Reviewerly/exHarmony huggingface dataset:
Research on WebShell Detection Method Based on Regularized Neighborhood Component Analysis (RNCA)

The variant, encryption, and confusion of WebShell results in problems in the detection method based on feature selection, such as poor detection effect and weak generalization ability. In order to solve this problem, a method of WebShell detection based on regularized neighborhood component analysis (RNCA) is proposed. The RNCA algorithm can effectively reduce the dimension of data while ensuring the accuracy of classification. In this paper, it is innovatively applied to a WebShell detection neighborhood, taking opcode behavior sequence features as the main research object, constructing vocabulary by using opcode sequence features with variable length, and effectively reducing the dimension of WebShell features from the perspective of feature selection. The opcode sequence selected by the algorithm is symmetrical with the source code file, which has great reference value for WebShell classification. On the issue of the single feature, this paper uses the fusion of behavior sequence features and text static features to construct a feature combination with stronger representation ability, which effectively improves the recognition rate of WebShell to a certain extent.
Constructing small genome graphs via string compression

Abstract Motivation The size of a genome graph—the space required to store the nodes, node labels and edges—affects the efficiency of operations performed on it. For example, the time complexity to align a sequence to a graph without a graph index depends on the total number of characters in the node labels and the number of edges in the graph. This raises the need for approaches to construct space-efficient genome graphs. Results We point out similarities in the string encoding mechanisms of genome graphs and the external pointer macro (EPM) compression model. We present a pair of linear-time algorithms that transform between genome graphs and EPM-compressed forms. The algorithms result in an upper bound on the size of the genome graph constructed in terms of an optimal EPM compression. To further reduce the size of the genome graph, we propose the source assignment problem that optimizes over the equivalent choices during compression and introduce an ILP formulation that solves that problem optimally. As a proof-of-concept, we introduce RLZ-Graph, a genome graph constructed based on the relative Lempel–Ziv algorithm. Using RLZ-Graph, across all human chromosomes, we are able to reduce the disk space to store a genome graph on average by 40.7% compared to colored compacted de Bruijn graphs constructed by Bifrost under the default settings. The RLZ-Graph scales well in terms of running time and graph sizes with an increasing number of human genome sequences compared to Bifrost and variation graphs produced by VGtoolkit. Availability The RLZ-Graph software is available at: https://github.com/Kingsford-Group/rlzgraph. Supplementary information Supplementary data are available at Bioinformatics online.
Artificial intelligence and copyright

Key words: copyright, work, artificial intelligence, computer program&#x0D; In the article, the problemsof legislation of Ukraine regarding the issues of copyright on works created due to artificialintelligence were analyzed. Particularly, who is the owner of copyright ofworks created due to artificial intelligence. On the one hand, it could be a developer ofa computer program, from the other hand, it could be a client or an employer. Because,it could happen that there is a situation when robots created something newand original, e.g., how it happened with the project “New Rembrandt”. In this case,computers created a unique portrait of Rembrandt. And here is a question, where isin this portrait original and intellectual works of developers of these computers andprograms. In the contrast, this portrait could be created without people who developedspecial machines, programs, and computers. The article’s author proposes to addinto Ukrainian legislation with following norm: the owner of the copyright createddue to artificial intelligence should be a natural person who uses artificial intelligencefor these purposes within the official relationship or on the basis of a contract. In caseof automatic generation of such work by artificial intelligence, the owner of copyrightshould be the developer.Also, another question arises, particularly, who will be responsible for the damagecaused by the artificial intelligence. As an example, of the solution for this issue Resolution2015/2103 (INL) was given, where is mentioned that human agent could be responsiblefor the caused damage. Because, it is not always a developer is responsiblefor the damage.Also, the legislation and justice practice of foreign countries was explored. Theways of overcoming mentioned problems in legislation of Ukraine were proposed.Such as changing our legislation and giving the exact explanation in who is the ownerof copyright on works created due to artificial intelligence and in which cases this personcould become an owner of the copyright. However, probably, these issues shouldbe resolved at international level regarding globalization.
Mining Non-Functional Requirements using Machine Learning Techniques

Background: Non-Functional Requirements (NFR) have a direct impact on the architecture of the system, thus it is essential to identify NFRs in the initial phases of software development.Aim: The work is based on extraction of relevant keywords from NFR descriptions by employing text mining steps and thereafter classifying these descriptions into one of the nine types of NFRs.Method: For each NFR type, keywords are extracted from a set of pre-categorized specifications using Information-Gain measure.Then models using 8 Machine Learning (ML) techniques are developed for classification of NFR descriptions.A set of 15 projects (containing 326 NFR descriptions) developed by MS students at DePaul University are used to evaluate the models. Results:The study analyzes the performance of ML models in terms of classification and misclassification rate to determine the best model for predicting each type NFR descriptions.The Naïve Bayes model has performed best in predicting "maintainability" and "availability" type of NFRs. Conclusion:The NFR descriptions should be analyzed and mapped into their corresponding NFR types during the initial phases.The authors conducted cost benefit analysis to appreciate the advantage of using the proposed models.
Developing Novel Activation Functions in Time Series Anomaly Detection with LSTM Autoencoder

Our proposal consists of developing two novel activation functions in time series anomaly detection, they have the capability to reduce the validation loss. The approach is based on a current activation function in Deep Learning, a very intensive field studied over time, in order to find the most suitable activation in a neural network. In order to achieve this purpose, we used an LSTM (Long Short-Term Memory) Autoencoder architecture, using these two novel functions to see the network's behavior through introducing them. The key point in our proposal is given by the learnable parameter, assuring more flexibility within the network in weights' updates, in fact, this property being more powerful than a predefined parameter that will bring a constraint due to its limit. We tested our proposal in comparison to other popular functions such as ReLU (Linear Rectifier Unit), hyperbolic tangent (tanh), Talu activation function. Also, the novelty of this paper consists of taking into consideration of piecewise behavior of an activation function in order to increase the performance of a neural network in Deep Learning.
On a Generalized Central Limit Theorem and Large Deviations for Homogeneous Open Quantum Walks

Abstract We consider homogeneous open quantum walks on a lattice with finite dimensional local Hilbert space and we study in particular the position process of the quantum trajectories of the walk. We prove that the properly rescaled position process asymptotically approaches a mixture of Gaussian measures. We can generalize the existing central limit type results and give more explicit expressions for the involved asymptotic quantities, dropping any additional condition on the walk. We use deformation and spectral techniques, together with reducibility properties of the local channel associated with the open quantum walk. Further, we can provide a large deviation principle in the case of a fast recurrent local channel and at least lower and upper bounds in the general case.
Almost Tight Approximation Algorithms for Explainable Clustering

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Almost Tight Approximation Algorithms for Explainable ClusteringHossein Esfandiari, Vahab Mirrokni, and Shyam NarayananHossein Esfandiari, Vahab Mirrokni, and Shyam Narayananpp.2641 - 2663Chapter DOI:https://doi.org/10.1137/1.9781611977073.103PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Recently, due to an increasing interest for transparency in artificial intelligence, several methods of explainable machine learning have been developed with the simultaneous goal of accuracy and interpretability by humans. In this paper, we study a recent framework of explainable clustering first suggested by Dasgupta et al. [11]. Specifically, we focus on the k-means and k-median problems and provide nearly tight upper and lower bounds. First, we provide an O(log k log log k)-approximation algorithm for explainable k-median, improving on the best known algorithm of O(k) [11] and nearly matching the known Ω(log k) lower bound [11]. In addition, in low-dimensional spaces d ≪ log k, we show that our algorithm also provides an O(d log2 d)-approximate solution for explainable k-median. This improves over the best known bound of O(d log k) for low dimensions [19], and is a constant for constant dimensional spaces. To complement this, we show a nearly matching Ω(d) lower bound. Next, we study the k-means problem in this context and provide an O(k log k)-approximation algorithm for explainable k-means, improving over the O(k2) bound of Dasgupta et al. and the O(dk log k) bound of [19]. To complement this we provide an almost tight Ω(k) lower bound, improving over the Ω(log k) lower bound of Dasgupta et al. Given an approximate solution to the classic k-means and k-median, our algorithm for k-median runs in time O(kd log2 k) and our algorithm for k-means runs in time O(k2 d). Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Impact of View-Dependent Image-Based Effects on Perception of Visual Realism and Presence in Virtual Reality Environments Created Using Multi-Camera Systems

Several recent works have presented image-based methods for creating high-fidelity immersive virtual environments from photographs of real-world scenes. In this paper, we provide a user-centered evaluation of such methods by way of a user study investigating their impact on viewers’ perception of visual realism and sense of presence. In particular, we focus on two specific elements commonly introduced by image-based approaches. First, we investigate the extent to which using dedicated image-based rendering algorithms to render the scene with view-dependent effects (such as specular highlights) causes users to perceive it as being more realistic. Second, we study whether making the scene fade out beyond a fixed volume in 3D space significantly reduces participants’ feeling of being there, examining different sizes for this viewing volume. To provide details on the virtual environment used in the study, we also describe how we recreated a museum gallery for room-scale virtual reality using a custom-built multi-camera rig. The results of our study show that using image-based rendering to render view-dependent effects can effectively enhance the perception of visual realism and elicit a stronger sense of presence, even when it implies constraining the viewing volume to a small range of motion.
Document-Grounded Goal-Oriented Dialogue Systems on Pre-Trained Language Model with Diverse Input Representation

Document-grounded goal-oriented dialog system understands users' utterances, and generates proper responses by using information obtained from documents.The Dialdoc21 shared task consists of two subtasks; subtask1, finding text spans associated with users' utterances from documents, and subtask2, generating responses based on information obtained from subtask1.In this paper, we propose two models (i.e., a knowledge span prediction model and a response generation model) for the sub-task1 and the subtask2.In the subtask1, dialogue act losses are used with RoBERTa, and title embeddings are added to input representation of RoBERTa.In the subtask2, various special tokens and embeddings are added to input representation of BART's encoder.Then, we propose a method to assign different difficulty scores to leverage curriculum learning.In the subtask1, our span prediction model achieved F1-scores of 74.81 (ranked at top 7) and 73.41 (ranked at top 5) in test-dev phase and test phase, respectively.In the subtask2, our response generation model achieved sacre-BLEUs of 37.50 (ranked at top 3) and 41.06 (ranked at top 1) in in test-dev phase and test phase, respectively.
UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction

Reading is a complex process which requires proper understanding of texts in order to create coherent mental representations.However, comprehension problems may arise due to hard-to-understand sections, which can prove troublesome for readers, while accounting for their specific language skills.As such, steps towards simplifying these sections can be performed, by accurately identifying and evaluating difficult structures.In this paper, we describe our approach for the SemEval-2021 Task 1: Lexical Complexity Prediction competition that consists of a mixture of advanced NLP techniques, namely Transformer-based language models, pre-trained word embeddings, Graph Convolutional Networks, Capsule Networks, as well as a series of handcrafted textual complexity features.Our models are applicable on both subtasks and achieve good performance results, with a MAE below 0.07 and a Person correlation of .73 for single word identification, as well as a MAE below 0.08 and a Person correlation of .79 for multiple word targets.Our results are just 5.46% and 6.5% lower than the top scores obtained in the competition on the first and the second subtasks, respectively.
Mapping Opencast Iron Mine and Mine Solid Waste Based on a New Spectral Index From Medium Spatial Resolution Satellite Data

Opencast iron mine and mine solid waste (OIM-MSW) are closely associated with a wide range of environmental issues, such as destroying soil structure, affecting air quality, and damaging biodiversity. Therefore, accurately grasping the distribution of OIM-MSW is of great significance to monitoring and managing environmental conditions. In order to reduce the impact of background features on the identification of OIM-MSW, we constructed an index based on medium spatial resolution satellite data, and proposed a semiautomatic method to determine the threshold value of the index. Experimental results indicated that: 1) The proposed index (OIM-MSWI) can effectively identify the OIM-MSW of which the OA (overall accuracy) and K (kappa coefficient) value are 0.98 and 0.75, respectively. 2) The index owns satisfying performance in identifying OIM-MSW in different months, and its recognition ability in plant growing season is better than that in the nonplant growing season. 3) Both Landsat 8 images and Sentinel 2 images have the potential to identify OIM-MSW via the proposed index, but the recognition accuracy of Landsat 8 images (OA = 0.98, K = 0.75) is slightly better than Sentinel 2 images (OA = 0.96, K = 0.64). (4) OIM-MSWI is effective in different climatic conditions, with OA and K of 0.98, 0.75 and 0.97, 0.63 in the cases of semiarid and humid zones, respectively. In conclusion, OIM-MSWI is a simple and effective index for identifying OIM-MSW, and can provide support for OIM-MSW monitoring and management.
Phase change memory: Operation, current challenges and future prospects

Phase change memory has become known and most advanced resistive memory technology to meet the consumer's applications. It is a non-volatile random access memory. It plays a vital role in order to overcome the increasing demand of powerful data storage devices. In the present paper, advantages over other memories have been discussed in detail. This review focuses on the device operation of PCM. The current challenges and future prospects have also been outlined.&#x0D;
SU-NLP at CASE 2021 Task 1: Protest News Detection for English

This paper summarizes our group's efforts in the multilingual protest news detection shared task, which is organized as a part of the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) Workshop.We participated in all four subtasks in English.Especially in the identification of event containing sentences task, our proposed ensemble approach using RoBERTa and multichannel CNN-LexStem model yields higher performance.Similarly in the event extraction task, our transformer-LSTM-CRF architecture outperforms regular transformers significantly.
An FST morphological analyzer for the Gitksan language

This paper presents a finite-state morphological analyzer for the Gitksan language.The analyzer draws from a 1250-token Eastern dialect wordlist.It is based on finite-state technology and additionally includes two extensions which can provide analyses for out-ofvocabulary words: rules for generating predictable dialect variants, and a neural guesser component.The pre-neural analyzer, tested against interlinear-annotated texts from multiple dialects, achieves coverage of (75-81%), and maintains high precision (95-100%).The neural extension improves coverage at the cost of lowered precision.
Reversible Data Hiding Based on Pixel-Value-Ordering and Prediction-Error Triplet Expansion

Pixel value ordering and prediction error expansion (PVO+PEE) is a very successful reversible data hiding (RDH) scheme. A series of studies were proposed to improve the performance of the PVO-based scheme. However, the embedding capacity of those schemes is quite limited. We propose a two-step prediction-error-triplet expansion RDH scheme based on PVO. A three-dimensional state transition map for the prediction-error triplet is also proposed to guide the embedding of the two-step scheme. By properly designing the state transitions, the proposed scheme can embed secret data or expand without embedding by modifying just a single entry of the triplet. The experimental results show that the proposed scheme significantly enlarges the embedding capacity of the PVO-based scheme and further reduces the distortion due to embedding.
Sarcasm detection of tweets without #sarcasm: data science approach

Identifying sarcasm present in the text could be a challenging work. In sarcasm, a negative word can flip the polarity of a positive sentence. Sentences can be classified as sarcastic or non-sarcastic. It is easier to identify sarcasm using facial expression or tonal weight rather detecting from plain text. Thus, sarcasm detection using natural language processing is major challenge without giving away any specific context or clue such as #sarcasm present in a tweet. Therefore, research tries to solve this classification problem using various optimized models. Proposed model, analyzes whether a given tweet, is sarcastic or not without the presnece of hashtag sarcasm or any kind of specific context present in text. To achieve better results, we used different machine learning classification methodology along with deep learning embedding techniques. Our optimized model uses a stacking technique which combines the result of logistic regression and long short-term memory (LSTM) recurrent neural net feed to light gradient boosting technique which generates better result as compare to existing machine learning and neural network algorithm. The key difference of our research work is sarcasm detection done without #sarcasm which has not been much explored earlier by any researcher. The metrics used for evolutionis F1-score and confusion matrix.
Privacy preserving association rule hiding using border based approach

&lt;div&gt;Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. &lt;/div&gt;
A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking

Despite years of work, a robust, widely applicable generic “symmetry detector” that can paral-lel other kinds of computer vision/image processing tools for the more basic structural charac-teristics, such as a “edge” or “corner” detector, remains a computational challenge. A new symmetry feature detector with a descriptor is proposed in this paper, namely the Simple Robust Features (SRF) algorithm. A performance comparison is made among SRF with SRF, Speeded-up Robust Features (SURF) with SURF, Maximally Stable Extremal Regions (MSER) with SURF, Harris with Fast Retina Keypoint (FREAK), Minimum Eigenvalue with FREAK, Features from Accelerated Segment Test (FAST) with FREAK, and Binary Robust Invariant Scalable Keypoints (BRISK) with FREAK. A visual tracking dataset is used in this performance evaluation in terms of accuracy and computational cost. The results have shown that combining the SRF detector with the SRF descriptor is preferable, as it has on average the highest accuracy. Additionally, the computational cost of SRF with SRF is much lower than the others.
Zero-shot Sequence Labeling for Transformer-based Sentence Classifiers

We investigate how sentence-level transformers can be modified into effective sequence labelers at the token level without any direct supervision.Existing approaches to zero-shot sequence labeling do not perform well when applied on transformer-based architectures.As transformers contain multiple layers of multihead self-attention, information in the sentence gets distributed between many tokens, negatively affecting zero-shot token-level performance.We find that a soft attention module which explicitly encourages sharpness of attention weights can significantly outperform existing methods.