Reviewerly/exHarmony huggingface dataset:
Interview with a biased machine

Deep learning, the technology behind what we like to call AI, is at its root, a highly sophisticated system of pattern recognition. These systems easily learn any pattern that is in a dataset. We have now built machines like OpenAI's GPT-3 that can read every story on the internet, work out the patterns of narratives and synthesize new stories back at us. But the GPT-3 doesn't generate text like people. It does not ponder or try to structure an argument. It is more like water flowing down a hill. The billions of weights in the language model represent a kind of high-dimensional landscape. In this chapter, we explore how this works in practice in an interview that ranges from the importance of fact-based decisions the belief that Reptilians, a race of aliens, are going to turn us all into mindless zombies by planting microchips in our brains.
The Petrographic Microscope: A Polarized Light Microscope

The petrographic microscope is a specialized microscope which uses plane polarized light. It has the usual components of a microscope and is additionally equipped with a smoothly rotating circular stage with a graduation of 360° or a stage goniometer and additional devices to understand the behavior of light in minerals in a thin section (Figs. 3.1 and 3.2). Light is plane polarized by a first polarizer, called the polarizer, and enters the mineral from below. Because of the atomic structure of anisotropic minerals, it is split into two rays, the ordinary and extraordinary rays, vibrating at an angle of 90° to each other.
Basic Concepts

The interaction of light with the atomic structure of minerals is the basic principle for the identification of minerals in a rock using the petrographic microscope.
Adaptive Channel Attention-Based Deformable Generative Adversarial Network for Underwater Image Enhancement

In this paper, to effectively strengthen quality of underwater image enhancement from both channel and spatial viewpoints, an adaptive channel attention-based deformable generative adversarial networks (ACADGAN) framework is established. Main contributions are as follows. 1) By virtue of multi-branch convolution architecture with dilated convolution mechanism, the adaptive channel attention (ACA) is devised, such that channel weight can be adaptively recalibrated, and thereby significantly contributing to preserving content features from channel viewpoint. 2) By augmenting offset position of sampling point with respect to convolution kernel, the deformable convolution network (DCN) is created, such that detailed information of underwater image can be dramatically retained from spatial aspect. 3) The ACADGAN scheme is eventually proposed by integrating ACA and DCN modules with a deep generative adversarial network. Comprehensive experiments demonstrate the remarkable effectiveness and superiority of the developed ACADGAN scheme.
CAMiSE: Content Addressable Memory-Integrated Searchable Encryption

Searchable symmetric encryption (SSE) is a special class of encryption schemes for computing directly over encrypted data. SSE aims to be significantly more efficient as compared to other solutions, such as fully homomorphic encryption (FHE), while leaking only minimal information to the adversary. SSE is particularly efficient and scalable for Boolean queries over large encrypted relational databases outsourced to third-party cloud service providers. However, practical implementations of SSE often suffer from performance bottlenecks due to randomised memory accesses for reads/writes and computation-intensive cryptographic operations. As a result, a gap exists today between theoretically efficient SSE algorithms and practically efficient SSE systems for real-world databases. In this paper, we address this longstanding open question that has otherwise hindered the widespread deployment of SSE over real cloud computing platforms. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAMiSE</i> –a fully associative memory-integrated framework for designing SSE systems with fast query processing over extremely large databases. We show a novel usage of custom-designed Content Addressable Memory ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAM</i> ), together with robust data access policies, to bridge the memory wall in traditional SSE implementations by minimising storage-access latencies due to randomised look-up operations during searches. Coupled with dedicated hardware accelerators for cryptographic operations, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAMiSE</i> achieves extremely fast and scalable query processing over encrypted relational databases. We prototype multiple well-known SSE algorithms and SSE data structures within our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAMiSE</i> framework. Our experiments show that these implementations achieve around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7\times $ </tex-math></inline-formula> speed-up over traditional software-based implementations while scaling smoothly to extensive real-world databases with millions of records.
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion Recognition

Emotion recognition in conversation (ERC) has received increasing attention from researchers due to its wide range of applications. As conversation has a natural graph structure, numerous approaches used to model ERC based on graph convolutional networks (GCNs) have yielded significant results. However, the aggregation approach of traditional GCNs suffers from the node information redundancy problem, leading to node discriminant information loss. Additionally, single-layer GCNs lack the capacity to capture long-range contextual information from the graph. Furthermore, the majority of approaches are based on textual modality or stitching together different modalities, resulting in a weak ability to capture interactions between modalities. To address these problems, we present the relational bilevel aggregation graph convolutional network (RBA-GCN), which consists of three modules: the graph generation module (GGM), similarity-based cluster building module (SCBM) and bilevel aggregation module (BiAM). First, GGM constructs a novel graph to reduce the redundancy of target node information. Then, SCBM calculates the node similarity in the target node and its structural neighborhood, where noisy information with low similarity is filtered out to preserve the discriminant information of the node. Meanwhile, BiAM is a novel aggregation method that can preserve the information of nodes during the aggregation process. This module can construct the interaction between different modalities and capture long-range contextual information based on similarity clusters. On both the IEMOCAP and MELD datasets, the weighted average F1 score of RBA-GCN has a 2.17 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 5.21% improvement over that of the most advanced method.
POVNet: Image-Based Virtual Try-On Through Accurate Warping and Residual

Virtual dressing room applications help online shoppers visualize outfits. Such a system, to be commercially viable, must satisfy a set of performance criteria. The system must produce high quality images that faithfully preserve garment properties, allow users to mix and match garments of various types and support human models varying in skin tone, hair color, body shape, and so on. This paper describes POVNet, a framework that meets all these requirements (except body shapes variations). Our system uses warping methods together with residual data to preserve garment texture at fine scales and high resolution. Our warping procedure adapts to a wide range of garments and allows swapping in and out of individual garments. A learned rendering procedure using an adversarial loss ensures that fine shading, etc. is accurately reflected. A distance transform representation ensures that hems, cuffs, stripes, and so on are correctly placed. We demonstrate improvements in garment rendering over state of the art resulting from these procedures. We demonstrate that the framework is scalable, responds in real-time, and works robustly with a variety of garment categories. Finally, we demonstrate that using this system as a virtual dressing room interface for fashion e-commerce websites has significantly boosted user-engagement rates.
Semantic Transformation Utilizing Graphs

Semantic transformation for data in motion has been a continuous problem, and various approaches have been proposed to solve it. This paper proposes a graph approach to handle semantic data transformations. By using a set of traversal patterns, an event log will provide the feature set for both machine-learning algorithms and graph algorithms to make missing node predictions. The paper compares the two approaches and presents significant differences in results.
AIRMan: An Artificial Intelligence (AI) Risk Management System

Artificial intelligence (AI) has emerged as one of the most formative technologies of the century and further gains importance to solve the big societal challenges (e.g. achievement of the sustainable development goals) or as a means to stay competitive in today’s global markets. The role as a key enabler in many areas of our daily life leads to a growing dependence, which has to be managed accordingly to mitigate negative economic, societal or privacy impacts. Therefore, the European Union is working on an AI Act, which defines concrete governance, risk and compliance (GRC) requirements. One of the key demands of this regulation is the operation of a risk management system for High-Risk AI systems. In this paper, we therefore present a detailed analysis of relevant literature in this domain and introduce our proposed approach for an AI Risk Management System (AIRMan).
Haskell usedata objects

When extending pandoc (or Quarto) with Lua filters, we interact with so-called Lua userdata objects. These objects are used to wrap document AST elements, making them accessible from Lua scripts. They mostly behave like normal Lua tables. This post is intended as a quick overview, listing interesting properties of userdata objects. Userdata objects Haskell-generated userdata objects have three main components: a type name, properties, and methods: Type name The name can be retrieved with...
AutoML – Optimal K Procedure

Clustering procedures are an important method in the AI domain. They help improve machine learning capabilities and allow the expansion and enrichment of a variety of services, functionalities and information for organizations, decision-makers, recommendations, predictions, etc. One of the major challenges of the k-means algorithm is selecting an optimal K cluster for the given classified datasets. Our research defines, implements and examines an AutoML procedure that combines numeric and categorical datasets, which are divided into subsets of numeric values, analyzed and compared. Then, via four different scores, the procedure selects an optimal k value according to the scores, after evaluating the square(n) observations and integrating the optimal k into the datasets, with an additional labeling value. The research presents experiments of the end-to-end AutoML procedure, which is implemented using two datasets. Furthermore, it elaborates on the advantages of this innovative procedure that analyzes, selects and implements the optimum k for a dataset.
ISO27001 as a Tool for Availability Management

Many companies seek to gain reputation by going through a certification process, and the standard of choice for many companies will be the ISO 27001:2013 standard [1], A thorough literature review on ISO27001 research [8] found that only 26% of the studies have cited outcomes of the ISO27001 certification, and only 3 papers focus explicitly on the impact of the standard. Neither of these focus on availability and risk, and no research provides real data from a risk registry showing the standard’s effect. In this research we analyse the controls of the standard from the point of view of a Network Operations Centre. We further analyse a global network operator’s risk registry over a 5-year period and show how the risks and risk improvements relate to the ISO27001 information security objectives. The results show that the implementation of ISO27001 caused a significant reduction of risk in all areas, and argues that the ISO27001 standard is well suited to reducing the operator’s risk related to the objects of confidentiality, integrity and availability.
Galaxy Zoo 3D: Identifying Bars, Spirals and Foreground Stars in MaNGA Galaxy Data

Abstract Galaxies, particularly disc galaxies, show a wide variety of internal structures (e.g. spirals, bars, and bulges). Mapping Nearby Galaxies at Apache Point Observatory (MaNGA, part of the fourth incarnation of the Sloan Digital Sky Surveys), obtained spatially resolved spectral maps for 10,010 nearby galaxies. Many results from MaNGA have collapsed this structure into azimuthally averaged radial gradients, or symmetric 2D shapes, but there is significantly more information about the effect internal structures have on the evolution of galaxies available if we can identify different internal structures. One of the simplest ways to identify irregular internal structures in galaxies is by visual inspection. By employing a citizen science technique to ask this question of N independent volunteers we have obtained quantitatively robust masks (and errors) for spirals and bars in MaNGA target galaxies. In addition to internal features the interface asked users to identify foreground stars and foreground/background galaxies.
Data reconstruction and recovery of deduplicated files having non-resident attributes in NTFS volume

The NTFS used in Windows Server operating system provides a function of removing duplicated areas by comparing them with other files in variable-length block units to use storage space efficiently. Although the data deduplication technology is already applied to the server systems such as cloud services, it is necessary to understand the operation of the Windows Server and the corresponding file system in order to view or extract files in digital forensic surveys. So in this paper, we analyze the process of deduplication and structure for reconstructing files with data deduplication function on the Windows Server using NTFS. Specific actions have structural differences depending on the version of the Windows Server. Until Windows Server 2012, deduplicated files with only resident attributes existed in the MFT entry. Still, in the 2016 and 2019, and 2022 versions released thereafter, deduplicated files have non-resident attributes, so the previous analysis results have a limitation in extracting files. Also, if the deduplicated file is deleted, even if the file carving technique is applied, it is stored in a fragmented state called a chunk. If a deduplicated file is deleted, and when its chunks are used by another file, chunks must remain allocated even if the file is deleted. This is different from traditional file recovery techniques, so we introduce a new perspective when recovering deduplicated files.
The utilization of a reading app in business English classes in higher education

The article presents the research of empirical mixed methods on the use of a modern mobile reading app compared to traditional forms of teaching in English business classes in higher education in Slovakia. The research belongs to the theoretical frame of the mobile assisted language learning field that has generated an interest since the early 1990 thanks to the development of mobile technologies, their features, and their functions all over the world. The research sample consisted of 40 freshman students from the University of Economics in Bratislava who were equally divided into control and experimental groups. The experimental treatment lasted one semester and was based on analyzing books on a biweekly basis from Blinkist, the reading app based on the protocol the authors created, where three higher-level thinking domains of Bloom’s taxonomy (Anderson &amp;amp; Krathwohl, 2001) were identified. Students were also asked to read/listen to the books every week. Even though the current findings indicate no relationship between enhancing English reading skills and incorporating reading apps in a foreign language learning, SWOT analysis from focus group interviews shows different results. It is regarding the positive perception of working with the reading app in business English classes. Based on these findings, the authors make pedagogical implications about using reading apps as a part of the curriculum of blended learning in business English classes in higher education.
Neutrosophic Cognitive Impact Study on Role Transformation of Teachers to Facilitators

The field of education is witnessing paradigm changes, especially with reference to characterization of teachers and their roles. The inception of digital learning portals has widened the information sources of the learners and masked the belief of teachers as the only pool of knowledge. The mutating pedagogies have defined teacher as a facilitator in a learning environment. This chapter investigates on the impacts caused by the transformation of a teacher's role to a facilitator in the intra- and inter- personal dimensions of teachers and also on the classroom environment. This research work also investigates the consequences of teachers adapting to new roles and the immediate impacts on the learners of various kinds. A mathematical model is formulated using neutrosophic cognitive maps to profoundly explore the impacts of role transformation. The consequential impacts analysed using neutrosophic representations are more promising in comparison with other kinds of demonstrations.
Multi-class Detection for Off The Shelf transfer-based Black Box Attacks

Nowadays, deep neural networks are used for a variety of tasks in a wide range of application areas. Despite achieving state-of-the-art results in computer vision and image classification tasks, neural networks are vulnerable to adversarial attacks. Various attacks have been presented in which small perturbations of an input image are sufficient to change the predictions of a model. Furthermore, the changes in the input image are imperceptible to the human eye. In this paper, we propose a multi-class detector framework based on image statistics. We implemented a detection scheme for each attack and evaluated our detectors against Attack on Attention (AoA) and FGSM achieving a detection rate of 70% and 75% respectivley, with a FPR of . The multi-class detector identifies 77% of attacks as adversarial, while remaining 90% of the benign images, demonstrating that we can detect out-of-the-box attacks.
Toward Evaluating the Robustness of Deep Learning Based Rain Removal Algorithm in Autonomous Driving

Autonomous driving systems have been widely adopted by automobile manufacturers, ushering in a new era of intelligent transportation. While adverse weather conditions continue to pose a significant challenge to its commercial application, as they can impact sensor data, degrade the quality of image transmission, and pose safety risks. Using neural network models to remove rain has shown significant promise in addressing this problem. The learning-based rain-removal algorithm discovers the deep connection between rainy pictures and non-rainy pictures by mining the information on raindrops and rain patterns. Nevertheless, the robustness of these rain removal algorithms was not considered, which poses a threat to autonomous vehicles. In this paper, we propose an optimized CW adversarial sample attack to explore the robustness of the rain removal algorithm. In our attacks, we generate a perturbation index of structural similarity that is difficult to detect through human vision and image pixel analysis, causing the similarity and image quality of the restored scene to be significantly degraded. To validate the realistic attack potential of the proposed method, a pre-trained State-of-the-art rain removal attack algorithm, RainCCN, is used as a potential victim of the proposed attack method. We demonstrate the effectiveness of our approach against a state-of-the-art rain removal algorithm, RainCCN, and show that we can reduce PSNR by 39.5 and SSIM by 26.4.
Membership Inference Vulnerabilities in Peer-to-Peer Federated Learning

Federated learning is emerging as an efficient approach to exploit data silos that form due to regulations about data sharing and usage, thereby leveraging distributed resources to improve the learning of ML models. It is a fitting technology for cyber physical systems in applications like connected autonomous vehicles, smart farming, IoT surveillance etc. By design, every participant in federated learning has access to the latest ML model. In such a scenario, it becomes all the more important to protect the model’s knowledge, and to keep the training data and its properties private. In this paper, we survey the literature of ML attacks to assess the risks that apply in a peer-to-peer (P2P) federated learning setup. We perform membership inference attacks specifically in a P2P federated learning setting with colluding adversaries to evaluate the privacy-accuracy trade offs in a deep neural network thus demonstrating the extent of data leakage possible.
THEORETICAL AND METHODOLOGICAL BASIS OF IMPLEMENTING THE PRINCIPLES OF ACADEMIC INDEPENDENCE TO THE MANAGEMENT OF HIGHER EDUCATION INSTITUTIONS

In the article, one of the theoretical and methodological bases of introducing the principles of academic independence is the importance of expressing students' opinions, expressing their concepts in a consistent form, making comments, asking questions, and cooperating with other students in the process of learning on their own time. In addition, the article provides information on the importance of improvement and changes in students' processes of collaborating with other students on their knowledge and learning, expressing their thoughts and opinions to others. The article provides instructions for putting into practice the theoretical-methodological foundations of the principles of academic independence and presents the methods and methods used to increase the learning processes with students and develop their own thoughts and opinions. These include interacting with students, developing feedback using manuals and articles, evaluating and improving the methods they use in their learning processes, defining their own learning framework, and learning in their own time. There are practical methods, such as using a record of one's thoughts about learning objectives and outcomes.