Reviewerly/exHarmony huggingface dataset:
Optimal Rate List Decoding over Bounded Alphabets Using Algebraic-geometric Codes

We give new constructions of two classes of algebraic code families that are efficiently list decodable with small output list size from a fraction 1-R-ε of adversarial errors, where R is the rate of the code, for any desired positive constant ε. The alphabet size depends only ε and is nearly optimal. The first class of codes are obtained by folding algebraic-geometric codes using automorphisms of the underlying function field. The second class of codes are obtained by restricting evaluation points of an algebraic-geometric code to rational points from a subfield . In both cases, we develop a linear-algebraic approach to perform list decoding, which pins down the candidate messages to a subspace with a nice “periodic” structure. To prune this subspace and obtain a good bound on the list size, we pick subcodes of these codes by pre-coding into certain subspace-evasive sets that are guaranteed to have small intersection with the sort of periodic subspaces that arise in our list decoding. We develop two approaches for constructing such subspace-evasive sets. The first is a Monte Carlo construction of hierearchical subspace-evasive (h.s.e.) sets that leads to excellent list size but is not explicit. The second approach exploits a further ultra-periodicity of our subspaces and uses a novel construct called subspace designs , which were subsequently constructed explicitly and also found further applications in pseudorandomness. To get a family of codes over a fixed alphabet size, we instantiate our approach with algebraic-geometric codes based on the Garcia–Stichtenoth tower of function fields. Combining this with pruning via h.s.e. sets yields codes list-decodable up to a 1-R-ε error fraction with list size bounded by O (1/ε), matching the existential bound for random codes up to constant factors. Further, the alphabet size can be made exp ( Õ (1/ε 2 )), which is not much worse than the lower bound of exp (Ω (1/ε)). The parameters we achieve are thus quite close to the existential bounds in all three aspects (error-correction radius, alphabet size, and list size) simultaneously. This construction is, however, Monte Carlo and the claimed list-decoding property only holds with high probability. Once the code is (efficiently) sampled, the encoding/decoding algorithms are deterministic with a running time O _ε ( N c ) for an absolute constant c , where N is the code’s block length. Using subspace designs instead for the pruning, our approach yields the first deterministic construction of an algebraic code family of rate R with efficient list decoding from 1-R-ε fraction of errors over an alphabet of constant size exp (Õ(1/ε 2 )). The list-size bound is upper bounded by a very slowly growing function of the block length N ; in particular, it is at most O(log ( r ) N ) (the r th iterated logarithm) for any fixed integer r . The explicit construction avoids the shortcoming of the Monte Carlo sampling at the expense of a slightly worse list size.
Deep learning for human-computer interaction

column Share on Deep learning for human-computer interaction Authors: Huy Viet Le University of Stuttgart University of StuttgartView Profile , Sven Mayer LMU Munich LMU MunichView Profile , Niels Henze University of Regensburg University of RegensburgView Profile Authors Info & Claims InteractionsVolume 28Issue 1January - February 2021 pp 78–82https://doi.org/10.1145/3436958Online:23 December 2020Publication History 1citation6,902DownloadsMetricsTotal Citations1Total Downloads6,902Last 12 Months1,392Last 6 weeks106 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Recent Trends in Software Quality Interrelationships: A Systematic Mapping Study

Despite the importance of software qualities, they are not well understood, especially in the context of the interrelationships between qualities. A number of systematic mapping studies have been conducted prior to 2015 to summarize the literature on the topic and to identify research gaps. To provide a better understanding of the current state of the art, we conducted a systematic mapping study on relevant studies from 2015 to 2019 through a database search and a subsequent snowballing approach. In total, 18 studies were selected as the study subjects wherein we evaluated the types of software quality interrelationships and the qualities that comprise them. Based on our findings, we report on the progress made to address previously identified research gaps.
Separation of Concerns Within Robotic Systems Through Proactive Computing

In this short paper, we first introduce a possible new model for designing and implementing software in robotic systems. This model is based on proactive scenarios, coded through dynamic sets of condition-action rules. Each scenario embeds the required rules and can be assembled dynamically with others, allowing the proactive system to achieve a unique objective or behavior and instruct the robot accordingly. Furthermore, a scenario is not aware of the existence of the other scenarios. In fact, it only contains information about a predefined central scenario, which oversees global decision making. In addition, each scenario knows where to enter its suggestions, thus allowing for a high degree in terms of separating concerns and modularity of code. Consequently, allowing easier development, testing and optimization of each scenario independently, possible reuse in different robots, and finally, a faster achievement of robust and scalable robotics software. We then show how to apply this programming model and its functionalities during runtime, by a proof of concept consisting of a virtual robot deployed in the Webots™ simulator. This simulator is controlled with four proactive scenarios (plus the central one), in charge of three different objectives.
A Comparative Study of Pretrained Language Models for Automated Essay Scoring with Adversarial Inputs

Automated Essay Scoring (AES) is a task that deals with grading written essays automatically without human intervention. This study compares the performance of three AES models which utilize different text embedding methods, namely Global Vectors for Word Representation (GloVe), Embeddings from Language Models (ELMo), and Bidirectional Encoder Representations from Transformers (BERT). We used two evaluation metrics: Quadratic Weighted Kappa (QWK) and a novel "robustness", which quantifies the models' ability to detect adversarial essays created by modifying normal essays to cause them to be less coherent. We found that: (1) the BERT-based model achieved the greatest robustness, followed by the GloVe-based and ELMo-based models, respectively, and (2) fine-tuning the embeddings improves QWK but lowers robustness. These findings could be informative on how to choose, and whether to fine-tune, an appropriate model based on how much the AES program places emphasis on proper grading of adversarial essays.
ExtPFA: Extended Persistent Fault Analysis for Deeper Rounds of Bit Permutation Based Ciphers with a Case Study on GIFT

Persistent fault analysis (PFA) has emerged as a potent fault analysis technique that can recover the secret key by influencing ciphertext distribution. PFA employs the persistent fault model that alters algorithm constants such as Sbox elements, and the fault endures until the system restarts. As the PFA fault model does not require high precision and controllability to carry out the attack, it has gained considerable attention from the cryptography research community. However, most of the research work highlights its application for investigating only the last round, albeit a persistent fault impacts all internal rounds too. In this work, we present an extension of the original PFA to recover deeper round keys of Substitution bit-Permutation Network (SbPN) based ciphers by leveraging its capability to affect every round. We use GIFT cipher as a case study and show the effectiveness of the proposed approach through simulation. We could recover the full master keys of both the GIFT cipher versions by retrieving the round keys up to the depth 2 and 4 for GIFT-128 and GIFT-64, respectively. We also analyzed the success rate of our approach on both the versions in two dimensions: Depth-wise and Hamming distance-wise. We observed that the number of ciphertexts required to recover the round key increases exponentially as we move deeper from the final round. Furthermore, the number of required ciphertexts to recover the key increases exponentially with Hamming distance between indexes of two identical elements in faulty Sbox. In GIFT-64, for Hamming distance of value 1 between the indexes, the round keys can be recovered in approximately 110, 290, and 750 ciphertexts for round number 28, 27, and 26, respectively, with a 100% success rate. For round 25, around 2000 ciphertexts are sufficient to recover the round key in 90% of the cases out of 1000 experiments. For GIFT-128, around 200 ciphertexts are enough to extract the last round key for the Hamming distance of value 1. For $$39^{th}$$ round, the round key can be recovered with a 100% success rate in roughly 380, 575, and 1100 ciphertexts for the Hamming distance 1, 2, and 3, respectively. However, for the same round with Hamming distance of value 4, the success rate is 75% for around 2000 ciphertexts.
A delay prediction model for high-speed railway: an extreme learning machine tuned via particle swarm optimization

Train delay prediction is a significant part of railway delay management, which is key to timetable optimization of Highspeed Railways (HSRs). In this paper, an extreme learning machine (ELM) tuned via particle swarm optimization (PSO) is proposed to predict train arrival delays of HSR lines. First, five characteristics (e.g., the plan running time between the present station and the next station, stations) are selected from nine characteristics as input variables for ELM by correlation coefficient matrix. Next, PSO algorithm is implemented to effectively resolve the hyperparameter adjustment of ELM, which overcomes tedious manual regulation for the number of hidden neurons. Finally, a case study of fifteen stations on Beijing-Kowloon (B-K) HSR line in China is proposed using the ELM tuned via PSO (ELM-PSO). The prediction performance of the proposed method is verified by comparison with six benchmark models. The results indicate that our method is superior to these baseline models in prediction accuracy.
Blockchain-based Solutions for IoT: A Tertiary Study

The combination of blockchain and Internet of Things (IoT) brings revolutionary changes to Industry 4.0. However, blockchain technology is immature now and the combination is under exploration. To understand the research status of applying blockchain-based solutions for IoT, we conduct a tertiary study that systematically reviews secondary studies in this domain. Through the processes of automated search and selection, this study obtains 11 secondary studies published between 2016 and 2020. Based on the rigorous data extraction and analysis, we have established a comprehensive overview of the current state, concerns, and critical findings of relevant studies focusing on blockchain-based solutions for IoT. Results involve typical challenges and characteristics of blockchain itself, the constrained nature of IoT, and specific domains that blockchain-based solutions are commonly applied to. Three directions are also highlighted for future research, i.e. addressing challenging quality attributes, consensus optimization, and integrating blockchain-based IoT with other new technologies.
Detection of Structural Behavior Anomalies in Hybrid Roof Systems

Hybrid roofs consist of a frame and flexible polymer membrane. The frame comprises steel beams, hinged struts and high-strength wire-ropes. During a long operational period the roofs are susceptible to adverse effects, which include designed impacts and unexpected influences. The latter ones result in, so-called, anomalous structural behavior, which should be detected as soon as it is possible in order to take corrective measures, preventing severe damage or structural collapse. The problem is aggravated with a huge manifold of eventual anomalies, which is not possible to be directly simulated in the design stage. Autoencoders, being a special type of Artificial Neural Networks, are used to distinguish anomalies from functionally operative behavior of the hybrid roofs. The more the input data deviate from normal structural response, the greater reconstruction error is produced by the Autoencoder. Thus, having received normal parameter set only, the Autoencoder is able to clearly detect anomalous structural response. Such an approach has already been successfully used in cyber- and financial security domains for intrusion and fraud detection, as well as in the field of monitoring of complex engineering systems. Its expansion on the hybrid roof systems contributes to operational reliability enhancement of promising building constructions.
Mastering Variation in Human Studies

The human factor is prevalent in empirical software engineering research. However, human studies often do not use the full potential of analysis methods by combining analysis of individual tasks and participants with an analysis that aggregates results over tasks and/or participants. This may hide interesting insights of tasks and participants and may lead to false conclusions by overrating or underrating single-task or participant performance. We show that studying multiple levels of aggregation of individual tasks and participants allows researchers to have both insights from individual variations as well as generalized, reliable conclusions based on aggregated data. Our literature survey revealed that most human studies perform either a fully aggregated analysis or an analysis of individual tasks. To show that there is important, non-trivial variation when including human participants, we reanalyze 12 published empirical studies, thereby changing the conclusions or making them more nuanced. Moreover, we demonstrate the effects of different aggregation levels by answering a novel research question on published sets of fMRI data. We show that when more data are aggregated, the results become more accurate. This proposed technique can help researchers to find a sweet spot in the tradeoff between cost of a study and reliability of conclusions.
Hybrid Computerized Face Recognition System Using Bag of Visual Words and MLP-Based BPNN

Nowadays in a situation like the Covid19 pandemic it is very sensitive to use biometric systems for attendance monitoring of employees. The reason is covid19 spreads from one person to another easily with a biometric system. It has become necessary for any organization to maintain an attendance monitoring system without taking fingerprints of any employee or a student. The automatic Face recognition system is best to alternate for the biometric system. An advanced automatic face recognition technique is proposed in this paper with the classification technique using Bag of Visual Words (BOVW) and Multi-Layer Perceptron (MLP) based Back Propagation Neural Network (BPNN). An Accuracy of 91% is achieved with the proposed methodology.
Digital technologies for organizing the intelligent learning environment

This article describes the problem of computer simulation of the educational process at a research university. We consider some models and methods of organizing the intelligent learning environment described by a number of researchers. We also come up with a few ideas of creating an intelligent learning environment and organizing the learning process, such as a hierarchical educational system, the model of which was tested in our previous studies.
Up Close &amp; Personal: Exploring User-preferred Image Schemas for Intuitive Privacy Awareness and Control

Effective end-user privacy management in everyday ubiquitous computing environments requires giving users complex, contextual information about potential privacy breaches and enabling management of these breaches in a timely, engaging and intuitive manner. In this paper, we propose using empirically grounded image schema-based metaphors to help design these interactions. Results from our exploratory user study (N=22) demonstrate end users' preferences for changes in physical attributes and spatial properties of objects for privacy awareness. For privacy control, end users prefer to exert force and create spatial movement. The study also explores user preferences for wearable vs. ambient form-factors for managing privacy and concludes that a hybrid solution would work for more users across more contexts. We thus provide a combination of form factor preferences, and a focused set of image schemas for designers to use when designing metaphor-based tangible privacy management tools.
Analysis of Oral Squamous Cell Carcinoma into Various Stages using Pre-Trained Convolutional Neural Networks

Abstract Oral cancer is a commonly prevalent disease in the world. Cancer begins when alterations in healthy cells take place and grow gradually. Finally, it forms a mass called cancer. In general cancer can be classified into malignant and benign. In malignant, the cells can grow and multiply affects other parts of the body but, benign does not spread. Among the cancers in the South East Asia, the cancers in oral cavity ranks among the third most common types of cancer. Oral Squamous Cell Carcinoma is a highly prevalent oral cancer affecting the head and neck more than 90% than other parts of the body. Until now, classification of Oral Squamous Cell Carcinoma classification into various stages is based on the cytological and architectural change which relies on the pathologist. Every pathologist while assessing the lesions of Oral Squamous Cell Carcinoma into various stages led to mistakes. To overcome this, Computer Aided Diagnosis gives the exact stages of Oral Squamous Cell Carcinoma into poorly differentiated, moderately differentiated and well differentiated. In this work, two Convolutional Neural Network Architectures Inception-v3 and Resnet50 are used as feature extractors. Then, the derived features are given to Multi-class Support Vector Machine and Random Forest. Random Forest and Multi-class Support Vector Machine classifies the Oral Squamous Cell Carcinoma into various stages namely poorly differentiated, moderately differentiated and well differentiated. The features obtained from Resnet50 when given to Random Forest gives the satisfactory performance of 92.08 %.
Manual and Autonomous Mode based Navigation using Human Robot Interaction on a Robotic Vehicle

This paper describes how different algorithms are implemented on autonomous robots like GoPiGo robot and Turtlebot. A* and behavioral-based algorithms are programmed and used in this work. The GoPiGo robot is a vehicle control device; it can work in either manual or autonomous mode. Based on human instruction, the robot can switch back and forth between manual and autonomous control. A switching-based module is used to select the signals in manual mode and automated control signals in the automated mode. This research is a work in progress, and the next stage is using the Turtlebot to achieve the implementation of this work.
A semi-supervised dynamic ensemble algorithm for IoT anomaly detection

In general, anomaly detection is to identify the abnormal samples from large-scale regular data, which is an interesting research topic in data mining and machine learning domains. In this paper, we propose an anomaly detection algorithm based on semi-supervised extreme learning machine for large-scale IoT data. Unlike supervised learning algorithm, semi-supervised learning algorithm needs to deal with many unlabeled and labeled data, and its performance is better than the former. Because unlabeled data is much convenient to access than labeled data, semi-supervised learning algorithm is widely used in many realworld scenarios. In this paper, for anomaly detection task of large-scale IoT data, we propose a dynamic ensemble algorithm via a combination of semi-supervised extreme learning machine (SSELM) and mutual information criteria. As a base classifier, SSELM has the characteristics of simple structure, convenient training and excellent generalization, we select the base classifiers for the target instance based on maximum relevance and minimum redundancy. Experimental results on four real-life datasets from UCI show that our proposed algorithm for IoT anomaly detection outperforms three state-of-the-art methods in terms of average classification accuracy.
Datasets for recognition of aggressive interactions of children toward robotic toys

The data is related to unwanted interactions between a person and a small robotic toy based on acceleration sensor embedded within the robotic toy. Three toys were considered namely, a stuffed panda, a stuffed robot, and an excavator. Each toy was embedded with an accelerometer to record the interactions. Five different unwanted interactions were performed by adult participants and children. The considered interactions were hit, shake, throw, pickup, drop, and idle for the no interaction case. The collected data contains the magnitude of the resultant acceleration from the interactions. The data was processed by extracting the instances of interactions. A secondary dataset was created from the original one by creating artificial sequences. This data article contains the processed data that can be used to explore different machine learning models and techniques in classifying such interactions. Online repository contains the files: https://doi.org/10.7910/DVN/FHOO0Q.
Efficient Model-Based Collaborative Filtering with Fast Adaptive PCA

A model-based collaborative filtering (CF) approach utilizing fast adaptive randomized singular value decomposition (SVD) is proposed for the matrix completion problem in recommender system. Firstly, a fast adaptive PCA framework is presented which combines the fixed-precision randomized matrix factorization algorithm [1] and accelerating skills for handling large sparse data. Then, a novel termination mechanism for the adaptive PCA is proposed to automatically determine a number of latent factors for achieving the near optimal prediction accuracy during the subsequent model-based CF. The resulted CF approach has good accuracy while inheriting high runtime efficiency. Experiments on real data show that, the proposed adaptive PCA is up to 2.7X and 6.7X faster than the original fixed-precision SVD approach [1] and svds in Matlab repsectively, while preserving accuracy. The proposed model-based CF approach is able to efficiently process the MovieLens data with 20M ratings and exhibits more than 10X speedup over the regularized matrix factorization based approach [2] and the fast singular value thresholding approach [3] with comparable or better accuracy. Compared with the deep-learning-based CF approach, the proposed approach is much more computationally efficient, with just marginal performance loss.