Reviewerly/exHarmony huggingface dataset:
Chinese college students’ preferences for mobile-assisted language learning classes and their effects on student engagement in English language classrooms

Purpose This research investigated college student preferences for and engagement in mobile-assisted language learning (MALL) classes and whether the level of class engagement differed among students according to class preferences.Design/methodology/approach Using a mixed-methods approach, 115 first-year college students who were experiencing an innovative learning method, i.e., taking both traditional and MALL classes simultaneously, were invited to participate in the survey, and six of them were also invited to participate in the interviews.Findings and originality/value The analyses indicated that participants were highly engaged in MALL classes and liked the flexibility and learning resources. A greater number of participants preferred traditional language classes over mobile-assisted classes due to real-time communication and being better able to focus in the former. Participants preferring for MALL classes were significantly more engaged in such learning approaches than those without such a preference. These results indicate that mobile technologies are effective in assisting language learning and teaching, and student preferences regarding class type shape their engagement in language classrooms. The reasons behind students' active engagement and the effects of class preference on student engagement in MALL classrooms are discussed. Implications for how to promote meaningful engagement in MALL classrooms are offered.
Decentra-Twitter Using Blockchain

Blockchain nowadays has become a hot topic in technology, finance, regulation, and the wider society in recent years. A plethora of permissions and permissionless blockchain platforms are available in the industry, and some could be used to develop a social networking site. This paper proposes a framework for secure, trustworthy social networking that also creates value for user-generated content by using a blockchain-enhanced framework for social networking. In this work, we design blockchain-based data storage and access a framework that will not totally dependent on centralized Twitter. We use the public blockchain and tools like Ganache, MetaMask, and Ethereum IDE for deploying the contracts. In the proposed work, metadata of the files are stored on the blockchain and we use networks like the goerli test network for occurring the Transactions using peer-to-peer networks. The decentralized data storage will help to eliminate the most traditional data failures and outages by securing and controlling the data.
Publishing as an Amateur Author, Probability of the ‘Improbable’

No abstract available
Fake Profile Identification in Online Social Networks Using Machine Learning

Social networking platforms are now a common aspect of daily life for most people. Every day, a large number of people create profiles on social networking sites and interact with others, regardless of their location or time of day. Social networking platforms not only benefit users, but also put their security and personal information at danger. To find out who is spreading hazards on social media, we must classify user profiles. The classification allows us to distinguish between legitimate profiles on social networks and fake profiles. We generally employ a range of methods for categorising fraudulent profiles on social networks. As a result, we must improve the social network phoney profile identification system's accuracy rate. In this research, we propose machine learning and natural language processing (NLP) approaches for fraudulent profile detection. Both the Nave Bayes algorithm and the Support Vector Machine (SVM) can be employed.
Degradation learning and Skip-Transformer for blind face restoration

Blindrestoration of low-quality faces in the real world has advanced rapidly in recent years. The rich and diverse priors encapsulated by pre-trained face GAN have demonstrated their effectiveness in reconstructing high-quality faces from low-quality observations in the real world. However, the modeling of degradation in real-world face images remains poorly understood, affecting the property of generalization of existing methods. Inspired by the success of pre-trained models and transformers in recent years, we propose to solve the problem of blind restoration by jointly exploiting their power for degradation and prior learning, respectively. On the one hand, we train a two-generator architecture for degradation learning to transfer the style of low-quality real-world faces to the high-resolution output of pre-trained StyleGAN. On the other hand, we present a hybrid architecture, called Skip-Transformer (ST), which combines transformer encoder modules with a pre-trained StyleGAN-based decoder using skip layers. Such a hybrid design is innovative in that it represents the first attempt to jointly exploit the global attention mechanism of the transformer and pre-trained StyleGAN-based generative facial priors. We have compared our DL-ST model with the latest three benchmarks for blind image restoration (DFDNet, PSFRGAN, and GFP-GAN). Our experimental results have shown that this work outperforms all other competing methods, both subjectively and objectively (as measured by the Fréchet Inception Distance and NIQE metrics).
Missing Child Identification System using Deep Learning and Multiclass SVM for E-police

Countless children go missing in India every year. In missing children, most children are not included. This article presents a deep learning method to identify missing children from multiple existing images of children with the help of face recognition. Citizens can send pictures of suspicious children to public places with signs and writings. The photo will be automatically compared to the saved photo of the missing child in the warehouse. Sort the photos of the children and choose the best photos from the missing children file. To this end, a deep learning model was trained to accurately identify missing children by matching information about missing children using face images provided by the public. Convolutional Neural Networks (CNNs) are deep learning techniques for image-based applications, this is face recognition. Extract face identifiers from images using the CNN pre-trained VGG-Face deep architecture. Compared to ordinary deep learning applications, our algorithm only uses network connection as high-level input and child identification is done by SVM trainers. Choose VGG-Face, the most effective CNN face recognition model, and train it appropriately to make deep learning models that are not affected by noise, light, contrast, obscuration, image posture and child age, and perform better based on previous Face recognition methods. In identifying missing children, Child identification achieved a 99.41% classification rate. Forty-three patients were evaluated
Interior design of bakery shops and cafes using sustainable materials in Malang city

Abstract The primary purpose of a bakery shop design is the convenience of visitors, but the design sustainability aspect is also something that must be considered. In the interior design of bakery shops and cafes, using environmentally friendly materials will be the topic of discussion in this study. The problem is using sustainable materials to create interior designs for bakery shops and cafes following the company’s image. The case study in this research is a bakery shop and café with a modern vintage style in Malang City. This study uses qualitative methods and design exploration. Data was collected through literature studies, field surveys, and interviews with café owners and users. The results of this study are the application of sustainable materials applied to the interior elements of this bakery shop and café design. The material is applied as a backdrop for walls, ceilings, and furniture. The results of this study are expected to be able to become a problem solution for textile waste generated due to the continuous development of technology and people’s lifestyles. This effort is to harmonize the technology development and maintain the environmental quality for human life in the future.
Predictors of Usability of a Mobile Intelligent Agent Information Provider for College Students

This study determined the factors that influenced the usability of a mobile-based intelligent agent called “AskRed.” The design-related factors were evaluated in terms of performance, accuracy, responsiveness, aesthetics, and completeness. The usability of the software was determined in terms of satisfaction and intention to re-use the software. The software received favorable ratings from the students. Experts' software evaluation recommended strengthening the design of the intelligent agent in terms of security, performance, completeness, and ease-of-use. Multiple regression analyses showed that performance and completeness influenced satisfaction and intention to re-use. Aesthetics and responsiveness influenced satisfaction but not intention to re-use. Responsiveness had a negative impact on satisfaction. The predictive powers of the regression equations are 58% and 73%. This study provided empirical evidence on the predictors of usability of an intelligent agent used in a university setting.
AROS: Affordance Recognition with One-Shot Human Stances

We present Affordance Recognition with One-Shot Human Stances (AROS), a one-shot learning approach that uses an explicit representation of interactions between highly articulated human poses and 3D scenes. The approach is one-shot since it does not require iterative training or retraining to add new affordance instances. Furthermore, only one or a small handful of examples of the target pose are needed to describe the interactions. Given a 3D mesh of a previously unseen scene, we can predict affordance locations that support the interactions and generate corresponding articulated 3D human bodies around them. We evaluate the performance of our approach on three public datasets of scanned real environments with varied degrees of noise. Through rigorous statistical analysis of crowdsourced evaluations, our results show that our one-shot approach is preferred up to 80% of the time over data-intensive baselines.
An Improved Quality of Service Design for Real-Time Services Over Future Generation Wireless Networks using Blockchain Technology

The sixth generation of wireless networks, or 6G, is expected to provide unprecedented levels of connectivity and communication capabilities. It is essential to make sure that the underlying infrastructure is dependable, secure, and trustworthy in order to meet the growing demand for high-speed and low-latency communication services. A flexible and scalable solution is also provided by the proposed infrastructure, which is essential to meet future demand for high-speed and low-latency communication services. Blockchain technology has been recognized as a possible option due to its capacity to offer security, accountability, and transparency. The proposed infrastructure is designed using permissioned distributed ledgers, which provide an efficient and secure solution to improve accountability, transparency, and security in the delivery of wireless services. The proposed method makes use of the benefits of permissioned distributed ledger technology and blockchain technology to provide a dependable and trustworthy infrastructure for 6G wireless networks. The proposed solution is modular and easily extendable, allowing for the integration of new services and applications as they become available
Step-wise discriminative learning on uncertain annotations for word sense disambiguation

Uncertain annotations with sparsity and uncertainty widely exist for many natural language processing tasks. In this paper, a new word sense disambiguation system is proposed, which integrates rich features and distributed discrimination algorithm, and constructs a method for learning on fuzzy annotations, which can effectively learn from sparse and uncertain annotations. The algorithm considers the elementary components in ascending order concerning their ambiguity degrees, which learns a concept labeler with promising performance with only an inventory extracted from an ontology. Experiments show that the performance of the algorithm significantly outperforms the intuitive generative unsupervised method with the same linguistic resource.
Early Diagnosis of Dementia Patients by SPADE Activity Prediction Algorithm

Abstract Dementia is not a specific disease, but a general term for age-related decline or loss of memory, cognitive abilities including problem solving and decision-making, and one’s own language, which significantly interfere with daily life. Researchers around the world have developed ways to automate the diagnosis of dementia through the use of machine learning and data mining approaches. The aim of this research project is to design and develop a day-to-day activity prediction algorithm in order to accurately identify and differentiate the dementia affected patients from the healthy subjects, to ensure early diagnosis of dementia development. This research advocates a novel algorithm called ‘Sequence Prediction via All Discoverable Episodes (SPADE)’ as a statistical tool to map activities of daily life (ADLs) in different groups of people in order to develop a unique parameter for precise diagnosis. The results of our experiment demonstrated a significant difference (i.e. 11 %) in the sequence prediction peak accuracy between the healthy subjects and the residents with dementia. SPADE demonstrated an adequate accuracy (i.e. 80 % on average), with an improvement of about 12 % compared to the performance of M-SPEED in inferring future occurrences of activities. It is thus evident that the algorithms for activity predictions show promise for early detection of dementia symptoms without the use of any expensive clinical procedure.
Dual adversarial attacks: Fooling humans and classifiers

Adversarial samples mostly aim at fooling machine learning (ML) models. They often involve minor pixel-based perturbations that are imperceptible to human observers. In this work, adversarial samples should fool both humans and ML models, which is important in two-stage decision processes. We perform changes on a higher abstraction level so that a target sample exhibits properties of a desired sample. Technically, we contribute by deriving a regularization scheme for autoencoders incorporating a classifier loss for smoothly interpolating between wildly different samples. The realism and effectiveness of generated samples are confirmed with a user study and other evaluations. Our experiments consider neural networks of four architectures, assessed on MNIST, FashionMNIST, QuickDraw and CIFAR-10. Results show that our scheme leads to superior performance compared to existing interpolation techniques: on average, other methods have an 11% higher failure rate when producing a sample that is of any of two interpolated classes. Furthermore, our attacks work in both white- and black-box settings.
Anatomically Integrated In-Place Visualization of Patient Data for Cooperative Tasks with a Case Study on a Neurosurgical Ward

The workflow in modern hospitals entails that the medical treatment of a patient is distributed between several physicians and nurses. This leads to intensive cooperation, which takes place under particular time pressure and requires efficient conveyance of relevant patient-related medical data to colleagues. This requirement is difficult to achieve with traditional data representation approaches. In this paper, we introduce a novel concept of anatomically integrated in-place visualization designed to engage with cooperative tasks on a neurosurgical ward by using a virtual patient's body as spatial representation of visually encoded abstract medical data. Based on the findings of our field studies, we provide a set of formal requirements and procedures for this kind of visual encoding. Moreover, we implemented a prototype on a mobile device that supports the diagnosis of spinal disc herniation and has been evaluated by 10 neurosurgeons. The physicians have assessed the proposed concept as beneficial, especially emphasizing the advantages of the anatomical integration such as intuitiveness and a better data availability due to providing all information at a glance. Particularly, four of nine respondents have stressed solely benefits of the concept, other four have mentioned benefits with some limitations and only one person has seen no benefits.
Detecting the overfilled status of domestic and commercial bins using computer vision

As the amount of waste being produced globally is increasing, there is a need for more efficient waste management solutions to accommodate this expansion. The first step in waste management is the collection of bins or containers. Each bin truck in a fleet is assigned a collection route. As the bin trucks have a finite amount of storage for waste, accepting overfilled bins may result in filling this storage before the end of the collection route. This creates inefficiencies as a second bin truck is needed to finish the collection route if the original becomes full. Currently, the recording and tracking of overfilled bins is a manual process, requiring the bin truck operator to undertake this task, resulting in longer collection route durations. To create a more efficient and automated process, computer vision methods are considered for the task of detecting the bin status. Video footage from a commercial collection route for two bin types, automated side loader (ASL) and front-end loader (FEL), was utilized to create appropriate computer vision datasets for the task of fully supervised object detection and instance segmentation. Selected state-of-the-art object detection and instance segmentation algorithms were used to investigate their performances on this proprietary dataset. A mean average precision (mAP) score of 0.8 or greater was achieved with each model, reflecting the effectiveness of using computer vision as a tool to automate the process of recording overfilled bins.
Modeling and simulation of a Tamil language encoder for advanced encryption technologies

This paper presents a Tamil language (TL) encoder that benefits advanced encryption technologies such as the advanced encryption standard (AES). It defines a product set of vowel and consonant sounds of the Tamil language and reveals its connection to Hardy-Ramanujan prime factors and Tamil letters as a one-to-one mapping. It also reveals that the Tamil letters, combined with the digits from 1 to 9, form a Galois field of 28 over an irreducible polynomial of degree 8. Additionally, it implements these two mathematical properties, models the TL encoder, and replaces the pre-round transformation of AES with the model to enhance cryptographic strengths. The cryptographic strengths are measured by the runs test scores of the bit sequences of the ciphers of AES and compared with that of English language. The modeling and simulation conclude that the TL encoder enhances the cryptographic strength of AES at every step of the encryption flow.
Estimating prediction horizon of reservoir computer on L63 system when observed variables are incomplete

Abstract Reservoir computer (RC) is an attractive neural computing framework that can well predict the dynamics of chaotic systems. Previous knowledge of the RC performance is established on the case that all variables in a chaotic system are completely observed. However, in practical circumstances the observed variables from a dynamical system are usually incomplete, among which there is a lack of understanding of the RC performance. Here we utilize mean error growth curve to estimate the RC prediction horizon on the Lorenz63 system (L63), and particularly we investigate the scenario of univariate time series. Our results demonstrate that the prediction horizon of RC outperforms that of local dynamical analogs of L63, and the state-space embedding technique can improve the RC prediction in case of incomplete observations. We then test the conclusion on the more complicated systems, and extend the method to estimate the intraseasonal predictability of atmospheric circulation indices. These results could provide indications for future developments and applications of the RC.
On the relation of method popularity to breaking changes in the Maven ecosystem

Software reuse is a common practice in modern software engineering to save time and energy while accelerating software delivery. Dependency managers like MAVEN offer a large ecosystem of reusable libraries that build the backbone of software reuse. Breaking changes, i.e., when an update to a library introduces incompatible changes that break existing client programs, are troublesome barriers to this library reuse. Semantic Versioning has been proposed as a practice to make it easier for the users to find safe updates by encoding the change impact in the version number. While this practice is widely studied from the framework perspective, no detailed insights exist yet into the ecosystem perspective. In this work, we study violations of semantic versioning in the MAVEN ecosystem for 13,876 versions of 384 artifacts to better understand the impact these violations have on the 7,190 dependent versioned packages. We found that 67% of the artifacts introduce at least one type of semantic versioning violation, either a breaking change or an illegal API extension in their history. An impact analysis on breaking methods that (direct or transitive) dependents reference, revealed strong centralization: 87% of publicly accessible methods are never used by dependents and among methods with at least one usage, half of the unique calls from dependents concentrate on only 35% of the defined methods. We also studied method popularity and could not find an indication that popularity affects stability: even popular methods break frequently. Overall, we confirm the previous result that Semantic Versioning is violated repeatedly in practice. Our results suggest that the frequency of breaking changes might be a sign of insufficient change-impact awareness on the ecosystem and we believe that developers require more adequate information, like method popularity, to improve their update strategies.
Transfer and Deep Learning-Based Gurmukhi Handwritten Word Classification Model

The world is having a vast collection of text with abandon of knowledge. However, it is a difficult and time-taking process to manually read and recognize the text written in numerous regional scripts. The task becomes more critical with Gurmukhi script due to complex structure of characters motivated from the challenges in designing an error-free and accurate classification model of Gurmukhi characters. In this paper, the author has customized the convolutional neural network model to classify handwritten Gurmukhi words. Furthermore, dataset has been prepared with 24000 handwritten Gurmukhi word images with 12 classes representing the month’s names. The dataset has been collected from 500 users of heterogeneous profession and age group. The dataset has been simulated using the proposed CNN model as well as various pretrained models named as ResNet 50, VGG19, and VGG16 at 100 epochs and 40 batch sizes. The proposed CNN model has obtained the best accuracy value of 0.9973, whereas the ResNet50 model has obtained the accuracy of 0.4015, VGG19 has obtained the accuracy of 0.7758, and the VGG16 model has obtained value accuracy of 0.8056. With the current accuracy rate, noncomplex architectural pattern, and prowess gained through learning using different writing styles, the proposed CNN model will be of great benefit to the researchers working in this area to use it in other ImageNet-based classification problems.