Reviewerly/exHarmony huggingface dataset:
Discovering Authorization Business Rules toward Detecting Web Applications Logic Flaws

Aggressive integration of validation checks into web framework software has altered the attack surface of web applications by reducing the opportunity for traditional injection flaws. The hacking community's reaction has shifted to a more subtle - and more challenging to detect - form of attacks, that of discovering and exploiting underlying application business logic. The lack of accurate business rules defining the final application product extends its logical vulnerability surface. We propose a novel black-box approach for discovering authorization business rules in web applications through users' dynamic behavior. Allowing applications that lack formal specifications to be better tested for logic vulnerabilities. Our approach discovers groups using agglomerative hierarchical clustering based on different profiling techniques that capture users' actions and privileges. We also automated the process of identifying the optimal number of roles. The results indicated high quality and stability in discovering business rules, even in smaller datasets.
Interdisciplinarity Inference Using a Cross Encoder between Research Projects

We propose an inference method that infers the feasibility of interdisciplinary research by integrating two existing research projects from the brief descriptions of those two projects and evaluated its performance. The proposed method utilizes a cross encoder that can classify various relationships between pairs of sentences as binary or multivalued. We demonstrated the effectiveness of the proposed method by experiments using the reports of the research projects conducted by the Grants-in-Aid for Scientific Research (KAKENHI). The proposed method achieved the F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score of 0.8318.
Synthetic Benchmark Creation for Few shot Image Classification and Its performance on meta-learning Algorithms

Meta-learning is the paradigm of learning how to learn, which eventually enables adapting to new tasks quickly with few data samples, owing to the training over a large number of tasks. Optimization-based approaches are of critical interest in all the different meta-learning algorithms as it generalizes to almost all kinds of tasks, from regression and classification to reinforcement learning. The idea of synthetic data sets used for performance evaluation is to create benchmark data sets that are diverse enough to encourage meta-learning. However, such data sets have been previously designed for analyzing and evaluating popular models like ANIL and MAML but only for regression tasks. This work extends the same to create synthetic data sets for the N-way classification problem. Analysis of our synthetic data set shows that using data that require a higher degree of representation change in the hidden layer of models is detrimental to the performance of ANIL. At the same time, MAML performs comparatively well under similar circumstances.
Detection and Tracking of Sport Players on Videodata Using Deep Learning Methods

This article presents the research results of JDE algorithm for detection and tracking athletes in video data. The developed convolutional neural network was trained and tested on the NVIDIA DGX-1 supercomputer. To analyze the quality of the model, MOTA metric was used, which is related to how the human eyes track targets in the video stream. The detection and tracking people on video data is relevant to many tasks of computer vision, in particular, in sport to collect statistics about players. The quality of JDE algorithm was assessed by the video fragments of basketball games filmed in the sports halls at P.G. Demidov Yaroslavl State University.
Appearance-based Driver's Gaze Mapping Using a Dash Camera

Recent gaze mapping studies indicated that consideration of both head position and eye gaze can benefit performance. Pushing this idea further, we propose an appearance-based method that, uses a combination of head position and eye gaze features. It is a low-cost, non-intrusive, and lightweight gaze mapping method that is not difficult to use or fatigues the driver. We proposed different strategies for the gaze estimation strategy using head position and eye gaze features. Within this proposal, we investigated combining the features one by one and with the other features in binary, triple, and quadruple to determine how they affect the estimation of gaze mapping. In addition to features, we also investigated how different camera positions affect gaze estimation. From these strategies, the strategy using OpenFace with SVM classifier (using gaze angle, head position_R features, head rotation_R features, and eye position WO-Z features) using camera position 2 outperformed all other methods and achieved a SCER rate of 85.64%, and LCER rate of 98.69% on the open dataset CAVEDB.
Equivariance Score Based on Canonical Correlation Analysis

Self-supervised learning (SSL) is a process of learning a general-purpose feature extractor using artificially generated label data. Depending on the application, invariance and equivariance are desired in image feature representation. Invariance is a property in which feature representation does not change before and after applying transformations such as image scaling and rotation to an input image. In contrast, equivariance refers to the property in which feature representations change in response to the transformation of the input image.In SSL, whether invariance or equivariance for a given transformation considerably affect the downstream target task. However, a method for measurement of equivariance is not yet be proposed. In this study, inspired by observation of feature expressions and canonical correlation analysis, a novel metric for equivariance measurement was proposed. We used the MNIST, fashion-MNIST, and CIFAR-10 datasets to verify the behavior of the proposed metric of same-degeneration for encoders learned in a rotation prediction task, a type of SSL.
Rapid exposure time estimation method for high-dynamic range surface

In structured light projection method, complex reflectivity distribution of high-dynamic range object surface is an important factor affecting the measurement accuracy. Image will be overexposed and underexposed with the complex reflectivity distribution of high-dynamic range object surface. Therefore, this paper proposed a method to estimate the reflectivity and exposure time distribution by only projecting two images with one exposure time. Then the optimal exposure time of different reflectivity region can be calculated accurately. The experiment result shows that the image quality is improved in the calculated exposure time, and the surface is reconstructed. It proved the proposed method is accurate and rapid in high-dynamic range surface measurement.
Improved Seeker Optimization Algorithm of Robot Path Optimization in Warehouse System

To address the problems of non-instantaneousness and poor adaptation to grid maps in the warehouse robot Seeker Optimization Algorithm (SOA) path planning, an improved method is proposed to use the concept of key nodes to represent path critical information for iterative search during population (path) initialisation and update, followed by node connection to form a complete path after the search is completed. Within this framework, a node correction method is firstly proposed to correct the key nodes after the optimization search, and secondly a node extension method is proposed to connect the nodes, in order to address the problem of node connection. Then a collision avoidance strategy of collision detection-replanning is proposed for the problem of multi-robot path anti-collision in the storage workshop by combining a reservation table of time and position. Finally, the improved SOA path planning and the proposed collision avoidance strategy are validated through software simulation. The results show that for single path planning, the improved SOA not only greatly reduces the seeking time greatly, but also improves the path redundancy significantly; and the proposed collision avoidance strategy with the improved SOA can effectively avoid robot collision in the storage workshop.
Evolutionary Optimization of Stereo Matching Parameters for Plant Shape Estimation

Plant growth is one of the most important components in production environment control. In recent years, the development of stereo imaging techniques and, hardware such as stereo camera allows for precise measurement of the 3D structure of an object. However, in order to monitor plant growth at multiple points in space and time, it is necessary to construct an affordable system from a practical viewpoint in agriculture. Therefore, in this study, we focus on parameter optimization in parallax estimation to achieve highly accurate plant height measurement even with inexpensive hardware. In particular, this study applies an optimization method, namely a Genetic algorithm, to several parameters of the basic algorithm for disparity estimation considering objective functions. In this study, we set our objective functions such as Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural SIMilarity (SSIM). The results of this study show that the optimization methods can improve the accuracy of disparity estimation for plant shape.
Designing and Building a Web-Based Book Sales and Tenancy Platform

Infrastructure development that continues to increase is one of the conveniences for the community to use the internet, which can access various digital content such as websites, chat, shopping, and others. More than half of the population in Indonesia accesses the internet every day, if it is used to make a product branding, it will be more time efficient and save costs. Books are one of the products that must exist in every generation because they are an important source of information in everyday events. This study aims to create a place for people who want to sell books that can make it easier for buyers or consumers. Utilizing a website that can speed up an activity and save time to sell or find books for sellers and consumers. This research was built using Python as a programming language, Flask Framework as a system framework, sqlite3 as data storage, and bootstrap 5 as a display foundation combined with style changes through CSS (Cascading Style Sheets). Agile development is a method used to develop marketplace platforms. The result of this research is a web-based book rental and tenancy marketplace platform.
A Novel Story Plots Extraction Based Rapid Reading Comprehension System to Assist the Education for Low-income Families

Poverty has posed many threats to the children's education. Children in low-income families tend to have uneducated and/or very busy parents, and their reading after school is less being instructed and supported by their parents even they want to support but without the capacity and/or sufficient time to read and understand the huge volume of stories and novels. In this paper, we aim to tackle the above challenges and develop a rapid reading comprehension system to assist those parents rapidly understanding the story content by proposing a novel system that is capable of generating and visualising story plots from the massive literature narratives. Meanwhile, the visualised story plots are represented as a sequenced knowledge graph, helping those uneducated or busy parents grasp the story's linguistic meanings and flow. Furthermore, a well-known fiction book, i.e., “Alice in Wonderland” is utilised as case studies to validate the proposed system. The primary experimental results reveal that story plot extraction can effectively provide explicit information for presenting storylines chapter by chapter at the early stage of this research initiative.
Dancing, not Wrestling: Moving from Compliance to Concordance for Secure Software Development

Secure software development has become an increasingly important focus for research in recent years, not least because of advances in technology such as AI, machine learning (AI/ML), robotics, and autonomous systems (RAS). AI/ML and RAS facilitate automated decision-making and have the capability to have a significant impact on society. As such this technology needs to be trustworthy, and secure software development is a key attribute for trustworthiness. Software developers frequently have responsibility and accountability for delivering secure code but limited authority over how this is achieved. Authority tends to lie with cyber security professionals who mandate security processes, tools and training, often with limited success. Our research objective was to better understand how to bridge this gap between software developers and cyber security practitioners so that authority, responsibility and accountability are shared equally. We took inspiration from healthcare research that looks at the relationship between compliance, adherence and concordance. We use this research as a lens through which to analyse qualitative data from 35 interviews with professional software developers. Our research suggests that if software developers and cyber security professionals move to a point of concordance in their interactions it could lead to the negotiation of more realistic cyber security solutions, as well as removing friction from the practice of software developers and ultimately lead to more secure and trustworthy systems.
Towards the Integration of Human Factors in Collaborative Decision Making for Secure Architecture Design

Designing a large and complex software system depends not only on the nature of the system itself, but also on human-centric characteristics of the team of architects, developers, and managers involved in the design activity. Each of these team members often comes with varying levels of knowledge, experience, attitudes, and behaviors (i.e., human factors) towards securing systems that impact the decision-making process of the individual team members and of the team as a whole. Thus, these human factors can influence architectural design decisions impacting many different system qualities including security. In this paper, we propose a framework for considering human factors in collaborative decision-making for secure architecture design. At the core of the proposed framework, are conceptual models for security human factors and architectural design decisions. We describe the steps and our preliminary results towards creating the proposed framework using a combination of model-driven engineering techniques and human science approaches. We also provide a simple design scenario to illustrate the envisioned design workflow of the proposed framework. With the proposed framework, we aim to improve our understanding of how decisions are made by a team of diverse members, and to provide better traceability of decisions impacting system security.
On Viable Statistical Metrics for Re-Embedding Network Steganalysis

Network steganalyses attempt to uncover hidden messages (steganograms) in network flows. These techniques are binary in that they classify if a flow contains steganograms or not. Moreover, most of these techniques assume the availability flows that do not contain any steganograms as baselines for comparison, an assumption that is hard to hold. A re-embedding steganalysis does not require any baseline, and moreover, it can not only detect the presence of steganograms but also estimate the amount of steganograms. Being able to estimate the amount of steganograms allows a network forensic expert to judge the damage caused by these hidden messages. This paper addresses the question of what statistical metrics might apply for effective re-embedding steganalysis of network traces. It presents an empirical comparison of several statistical metrics in the light of their effectiveness in re-embedding steganalysis.
Software Evolution Management with Differential Facts

Many techniques have been proposed to mine knowledge from software artefacts and solve software evolution management tasks. To promote effective reusing of those knowledge, we propose a unified format, differential facts, to represent software changes across versions as well as various relations within each version, such as call graphs. Based on queryable formats, differential facts can be manipulated to implement complex evolution management tasks. Since facts once extracted can be shared among different tasks, the reusability brings improvements to overall performance. We validate the technique and show its benefits of being efficient, flexible, and easy to implement, with several applications, including semantic history slicing, regression test selection, documentation error detection and client-specific usage patterns discovery.
Detecting Inconsistencies in If-Condition-Raise Statements

Developers use exceptions guarded by conditions to abort the execution when a program reaches an unexpected state. However, sometimes the condition and the raised exception do not imply the same stopping reason, in which case, we call them inconsistent if-condition-raise statements. The inconsistency can originate from a mistake in the condition or the exception message. This paper presents IICR-Finder, a deep learning-based approach to detect inconsistent if-condition-raise statements. The approach reasons both about the condition’s logic and the natural language of the exception message and raises a warning in case of inconsistency. We present six techniques to automatically generate large numbers of inconsistent statements to train two neural models based on binary classification and triplet loss. We apply the approach to 210K if-condition-raise statements extracted from 42 million lines of Python code. It achieves a precision of 72% at a recall of 60% on a dataset of past bug fixes. Running IICR-Finder on open-source projects reveals 30 previously unknown bugs, ten of which we reported, with eight confirmed by the developers.
Domain Adaptation of Transformer-Based Models Using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback

Understanding customer feedback is becoming a necessity for companies to identify problems and improve their products and services. Text classification and sentiment analysis can play a major role in analyzing this data by using a variety of machine and deep learning approaches. In this work, different transformer-based models are utilized to explore how efficient these models are when working with a German customer feedback dataset. In addition, these pre-trained models are further analyzed to determine if adapting them to a specific domain using unlabeled data can yield better results than off-the-shelf pre-trained models. To evaluate the models, two downstream tasks from the GermEval 2017 are considered. The experimental results show that transformer-based models can reach significant improvements compared to a fastText baseline and outperform the published scores and previous models. For the subtask Relevance Classification, the best models achieve a micro-averaged $F1$-Score of 96.1 % on the first test set and 95.9 % on the second one, and a score of 85.1 % and 85.3 % for the subtask Polarity Classification.
A DCA‐based sparse coding for video summarization with MCP

Abstract Video summarization offers a summary version that conveys the primary information of a longer video. The main challenges of video summarization are related to keyframe extraction and saliency mapping. Thus, this work proposes a sparse coding model for keyframe extraction and saliency mapping applications. Specifically, the minimax concave penalty (MCP) is utilized as a sparse regularization scheme and the regularized non‐convex MCP problem is solved by decomposing MCP into two convex functions and the convex function's algorithm difference is relied on to solve the resulting sub‐problems. The experimental results demonstrate higher compressed keyframes and saliency maps than current state‐of‐the‐art algorithms. In particular, the model attains a lower summary length of 34% and 19% compared to sparse modeling representation selection (SMRS) and sparse modeling using the determinant sparsity measure (SC‐det), respectively. In addition, the developed scheme has a shorter computation time, requiring 82% and 33% less time than the ITTI and the dense and sparse reconstruction (DSR) methods.
Channel Correlation Distillation for Compact Semantic Segmentation

Knowledge distillation has been widely applied in semantic segmentation to reduce the model size and computational complexity. The prior knowledge distillation methods for semantic segmentation mainly focus on transferring the spatial relation knowledge, neglecting to transfer the channel correlation knowledge in the feature space, which is vital for semantic segmentation. We propose a novel Channel Correlation Distillation (CCD) method for semantic segmentation to solve this issue. The correlation between channels tells how likely these channels belong to the same categories. We force the student to mimic the teacher by minimizing the distance between the channel correlation maps of the student and the teacher. Furthermore, we propose the multi-scale discriminators to sufficiently distinguish the multi-scale differences between the teacher and student segmentation outputs. Extensive experiments on three popular datasets: Cityscapes, CamVid, and Pascal VOC 2012 validate the superiority of our CCD. Experimental results show that our CCD could consistently improve the state-of-the-art methods with various network structures for semantic segmentation.
Hashed Access Policy-Based Secure Data Transmission Using SDHAK-ECC

Cloud computing (CC), which provides numerous benefits to customers, is a new revolution in information technology. The benefits are on-demand, support, scalability, along with reduced cost usage of computing resources. However, with the prevailing techniques, the system’s authentication is still challenging and it leads to being vulnerable. Thus, utilizing Barrel Shift-centric Whirlpool Hashing-Secure Diffie Hellman ASCII Key-Elliptic Curve Cryptography (BSWH-SDHAK-ECC), the hashed access policy (AP)-grounded secure data transmission is presented in this paper. The data owner (DO) registers their information initially. The user login and verify their profile grounded on the registration. The user selects the data to upload to the Cloud Server (CS) after successful verification. The AP is created; after that, the image attributes are extracted; also, utilizing the BSWH approach, a hash code is produced for the AP. Concurrently, by utilizing the Adaptive Binary Shift-based Huffman Encoding (ABSHE) technique, the selected image is compressed. Also, by utilizing the SDHAK-ECC algorithm, the compressed image is encrypted. Lastly, to the CS, the created AP along with the encrypted image is uploaded. The data user sent the request to access and downloads the data. After that, the AP was provided to the user by the data owner. Next, the user sends it to the CS, which checks its AP with the user’s AP. When the AP is matched with the cloud AP, the encrypted data is downloaded and decrypted. Finally, the experimental outcomes revealed that the proposed model achieved a higher security value of 0.9970 that shows the proposed framework’s efficient performance in contrast to the prevailing techniques.