Reviewerly/exHarmony huggingface dataset:
Evaluation Validity of Oral English Test App in Mobile Learning Environment

In the post-epidemic era, as a good auxiliary tool the oral test app on the mobile learning terminal has taken a significant part in pre-class preparation, in-class task teaching, and after-class consolidation and expansion, especially in realizing the intelligence of the oral test and ensuring the fairness of the oral test. In this study, FACETS, a multi-facet Rasch model measurement program, was used to measure the consistency and the rater severity between the automatic scoring system of mobile intelligent apps and 5 expert raters' rating on 197 examinees' speaking records derived from a mock examination of the mobile terminal-assisted oral English mock test. The study found that: it has an enormous and decisive influence on the students' score distribution in spite of the significant difference in the severity of the mobile app automatic scoring and the expert scorer's scoring. The low bias rate of mobile intelligent apps automatic scoring imply that mobile app automatic scoring is more suitable and normative than human raters in the aspect of inner-consistency.
Cloud service and interactive IoT system application in the service management mode of logistics enterprises

In order to standardize the functions of the Internet of Things interactive system, such as cost constraints, probability, and speed, the basic principle is to expand the ability to express data flow or cost behavior, and to build logic to describe the nature of the Internet of Things. In order to integrate the MODEST automatic verification tool, this article investigates the mechanism of converting PPTCA reaction models into action programs using random time automata, theoretically supporting the use of the tool. This paper studies the theory and technology of enterprise logistics service management and introduces the B/S system structure, the database technology used, and the current research status at home and abroad. When analyzing program requirements, first use dynamic and static functional models to explain system business requirements and distinguish systems. The general design and detailed design of the system are carried out, and the functional structure of the system is established. The system performs user management module, company information management, customer service, transaction order and report management, user management module, user connection and registration, employee personal information management, order management, and other functions of the customer service module. In addition, users and developers conduct transactions by accessing the cloud computing service platform created by operators, and there are factors that promote each other between users and developers. Sending and completing service functions, the transaction control module performs order management, order comparison management and other functions, and the report management module performs the reporting function of customer information and user information reports.
An optimised multi-channel neural network model based on CLDNN for automatic modulation recognition

To achieve high accuracy blind modulation identification of wireless communication, a novel multi-channel deep learning framework based on the Convolutional Long Short-Term Memory Fully Connected Deep Neural Network (MC-CLDNN) is proposed. To make network training more efficient, we use the Gated Recurrent Unit (GRU) sequence model as the substructure. Furthermore, the skip connection is added to alleviate the problem of gradient disappearance in the network training and reduce the negative effect of pooling layer processes time series data on the subsequent sequence model. We test the feasibility of the model based on two open-source data sets RadioML2016.10a and RadioML2016.10b. The simulation results show that the proposed model can identify most modulation modes efficiently under the influence of various factors such as Additive White Gaussian Noise (AWGN), multipath fading, frequency offset. In the Signal-to-Noise Ratio (SNR) range of 0-18 dB, the overall recognition accuracy of the MC-CLDNN can reach 93% and the area under the Receiver Operating Characteristic (ROC) curve accounts for more than 99%. Therefore, the model has the characteristics of high recognition accuracy and strong generalisation ability. Its comprehensive performance is better than most of the existing deep learning models.
Anonymous System for Fully Distributed and Robust Secure Multi-Party Computation

In secure multi-party computation (SMPC), it is considered that multiple parties that are known to each other evaluate a function over their private inputs in a secure fashion. The participating parties do not learn anything about each other's private inputs beyond what can be deduced from their own input and output. The assumption that the parties know each other, however, does not seem suitable for all potential applications of SMPC. In some applications participants may not only want to hide their private inputs and outputs, but may also want to hide the fact that they are participating in a given function evaluation in the first place. We therefore propose an anonymous system for SMPC that allows parties to anonymously evaluate a function of their private inputs in a fully distributed and secure fashion. The proposed system allows authorized parties to execute an SMPC protocol robust with penalty against a dishonest majority in the presence of a malicious adversary. During the protocol execution, the system guarantees that all participating parties stay anonymous w. r. t. each other as well as any third parties. In addition, it guarantees that in each function evaluation all participating parties are unique, i. e., no party can participate as more than one entity.
Sistem Pendukung Keputusan Pemilihan Instruktur Bimbingan Belajar Menggunakan Metode PSI (Preference Selection Index)

Service activities aim for the long term in creating HR (Human Resources) who have knowledge and creativity in the field of computers, especially in graphic design and multimedia applications. This training aims to train students in using tools in graphic design applications, for example. Corel Draw, so students are expected to know how to plan and develop students' creative abilities in designing. This activity is carried out using the social method of graphic design applications using the Corel Draw application, and an instructor who is an expert in the field of graphic design. to guide the operation of the application, thereby helping trainees in the practice of making designs. Decision support system, is a system that is used to facilitate decision making in selecting a graphic design training instructor, where the decision making process is basically choosing alternative decisions in determining the eligibility of a person to become a design training instructor, the assessment can be seen from several assessment criteria, namely experience, consistent, creative and communicative. In facilitating the selection process, a decision support system is used using the PSI (Preference Selection Index) method to produce the best weights and preferences. So that the final result obtained as a tutoring instructor is alternative A11 on behalf of Sriwulan with the first rank with the highest score of 0.9196
Challenges and Opportunities for Interactive Technology to Support Parents of HIV-Positive Children in Ethiopia in the Disclosure Process

Fearing stigma, parents often hide their children's HIV diagnosis from them, and postpone disclosure, in turn negatively impacting children's well-being. Our work explores whether interactive technology can support disclosure. In the first study, we examine disclosure experiences and the role of interactive technology from the perspective of HIV-positive children and parents. Through Thematic Analysis, we highlight how disclosure is linked with parents' own experience of HIV, and that disclosure needs to be viewed as a process. On this basis, we contribute an experience prototype that guides parents through an incremental disclosure process using interactive storytelling. In a second study, we evaluate the prototype through interviews with six parents. Leveraging Interpretative Phenomenological Analysis, we show that the prototype has potential to transform how parents understand and approach disclosure. Based on our results, we present further design directions, and discuss the (limitations of the) role that technology can play in this context.
Evaluating Across-Hinge Dragging with Pen and Touch on Curved and Foldable Displays

Foldable touch screens are increasingly popular, but little research has explored how the hinge impacts usability and performance. We evaluate across- and along-hinge drag gestures on a series of prototypes emulating foldable all-screen laptops with a curved hinge radius ranging from 1mm to 24mm. Results show that using a large 24mm hinge radius instead of a small 1mm hinge radius can decrease drag time by 13% and movement variability by 7% for touch input. However, hinge radius had no effect on performance for pen input. Further, we found that dragging along the hinge was up to 30% faster than dragging across the hinge, especially when dragging across at an acute angle to the hinge. Using these results, we demonstrate use cases for across- and along-hinge gestures. Our findings provide guidance for hardware and interaction designers seeking to create foldable touchscreen devices and their accompanying software.
Complementary Product Recommendation using Siamese Neural Network

Online catalogs on e-commerce websites are sometimes too overwhelming where customers have a choice of as much variety and richness to find what they need in one place. In e-commerce websites, recommendation systems are crucial since they enhance the user experience by assisting visitors in finding what they want by recommending products. These suggestions can be based on user traits, demographics, past purchases, or search history. In this paper, we focus on identifying a complementary relationship between products, we have made a content-based recommendation system for discovering complementary products using Siamese Neural Networks (SNN). Algorithms like this have a lot of potential to increase the average purchase amount on an e-commerce website by recommending comparable products. After implementing the network we propose an extension of the network of the SNN approach to handling more products and will improve the time for recommending products by the KNN algorithm.
Beyond prototyping boards: future paradigms for electronics toolkits

Electronics prototyping platforms such as Arduino enable a wide variety of creators with and without an engineering background to rapidly and inexpensively create interactive prototypes. By opening up the process of prototyping to more creators, and by making it cheaper and quicker, prototyping platforms and toolkits have undoubtedly shaped the HCI community. With this workshop, we aim to understand how recent trends in technology, from reprogrammable digital and analog arrays to printed electronics, and from metamaterials to neurally-inspired processors, might be leveraged in future prototyping platforms and toolkits. Our goal is to go beyond the well-established paradigm of mainstream microcontroller boards, leveraging the more diverse set of technologies that already exist but to date have remained relatively niche. What is the future of electronics prototyping toolkits? How will these tools fit in the current ecosystem? What are the new opportunities for research and commercialization?
Criptopias: Speculative Stories Exploring Worlds Worth Wanting

In a manner of cripping access in technology, we use the concept of criptopias and what it might bring to technology research and Human-Computer Interaction along a range of speculative stories that explore desirable worlds from a crip perspective. We stretch our bodyminds into the past, present and futures to identify how we might thrive in worlds that welcome us, instead of giving us the persistent notion of being considered as an afterthought. Such a collection is necessarily eclectic and not oriented on providing solutions; rather, we carefully tread forward on trying to find different stories we tell to and about ourselves through speculative explorations of how disability-centred interactions could be shaped. However, as we discuss briefly at the end, allowing ourselves to desire difference has the tendency to throw us back into the lack of a given status quo, making such an endeavour surprisingly painful to endure – while simultaneously providing wholesome alternatives worth fighting for in solidarity.
Describe, Spot and Explain: Interpretable Representation Learning for Discriminative Visual Reasoning

Despite the recent success achieved by deep neural networks (DNNs), it remains challenging to disclose/explain the decision-making process from the numerous parameters and complex non-linear functions. To address the problem, explainable AI (XAI) aims to provide explanations corresponding to the learning and prediction processes for deep learning models. In this paper, we propose a novel representation learning framework of Describe, Spot and eXplain (DSX). Based on the architecture of Transformer, our proposed DSX framework is composed of two learning stages, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">descriptive prototype learning</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">discriminative prototype discovery</i> . Given an input image, the former stage is designed to derive a set of descriptive representations, while the latter stage further identifies a discriminative subset, offering semantic interpretability for the corresponding classification tasks. While our DSX does not require any ground truth attribute supervision during training, the derived visual representations can be practically associated with physical attributes provided by domain experts. Extensive experiments on fine-grained classification and person re-identification tasks qualitatively and quantitatively verify the use our DSX model for offering semantically practical interpretability with satisfactory recognition performances.
Salience-based stakeholder selection to maintain stakeholder coverage in solving the next release problem

The quantification of stakeholders plays a fundamental role in the selection of appropriate requirements, as their judgement is a significant criterion, as not all stakeholders are equally important. The original proposals modelled stakeholder importance using a weighting approach that may not capture all the dimensions of stakeholder importance. Furthermore, actual projects involve a multitude of stakeholders, making it difficult to consider and compute all their weights. These facts lead us to search for strategies to adequately assess the importance concept, reducing the elicitation effort. We propose grouping strategies as a means of reducing the number of stakeholders to manage in requirement selection while maintaining adequate stakeholder coverage (how selection meets stakeholder demands). Our approach is based on the salience of stakeholders, defined in terms of their power, legitimacy, and urgency. Diverse strategies are applied to select important stakeholder groups. We use k-means, k-medoids, and hierarchical clustering, after deciding the number of clusters based on validation indices. Each technique found a different group of important stakeholders. The number of stakeholder groups suggested experimentally (3 or 4) coincides with those indicated by the literature as definitive, dominant, dependent, and dangerous for 4 groups; or critical, major, and minor for 3 groups. Either for all the stakeholders and for each important group, several requirements selection optimisation problems are solved. The tests do not find significant differences in coverage when important stakeholders are filtered using clustering, regardless of the technique and number of groups, with a reduction between 66.32% and 87.75% in the number of stakeholders considered. Applying clustering methods to data obtained from a project is useful in identifying the group of important stakeholders. The number of suggested groups matches the stakeholders’ theory, and the stakeholder coverage values are kept in the requirement selection.
An unhackable quantum internet

Entangled photons of light are beeing sent under the noisy streets of New York to test a prototype network, reports Karmela Padavic-Callaghan
Start small: Training controllable game level generators without training data by learning at multiple sizes

A level generator is a tool that generates game levels from noise. Training a generator without a dataset suffers from feedback sparsity, since it is unlikely to generate a playable level via random exploration. A common solution is shaped rewards, which guides the generator to achieve subgoals towards level playability, but they consume effort to design and require game-specific domain knowledge. This paper proposes a novel approach to train generators without datasets or shaped rewards by learning at multiple level sizes starting from small sizes and up to the desired sizes. The denser feedback at small sizes negates the need for shaped rewards. Additionally, the generators learn to build levels at various sizes, including sizes they were not trained for. We apply our approach to train recurrent auto-regressive generative flow networks (GFlowNets) for controllable level generation. We also adapt diversity sampling to be compatible with GFlowNets. The results show that our generators create diverse playable levels at various sizes for Sokoban, Zelda, and Danger Dave. When compared with controllable reinforcement learning level generators for Sokoban, the results show that our generators achieve better controllability and competitive diversity, while being 9x faster at training and level generation.
Multimodal Medical Images using Rigid Iconic Registration based on Flower Pollination Algorithm and Butterfly Optimization Algorithm

One of the numerous challenges of modern image processing is image registration. Information from many images often emerges in slightly different forms and is highly compatible. Spatial alignment is crucial to merge essential and valuable information from several images properly. The term "registration" describes this procedure. Find a transformation that results in a model that closely resembles the reference image [1].Mainly, this work is concerned with implementing two optimization algorithms: the Flower Pollination Algorithm (FPA) and the Butterfly Optimization Algorithm (BOA). To measure the efficacy of these methods, we compare the transformed image to the original by computing the mutual information between the two. The effectiveness of these methods was assessed using SSIM, EQM, and MI measures. Results from the experiments indicate that the BOA outperforms the FPA.
Demonstrating Virtual Teamwork with Synchrobots: A Robot-Mediated Approach to Improving Connectedness

The increased prevalence of online collaborative work, through necessity or preference, is accompanied by measurable drops in satisfaction, creativity and energy, often termed "zoom fatigue." As loss of physical co-presence and associated nonverbal communication are identified as contributors, we introduce Synchrobots – robots designed to channel human biophysiology for group connectedness. We propose an Interactivity demo wherein two participants perform an online problem-solving task while wearing physiological sensors and holding a Synchrobot as it physically renders a translation of their partner's heartrate. The setup involves two stations, each with a laptop running Zoom, a set of wearable sensors recording heart rate, respiratory rate, and electrodermal activity, and a Synchrobot. After the problem-solving task, we will invite participants to reflect on how connected they felt with each other as well as their satisfaction with the collaboration quality. Participants may consent to release this data for later inclusion as part of a study.
FIANCEE: Faster Inference of Adversarial Networks via Conditional Early Exits

Generative DNNs are a powerful tool for image synthesis, but they are limited by their computational load. On the other hand, given a trained model and a task, e.g. faces generation within a range of characteristics, the output image quality will be unevenly distributed among images with different characteristics. It follows, that we might restrain the models complexity on some instances, maintaining a high quality. We propose a method for diminishing computations by adding so-called early exit branches to the original architecture, and dynamically switching the computational path depending on how difficult it will be to render the output. We apply our method on two different SOTA models performing generative tasks: generation from a semantic map, and cross-reenactment of face expressions; showing it is able to output images with custom lower-quality thresholds. For a threshold of LPIPS <=0.1, we diminish their computations by up to a half. This is especially relevant for real-time applications such as synthesis of faces, when quality loss needs to be contained, but most of the inputs need fewer computations than the complex instances.
The Digital Reading Condition. Maria Engberg, Iben Have and Birgitte Stougaard Pedersen (eds)

Journal Article The Digital Reading Condition. Maria Engberg, Iben Have and Birgitte Stougaard Pedersen (eds) Get access The Digital Reading Condition. Maria Engberg, Iben Have and Birgitte Stougaard Pedersen (eds).London: Routledge, 2023. 256 pp. ISBN: 978-1-032-07812-0. $44.95 (Pbk). Jin Qiu, Jin Qiu School of Foreign Languages, Chongqing Jiaotong University, China Email: 88235299@qq.com Search for other works by this author on: Oxford Academic Google Scholar Xin Su Xin Su School of Foreign Languages, Chongqing Jiaotong University, China Search for other works by this author on: Oxford Academic Google Scholar Digital Scholarship in the Humanities, Volume 38, Issue 2, June 2023, Pages 912–914, https://doi.org/10.1093/llc/fqad029 Published: 22 April 2023
Correction

This article refers to:Turning social inclusion into exclusion during collaborative learning between students with and without SEN
Phishing Attack Detecting System Using DNS and IP Filtering

This study examines the different types of phishing attacks, which are a major threat to digital security. Phishing involves the use of fraudulent messages to deceive recipients, including email spoofing, spear phishing, phone phishing, clone phishing, pharming, HTTP phishing, man-in-the-middle attacks, and fast-flux phishing. Attackers can gather information about their targets from public sources such as social media networks, including work history, interests, and activities. The study developed a filtered website that detects fraudulent links based on the internet protocol (IP), register date, and domain name server (DNS) of each website. While further research is needed to improve the effectiveness of the site, this marks an important step towards enhancing digital security.