Reviewerly/exHarmony huggingface dataset:
Word Use Equivalence and Hierarchical Word Tiers

A ranked word list provides information about the position of each word in the list. However, retaining and employing the measure used to generate the ranked list can yield additional information about the words. If ω denotes the prevalence of a word in a corpus, then not only can the values of ω be ordered, their values can be compared to one another, and words having similar values can be grouped together into equivalence classes. Measures of word prevalence include mean text frequency, the dispersion of words across texts in a corpus, or a measure that combines frequency and dispersion. In this paper, we examine the concepts of word equivalence classes and hierarchical word tiers and apply these concepts to the words in the British National Corpus (BNC). Hierarchical word tiers can be constructed without the knowledge of all pairwise comparisons of the words under study. By grouping words that have similar values of prevalence, the ranked ordered list reduces to an informative set of hierarchical word tiers where each tier contains words that are similar to one another in terms of their use in the corpus.
The role of cooperating teachers and the training program in the development of professional identity among pre-service special education teachers

Field experience is considered one of the most important and powerful components of teacher education programs. The current study examined the development of the professional identity of pre-service special education teachers during their practical field experience period. The study focused on their relationships with their cooperating teachers and the type of training program (teacher training college [TTC] versus university) using a mixed-methods approach. Eighty-four participants took part in the quantitative design and nineteen participants took part in the qualitative design. Questionnaires and reflective diaries were used to examine changes experienced by participants during one academic year. TTC students reported higher levels than university students in relation to two factors of professional identity: self-efficacy and confidence in professional choice. Students’ relationships with their cooperating teacher were positive and improved over time among both study groups. Implications for research and practice are discussed.
Note about Passive Continuous Variable Quantum Key Distribution over Turbulent Atmospheric Channel

Continuous variable quantum key distribution (CVQKD) has been implemented over the atmospheric channels over free space. However, atmospheric turbulence weakens the quality of the transmitting quantum signals and hence decreases the secret key rate of the system. Here, we suggest an atmospheric turbulence channel model that involves atmospheric turbulence bubbles and demonstrates the implementation feasibility of passive CVQKD with spectrum resources in the terahertz band over the atmospheric turbulence channel. We achieve the channel transmittance characterized by the refractive index and the wavefront distortions. Moreover, an adaptive optics (AO) unit is used for performance improvement while considering the effect of the thermal noise and excess noise on the atmospheric turbulence bubble-modeled channel. Numerical simulations show that the AO-involved detection scheme can result in reductions in excess noise when being faced with the floating clouds and mist in atmospheric turbulence, which results in performance improvements in terms of secret key rate, which confirms the utility of the high-rate and long-distance CVQKD in terahertz (THz) for practical implementations.
A Research Agenda for Collaborative Roadmapping Supported by Blockchain Technology

Roadmapping is a well-known management approach that helps with the collaborative development and management of technology and innovation planning. In this context, the management of shared information is critical to effective and reliable collaboration. Blockchain technology supports disruptive and distributed IP management solutions, especially in a multi-stakeholder environment. This research investigates opportunities to apply blockchain to support collaborative roadmapping. First, a literature review was conducted to identify potential benefits provided by blockchain technologies. The connections between the collaborative roadmapping gaps and potential blockchain benefits were translated into research opportunities. Blockchain by nature can support two unsolved gaps in collaborative roadmapping: confidentiality and security. Additionally, the research reveals three new potential benefits of blockchain technologies: certification, shared value, and traceability.
Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System

Electricity demands are increasing significantly and the traditional power grid system is facing huge challenges. As the desired next-generation power grid system, smart grid can provide secure and reliable power generation, and consumption, and can also realize the system's coordinated and intelligent power distribution. Coordinating grid power distribution usually requires mutual communication between power distributors to accomplish coordination. However, the power network is complex, the network nodes are far apart, and the communication bandwidth is often expensive. Therefore, how to reduce the communication bandwidth in the cooperative power distribution process task is crucially important. One way to tackle this problem is to build mechanisms to selectively send out communications, which allow distributors to send information at certain moments and key states. The distributors in the power grid are modeled as reinforcement learning agents, and the communication bandwidth in the power grid can be reduced by optimizing the communication frequency between agents. Therefore, in this paper, we propose a model for deciding whether to communicate based on the causal inference method, Causal Inference Communication Model (CICM). CICM regards whether to communicate as a binary intervention variable, and determines which intervention is more effective by estimating the individual treatment effect (ITE). It offers the optimal communication strategy about whether to send information while ensuring task completion. This method effectively reduces the communication frequency between grid distributors, and at the same time maximizes the power distribution effect. In addition, we test the method in StarCraft II and 3D environment habitation experiments, which fully proves the effectiveness of the method.
An Information Extraction and Thorough Understanding Method for Test-question Graph of Junior High School Physical Mechanical Motion

Intelligent problem-solving technology is a typical application of artificial intelligence in the educational field. The purpose of intelligent problem-solving is to enable machine to solve problems like human beings and help people to find useful and accurate information in the test-questions. Correct understanding of test-questions is one of the key techniques of intelligent problem-solving. The existing methods are mainly to extract simple relations from text and graphs of test-questions. In the absence of text, deep understanding of graph elements and complex relationships are difficult, which is a challenge for machines. However, the information contained in motion test-question graphs is more abundant than that in the text, which is more beneficial for machine to solve problems. To solve this problem, based on the graph classification and characteristics analysis on test-questions, a novel automatic and weak text-dependent information extraction and thorough understanding method for test-question graph of junior high school physical mechanical motion is proposed (TQGIEU). It can extract information and understand regarding to test-question graphs based on several image preprocessing techniques and neural network topologies combined with the characteristics of test-question graphs, without relying on text processing. It will generate a readable mechanical motion solution at last. It fills the blank of automatic information extraction and thorough understanding for the test-question research of physical mechanical motion. The experimental results show that the accuracy of graph classification and line segmentation model are 99.60% and 90.87% respectively. The average accuracy of TQGIEU on test dataset is 83.08%, and the total F1 score of TQGIEU is 0.859, indicating that TQGIEU can provide a good information extraction and thorough understanding service for test-question graphs of junior high school physical mechanical motion with high performance.
Exploring the dynamics of graph algorithms

Abstract In this paper, we describe an interactive visualization tool for representing the dynamics of graph algorithms. To reach this goal, we designed a web-based framework which illustrates the dynamics as time-to-space mappings of dynamic graphs. Such static diagrams of dynamic data have the benefit of being able to display longer time spans in one view, hence supporting the observer with comparison tasks which is challenging or even impossible for graph algorithm animations. Our tool can show details about how an algorithm traverses a graph step-by-step in a static and animated fashion, for graph algorithm exploration as well as educational purposes. The animation together with the time-to-space mapping hence forms an overview-and-detail approach. We also allow changing of speed, replaying, stopping, storing intermediate stages with parameter configurations, as well as measuring and monitoring performance and memory consumption to eventually identify bottlenecks in a graph algorithm. By using flight carrier data from the United States Department of Transportation and a network of autonomous systems we demonstrate how we used the tool to explore two standard graph-theoretic algorithms. Finally, we discuss scalability issues and limitations. Graphical abstract
Coherent adversarial deepfake video generation

Deepfake video has been rapidly developed and attracted public concerns due to its potential wide applications, deepfake videos can be easily distinguished by DNN-based detection approaches. As the vulnerability of DNNs, the adversarial attack can be an effective way to deteriorate the ability of deepfake detection, but current adversarial attack techniques are commonly designed for individual images, which are easily perceived at the video level. To reveal the weakness of current attack methods, we first propose a robust detector utilizing the temporal consistency to discriminate between the clean and perturbed ones aiming at weakly adversarial deepfake videos, achieving maximum success rates of 100%. Then we propose a novel framework for generating high-quality adversarial deepfake videos which can fool deepfake detectors and evade the detection of adversarial perturbations simultaneously. Two pivotal techniques are utilized for improving the visual quality and the imperceptibility of adversarial perturbations: (i) Optical flow is adopted to restrict the temporal coherence of adversarial perturbations among frames; (ii) An adaptive distortion cost that can measure the complexity of each frame and help to keep the adversarial modification imperceptible. We demonstrate the effectiveness of our methods in disrupting representative DNN-based deepfake detectors. Extensive experiments are conducted to show the great improvement in coherence, visual quality, and imperceptibility of the adversarial deepfake videos. Furthermore, We hope that our adversarial deepfake generation framework can shed some light on the detection methods to fix their weakness.
Digitalizing and Upgrading Severe Acute Respiratory Infections Surveillance in Malta: System Development

In late 2020, the European Centre for Disease Prevention and Control and Epiconcept started implementing a surveillance system for severe acute respiratory infections (SARI) across Europe.We sought to describe the process of digitizing and upgrading SARI surveillance in Malta, an island country with a centralized health system, during the COVID-19 pandemic from February to November 2021. We described the characteristics of people included in the surveillance system and compared different SARI case definitions, including their advantages and disadvantages. This study also discusses the process, output, and future for SARI and other public health surveillance opportunities.Malta has one main public hospital where, on admission, patient data are entered into electronic records as free text. Symptoms and comorbidities are manually extracted from these records, whereas other data are collected from registers. Collected data are formatted to produce weekly and monthly reports to inform public health actions. From October 2020 to February 2021, we established an analogue incidence-based system for SARI surveillance. From February 2021 onward, we mapped key stakeholders and digitized most surveillance processes.By November 30, 2021, 903 SARI cases were reported, with 380 (42.1%) positive for SARS-CoV-2. Of all SARI hospitalizations, 69 (7.6%) were admitted to the intensive care unit, 769 (85.2%) were discharged, 27 (3%) are still being treated, and 107 (11.8%) died. Among the 107 patients who died, 96 (89.7%) had more than one underlying condition, the most common of which were hypertension (n=57, 53.3%) and chronic heart disease (n=49, 45.8%).The implementation of enhanced SARI surveillance in Malta was completed by the end of May 2021, allowing the monitoring of SARI incidence and patient characteristics. A future shift to register-based surveillance should improve SARI detection through automated processes.
Reconfigurable Architecture for Image Encryption Using a Three-Layer Artificial Neural Network

Multimedia security is important as privacy and protection has to be ensured when images from military, medical and other confidential services are sent through the unprotected channel. In this paper, an attempt to develop reconfigurable hardware for an encryption scheme using FPGA has been carried out and tested successfully for its features. In this proposed encryption scheme, chaos and a novel artificial neural network-based diffusion have been used along with zig-zag permutation for pixel level and novel enhanced Fisher-Yates permutation for bit level. The ANN is composed of additive and multiplicative convolution and the 2D cellular automata as an activation function for diffusion. A novel key generation algorithm is adopted to produce the key values for generating the chaos sequence. Every image gets a new key, hence differential attack possibility is negligible. The encryption algorithm is designed in MATLAB and coded in Verilog HDL to develop it as reconfigurable hardware. The hamming distance is analyzed to know the difference between the key sequence generated. The average hamming distance is 0.5111 and the highest value is 0.8796 to show that the randomness of the sequence. Similarly, equidistribution, autocorrelation and periodicity analysis are performed. The NIST SP 800–22 randomness test was also performed on keys and encrypted images to analyze its randomness. The results obtained from the encryption process are tested for their efficiency related to statistical and differential attacks. It gives better results when compared with a few encryption algorithms in the literature.
How will challenges in micro- and nanofabrication impact the development of quantum technologies?

The development of useful quantum technologies will heavily rely on assembling many sub-systems that exhibit quantum properties. These systems do not spontaneously assemble themselves into useable quantum machines: they rely on advanced fabrication techniques at micro- and nanometre scales. Examples of such techniques include the fabrication of electrodes and waveguides for trapped ions, of Josephson junctions and microwave chips for superconducting circuits, of electrodes for the control of quantum dots or the fabrication of low-disorder semi-conductors for the operation of Majorana Zero Modes.
In Search of Gaps between Languages and Wordnets: the Case of Polish-English WordNet

Abstract The results of manual mapping of Polish plWordNet onto English Princeton WordNet revealed a number of gaps and mismatches between those interlinked lexical resources. Preliminary studies have shown that they embrace wordnet-specific and language-specific differences, and in this exploratory study we focus on the latter, also called lacunae. Capitalising on the system of equivalence types and features for linking wordnet senses (Rudnicka et al. 2019), we present a semi-automatic, rule-based diagnostic system developed specifically for systematic detection and classification of gaps and mismatches between wordnets. First, focusing on noun synsets, we aim to identify those network fragments that are the most prone to reveal lexical and referential gaps (Svensén 2009). Second, we attempt to identify areas in an interlinked Polish-English wordnet that require resource expansion or modification of the existing network of inter-lingual relations.
Assessment of Psychological Impact Due to the Pandemic in Society and Education in Colombia with Machine Learning

After 20 months of the pandemic, the health situation in the world has evolved favorably with vaccination in most of the countries that have had access to it. However, the impact on society from the point of view of people’s behavior and their relationship with physical health is being evaluated by stakeholders. It helps states and institutions to formulate public policies to achieve social adaptation to new conditions, after pandemic. Sociodemographic conditions interact with the environment produced by the pandemic and can cause physical and psychological affectation that is reflected in the behavior of the person, such as the reaction to a new threatening situation (stress). This response is used to evaluate the degree of mentioned affectation of people. Models based on machine learning are proposed to evaluate and predict this level as well as the consequence in the educational process.
Production Cell Structure Constructor for Remote Laboratory Experiments

The paper presents a method for extending the diversity of virtual experiments in remotely controlled laboratories. Using the example of experiments controlling the model of a manufacturing cell, it is shown how a variety of different experiments can be achieved by synthesizing new variants of the structure of this manufacturing cell. The manufacturing cell is used to process workpieces and consists of elements such as conveyors, turntables, processing devices, and others. Each of these devices is represented by a variety of discrete sensors and actuators, where the sensors are inputs and the actuators are outputs to the control system, which must be designed by the student as a finite state machine (FSM). The devices with their sensors and actuators, as well as visual and virtual device models, are grouped together as a structure constructor. Here, the visual model of a device is a set of graphic objects whose visual properties depend on the values of the tags of the virtual model. The virtual model of the device specifies the graphical (tag values) and functional (values of the inputs of the FSM) response of the device model to the control signals of the FSM. The requirements for the structure of the manufacturing cell are determined by the sequence of operations for processing the workpiece. As a result of the synthesis of the manufacturing cell structure, the following are determined: the composition and location of the devices on the working field of the system; their visual reactions to temporal changes in the values of the outputs of the FSM; the values of the inputs in response to the control signals (=outputs of the FSM); and the values of the variables of the relationship with neighboring devices. The proposed method can be useful in organizing a laboratory workshop on the design of parallel and hierarchical control automata.
Personality Traits of Twitter Bullies Using Deep Learning

Research in the phycology domain pointed out a relationship between the personality traits and the ability of a person to bully or be bullied. This paper aims at analyzing the personality traits, in terms of the Big Five model (BFM), of cyberbullies on Twitter communities. To achieve our objective, two deep learning-based models were trained to detect the BFM traits from a text; the first of them is a multi-label CNN based classifier while the second includes five independent binary CNN classifiers (one for each trait). The trained models were applied to a tweeter dataset of cyberbullying people, in order to identify their BFM personality traits. Experimental results showed that the characteristics of cyberbullies are high neuroticism, high extroversion, and low agreeableness. Moreover, modelling the BFM personality prediction problem using five binary classifiers achieves higher accuracy than utilizing one multi-label classifier.
A Privacy Preserving Mixed Reality Approach for Industrial Remote Maintenance Applications

Augmented Reality (AR) is used in various industrial contexts like on-site maintenance and can further provide the basis for remote maintenance and remote support applications. This work presents a novel coupled AR-VR approach that supports all common remote-service interactions without transmitting a live camera feed. Instead of that, the expert is using a VR headset and only sees the virtual 3D representation of the machinery. Both can interact with an avatar of each other in AR or VR respectively. A prototype has been implemented using the Microsoft HoloLens 2 AR HMD and the HP Reverb VR headset. Participants connect to a central server component that provides static assets, like the required 3D models, and a second server for real-time collaboration and synchronization of movements, voice, and annotations. By design, this application does not transfer camera data unless especially requested and allowed by the technician.
AES Hardware Implementation Based on FPGA with Improved Throughput

AES (Advanced Encryption Standard) is one of secured symmetric key cryptographic algorithms. AES can be implemented software or hardware. Compared to software implementation, the hardware implementation is much faster, being used in applications which require higher data rate, such as routers. This paper presents the hardware implementation of AES by using Altera SoCKit board. This hardware implementation is based on efficient methods to implement internal operations, Key generation and AES controller. The paper presents a comparison of two implementations that may be used by students during the cryptographic laboratory in order to explore the operation of the AES encryption algorithm.