Reviewerly/exHarmony huggingface dataset:
Position and structure-aware graph learning

Graph representation learning methods have gained great popularity in tackling various analytics tasks, such as node classification, link prediction and graph classification. However, most of graph representation learning methods merely consider the local information around the nodes. In addition, information about the global relative position information of each node is not fully taken into account. Recently, Position-aware Graph Neural Networks have been proposed to capture positions of nodes with respect to the anchor nodes and embed this position information into the feature representation, which aims to distinguish nodes which are locally structurally similar. This network aggregates position information from the randomly sampled anchor set nodes to the given target node. With such randomness, the sampled nodes lack particularity, and not all of them are significant enough in the graph to be used as position reference points. To address these issues, we propose a Position and Structure-aware Graph Learning framework (PSGL). The proposed PSGL extracts local topology information of nodes through the structure representation module, obtains anchor set nodes through graph pooling and further calculates global position information of the nodes to each anchor set node. The attention mechanism is used to weigh the obtained local topology and the global position information adaptively. Our proposed PSGL is capable of learning structural and positional information adaptively and encoding more informative characteristics in real-world networks. Our experimental results demonstrate that the proposed PSGL outperforms the state-of-the-art graph representation learning methods. Our code is available at https://github.com/leaf-ygq/PSGL.
Classification of single‐cell cervical pap smear images using <scp>EfficientNet</scp>

Abstract Cervical cancer is the second leading cause of death in women in developing nations like India. The automated cervical cancer screening systems play a critical role in diagnosing the ailment at an early stage. They can also assist in routine cervical cancer screening and decision‐making processes. The underlying binary classifier for detecting cancerous cells is necessary for building computerized screening systems. Thus, this work aims to develop a robust binary classifier capable of classifying single cervical cells as normal and cancerous. The current work aims to improvise the EfficientNet‐B7 with empirical resolution parameters and skilful consolidation of the outcomes of the inner layers using the global pooling layer to improve the binary classification result. The proposed model is trained and evaluated on three independent pap smear datasets. The class activation heatmap visualizes the classification model for better interpretability. The performance of the autonomous classification tasks indicates that the deep learning‐based binary classifier is robust in classifying cancerous cells as it achieves accuracy of 94% on Herlev Dataset which is 2% higher than the other state‐of‐the‐art methods and comparable accuracy of nearly 99% on other cervical cell datasets using the proposed methodology.
GRM: Gaussian response module for visual tracking

Online Discriminative Correlation Filters have excellent performance in visual object tracking. It always divides the tracking network as a classification network and a regression network, which makes the regression network lack classification information and makes it hard to leverage its advantages. To address this problem, we propose the ATOM_GRM tracker to append the classification information to the network. Specifically, we design static and dynamic Gaussian response modules to encode the raw target position from classifier to regression network by multistage. In Particular, the parameters and fusion method of two Gaussian response modules is designed in different ways according to their location in the network. Moreover, we propose the ratio of height and width of the bounding box instead of intersection of union as the predictor, and two auxiliary training heads are used in the regression network. It makes the regression network better distinguish bounding boxes. Extensive experiments conducted on four public benchmarks, i.e., OTB100, GOT10k, LaSOT, Trackingnet, demonstrate the effectiveness of the proposed method. The proposed ATOM_GRM achieves 0.556 → 0.596 AO compared with the baseline ATOM on GOT10k.
Implementation of a deep convolution neural network model for identifying and classifying Pleuropulmonary Blastoma on DNA sequences

Pleuro Pulmonary Blastoma is a lung cancer affecting children's lung tissues. The development of the DICER1 gene in the patients leads to PPB. This disease causes trouble breathing along with lung infections. It can only be found by analyzing the DNA sequence of the PPB tissues. The DNA sequence of the tissues affected by cancer is extracted from the infections. The DNA sequences reveal the hereditary cause of the PPB, which can help in detecting the abnormalities of their family members. DNA sequences are complex, and it is impossible to rely on manual clinical methods to give faster and more accurate predictions. Several research works have suggested machine learning models which process the DNA sequences to detect PPB. However, machine learning algorithms cannot understand the complex DNA patterns of cancer tissues. This paper designs and implements a deep learning algorithm, a novel Deep Convolution Neural Network, to overcome such difficulties. The whole DNA sequence can be processed by more layers involved in the DCNN. The proposed DCNN learns the DNA sequence data part by part and compares it with the ground truth values, identifying odd data and abnormal sequence patterns to predict the presence of PPB. The DCNN is implemented in Python software with the DICER1 gene dataset for predicting the PPB. From the result of the experiment, the efficacy of the proposed DCNN model is verified and compared with other traditional models to evaluate the performance. The proposed CNN obtained 98.67% of accuracy for CT image classification and 96% of accuracy for DICER1-DNA data analysis.
Self supervised learning and the poverty of the stimulus

Diathesis alternations are the possible expressions of the arguments of verbs in different, systematically related subcategorization frames. Semantically similar verbs such as spill and spray can behave differently with respect to the alternations they can participate in. For example one can “spill/spray water on the plant”, but while one can “spray the plant with water”, it is odd to say “spill the plant with water”. “Spray” is a verb which can alternate between syntactic frames while “spill” is not alternating. How human speakers learn the difference between such verbs is not clearly understood, because the primary linguistic data (PLD) they receive does not appear sufficient to infer the knowledge required for adult competence. More generally the poverty of the stimulus (POS) hypothesis states that the PLD is not sufficient for a learner to infer full adult competence of language. That is, learning relies on prior constraints introduced by the language faculty. We tested state-of-the-art machine learning models trained by self supervision, and found some evidence that they could in fact learn the correct pattern of acceptability judgement in the locative alternation. However, we argued that this was partially a result of fine-tuning which introduced negative evidence into the learning data, which facilitated shortcut learning. Large language models (LLMs) cannot learn some linguistic facts from normal language data, but they can compensate to some extent by learning spurious correlated features when negative feedback is introduced during the training cycle.
Tunnel: Parallel-inducing sort for large string analytics

The suffix array is a crucial data structure for efficient string analysis. Over the course of twenty-six years, sequential suffix array construction algorithms have achieved O(n) time complexity and in-place sorting. In this paper, we present the Tunnel algorithm, the first large-scale parallel suffix array construction algorithm with a time complexity of Onp based on the parallel random access machine (PRAM) model. The Tunnel algorithm is built on three key ideas: dividing the problem of size O(n) into p sub-problems of reduced size Onp by replacing long suffixes with shorter prefixes of size at most a constant D; introducing a Tunnel mechanism to efficiently induce the order of a set of suffixes with long common prefixes; developing a strategy to transform a partially ordered suffix set into a total order relation by iteratively applying the Tunnel inducing method. We provide a detailed description of the algorithm, along with a thorough analysis of its time and space complexity, to demonstrate its correctness and efficiency. The proposed Tunnel algorithm exhibits scalable performance, making it suitable for large string analytics on large-scale parallel systems.
DeepPortraitDrawing: Generating human body images from freehand sketches

Various methods for generating realistic images of objects and human faces from freehand sketches have been explored. However, generating realistic human body images from sketches is still a challenging problem. It is, first because of the sensitivity to human shapes, second because of the complexity of human images caused by body shape and pose changes, and third because of the domain gap between realistic images and freehand sketches. In this work, we present DeepPortraitDrawing, a deep generative framework for converting roughly drawn sketches to realistic human body images. To encode complicated body shapes under various poses, we take a local-to-global approach. Locally, we employ semantic part auto-encoders to construct part-level shape spaces, which are useful for refining the geometry of an input pre-segmented hand-drawn sketch. Globally, we employ a cascaded spatial transformer network to refine the structure of body parts by adjusting their spatial locations and relative proportions. Finally, we use a style-based generator as the global synthesis network for the sketch-to-image translation task which is modulated by segmentation maps for semantic preservation. Extensive experiments have shown that given roughly sketched human portraits, our method produces more realistic images than the state-of-the-art sketch-to-image synthesis techniques.
Warnings: Violation symptoms indicating architecture erosion

As a software system evolves, its architecture tends to degrade, and gradually impedes software maintenance and evolution activities and negatively impacts the quality attributes of the system. The main root cause behind architecture erosion phenomenon derives from violation symptoms (i.e., various architecturally-relevant violations, such as violations of architecture pattern). Previous studies focus on detecting violations in software systems using architecture conformance checking approaches. However, code review comments are also rich sources that may contain extensive discussions regarding architecture violations, while there is a limited understanding of violation symptoms from the viewpoint of developers. In this work, we investigated the characteristics of architecture violation symptoms in code review comments from the developers’ perspective. We employed a set of keywords Related to violation symptoms to collect 606 (out of 21,583) code review comments from four popular OSS projects in the openStack and qt communities. We manually analyzed the collected 606 review comments to provide the categories and linguistic patterns of violation symptoms, as well as the reactions how developers addressed them. Our findings show that: (1) three main categories of violation symptoms are discussed by developers during the code review process; (2) The frequently-used terms of expressing violation symptoms are “inconsistent” and “violate”, and the most common linguistic pattern is Problem Discovery; (3) Refactoring and removing code are the major measures (90%) to tackle violation symptoms, while a few violation symptoms were ignored by developers. Our findings suggest that the investigation of violation symptoms can help researchers better understand the characteristics of architecture erosion and facilitate the development and maintenance activities, and developers should explicitly manage violation symptoms, not only for addressing the existing architecture violations but also preventing future violations.
Objects of forensic documentary science

The article is devoted to the scientific analysis of the transformation of the “document” concept as an object of forensic research and to the identification of the main directions of development of forensic documentary science. The methodological basis of the study is the dialectical, formal-logical, formal-legal approaches, and a number of general scientific methods (historical, functional, praxeological). The systematic approach has ensured the comprehensive nature of the study and the unification of its results with a general theoretical conclusion. The scientific novelty lies in the establishment of promising areas for the development of an innovative methodology for conducting forensic document examination in the context of the use of special technical means and the methodology for the use of modern software products.&#x0D; With the development of digital technologies, forensic science, in particular documentary science, has faced a significant number of new objects that require different approaches to research methods and tools. The transformation of the document as an object of forensic document science has significantly increased the demand for innovative technologies for research, making it necessary to integrate science and digital technologies in the investigation of crimes. The increase in the number of offences requiring the examination of electronic data storage devices poses new challenges for forensic experts, requiring modern means of recording and comparing them with standards. Forensic document research has acquired a different meaning and faced new challenges. With the development of digital technologies, forensic documentary requires the improvement of methods and means of researching information storage media.&#x0D; The transformation of the “document” concept has led to its definition as a carrier of information regardless of the way it is recorded. In parallel with the study of the classical written paper document, forensic science has in a short time mastered the methods and means of studying plastic cards, digital storage devices and electronic money. Forensic science defines a document as an object such as video documents, photo documents, film documents, etc. The current level of research is not determined by the traditional comparison with a reference sample, and sometimes such a sample does not exist at all. The research of a document cannot be considered complete without determining the originality of the information it contains, without comparing this information with other possible storage media of the same information.
The significance of computer technologies in detecting plagiarism in scientific works

The article analyses problematic issues regarding the importance of using computer technologies in detecting plagiarism in scientific works. It was determined that the problem of plagiarism in science is not only a matter of legislation, but a manifestation of disdain for scientific ethics and moral standards by the scientists themselves. The issue of academic plagiarism must be solved comprehensively and with joint efforts, by applying effective mechanisms to prevent plagiarism.&#x0D; There is no single, complete, comprehensive and generally accepted definition of the category of plagiarism. The essence of plagiarism is the assignment of authorship to someone else’s original (literary, artistic, scientific) work or its part.&#x0D; The development of information technologies creates conditions for detecting illegal borrowing. Special computer programmes and online services check works for originality much faster. The essence and nature of computer technologies, their importance in detecting plagiarism in scientific works are studied. Positive and negative points in the application of computer anti-plagiarism technologies were identified, and an appropriate assessment was given to them.&#x0D; It is substantiated that plagiarism has a certain personification and specificity in various spheres and branches of human activity. The anti-plagiarism system almost does not take into account official parts of scientific works (cover letter, introduction, references to normative legal acts, conclusions, list of used sources) and evaluates them as plagiarism. The algorithm of the anti-plagiarism service must take into account constant stylistic turns and peculiarities of certain branches of science and technology. We believe that the results of new interpretations of scientific work should not be completely new. It is noted that checking with online services does not always reveal translations of texts or borrowings from editions not published online.&#x0D; Accordingly, the results of information processing with the help of specialized anti-plagiarism programmes and services cannot independently be circumstances that allow making reasonable conclusions about the presence or absence of plagiarism in a scientific work. Anti-plagiarism services and programs really help in detecting plagiarism as an additional tool for searching for possible matches of the text, followed by their analysis and processing by a specialist.&#x0D; But the situation with plagiarism still requires the use of plagiarism checking services (even with all their shortcomings). However, no matter how attractive and easy-to-use anti-plagiarism services are, it is necessary to do some work to improve information culture and respect for the copyrights of others.
MITra: A Framework for Multi-Instance Graph Traversal

This paper presents MITra, a framework for composing multi-instance graph algorithms that traverse from multiple source vertices simultaneously over a single thread. Underlying MITra is a model of multi-instance traversal that uniformly captures traversal sharing across instances. Based on this, MITra provides a programming model that allows users to express traversals by declaring vertex ranks and specify computation logic via an edge function. It synthesizes multi-instance traversal algorithms from declared vertex ranks and edge functions adopted from classic single-instance algorithms, automatically sharing computation across instances and benefiting from SIMD. We show that MITra can generate multi-instance algorithms provably better than existing ones, while being more expressive than traditional frameworks. In addition to the ease of programming, we experimentally verify that MITra is on average an order of magnitude faster than approaches based on existing frameworks for common graph algorithms, and is comparable to the state-of-the-art highly optimized one-off algorithms.
CommunityAF: An Example-Based Community Search Method via Autoregressive Flow

Example-based community search utilizes hidden patterns of given examples rather than explicit rules, reducing users' burden and enhancing flexibility. However, existing works face challenges such as low scalability, high training cost, and improper termination during the search. Aiming at tackling all these issues, this paper proposes a community search framework named CommunityAF with three well-designed components. The first is a GNN (graph neural network) component that combines community-aware structure features to incrementally learn node embeddings over a large graph for the other two components. The second is an autoregres-sive flow-based generation component designed for fast training and model stability. The third is a scoring component that evaluates the communities and provides scores for a stable termination. Moreover, to show that CommunityAF has the sufficient expressive power to cover the rules, we demonstrate that the scoring component with node features weighted by degree-related factors is able to mimic the existing structure-based community metrics. We introduce a square ranking loss to guide the training of the scoring component, and further devise a flexible termination strategy based on the inferred score change pattern over a sequence of candidate communities using beam search. We compare CommunityAF with four different categories of community search methods on six real-world datasets. The results illustrate that CommunityAF outperforms these community search methods, and achieves an average 15.3% improvement in effectiveness and 4x to 20x speedups on different datasets relative to the state-of-the-art generative method.
Autonomously Computable Information Extraction

Most optimization techniques deployed in information extraction systems assume that source documents are static. Instead, extracted relations can be considered to be materialized views defined by a language built on regular expressions. Using this perspective, we can provide an efficient verifier (using static analysis) that can be used to avoid the high cost of re-extracting information after an update. In particular, we propose an efficient mechanism to identify updates for which we can autonomously compute an extracted relation. We present experimental results that support the feasibility and practicality of this mechanism in real world extraction systems.
Information-Theoretically Secure and Highly Efficient Search and Row Retrieval

Information-theoretic or unconditional security provides the highest level of security --- independent of the computational capability of an adversary. Secret-sharing techniques achieve information-theoretic security by splitting a secret into multiple parts (called shares ) and storing the shares across non-colluding servers. However, secret-sharing-based solutions suffer from high overheads due to multiple communication rounds among servers and/or information leakage due to access-patterns ( i.e. , the identity of rows satisfying a query) and volume ( i.e. , the number of rows satisfying a query). We propose S 2 , an information-theoretically secure approach that uses both additive and multiplicative secret-sharing, to efficiently support a large class of selection queries involving conjunctive, disjunctive, and range conditions. Two major contributions of S 2 are: ( i ) a new search algorithm using additive shares based on fingerprints, which were developed for string-matching over cleartext; and ( ii ) two row retrieval algorithms: one is based on multiplicative shares and another is based on additive shares. S 2 does not require communication among servers storing shares and does not reveal any information to an adversary based on access-patterns and volume.
A Comparative Analysis of Gradient-Based Optimization Methods for Machine Learning Problems

In this study, we compare and contrast the seven most widely used gradient-based optimization algorithms of the first order for machine learning problems. These methods are Stochastic Gradient Descent with momentum (SGD), Adaptive Gradient (AdaGrad), Adaptive Delta (AdaDelta), Root Mean Square Propagation (RMSProp), Adaptive Moment Estimation (Adam), Nadam (Nestrove accelerated adaptive moment estimation) and Adamax (maximum adaptive moment estimation). For model creation and comparison, three test problems based on regression, binary classification and multi-classification are addressed. Using three randomly selected datasets, we trained the model and evaluated the optimization strategy in terms of accuracy and loss function. The total experimental results demonstrate that Nadam outperformed the other optimization approach across these datasets, but only in terms of correctness, not in terms of time. Adam optimizer has the best performance in terms of time and accuracy.