Reviewerly/exHarmony huggingface dataset:
Representational and Computational Changes in Heritage Language Grammars

Abstract The notion of complexity has been applied to descriptions and comparisons of languages and to explanations related to ease and difficulty of various linguistic phenomena in first and second language acquisition. It has been noted that compared to baseline grammars, heritage language grammars are less complex, displaying morphological simplification and structural shrinking, especially among heritage speakers with lower proficiency in the language. On some recent proposals of gender agreement in Spanish and Norwegian (Fuchs et al., 2015; Lohndal &amp; Putnam, 2020), these differences are representational, affecting the projection of functional categories and feature specifications in the syntax. An alternative possibility is that differences between baseline and heritage grammars arise from computational considerations related to bilingualism, affecting speed of lexical access and feature reassembly online in the minority language. We illustrate this proposal with empirical data from gender agreement and differential object marking. Although presented as alternatives, the representational and computational explanations are not incompatible, and may both be adequate to capture varying levels of variability modulated by linguistic proficiency. These proposals formalize bilingual acquisition models of grammar competition and directly relate the availability and type of input (the acquisition evidence) to the locus and nature of the grammatical differences between heritage and baseline grammars.
Reservoir computing using photon-magnon coupling

The current demand for large-volume data processing has led to the emergence of brain-inspired devices and algorithms, such as reservoir computing (RC), a promising computational framework for temporal-data processing. As inspired by a demonstration of RC using spin-torque nano-oscillators [Marković et al., Appl. Phys. Lett. 114, 012409 (2019)], we experimentally demonstrated RC using the non-linear dynamical responses of photon-magnon coupling (PMC) modes, i.e., the magnitude, phase, and frequency of the transmission spectra in a specially designed hybrid system consisting of an inverted split-ring resonator and an yttrium iron garnet film. Through the outputs decoded from the magnitude and frequency of the PMC transmission spectra, we experimentally achieved a 100% classification rate for recognitions of non-degenerate sine and square waveforms in a wide range of DC magnetic fields (DC currents) center, thanks to the extremely high signal-to-noise ratios and the non-linearity of the dynamical variables. The experimental realization of RC based on PMC can pave an alternative pathway to the development of high-performance RC devices.
Fostering instructor-student argumentative interaction in online lecturing to large groups: a study amidst the Covid-19 pandemic

e Covid-19 pandemic has accelerated the development and implementation of online education in higher education institutions around the globe.Online lecturing to large groups has been part of this acceleration.e problem is that synchronous face-toface lectures and synchronous online lectures are widely criticized for reinforcing the hegemony of instructor-centered traditional approaches and rarely involving instructor-student and student-student argumentative interaction.is study aimed to provide evidence that online informal formative assessment (OIFA) can be used to provide undergraduates with explicit opportunities to participate in instructor-student argumentative interaction.An OIFA-based pedagogical strategy was implemented amidst the Covid-19 pandemic in the online lecturing sessions of a science course with 76 undergraduates (40 females and 36 males, 16-23 years old) in Colombia.It was found that OIFA can contribute to instructor-student argumentative interaction as well as to being able to better address undergraduate learning needs.Practical implications for university science education in the pandemic and post-pandemic eras are discussed.
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition

Most reinforcement learning methods for dialog policy learning train a centralized agent that selects a predefined joint action concatenating domain name, intent type, and slot name.The centralized dialog agent suffers from a great many user-agent interaction requirements due to the large action space.Besides, designing the concatenated actions is laborious to engineers and maybe struggled with edge cases.To solve these problems, we model the dialog policy learning problem with a novel multi-agent framework, in which each part of the action is led by a different agent.The framework reduces labor costs for action templates and decreases the size of the action space for each agent.Furthermore, we relieve the non-stationary problem caused by the changing dynamics of the environment as evolving of agents' policies by introducing a joint optimization process that makes agents can exchange their policy information.Concurrently, an independent experience replay buffer mechanism is integrated to reduce the dependence between gradients of samples to improve training efficiency.The effectiveness of the proposed framework is demonstrated in a multi-domain environment with both user simulator evaluation and human evaluation.
The Use of ICT for Communication between Teachers and Students in the Context of Higher Education Institutions

Recently, the communication paradigm has been changing in society in the higher education context because of the ease of access to the Internet and the high number of mobile devices. Thus, universities have increased their interest in accepting different and sophisticated communication technologies to improve student participation in the educational process. This study aimed to assess how students and teachers use communication technologies to communicate with each other and what their expectations, satisfaction, and attitudes regarding the results of this use are. An analysis model was used in a case study at the University of Aveiro to support the study. Data were obtained through an online questionnaire, which collected 570 responses from students and 172 responses from teachers. These data were processed through descriptive statistics techniques and inference tests (t-tests). The primary outcomes are that publishing and sharing technologies and electronic mail are the most commonly used communication technologies by students and teachers, suggesting that their use will not decline soon. However, other communication technologies were also revealed to be widely used and accepted, with excellent levels of confirmation of expectation.
Lessons Learned Developing and Using a Machine Learning Model to Automatically Transcribe 2.3 Million Handwritten Occupation Codes

Machine learning approaches achieve high accuracy for text recognition and are therefore increasingly used for the transcription of handwritten historical sources. However, using machine learning in production requires a streamlined end-to-end pipeline that scales to the dataset size and a model that achieves high accuracy with few manual transcriptions. The correctness of the model results must also be verified. This paper describes our lessons learned developing, tuning and using the Occode end-to-end machine learning pipeline for transcribing 2.3 million handwritten occupation codes from the Norwegian 1950 population census. We achieve an accuracy of 97% for the automatically transcribed codes, and we send 3% of the codes for manual verification . We verify that the occupation code distribution found in our results matches the distribution found in our training data, which should be representative for the census as a whole. We believe our approach and lessons learned may be useful for other transcription projects that plan to use machine learning in production. The source code is available at https://github.com/uit-hdl/rhd-codes.
Hybrid-based bat optimization with fuzzy C-means algorithm for breast cancer analysis

Background: Breast cancer is one of the most frequent types of cancer among women and early identification can reduce the mortality rate drastically. Feature selection is one of the significant tasks in the breast cancer analysis process. Several types of feature selection algorithms have been implemented to select the most appropriate feature for breast cancer analysis. However, they have to take a longer time to converge, over-fitting problems and providing less accuracy. Hence, a hybrid bat optimization algorithm combined with chaotic maps and fuzzy C-means clustering algorithm (BSCFC) is proposed for feature selection. Aims and Objectives: An integrated optimized bat optimization algorithm combined with chaotic maps and fuzzy C-means clustering algorithm (BSCFC) is proposed to determine the relevant feature. Materials and Methods: Breast cancer mini-Mammographic Image Analysis Society database (MIAS) dataset is used for analysis. Further, median filters are used for preprocessing, Region of Interest (ROI) was utilized for segmentation, gray level co-occurrence matrix (GLCM), and texture analysis are utilized in the feature extraction process. A hybrid bat optimization algorithm combined with chaotic maps and fuzzy C-means clustering algorithm (BSCFC) is proposed for feature selection. K nearest neighbor (KNN) classifier is used for classification. Results: Performance of the proposed system is evaluated using standard measures and achieved the highest accuracy rate of (98.2%), specificity of (97.3%), and sensitivity of (98.3%) as compared to other relevant methods such as bat, chaotic bat, chaotic crow search, ant lion optimization, and chaotic ant lion optimization algorithm. Conclusion: The proposed BSCFC algorithm is designed to improve the performance of convergence speed and control balance between exploration and exploitation rate using five types of chaotic maps namely sinusoidal, sine, gauss, logistic, and tent maps. The results show that the BSCFC with sinusoidal maps can significantly boost the classification performance of the BSCFC algorithm in classifying the breast cancer images with reduced features, which in turn optimizes the radiologists' time for their interpretation.
Trustworthy Cross-Organizational Collaborations with Hybrid On/Off-Chain Declarative Choreographies

Business Process Management communities increasingly adopt the blockchain technology to support trustworthy decentralized execution of processes. In this context, the interest in business process choreographies rises as they offer a distributed way to compose and control cross-organizational processes. In choreographies, the process view is distributed between participants to limit privacy leakages. Hence, the process observability (i.e., who knows what) is challenging. On one side, partners have no insight into each other’s orchestration and communicate peer-to-peer via the public view. On the other side, they have to maintain their internal orchestrations’ states consistent with the choreography’s global state. The need to ensure a privacy-preserving method to enforce a blockchain-based execution thus rises. In the present work, we propose a unified solution for the hybrid on/off-chain generation and execution of business process choreographies. The public view, shared understanding of the cross-organizational process, is triggered by the on-chain smart contract. Participants generate their private views off-chain using this on-chain public view. They execute afterward the private views in their off-chain process execution engine. Our prototypical implementation demonstrates the feasibility of the approach .
Supervised Word Sense Disambiguation with Recurrent Neural Network Model

Disambiguating words is a branch of artificial intelligence that deals with natural language processing. The dissatisfaction of the motive of the word deals with the polysemy of the ambiguous word, processing a single word in natural language, having two or more meanings where the corresponding context discriminates the meaning. Humans are intelligent enough to derive the meaning of the word because they are a biological neural network. Computers can be trained in such a way that they should function similarly to biological neural networks. There are four different suggested approaches to clutter as the knowledge-dependent approach and the machine learning based models which are further classified as supervised, semi-supervised and unpublished learning models. The purpose of this research is to improve better communication between computers and humans. The discussed model used a supervised learning approach with recurrent neural networks.
PartImageNet: A Large, High-Quality Dataset of Parts

It is natural to represent objects in terms of their parts. This has the potential to improve the performance of algorithms for object recognition and segmentation but can also help for downstream tasks like activity recognition. Research on part-based models, however, is hindered by the lack of datasets with per-pixel part annotations. This is partly due to the difficulty and high cost of annotating object parts so it has rarely been done except for humans (where there exists a big literature on part-based models). To help address this problem, we propose PartImageNet, a large, high-quality dataset with part segmentation annotations. It consists of 158 classes from ImageNet with approximately 24, 000 images. PartImageNet is unique because it offers part-level annotations on a general set of classes including non-rigid, articulated objects, while having an order of magnitude larger size compared to existing part datasets (excluding datasets of humans). It can be utilized for many vision tasks including Object Segmentation, Semantic Part Segmentation, Few-shot Learning and Part Discovery. We conduct comprehensive experiments which study these tasks and set up a set of baselines.
Opportunities and Challenges of Using Blockchain Technology in Government Accounting in Brazil

New technologies lead to significant changes in how public and private organizations structure their processes and activities. This study aims to identify the challenges and opportunities of using blockchain technology in government accounting in Brazil. This is a descriptive research using quantitative and qualitative data, collected through a questionnaire applied to a non-probabilistic sample of 94 professionals. The data were submitted to descriptive statistical analysis and content analysis, based on seven categories: technology, government accounting, security, transparency, control, change, and knowledge. For 89.4% of respondents, blockchain can improve government accounting and be applied to financial transactions, auditing, and asset transfers. The technology offers the benefits of trust and control, information security, and control against fraud and corruption. For 98.9% of the sample, the challenges of using blockchain technology in government accounting are the lack of knowledge about the technology and its cost-benefit and implementation, difficulties in replacing or adapting systems, and few blockchain use cases demonstrating the technology’s use and application.
X-ray Security Inspection Image Detection Algorithm Based on Improved YOLOv4

The existing object detection algorithms have low recognition accuracy for prohibited items due to the complex background, large variation of target scale, and mutual occlusion of objects in X-ray security inspection images. In order to accurately identify prohibited items in real-time, an X-ray security inspection image detection algorithm based on improved YOLOv4 is proposed. Firstly, deformable convolution is introduced into the network to improve the feature extraction ability of prohibited items. Then, GHM loss is used to optimize the loss function, so that the model can focus on the difficult classification samples that are more effective for training improvement. Finally, the non-maximum suppression method combining soft NMS and DIoU NMS is used to improve the detection ability of the algorithm for occluded targets. Experiments on the X-ray security inspection image dataset show that the mAP of the improved algorithm reaches 91.4%, which is 3.3% higher than the YOLOv4, and the detection speed meets the real-time requirements.
The illusion of having a large virtual body biases action-specific perception in patients with mild cognitive impairment

Abstract The illusion of having a large body makes us perceive objects as smaller than they really are. This action-specific perception effect occurs because we perceive the property of an object (i.e., size) differently according to our unique action capability (i.e., the affordance of body size). Although the body-ownership illusion contributing to this action-specific perception has been studied, its effects remain unclear in neurological patients. We examined the action-specific perception impairments of MCI patients by means of body-ownership illusion in a non-immersive virtual reality environment. Twenty healthy young adults, 21 healthy older adults, and 15 MCI patients were recruited. We assessed their “original-body action-specific perception” and “enlarged-body action-specific perception” using the original and enlarged sizes of their virtual bodies, respectively. The MCI patients’ original-body action-specific perception was no different than that of the healthy controls ( p = 0.679). However, the enlarged-body action-specific perception of the MCI patients was significantly biased ( p &lt; 0.001). The inclusion of the enlarged-body action-specific perception provides additional discriminative power for early diagnosis of MCI (89.3% accuracy, 75.0% sensitivity, 100.0% specificity, and 87.5% balanced accuracy).
Through (Tracking) Their Eyes: Abstraction and Complexity in Program Comprehension

Previous studies on writing and understanding programs presented evidence that programmers beyond a novice stage utilize plans or plan-like structures. Other studies on code composition showed that learners have difficulties with writing, reading, and debugging code where interacting plans are merged into a short piece of code. In this article, we focus on the question of how different code-composition strategies and the familiarity with code affect program comprehension on a more abstract, i.e., algorithmic level. Using an eye-tracking setup, we explored how advanced students comprehend programs and their underlying algorithms written in either a merged or abutted (sequenced) composition of code blocks of varying familiarity. The effects of familiarity and code composition were studied both isolated and in combination. Our analysis of the quantitative data adds to our understanding of the behavior reported in previous studies and the effects of plans and their composition on the programs’ difficulty. Using this data along with retrospective interviews, we analyze students’ reading patterns and provide support that subjects were able to form mental models of program execution during task performance. Furthermore, our results suggest that subjects are able to retrieve and create schemata when the program is composed of familiar templates, which may improve their performance; we found indicators for a higher element-interactivity for programs with a merged code composition compared to abutted code composition.
Perceptual hashing for color images

Currently, the reported image hashing schemes cannot perform satisfactorily in some aspects, such as rotation robustness. To this end, a perceptual color image hashing scheme is proposed. First, the original image is normalized and smoothed by Gaussian low-pass filter. The obtained secondary image is divided into a series of ring-ribbons with different radii and the same number of pixels. Then, textural and color features are extracted in local and global manners. Specifically, local textural features are extracted on luminance values of the ring-ribbons using quadtree decomposition, and global textural features are extracted by gray level co-occurrence matrix. Local color features of significant corner points are extracted on outer boundaries of ring-ribbons through color vector angles, and global color features are extracted by low-order moments. The extracted features are concatenated after quantization and permutation to generate the final hash. Receiver operating characteristic curves verified the effectiveness of our scheme, including robustness, discrimination, and security, which can be effectively applied in content authentication and tampering detection.
The Effect of Hyperparameter Selection on the Personification of Customer Population Data

We explore the effects of hyperparameter selections on the personification accuracy of customer analytics data from a corporate YouTube channel with an audience in the hundreds of thousands and customer interactions in the tens of millions. Using non-negative matrix factorization, we generate personas sets from 5 to 15 using the customer analytics data, with the number of personas being the changing hyperparameter. We then compare the gender, age, nationality, and topical interests of the personas across each of the 11 persona sets using the average of the 110 generated personas as the baseline. This analysis shows that hyperparameter selection significantly alters the personification of the analytics data, with the effect most apparent with age representation. The set of 10 personas provides one of the most accurate representations across all attributes, indicating that this may be a good default hyperparameter for personification. Future research can explore other personification attributes with other customer analytics datasets.
CodeMiko

While VTubing has grown in popularity over the last few years, CodeMiko stands out by offering high fidelity motion and facial capture and a higher degree of interactivity due to Twitch chat's ability to affect her streams. CodeMiko has created a new form of entertainment inside of Twitch by mixing gaming, live interactivity, and traditional scripted content. New interactions are created all the time, making it an incredibly exciting experience for the viewers and makes them want to keep watching. To bring CodeMiko to life, all you need is motion capture through an Xsens suit and facial capture through an iPhone live link. Her fingers are captured using Manus VR. Most of her expressivity is done through her animation blueprint and I've added blendshapes with squash and stretch to make her face feel more alive. Heightened interactivity with chat is one of the most unique aspects of the CodeMiko show. When I connect to Twitch chat, tools that I created allow the viewers to play with the character morph screen, while others boast arcade-like features. Chat can make her explode, throw balls and try to hit her, or make her fart. Chat can also visit Miko's world as a little frog, which Miko can then grab and throw around if she wants to. As an example, I have connected to chat and they will play with the character morphs live to change CodeMiko's body1.
An Insight into Encrypted DNS protocol: DNS over TLS

Domain Name Service (DNS) is one of the predominant protocols in network and communication. In the current implementation, a DNS query and response precedes the connection between any two hosts on the internet. This protocol translates the domain name in plain text to an IP address. A critical privacy breach arises here due to the plain text DNS query. In the recent years, encrypted DNS protocols mainly DNS over HTTPS (DoH) and DNS over TLS (DoT) have surfaced. This paper provides an insight into the DoT protocol and its session establishment according to the RFC. Furthermore, we have brought out the differences between various encrypted DNS protocols. In addition to this we highlight the privacy and information leakage associated with DoT protocol and how despite encryption, there is a necessity for thorough analysis of the threats to privacy which should be examined and rectified.