on microscope images and vessel segmentation on digital subtraction angiographies (DSA). The results show that our annotation-free method provides a considerable improvement over the traditional methods and achieves comparable accuracies with fully supervised methods.
Pearson 2025: Transforming teaching and privatising education data

This research report critically analyses the business strategy of multinational education company, Pearson, and identifies concerns for education stakeholder and policymakers.
La práctica holística de las competencias digitales docentes: diagnóstico y prospectiva

Digital teaching competences emerge in the context of educational activity as a mechanism of development with high possibilities of success and feasibility. For teachers, the appropriate use of information and communication technologies (ICT) means the possibility of more agile, efficient, and effective communication with students. In this context, ICT are a fundamental means of production, dissemination, and assessment to meet educational needs and build meaningful learning. This study focuses on the creation of a diagnosis of digital teaching competences and the development of a prospective that helps the participants achieve better application of ICT in the context of building learning. This research identifies and describes digital competences and then develops sub- competences for each of them, which allows more significant pathways of action to be plotted out regarding the incorporation of ICT into the teaching-learning process. The instrument developed was applied to a group of teachers involved in the Bachelor&rsquo;s Degree Program in Digital Arts at the Universidad de Guanajuato, Mexico. The analysis of the data enabled the development of a plan that would encourage better practices among the teachers on this program. &nbsp