Reviewerly/exHarmony huggingface dataset:
How My Students and I (Re)Discovered the Joy of Computing in CS2

research-article Share on How My Students and I (Re)Discovered the Joy of Computing in CS2 Author: Victoria C. Chávez Northwestern University Northwestern UniversityView Profile Authors Info & Claims ACM InroadsVolume 14Issue 2June 2023pp 36–39https://doi.org/10.1145/3596918Published:19 May 2023Publication History 0citation20DownloadsMetricsTotal Citations0Total Downloads20Last 12 Months20Last 6 weeks20 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Attention-based Bayesian inferential imagery captioning maker

Computer Vision (CV) and Natural Language Processing (NLP) algorithms are used to provide textual descriptions of an image known as "image captioning." The performance of contemporary models has been improved by applying Deep Learning (DL) approaches to this issue. There is no way to tell whether parts of a picture are more significant than others, or to explain why certain phrases were chosen as captions. As a workaround for these issues, this study suggests an explainable image captioning model, which uses certain elements in an image to produce captions that can visually explain what they represent. An evaluation of the model's efficacy has been done using datasets like MSCOCO, Flickr8K, and Flickr30K. Some qualitative findings are reported. The proposed image captioning model achieved an accuracy of 90%.
A Stand-Alone Augmented Reality Intervention for Chronic Pain Using Embodied Systolic Stimulation

Chronic pain presents a tremendous personal, societal, and financial burden. Treatment options are often limited to managing symptoms as opposed to treating the causes, and the consequences of pharmacological treatments can be immensely harmful as evidenced by the on-going opioid epidemic. Virtual Reality interventions and digital therapeutics provide a new tool for therapists but are currently limited to pain-distraction or digitizing traditional approaches that are similarly focused on pain management. However, recent research suggests that potentially neurorestorative, non-pharmacological treatments are conceivable using personalized, embodied, multimodal feedback. In particular, i) providing visual feedback of the affected body part, ii) increasing its embodiment using multisensory stimulation, and iii) timing stimulation to the systole of the ECG cycle have been linked with analgesic effects in induced acute and chronic pain. Based on these findings and methods, we here propose a stand-alone solution that aims to provide neurorestorative feedback for individuals suffering from chronic pain by visualizing interoceptive signals and mapping them onto the patients affected body part using Augmented Reality, tapping into the three aforementioned principles. Our initial findings indicate the feasibility, acceptance, and stimulus adherence of the device and intervention, whose efficacy will be evaluated in an upcoming randomized controlled clinical trial.
Sample Reweighting for Label Denoising of Neural Activity Data

Neural decoding is a powerful technique to explore the relationship between neural activities and behaviors. It often needs massive accurately labeled data to train a model for behavior prediction. However, it is not easy to obtain accurate annotations for massive data, and the label noise is sometimes inevitable and needs to be denoised first. For annotation correction, we propose a sample reweighting method to denoise noisy labels. This method utilizes a small clean validation dataset to assign weights to the training data with label noise. A deep neural network model can be trained based on the weighted training data and the validation data. Based on the neural network model, new labels can be predicted for training data to realize the label denoising. The label denoising experiment is conducted on a functional magnetic resonance imaging dataset with class imbalance. The results show that the sample reweighting method can effectively denoise labels under different annotation qualities or noise levels for each class and it outperforms the baseline methods (validation only and semi-supervised learning). The sample reweighting method can also effectively handle the class imbalance problem. The proposed method is an effective way to tackle the noisy label problem in neural decoding.
EM Side-Channel Analysis of Data Leakage Near Embedded Bluetooth Low Energy Modules

A two-phase measurement method is presented to evaluate Bluetooth low energy (BLE) modules’ vulnerability to electromagnetic side-channel analysis attacks. In both phases, signals are collected by probing the fields near the surface of a chip that operates as a generic attribute profile (GATT) server, while it receives and processes data via Bluetooth. In Phase I, first optimal measurement configurations—time intervals, probe positions, probe orientations—are identified by computing the analysis-of-variance (ANOVA) F-statistic on signals collected from a multitude of such configurations while a few carefully chosen data are repeatedly sent to the GATT server. Then, these configurations are used to construct a reference database by collecting additional signals as specific additional data are sent to the GATT server. In Phase II, the chip is monitored using the optimal measurement configurations and the collected signals are compared to those in the database to extract the data. This side-channel analysis attack is shown to recover the Hamming weights of arbitrary data sent to the GATT server with ~99% success rate.
Deep Learning-based RF Fingerprint Authentication with Chaotic Antenna Arrays

Radio frequency (RF) fingerprinting is a tool which allows for authentication by utilizing distinct and random distortions in a received signal based on characteristics of the transmitter. We introduce a deep learning-based authentication method for a novel RF fingerprinting system called Physically Unclonable Wireless Systems (PUWS). An element of PUWS is based on the concept of Chaotic Antenna Arrays (CAAs) that can be cost effectively manufactured by utilizing mask-free laser-enhanced direct print additive manufacturing (LE-DPAM). In our experiments, using simulation data of 300 CAAs each exhibiting 4 antenna elements, we test 5 different convolutional neural network (CNN) architectures under different channel conditions and compare their authentication performance to the current state-of-the-art RF fingerprinting authentication methods.
Distributed Truss Computation in Dynamic Graphs

Large-scale graphs usually exhibit global sparsity with local cohesiveness, and mining the representative cohesive subgraphs is a fundamental problem in graph analysis. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -truss is one of the most commonly studied cohesive subgraphs, in which each edge is formed in at least <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k-2$</tex> triangles. A critical issue in mining a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -truss lies in the computation of the trussness of each edge, which is the maximum value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> that an edge can be in a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -truss. Existing works mostly focus on truss computation in static graphs by sequential models. However, the graphs are constantly changing dynamically in the real world. We study distributed truss computation in dynamic graphs in this paper. In particular, we compute the trussness of edges based on the local nature of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -truss in a synchronized node-centric distributed model. Iteratively decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition algorithm. Moreover, the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the computation. Extensive experiments have been conducted to show the scalability and efficiency of the proposed algorithm.
A Copyright-Preserving and Fair Image Trading Scheme Based on Blockchain

With the proliferation of the Internet, particularly the rise of social media, digital images have gradually become an important part of life, and trading platforms have emerged for buying and selling images. However, traditional image trading service providers may disclose users' private information for profit. Additionally, many image trading platforms disregard the fairness of a transaction and the issue of copyright protection after an image is sold. This neglect harms the interests of users and affects their enthusiasm for trading. A secure way to safely transact images is needed. We proposed a copyright-preserving and fair image trading scheme based on blockchain, which combines amplifying locality-sensitive hashing with searchable symmetric encryption to achieve safe image retrieval on blockchain and ensure the credibility of the image retrieval process. Additionally, we use digital fingerprint and watermark technologies to realize the copyright protection of images and use smart contracts to achieve fair transaction processes. The experimental results show that our scheme can protect image copyrights and realize a fair trading process while ensuring efficiency.
THE SEMANTIC – STRUCTURAL PRIORITIES OF ICT TERMINOLOGY

The article is devoted to the study of the structural and semantic characteristics of computer terminology in English. The peculiarities of the word-formation process and the functioning of English computer terms are analysed. Moreover, as the technology is coming into every sphere of the life, the terminology is also should be obtained each branch of science eventually. This importance of ICT terminology is discussed widely.
Solution to a general version of a degree sequence variant of the Erdős–Sós conjecture

Let P denote a graph property, such as "being k-connected", "being hamiltonian", or "containing H as a subgraph", etc. A graphic sequence π is potentially P-graphic if there is a realization of π having the property P. In 1991, Erdős et al. introduced the following problem: Determine the minimum even integer σ(P,n) such that every n-term graphic sequence with sum at least σ(P,n) is potentially P-graphic. The parameter σ(P,n) is known as the potential function of P, and can be viewed as a degree sequence variant of the classical extremal function ex(P,n). A simple graph G is an ℓ-tree if G=Kℓ+1, or G has a vertex whose neighborhood is a clique of order ℓ, and G−v is an ℓ-tree. Clearly, 1-trees are the usual trees. For ℓ≥1 and k≥ℓ+1, let P(ℓ,k) be the graph property "containing every ℓ-tree on k vertices as a subgraph". For ℓ=1, Yin and Li determined σ(P(1,k),n) for k≥2 and n≥92k2+192k. This is a degree sequence variant of the Erdős–Sós conjecture. For ℓ=2, Zeng et al. determined σ(P(2,k),n) for k≥3 and n sufficiently large. In this paper, we consider the most general case ℓ≥3, and determine σ(P(ℓ,k),n) for ℓ≥3, k≥ℓ+1 and n sufficiently large.
Multi-Stage Prompt Tuning for Political Perspective Detection in Low-Resource Settings

Political perspective detection in news media—identifying political bias in news articles—is an essential but challenging low-resource task. Prompt-based learning (i.e., discrete prompting and prompt tuning) achieves promising results in low-resource scenarios by adapting a pre-trained model to handle new tasks. However, these approaches suffer performance degradation when the target task involves a textual domain (e.g., a political domain) different from the pre-training task (e.g., masked language modeling on a general corpus). In this paper, we develop a novel multi-stage prompt tuning framework for political perspective detection. Our method involves two sequential stages: a domain- and task-specific prompt tuning stage. In the first stage, we tune the domain-specific prompts based on a masked political phrase prediction (MP3) task to adjust the language model to the political domain. In the second task-specific prompt tuning stage, we only tune task-specific prompts with a frozen language model and domain-specific prompts for downstream tasks. The experimental results demonstrate that our method significantly outperforms fine-tuning (i.e., model tuning) methods and state-of-the-art prompt tuning methods on the SemEval-2019 Task 4: Hyperpartisan News Detection and AllSides datasets.
Optimal hybrid classification model for event recommendation system

There is a growing need for recommender systems and other ML-based systems as an abundance of data is now available across all industries. Various industries are currently using recommender systems in slightly different ways. These programs utilize algorithms to propose appropriate products to consumers based on their prior choices and interactions. Moreover, Systems for recommending events to users suggest pertinent happenings that they might find interesting. As opposed to an object recommender that suggests books or movies; event-based recommender systems typically require distinct algorithms. A developed event recommendation method is introduced which includes two stages: feature extraction and recommendation. In stage, I, a Set of features like personal willingness, community willingness, informative content, edge weight, and node interest degree are extracted. Stage II of the event recommendation system performs a hybrid classification by combining LSTM and CNN. In the LSTM classifier, optimal tuning is done by Improvised Cat and Mouse optimization (ICMO) algorithm. The results of the ICMO technique at an 80% training percentage have the maximum sensitivity value of 95.19%, whereas those of the existing approaches SSA, DINGO, BOA, and CMBO have values of 93.89%, 93.35%, 92.36%, and 92.24%. Finally, the best result is then determined by evaluating the whole performance.
An Ontology as Support for Specification of Non-functional Requirements of AAL Systems Considering Compliance Aspects

Abstract Ambient assisted living (AAL) is a technological approach that emerged to meet the demands of seniors and people with disabilities. Because they are considered complex and multidisciplinary systems, it is necessary to identify and define which elements need to compose these systems. These challenges and the need for assistance in specifying requirements have led the academic community to explore and establish new approaches for the development of AAL systems. For this, there is the integration of different areas of knowledge, such as ontology, requirements engineering and compliance. Therefore, this work presents a central ontology to support the specification of requirements in AAL systems, where the elements that are part of the type of system are integrated. With the use of this ontology, it was possible to develop a domain ontology for vertical elevation platforms, where a validation was carried out with the industry in relation to the elements that constitute it and, later, a scenario was built to verify the syntactic correction of ontology. With the use of ontology, it will be possible to standardize the understanding of the associated terms and, at the same time, verify the relationships between the elements and help the designer in the requirements specification stage.
Building robust neural networks under adversarial machine learning attacks by using biologically-inspired neurons

At the core of every artificial neural network, lie neurons which are approximate models of their biological counterparts. However, networks that are made of artificial neurons can become unreliable in the face of adversarial machine learning attacks. We traced this issue back to its roots and created a reliable artificial neuron (called binomial neuron) that can resist adversarial attacks such as DeepFool, Fast Gradient Sign Method (FGSM), Pixel, Square and Carlini and Wagner's (C&W) better. We propose to replace the input value of an artificial neuron with the expectation of a Bernoulli random variable. This random variable is made up of the spikes fired by the previous-layer binomial neuron upon crossing a randomly generated threshold. Depending on how long the neuron waits to aggregate the spikes and find the expectation, different model variants can be created. We conducted extensive experiments to evaluate the reliability of this new neuron. The results confirm the increase in the robustness/accuracy under adversarial attacks, or equivalently, the substantial increase in the amount of distortion attackers need to add to images in order to create successful adversarial samples. These distortions pass the perceptibility range to the extent that approach total image destruction.
The 2022 World Computer Chess Championships

After two years of pandemic-related cancellations, the World Computer Chess Championship returned in July 2022. The title of World Computer Chess Champion, first contested in 1974, was successfully defended by Komodo. The World Chess Software Championship was won by Ginkgo. The World Computer Speed Chess Championship title was shared by Leela Chess Zero and Ginkgo.
Recurrent Neural Network to Predict Saccade Offset Time Points from Electrooculogram Signals for Automatic Measurement of Eye-Fixation-Related Potential

Eye-fixation-related potential (EFRP)—an event-related potential that is time-locked to the saccade offset (SO)—can be measured without synchronizing with time when external stimuli occur. Such an advantage in measurement enables the mean amplitude of the EFRP to be used to estimate the cognitive workload, which is known to change the amplitude, under real-world conditions. However, to observe EFRPs reliably, the SO timing must be correctly and consistently determined in milliseconds owing to the high temporal resolution of the electroencephalogram (EEG). As the electrooculogram (EOG) is commonly measured simultaneously with the EEG and the SO timing is reflected as a steep change in the waveforms, attempts have been made to determine the SO timing from EOG signals visually (the VD method). However, the SO timing detected by the VD method may be inconsistent across trials. We propose a gated recurrent unit—a recurrent neural network model—to detect the SO timing from EOGs consistently and automatically. We used EOG data from a task that mimics visual inspections, in which participants periodically traversed their eyes from left to right, for the model training. As a result, the amplitudes of the EFRPs based on the proposed method were significantly larger than those based on the VD method and the previous automatic method. This suggests that the proposed method can prevent the decrease in EFRP amplitudes owing to the inconsistent determination of the SO timing and increase the applicability of cognitive workload estimation using the EFRP in real-world environments.
A risk-averse learning machine via variance-dependent penalization

Based on Hoeffding’s inequality, many popular regression and classification models in supervised learning relax the expected risk minimization problem to the empirical risk minimization problem. Nevertheless, the recent theoretical results disclose that the bound of Bernstein’s inequality - which includes variance information - is often significantly tighter than the bound of Hoeffding’s inequality. In this paper, based on the empirical Bernstein bound, we proposed a risk-averse learning machine, which can achieve better generalization performance by trading off good loss performance (approximation error) and small variance (estimation error) as well as suitable complexity of the model. We prove that the resulting learning machine is tractable for many popular loss functions. Moreover, to solve our model which is mostly non-convex because of the square root term, we introduce an extra variable to get rid of the square root and obtain an optimization problem that is convex in most cases for two variables respectively. Then Newton’s method can be used to solve it alternately. The experimental results on artificial and benchmark datasets demonstrate that the proposed models can achieve better performance compared to other existing empirical risk minimization models.
Using machine learning to predict cardiovascular risk using self-reported questionnaires: Findings from the 45 and Up Study

Machine learning has been shown to outperform traditional statistical methods for risk prediction model development. We aimed to develop machine learning-based risk prediction models for cardiovascular mortality and hospitalisation for ischemic heart disease (IHD) using self-reported questionnaire data.The 45 and Up Study was a retrospective population-based study in New South Wales, Australia (2005-2009). Self-reported healthcare survey data on 187,268 participants without a history of cardiovascular disease was linked to hospitalisation and mortality data. We compared different machine learning algorithms, including traditional classification methods (support vector machine (SVM), neural network, random forest and logistic regression) and survival methods (fast survival SVM, Cox regression and random survival forest).A total of 3687 participants experienced cardiovascular mortality and 12,841 participants had IHD-related hospitalisation over a median follow-up of 10.4 years and 11.6 years respectively. The best model for cardiovascular mortality was a Cox survival regression with L1 penalty at a re-sampled case/non-case ratio of 0.3 achieved by under-sampling of the non-cases. This model had the Uno's and Harrel's concordance indexes of 0.898 and 0.900 respectively. The best model for IHD hospitalisation was a Cox survival regression with L1 penalty at a re-sampled case/non-case ratio of 1.0 with Uno's and Harrel's concordance indexes of 0.711 and 0.718 respectively.Machine learning-based risk prediction models developed using self-reported questionnaire data had good prediction performance. These models may have the potential to be used in initial screening tests to identify high-risk individuals before undergoing costly investigation.