Reviewerly/exHarmony huggingface dataset:
Comparison of Machine Learning and Deep Learning models for Cervical Cancer Prediction

Traditional cervical cancer type categorization relies heavily on the pathologist&#x2019;s experience, which is inefficient and inaccurate. Colposcopy is an important part of preventing cervical cancer. Over the last 50 years, colposcopy, in combination with precancer screening and therapy, has played a critical role in lowering the incidence and death of cervical cancer. It justifies the importance of predicting cervical cancer at an early stage. For prediction, the proposed approach makes use of the UCI data repository and machine learning classifiers. The data is preprocessed, and the repository is updated with feature extraction and validations. After that, the pre-processed data is then applied to various machine learning classifiers namely Logistic Regression, Random Forest, Gradient Boosting, Support vector machine (SVM), and all its ensemble classifier. The ensemble classifier has not surpassed the results of individual classifier and thus preferred deep learning model using artificial neural network. The comparison is done in terms of accuracy and fmeasure along with recall and precision. The results revealed that the neural network model is the best with 97.67&#x0025; of accuracy and 98.78&#x0025; of F-score.
Visual measurement of valve opening area with improved subpixel edge location

The opening area error of the gas valve is mainly caused by machining, assembling errors and transmission clearances. Accurate measurement of the opening area is important for the production quality inspection of gas valves. This paper proposes a novel solution with a specific measurement system. Due to the reflective effect of the valve surface, the valve opening’s saliency is enhanced by the combination of top lighting and camera exposure. Then, a partial image is used to extract the single closed edge of the valve opening. To obtain the precise edge position, a subpixel location based on the rotating area effect is applied. Finally, the area computation based on the minimum circumscribed circle and the maximum inner circle of the edge is provided. The experiment results prove the system reliability, and the general significances of the proposed algorithms.
Return-Oriented Programming Gadget Catalog for the Xtensa Architecture

This paper shows that the modern high customizable Xtensa architecture for embedded devices is exploitable by Return-Oriented Programming (ROP) attacks. We used a simple Hello-World application written with the RIOT OS as an almost minimal code basis for determining if the number of gadgets that can be found in this code base is sufficient to build a reasonably complex attack. We determined 859 found gadgets which are sufficient to create a gadget catalog for the Xtensa. Despite the code basis used being really small, the presented gadget catalog provides Turing completeness, which allows an arbitrary computation of any exploit program.
Understanding the Negative Aspects of User Experience in Human-likeness of Voice-based Conversational Agents

With advances in artificial intelligence technology, Voice-based Conversational Agents (VCAs) can now imitate human abilities, sometimes almost indistinguishably from humans. However, concerns have been raised that too much perceived similarity can trigger threats and fears among users. This raises a question: Should VCAs be able to imitate humans perfectly? To address this, we explored what influences the negative aspects of user experience in human-like VCAs. We conducted a qualitative exploratory study to elicit participants' perceptions and feelings of human-like VCAs through comparable video prototypes of human–agent conversation and human–human conversation. We discovered that the dialogues of the human-likeness outside of the expressed purpose of a VCA and expressions pretending to come from a human identity could lead to negative experiences with VCAs. Based on our findings, we discussed design directions for overcoming potential issues of human imitation.
Materialising the Immaterial: Provotyping to Explore Voice Assistant Complexities

Voice assistants (VAs), typically promoted as omniscient conversational butlers, still remain below users’ expectations. Interaction designers seem to struggle bringing in user perspectives necessary to develop more meaningful VA applications beyond simple use cases such as playing music. One of the reasons might be the immateriality of cloud-based VA technology making it difficult to comprehend such complex and ever-evolving systems. In this paper, we investigate provotyping as a design tool for ‘materialising the immaterial’. In our case study, teams of multidisciplinary experts devised twelve provotypes to explore intangible VA technology. We present and discuss three generalisations in respect to the role of provotypes for the exploration of VAs. Our findings show provotypes can serve as the necessary props by which we can bring in missing perspectives around this technology and generate material which enables designers to speculate, debate, and sketch out ideas for meaningful futures of VA applications.
Cancer Prediction Using Random Forest and Deep Learning Techniques

Cancer is a medical condition in which cells divide abnormally in the human body and spread to other body parts. If the prognosis of cancer is done well in advance, it is curable. It is estimated that 22.2 million individuals would be impacted by cancer between now and 2030. There are about 100 different forms of cancer, which are categorized based on criteria including age, gender, and race/ethnicity. Cancer in organs such as the lung, liver, kidney, breast, and brain was investigated in the present study. The data for the Lung Cancer study came from the Kaggle online system, the TCIA informed on kidney cancer (The Cancer Imaging Archive), the HCC Liver Cancer dataset, the Brain Cancer dataset from data. World, and the Wisconsin dataset for breast cancer. The proposed approaches to classify cancer's malignancy were Random Forest Classifier, Convolutional Neural Network, and ResNet50. Lung, Liver, Kidney, Breast, and Brain cancers had accuracy rates of 99 percent, 75.75 percent, 88.09 percent, 96 percent, and 81 percent, respectively.
Chaining models of serial recall can produce positional errors

A major argument for positional-coding over associative chaining models of immediate serial recall has been the high probability that an error from a prior list will appear in its correct serial-position, so-called “protrusions.” Here we show that a chaining model can produce protrusions if it includes three characteristics that have been incorporated into published chaining models: (a) a “start-signal” item is associated with all first list-items, (b) memory is not cleared following each list, and (c) the retrieval cue for each item is always the full non-redintegrated retrieved information, regardless of the response. The model covertly recalls all studied lists in parallel (weighted by recency), such that when prior-list items intrude, they predominantly occur at the correct output position. In addition to fitting prior protrusion data, we report two new data sets that question the ubiquity of the simple protrusion-dominance characteristic. These findings show that protrusions cannot falsify an associative basis for serial-order memory and speak to the plausibility of mixture models.
GaccO - A GPU-accelerated OLTP DBMS

In this paper, we present GaccO - a main memory DBMS for GPU-accelerated OLTP. For executing OLTP workloads, GaccO implements a novel scheme that splits the execution across the CPU and the GPU. Using such a co-execution scheme GaccO can thus not only efficiently make use of the vectorized execution of the GPU by grouping transactions of the same type into batches, but it can also support databases larger than device memory by leveraging CPU memory in addition to the GPU memory. In our evaluation with TPC-C, we show that GaccO can thus speed-up OLTP workloads by up to 6 times compared to a pure CPU-based OLTP execution.
Artificial intelligence-based approach for Univariate time-series Anomaly detection using Hybrid CNN-BiLSTM Model

Anomaly detection is a critical issue that has been extensively researched across a wide range of study fields and application domains. The main purpose of an anomaly detection model is to determine which instances stand out as being dissimilar to all others. Such instances are known as anomalies. Due to the common need for analyzing huge real-world data sets, automated data-driven models based on artificial intelligence techniques have become the most trending field of research. Deep learning techniques outperform other traditional machine learning and statistical methods as the scale of data increases. They have the power to learn very complex hierarchical feature relations within high-dimensional input data. In this paper, we suggested a hybrid deep learning technique that combines one-dimensional convolutional neural networks (1D CNN) with bidirectional long short-term memory (BiLSTM) for the detection of anomalies in univariate time series. The suggested model (1D CNN-BiLSTM) was tested and verified by the benchmark datasets and compared to state-of-the-art algorithms. The experimental results ensured that the proposed method delivers on its promises.
Design of substitution nodes (S-Boxes) of a block cipher intended for preliminary encryption of confidential information

This paper considers a new method for obtaining an S-box, which is one of the nonlinear transformations used in modern block-symmetric cipher systems. This method is based on modular arithmetic, that is, exponentiation modulo polynomial in extended Galois fields GF28. The indicators and criteria of efficiency of the obtained S-box (balance, Hamming distance, distribution criteria, autocorrelation, algebraic immunity, cyclic structure of the S-box) are analyzed. The cryptographic characteristics are presented in comparison with the substitution boxes of known modern block ciphers. In addition, the resulting S-box was investigated by the methods of linear and differential cryptanalysis. In the future, the proposed S-box will be used in the developed encryption algorithm designed for the pre-encryption of confidential information.
Transformer-based vehicle detection for surveillance images

Dense vehicle detection in rush hours is important for intelligent transportation systems. Most existing object detection methods can work well in off-peak vehicle detection for surveillance images. However, they may fail in dense vehicle detection in rush hours due to severe overlapping. To address this problem, a dense vehicle detection network is proposed by embedding the deformable channel-wise column transformer (DCCT) into the current you only look once (YOLO)-v5l network with a novel asymmetric focal loss (AF loss). The proposed DCCT fully extracts the column-wise occlusion information of vehicles in the images and guides the network to pay more attention to the visible area of partially occluded vehicles to improve the detection and positioning accuracy of weak feature targets. The proposed AF loss is used to balance the performance between easy and hard targets and address class imbalance. Extensive results demonstrate that the proposed network can accurately detect on-road densely located vehicles, even the minority classes in real time. Compared with the baseline YOLO-v5l, the mean average precision is improved by 3.93%, and it achieves comparable results with the existing state-of-the-art methods on the UA_Detrac dataset.
Experimental Validation of a Chaotic Jerk Circuit Based True Random Number Generator

A method for true random number generation by directly sampling a high frequency chaotic jerk circuit is explored. A method for determination of the maximum Lyapunov exponent, and thus the maximum bit rate for true random number generation, of the jerk system of interest is shown. The system is tested over a wide range of sampling parameters in order to simulate possible hardware configurations. The system is then implemented in high speed electronics on a small printed circuit board to verify its performance over the chosen parameters. The resulting circuit is well suited for random number generation due to its high dynamic complexity, long term aperiodicity, and extreme sensitivity to initial conditions. This system passes the Dieharder RNG test suite at 3.125 Mbps.
Unsupervised learning of light field depth estimation with spatial and angular consistencies

Learning-based depth estimation from light fields has made significant advances in recent years, however, most of these work abandon the traditional non-learning based formulations and start over with an end-to-end deep network framework. Actually traditional methods have also presented many effective and evident depth cues or constraints for light field depth estimation. In this paper, we try to combine the advantages of the learning and non-learning strategies, and incorporate the traditional light field constraints into an unsupervised framework by using a learnable approximation scheme to yield differentiable unsupervised loss functions. Specifically, we first propose an unsupervised coarse-to-fine network architecture for light field depth estimation, and then design an adaptive spatio-angular consistency loss combined with the differentiable versions of modified traditional constraints. Comparative experiments demonstrate that our method is superior to the state-of-the-art unsupervised methods.
Affective Spamming on Twitch: Rhetorics of an Emote-Only Audience in a Presidential Inauguration Livestream

This article analyzes what is at stake when social media platforms restrict the modes in which audience members can publicly compose and communicate. More specifically, we are concerned with how platforms adjust users' multimodal affordances during livestreaming public events, and how these adjustments affect public deliberation. This article focuses on an historic, political Twitch livestream: U.S. President Joe Biden's inaugural address on January 20, 2021. For rhetoric and writing scholars, this event is significant for two reasons: (1) it is the first presidential inauguration to be livestreamed, officially, on Twitch by the president's committee, and (2) the livestream's chat was restricted to "emote-only," meaning online audience members could only communicate with Twitch emotes in the "live chat" space of the stream. Based on an analysis of more than 12,000 comments, our findings support a theory of what we call affective spam, a more nuanced, visual-content-based form of spam that online audiences use to influence public communication and deliberation on social media during live events.
Multiple-solutions RANSAC for finding axes of symmetry in fragments of objects

The problem of “finding best lines passing through a set of straight lines” has appeared in applications such as archaeological pottery analysis, precision manufacturing, and 3D modelling. In these applications, an instance of this problem is finding the symmetry axis of a symmetrical object from a set of its surface normal lines. We show that the mentioned instance of the problem may have two meaningful local minima, one of which is the symmetry axis, a fact that has been neglected in the literature. A multiple-solutions RANSAC algorithm is proposed for finding initial estimates of both local minima in the presence of outliers. Then, a coordinate-descent algorithm is presented that starts from these initial estimates and finds the local minima of the problem. The proposed coordinate-descent method does not involve any line search procedure, and its convergence is guaranteed. We also provide a proof for the rate of the convergence.
Worst-case to average-case reductions via additive combinatorics

We present a new framework for designing worst-case to average-case reductions. For a large class of problems, it provides an explicit transformation of algorithms running in time T that are only correct on a small (subconstant) fraction of their inputs into algorithms running in time O(T) that are correct on all inputs.
Portfolio of Loose Ends

Digital Craftsmanship, and other explorative design research practices using digital fabrication, depend on sample making and material exploration. Rather than describing a method of making use of the information present in samples co-inhabiting a timeline, this pictorial reports on the loose ends that may emerge out of the main journey, when taking a traveler's approach to making. By loose ends, we mean successful samples that are not suitable to the main inquiry of the present design research process, but that under certain circumstances can become starting points for new investigation lines. We describe four examples of such loose ends and introduce the concept of fellow travelers as a metaphor for describing the process of co-inhabiting a timeline and a journey while ultimately having diverging goals and outcomes.
CPN4M: Testing Multi-Agent Systems under Organizational Model Moise+ Using Colored Petri Nets

Multi-agent systems (MASs) are distributed and complex software that demand specific software engineering features. Testing is a critical phase when validating software, and it is also difficult to conceive and execute. Designing systems under a multi-agent paradigm is even more difficult because of properties such as autonomy, reactivity, pro-activity, and social skills. Any multi-agent system has at least three main dimensions: the individual and social levels and communication interfaces. Considering an approach for testing a dimension specifically, we deal with the social level as an organizational model in this paper. It imposes some restrictions on agents’ behavior through a set of behavioral constraints. In this sense, an error in the organization can occur when the allocated resources are not enough for executing plans and reaching goals. This work aims to present a whole framework for analyzing and testing MAS social level under organizational model Moise+. This framework uses a Moise+ specifications set as information artifact mapped in a colored Petri net (CPN) model, named CPN4M, as a test case generation mechanism. CPN4M uses two different test adequacy criteria: all-paths and state-transition path. In this paper, we have formalized the transition from Moise+ to CPN, the procedures for test case generation, and executed some tests in a case study. The results indicate that this methodology can increase the correction degree for a social level in a multi-agent system specified by a Moise+ model, indicating the system context that can lead the MAS for failures.
The FCG Editor: An innovative environment for engineering computational construction grammars

Since its inception in the mid-eighties, the field of construction grammar has been steadily growing and constructionist approaches to language have by now become a mainstream paradigm for linguistic research. While the construction grammar community has traditionally focused on theoretical, experimental and corpus-based research, the importance of computational methodologies is now rapidly increasing. This movement has led to the establishment of a number of exploratory computational construction grammar formalisms, which facilitate the implementation of construction grammars, as well as their use for language processing purposes. Yet, implementing large grammars using these formalisms still remains a challenging task, partly due to a lack of powerful and user-friendly tools for computational construction grammar engineering. In order to overcome this obstacle, this paper introduces the FCG Editor, a dedicated and innovative integrated development environment for the Fluid Construction Grammar formalism. Offering a straightforward installation and a user-friendly, interactive interface, the FCG Editor is an accessible, yet powerful tool for construction grammarians who wish to operationalise their construction grammar insights and analyses in order to computationally verify them, corroborate them with corpus data, or integrate them in language technology applications.
SGFS: A semi-supervised graph-based feature selection algorithm based on the PageRank algorithm

Feature selection is the process of choosing a subset of pertinent features to use in the model building during the machine learning and data mining process. Irrelevant and redundant features are reduced by this process while improving learning performance. Although in most real-world applications, it is time-consuming and expensive to collect labeled data. Plentiful unlabeled data is easily available. Semisupervised feature selection methods are used to tackle this problem, in which both unlabeled and labeled data are used to assess feature relevance. This paper suggests a new semisupervised feature selection algorithm based on the PageRank centrality concept for the partially labeled data, which is an efficient method for calculating the prominence of web pages on the Internet. The proposed method, called semi-supervised graph-based feature selection (SGFS), crafts a complete weighted graph based on the relevancy with labels and redundancy of features. PageRank algorithm estimates the prominence of each graph vertex (feature). This algorithm has been tested against multiple semi-supervised feature selection methods based on several datasets with various dimensions to prove the accurate performance of the proposed algorithm.
Human Autonomy in the Era of Augmented Reality—A Roadmap for Future Work

Augmented reality (AR) has found application in online games, social media, interior design, and other services since the success of the smartphone game Pokémon Go in 2016. With recent news on the metaverse and the AR cloud, the contexts in which the technology is used become more and more ubiquitous. This is problematic, since AR requires various different sensors gathering real-time, context-specific personal information about the users, causing more severe and new privacy threats compared to other technologies. These threats can have adverse consequences on information self-determination and the freedom of choice and, thus, need to be investigated as long as AR is still shapeable. This communication paper takes on a bird’s eye perspective and considers the ethical concept of autonomy as the core principle to derive recommendations and measures to ensure autonomy. These principles are supposed to guide future work on AR suggested in this article, which is strongly needed in order to end up with privacy-friendly AR technologies in the future.