Reviewerly/exHarmony huggingface dataset:
Algorithm identification in programming assignments

Current autograders of programming assignments are typically program output based; they fall short in many ways: e.g. they do not carry out subjective evaluations such as code quality, or whether the code has followed any instructor specified constraints; this is still done manually by teaching assistants. In this paper, we tackle a specific aspect of such evaluation: to verify whether a program implements a specific algorithm that the instructor specified. An algorithm, e.g. bubble sort, can be coded in myriad different ways, but a human can always understand the code and spot, say a bubble sort, vs. a selection sort. We develop and compare four approaches to do precisely this: given the source code of a program known to implement a certain functionality, identify the algorithm used, among a known set of algorithms. The approaches are based on code similarity, Support Vector Machine (SVM) with tree or graph kernels, and transformer neural architectures based only source code (CodeBERT), and the extension of this that includes code structure (GraphCodeBERT). Furthermore, we use a model for explainability (LIME) to generate insights into why certain programs get certain labels. Results based on our datasets of sorting, searching and shortest path codes, show that GraphCodeBERT, fine-tuned with scrambled source code, i.e., where identifiers are replaced consistently with arbitrary words, gives the best performance in algorithm identification, with accuracy of 96--99% depending on the functionality. Additionally, we add uncalled function source code elimination to our pre-processing pipeline of test programs, to improve the accuracy of classification of obfuscated source code.
Unsupervised Domain Adaptation for Video Transformers in Action Recognition

Over the last few years, Unsupervised Domain Adaptation (UDA) techniques have acquired remarkable importance and popularity in computer vision. However, when compared to the extensive literature available for images, the field of videos is still relatively unexplored. On the other hand, the performance of a model in action recognition is heavily affected by domain shift. In this paper, we propose a simple and novel UDA approach for video action recognition. Our approach leverages recent advances on spatio-temporal transformers to build a robust source model that better generalises to the target domain. Furthermore, our architecture learns domain invariant features thanks to the introduction of a novel alignment loss term derived from the Information Bottleneck principle. We report results on two video action recognition benchmarks for UDA, showing state-of-the-art performance on HMDB ↔ UCF, as well as on Kinetics→NEC-Drone, which is more challenging. This demonstrates the effectiveness of our method in handling different levels of domain shift. The source code is available at https://github.com/vturrisi/UDAVT.
A Novel Approach to Identify Difficult Words in English to Promote Vocabulary Acquisition of Children Dually Diagnosed with Autism Spectrum Disorder and Hearing Loss

Usage of complex words causes significant problems not only in reading but in writing as well and eventually leads to poor academic achievement of students, poorer particularly for hearing impaired children. The dual diagnosis of Autism Spectrum Disorder (ASD) and hearing impairment pose additional challenges mainly due to the difficulties that come with making accurate decisions. Hence, parents must be provided with the information about the signs and symptoms of ASD and deafness or partial hearing loss, as well as appropriate intervention strategies. Although different learning activities can be used to enlarge such children’s vocabulary, if the presented words are difficult to learn, it will be very hard to realize this. Identifying difficult words and replacing them with simple ones both make the readability of a text easier and help such children enhance their vocabulary knowledge in a shorter period of time. Therefore, in this study we propose a classification approach that identifies difficult words among a given set of words in English. The lexical and semantic features of the words in the dataset were extracted based on the language rules specific to hearing impaired children. In the classification approach, five popular classification algorithms were used and the algorithms' performance in identifying difficult words was evaluated using various performance metrics. As the results show, the K-Nearest Neighbors algorithm is the most suitable algorithm for identifying difficult words in English for the target group.
Compartive Study On Parkinson’s Disease Using Machine Learning

Parkinson’s disease is a nerve disorder disease which affects the movements or functioning of a body. There are five stages in Parkinson’s disease, in the starting stage speech becomes soft and the condition goes on worsen by the last stage where the individual cannot stand on their own. Many machine learning models give a moderate level of accuracy. In order to increase the accuracy of Machine Learning models to detect Parkinson’s disease Boosting algorithm is implemented in framework. Boosting is an ensemble machine learning algorithm which increases the model accuracy to emphasize that the accuracy of previous trained models is less or misclassified. One of the ensemble machine Learning algorithms that uses Gradient boosting Framework is XGBoost, it is a decision-tree-based algorithm. This project would be trying to incorporate XGBoost for detection of Parkinson’s disease to have a higher rate of accuracy, precision and recall. The inputs for this algorithm will be several voice recordings of patients and healthy people to identify early-stage symptoms.
Multi-Agent Routing Optimization for Underwater Monitoring

The exploration of the underwater environment has always been a relevant field for science and technology, in order to enlarge our knowledge of this mainly unexplored environment. Some of the main challenges in the underwater environment are the vehicles’ limited battery life and the absence of infrastructures for communication or geo-positioning. Because of these reasons, large vehicles able to navigate collecting data for long distances underwater require performant and high-cost instruments. In this paper, we propose a different approach applied to a fleet of small autonomous underwater vehicles (AUVs) $$^{*}$$ . We assume a coarse grained map is already available from satellite measurements and the set of robots is used to get detailed information on sea bottom features. A coverage path planning strategy is designed for the AUVs’ fleet. This is based on an optimization problem belonging to the large class of vehicle routing problems. We provide an exact method for finding both the optimal routing for the fleet of drones and their starting position. This is a tool that can be applied on monitoring mission performed a fleet of AUVs. Specifically, a typical use case is the monitoring of health status and growth rate of corals on the coral reef near the coast.
MSCE-Net: Multi-scale Spatial and Channel Enhancing Net based on Attention for Cloud Image Classification

The difference between cloud types is mainly present in appearance but has problems of high similarity and insignificant appearance between classes. In addition, the existing approaches separately use the channel and spatial attention machines in a stage of the network that loses the attention in multiple scales and the relationship between channel and spatial attention. Therefore, this study proposed a framework, namely multi-scale spatial and channel enhancing Net (MSCE-Net), based on attention mechanisms to enhance the feature learning ability of the network for cloud image classification. The designed multi-scale spatial and channel attentions consider information between scales and concatenate spatial and temporal attention at each scale to consider the relationship between spatial and channel attention and make the network focus on the appearance of the cloud image, which is significantly different between various clouds. The main contributions of this paper are as follows: (1) we construct a new cloud image dataset with ten categories with a total of 6000 images; (2) we generate multi-scale spatial-based and channel-based enhancing factors to enhance the spatial and channel features, respectively, at each scale; (3) we concatenate spatial and temporal attention at each scale to consider the relationship between spatial and channel attention at each scale. Experimental results show that the accuracies of the proposed framework suppress the state-of-the-art approaches and achieve 85.11%. Moreover, the ablation experiments prove the eﬃciency of the proposed strategies.
The Role of the Number of Examples in Convolutional Neural Networks with Hebbian Learning

Both synaptic plasticity rules (the so-called Hebbian rules) and Convolutional Neural Networks are based on or inspired by well-established models of Computational Neuroscience about mammal vision. There are some theoretical advantages associated with these frameworks, including online learning in Hebbian Learning. In the case of Convolutional Neural Networks, such advantages have been translated into remarkable results in image classification in the last decade. Nevertheless, such success is not shared in Hebbian Learning. In this paper, we explore the hypothesis of the necessity of a wider dataset for the classification of mono-instantiated objects, this is, objects that can be represented in a single cluster in the feature space. By using 15 mono-instantiated classes, the Adam optimizer reaches the maximum accuracy with fewer examples but using more epochs. In comparison, Hebbian rule BCM demands more examples but keeps using real-time learning. This result is a positive answer to the principal hypothesis and enlights how Hebbian learning can find a niche in the mainstream of Deep Learning.
Image Grid Recognition and Regression for Fast and Accurate Face Detection

CNN-based face detection methods have achieved significant progress in recent years. However, for high performance face detection, there are still many challenging problems, e.g., the speed-accuracy balance and the performance degradation in adverse conditions. In this paper, by taking advantage of the characteristic of CNN, we propose an effective anchor generation and bounding-box regression method that can make a good balance between speed and accuracy, and also work well in bad conditions. The classic structure of CNN produces pyramid-like feature maps due to the pooling or other downscale operations. According to the size of a feature map, we divide the image into grids. Each grid corresponds to a point in the feature map. We make the corresponding feature point responsible for identifying the content of the grid. If this grid area belongs to the face area, it is a natural anchor for face bounding-box regression. Since this anchor is square, it is reasonable to use it to predict the face bounding-box which is square-like. The points in the lower-level feature map correspond to smaller grids, which are dedicated to predicting the bounding-boxes of smaller faces. The points in the higher-level feature maps correspond to larger grids, which are responsible for predicting the bounding-boxes of larger faces. Hence our method can effectively detect multi-scale faces. With this effectiveness, our method can achieve a high detection accuracy using fewer parameters which leads to a fast detection speed. The experiments demonstrate the effectiveness of our method.
The Relationship Between Social Experiment Video of ‘Catcalling’ in YouTube and Multimodal Analysis

The existence of language in society helped us minimize misunderstanding on the interpretation of meaning as humans. The different style of language makes human understand the meaning in different ways. A multimodal anlaysis described the language from various viewpoints because the relations in multimodal aspects symmetrically form a meaning. This study showed the social problem that occurs in society is called catcalling. Catcalling is a form of sexual harassment described as giving verbal or nonverbal sexual attention by strangers on the street or in public places. This study analyzed catcalling on social experiment video in YouTube Channel by Rob Bliss using multimodal analysis. This study focused on how the semiotic systems interpret the catcalling meaning in the social experiment video and explain how catcalling is considered as sexual harassment in multimodal viewpoints. This study used descriptive quantitative as a research method. The data will be processed through ELAN (EUDICO Linguistic Annotator) Version 5.9. This study found the verbal and nonverbal language in the social experiment video. Meanwhile, multimodal analysis by Michele Anstey and Geoff Bull demonstrated three semiotic systems contained in the social experiment video: 1) Linguistic, 2) Visual, and 3) Gestural. The theory explains that multimodal can be used as an analysis media focused on text, images, and video analysis. This study tries to provide an explanation for the understanding of meaning extensively with the existence of more than one semiotic system found in the social experiment video. Following this study, catcalling can be seen from various existing viewpoints, not only by women but also by men as the perpetrators of catcalling.
Supervised Contrastive Learning-Based Deep Hash Retrieval for Remote Sensing Image

With the development of remote sensing technology, the earth observation data shows a blowout growth. How to quickly and accurately retrieve the target content from the massive data has become a noteworthy task. Recently, the methods based on convolutional neural networks (CNN) have been far ahead in remote sensing retrieval. However, due to the diversity of sources acquired, remote sensing images with the same semantic information may have great visual differences. The pre-trained CNN is not always able to successfully extract representative and distinguishing features to recognize the target content. To address this problem, a supervised contrastive learning-based deep hash retrieval method (SCLDHR) is introduced in this paper, which effectively uses label information to gather image features belonging to the same class and separate image features of different classes in the embedding space. Furthermore, a quantization function in the hash space is designed to improve hashing quality. The experimental results conducted on three datasets show that SCLDHR can achieve competitive retrieval performance compared with state-of-the-art methods.
On the Linguistic Argument for the Adoption of the Library Subject Heading <i>Noncitizen</i>

This linguistic study contributes to the decades-long discussion on the inappropriateness of the Library of Congress Subject Heading illegal aliens by examining its absence in a corpus of US Supreme Court oral arguments and by evaluating automatic translation tool results related to keywords as well as a corpus extract. This linguistic study confirms the ideological bias of the illegal aliens subject heading compared to a plethora of other expressions that legal scholars may use to describe the situations implied under the more neutral umbrella term noncitizen. The automatic translation results for alien and illegal alien also support the notion that the term illegal alien is confined to a United States historical context, which hinders its international comprehension as a subject heading.
Understanding Designers’ Use of Prototypes in Smart Product Design

Various smart products with sensing and intelligent features have been emerging in the market, the design of which encounters new challenges from continuous technological advancement related to hardware, software, and the Internet. Among them, the communication barrier between designers and stakeholders from disparate backgrounds is a crucial one to overcome, considering that the information explained by the designers strongly depends on their professional experience. Previous literature clearly highlights the importance of prototyping in product development to support communication and development of ideas as they emerge in the team and beyond. The importance of communication and collaborative work/development of ideas is even more emphasised on smart product development because of its interdisciplinary nature; hence, appropriate prototyping is also vital. However, research related to prototyping in smart product design is scarce. This study aims to comprehensively understand the differences in the prototyping between smart products and traditional ones (non-smart features). A survey was conducted to capture prototyping practices in the early stage of smart product design (SPD) compared to traditional product design (TPD) group. It is found that there are differences in the frequency of use and satisfaction with different prototyping tools between the SPD and TPD groups. From the perspective of prototyping tools’ dimensions (of fidelity), SPD designers pay more attention to the interactivity and environment of the tools, especially at the front end of the design process. The findings contribute to better supporting practices, and potentially guide the development of future prototyping tools that are specific to SPD.
Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition

This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER). The idea is to create complementary representations promoting diver-sity by varying the autoencoders' initialization, architecture, and training data. SVM, Bagging, Random Forest, and a dynamic ensemble selection method are evaluated as final classification methods. Experimental results on JAFFE and Cohn-Kanade datasets using a leave-one-subject-out protocol show that FER methods based on the proposed diverse representations compare favorably against state-of-the-art approaches that also explore unsupervised feature learning.
A Novel Deep Learning Model for Link Prediction of Knowledge Graph

Link prediction of knowledge graph is a relatively widely studied task in knowledge graph completion, the purpose of which is to complete the incomplete triples according to the original knowledge triples of the knowledge graph. To solve the problem of handling the heterogeneous neighborhood and the injective problem in the link prediction of knowledge graph, we propose a novel deep learning model called Transformation Assumptions with Message Passing Aggregation Network (TMPAN). TMPAN can effectively deal with the heterogeneous neighborhood information by introducing TransGCN’s transformation assumptions into DPMPN, which transforms head entities to tail entities using relationships as transformation operators. TMPAN also solves the injective problem caused by the single-aggregation operation by employing the multiple aggregators of the Principal Neighborhood Aggregation network (PNA) model. We comprehensively evaluate our model compared to typical baseline models by conducting experiments on two public datasets, FB15K-237 and YAGO3-10. The experimental results show the effectiveness of our model in the link prediction task.
Adv-IFD: Adversarial Attack Datasets for An Intelligent Fault Diagnosis

Deep learning techniques have been widely applied for performing intelligent fault diagnosis (IFD) for applications such as bearing fault diagnosis, wind turbines and drilling operations. Notwithstanding the huge success of deep learning-based models for intelligent fault diagnosis, their vulnerability against adversarial attacks have been neglected to a great extent. The adversarial attacks aim to craft an adversarial sample by addition of imperceptible perturbation to its clean data sample. In this paper, we investigate the performance of different deep models against four state-of-the-art adversarial attacks. A total of four deep models have been tested against untargeted white-box adversarial attacks. Moreover, analysis of transferability of adversarial examples across different deep models is also inspected. Experiments and results reveal that deep models for IFD are highly susceptible to adversarial examples crafted with four state-of-the-art adversarial attacks. The proposed work presents an extensive insight on adversarial samples of machinery vibration signals of the CWRU dataset. Additionally, we are also releasing a first ever adversarial attack dataset for IFD, i.e. Adv-IFD. The code used in this work and adversarial attack datasets are available at: https://github.com/achyutmani/ADV-IFD
UAV 3-D Path Planning Based on High-Resolution DSM, DTM, and True Orthomosaic

In this letter, it is proposed an offline, shortest, and safer path planning algorithm for a multirotor Unoccupied Aerial Vehicle (UAV). The aim is to generate a UAV trajectory that avoids specific obstacles on the ground while maintaining a safe and legal flight height. A free-collision path is generated by simulating the UAV trajectory over a restriction map, obtained contemplating buildings footprint and surroundings over a true-orthomosaic, using the A* algorithm for 2D path planning, combined with both the Digital Terrain Model (DTM) and Digital Surface Model (DSM) to analyze the altimetric safety trajectory. The sub-optimal path is smoothed by cartographic generalization, adopting the parameter of generalization equal to the binary image resolution that corresponds to the restriction map. The experiments show that the algorithm can generate a 3D path that is efficient and safe, for both the UAV and the objects on and above ground, according to the regulations to access the Brazilian airspace.
Secure, Interoperable, End-to-End Industry 4.0 Service Platform for Lot-Size-One Manufacturing

The paper introduces a novel secure, interoperable, and end-to-end industry 4.0 service platform for lot-size-one man-ufacturing. The challenges faced by the European manufacturing industry that prevent it from embracing such a new paradigm are outlined. Then, the platform architecture and operational steps are summarised.
The Creative Legacy of Ivan Sergeevich Shmelev: the Writer's Archive in the Russian State Library

The article is devoted to the history of the formation of the personal archive of the famous Russian writer Ivan Sergeyevich Shmelev (1873-1950), stored in the Department of Manuscripts of the Russian State Library (RSL). The composition of the fund is disclosed, its most important sections are listed. Through the main events of I.S. Shmelev's life, his creative path is shown, works written in the period 1894 — early 1920s, i.e. the time of I.S. Shmelev's life in Russia before emigration, draft autographs stored in the archive in several editions are presented. The sections of the fund, including articles, notebooks, sketches and excerpts of unidentified works, were analyzed, the correspondence of the writer, the materials of S.I. Shmelev, his son, as well as various materials of the 1925-1940s, documents related to the memory of the writer were studied. Individual materials of I.S. Shmelev, stored in other funds of the RSL, are reflected.