Reviewerly/exHarmony huggingface dataset:
Edge Learning for 6G-Enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE) applications and future networks (e.g., sixth-generation (6G) networks) has raised a number of operational challenges and limitations, for example in terms of security and privacy. Edge learning is an emerging approach to training models across distributed clients while ensuring data privacy. Such an approach when integrated in future network infrastructures (e.g., 6G) can potentially solve challenging problems such as resource management and behavior prediction. However, edge learning (including distributed deep learning) are known to be susceptible to tampering and manipulation. This survey article provides a holistic review of the extant literature focusing on edge learning-related vulnerabilities and defenses for 6G-enabled Internet of Things (IoT) systems. Existing machine learning approaches for 6G–IoT security and machine learning-associated threats are broadly categorized based on learning modes, namely: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G–IoT intelligence. We also provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, namely: backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a comparative summary of the state-of-the-art defense methods against edge learning-related vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed.
Federated Learning - Opportunities and Application Challenges

In this paper our intention was to present a brief literature review focused on the latest research of the Federated Learning paradigm in order to identify current research trends, possible future directions of development, and challenges in this area. Federated learning as a new, powerful distributed intelligent paradigm can take on various forms in order to fit a diverse set of problems in a wide range of domains, economy, finance, medicine, agriculture and other industrial sectors. Based on presented research results, several key opportunities for future work can be identified and some emerging are connected to communication costs and performance of federated models trained by different algorithms.
Pathologist Computer-Aided Diagnostic Scoring of Tumor Cell Fraction: A Swiss National Study

Tumor cell fraction (TCF) estimation is a common clinical task with well-established large interobserver variability. It thus provides an ideal test bed to evaluate potential impacts of employing a tumor cell fraction computer-aided diagnostic (TCFCAD) tool to support pathologists’ evaluation. During a National Slide Seminar event, pathologists (n = 69) were asked to visually estimate TCF in 10 regions of interest (ROIs) from hematoxylin and eosin colorectal cancer images intentionally curated for diverse tissue compositions, cellularity, and stain intensities. Next, they re-evaluated the same ROIs while being provided a TCFCAD-created overlay highlighting predicted tumor vs nontumor cells, together with the corresponding TCF percentage. Participants also reported confidence levels in their assessments using a 5-tier scale, indicating no confidence to high confidence, respectively. The TCF ground truth (GT) was defined by manual cell-counting by experts. When assisted, interobserver variability significantly decreased, showing estimates converging to the GT. This improvement remained even when TCFCAD predictions deviated slightly from the GT. The standard deviation (SD) of the estimated TCF to the GT across ROIs was 9.9% vs 5.8% with TCFCAD (P < .0001). The intraclass correlation coefficient increased from 0.8 to 0.93 (95% CI, 0.65-0.93 vs 0.86-0.98), and pathologists stated feeling more confident when aided (3.67 ± 0.81 vs 4.17 ± 0.82 with the computer-aided diagnostic [CAD] tool). TCFCAD estimation support demonstrated improved scoring accuracy, interpathologist agreement, and scoring confidence. Interestingly, pathologists also expressed more willingness to use such a CAD tool at the end of the survey, highlighting the importance of training/education to increase adoption of CAD systems.
Cloaking a qubit in a cavity

Cavity quantum electrodynamics (QED) uses a cavity to engineer the mode structure of the vacuum electromagnetic field such as to enhance the interaction between light and matter. Exploiting these ideas in solid-state systems has lead to circuit QED which has emerged as a valuable tool to explore the rich physics of quantum optics and as a platform for quantum computation. Here we introduce a simple approach to further engineer the light-matter interaction in a driven cavity by controllably decoupling a qubit from the cavity's photon population, effectively cloaking the qubit from the cavity. This is realized by driving the qubit with an external tone tailored to destructively interfere with the cavity field, leaving the qubit to interact with a cavity which appears to be in the vacuum state. Our experiment demonstrates how qubit cloaking can be exploited to cancel the ac-Stark shift and measurement-induced dephasing, and to accelerate qubit readout. In addition to qubit readout, applications of this method include qubit logical operations and the preparation of non-classical cavity states in circuit QED and other cavity-based setups.
RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising

A common problem in the field of deep-learning-based low-level vision medical images is that most of the research is based on single task learning (STL), which is dedicated to solving one of the situations of low resolution or high noise. Our motivation is to design a model that can perform both SR and DN tasks simultaneously, in order to cope with the actual situation of low resolution and high noise in low-level vision medical images. By improving the existing single image super-resolution (SISR) network and introducing the idea of multi-task learning (MTL), we propose an end-to-end lightweight MTL generative adversarial network (GAN) based network using residual-in-residual-blocks (RIR-Blocks) for feature extraction, RIRGAN, which can concurrently accomplish super-resolution (SR) and denoising (DN) tasks. The generator in RIRGAN is composed of several residual groups with a long skip connection (LSC), which can help form a very deep network and enable the network to focus on learning high-frequency (HF) information. The introduction of a discriminator based on relativistic average discriminator (RaD) greatly improves the discriminator’s ability and makes the generated image have more realistic details. Meanwhile, the use of hybrid loss function not only ensures that RIRGAN has the ability of MTL, but also enables RIRGAN to give a more balanced attention between quantitative evaluation of metrics and qualitative evaluation of human vision. The experimental results show that the quality of the restoration image of RIRGAN is superior to the SR and DN methods based on STL in both subjective perception and objective evaluation metrics when processing medical images with low-level vision. Our RIRGAN is more in line with the practical requirements of medical practice.
Unraveling Digital Transformation in Banking: Evidence from Romania

This research probes into the digital transformation shifts in Romania and sets them against a backdrop of certain EU countries. Its primary objective is to spotlight digitalization’s significance and assess its level of integration within the Romanian banking landscape. Our approach relies on a detailed examination of the adoption of digital banking instruments in Romania through correlation and ANOVA assessments. The ANOVA analysis of the DESI index and its associated dimensions reveals how Romania’s digital transformation stands in relation to other EU member states. Our findings emphasize the numerous advantages Romanian banks have garnered from increasingly embracing digital innovations and artificial technologies. These perks span from optimized operations and efficiency to enhanced customer experiences and a sharpened competitive advantage. The research indicates a strong positive correlation between a bank’s return on assets and its liquid assets to deposits and short-term funding ratios. This suggests that as digital integration deepens, there is a marked upturn in financial robustness. Additionally, the study sheds light on the perks of individuals adopting digital banking offerings and delves into factors that propel and impede the digital evolution in the banking arena. Overall, this paper presents valuable insights into Romania’s digital banking trajectory and the sector’s long-term viability.
On the equivalence of $$\mathbb {Z}_{p^s}$$-linear generalized Hadamard codes

Abstract Linear codes of length n over $$\mathbb {Z}_{p^s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Z</mml:mi> <mml:msup> <mml:mi>p</mml:mi> <mml:mi>s</mml:mi> </mml:msup> </mml:msub> </mml:math> , p prime, called $$\mathbb {Z}_{p^s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Z</mml:mi> <mml:msup> <mml:mi>p</mml:mi> <mml:mi>s</mml:mi> </mml:msup> </mml:msub> </mml:math> -additive codes, can be seen as subgroups of $$\mathbb {Z}_{p^s}^n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>Z</mml:mi> <mml:mrow> <mml:msup> <mml:mi>p</mml:mi> <mml:mi>s</mml:mi> </mml:msup> </mml:mrow> <mml:mi>n</mml:mi> </mml:msubsup> </mml:math> . A $$\mathbb {Z}_{p^s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Z</mml:mi> <mml:msup> <mml:mi>p</mml:mi> <mml:mi>s</mml:mi> </mml:msup> </mml:msub> </mml:math> -linear generalized Hadamard (GH) code is a GH code over $$\mathbb {Z}_p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Z</mml:mi> <mml:mi>p</mml:mi> </mml:msub> </mml:math> which is the image of a $$\mathbb {Z}_{p^s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Z</mml:mi> <mml:msup> <mml:mi>p</mml:mi> <mml:mi>s</mml:mi> </mml:msup> </mml:msub> </mml:math> -additive code under a generalized Gray map. It is known that the dimension of the kernel allows to classify these codes partially and to establish some lower and upper bounds on the number of such codes. Indeed, in this paper, for $$p\ge 3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> prime, we establish that some $$\mathbb {Z}_{p^s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Z</mml:mi> <mml:msup> <mml:mi>p</mml:mi> <mml:mi>s</mml:mi> </mml:msup> </mml:msub> </mml:math> -linear GH codes of length $$p^t$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>p</mml:mi> <mml:mi>t</mml:mi> </mml:msup> </mml:math> having the same dimension of the kernel are equivalent to each other, once t is fixed. This allows us to improve the known upper bounds. Moreover, up to $$t=10$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>t</mml:mi> <mml:mo>=</mml:mo> <mml:mn>10</mml:mn> </mml:mrow> </mml:math> if $$p=3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> or $$t=8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>t</mml:mi> <mml:mo>=</mml:mo> <mml:mn>8</mml:mn> </mml:mrow> </mml:math> if $$p=5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>5</mml:mn> </mml:mrow> </mml:math> , this new upper bound coincides with a known lower bound based on the rank and dimension of the kernel.
Structural Alignment for Network Pruning through Partial Regularization

In this paper, we propose a novel channel pruning method to reduce the computational and storage costs of Convolutional Neural Networks (CNNs). Many existing one-shot pruning methods directly remove redundant structures, which brings a huge gap between the model before and after network pruning. This gap will no doubt result in performance loss for network pruning. To mitigate this gap, we first learn a target sub-network during the model training process, and then we use this sub-network to guide the learning of model weights through partial regularization. The target sub-network is learned and produced by using an architecture generator, and it can be optimized efficiently. In addition, we also derive the proximal gradient for our proposed partial regularization to facilitate the structural alignment process. With these designs, the gap between the pruned model and the sub-network is reduced, thus improving the pruning performance. Empirical results also suggest that the sub-network found by our method has a much higher performance than the one-shot pruning setting. Extensive experiments show that our method can achieve state-of-the-art performances on CIFAR-10 and ImageNet with ResNets and MobileNet-V2.
DALL-EVAL: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models

Recently, DALL-E [45], a multimodal transformer language model, and its variants including diffusion models have shown high-quality text-to-image generation capabilities. However, despite the realistic image generation results, there has not been a detailed analysis of how to evaluate such models. In this work, we investigate the visual reasoning capabilities and social biases of different text-to-image models, covering both multimodal transformer language models and diffusion models. First, we measure three visual reasoning skills: object recognition, object counting, and spatial relation understanding. For this, we propose PaintSkills, a compositional diagnostic evaluation dataset that measures these skills. Despite the high-fidelity image generation capability, a large gap exists between the performance of recent models and the upper bound accuracy in object counting and spatial relation understanding skills. Second, we assess the gender and skin tone biases by measuring the gender/skin tone distribution of generated images across various professions and attributes. We demonstrate that recent text-to-image generation models learn specific biases about gender and skin tone from web image-text pairs. We hope our work will help guide future progress in improving text-to-image generation models on visual reasoning skills and learning socially unbiased representations. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Using Technology as Learning Media to Enrich ESP Students' Speaking Skill for Indonesian Higher Education Students

For everyone, learning a new language is a really difficult undertaking. It's considerably more difficult to receive an education in a foreign tongue. It takes a lot of time to learn how to read, understand, speak, and write flawlessly in a language. ESL students who attempt to navigate this challenging experience while living abroad encounters numerous language and communication issues. This qualitative case study research project's main goal was to identify the educational technology tools that participants used to advance their language and communication abilities. This study is based on the research question: "To what extent do Indonesian higher education students use technology on their own, outside of the framework of ESP class, to develop English language and communication skills?. The findings of this study imply that students use technology in their daily life for a variety of functions, particularly for their education. The use of educational technology tools is also considered as assisting teachers and students in becoming more successful, effective, and useful individuals in their daily lives. As conclusion that using technology as learning media can enrich ESP students' speaking skill for Indonesian higher education students.
Feature Selection as State-Space Search: An Empirical Study in Clustering Problems

In this paper we treat the problem of feature selection in unsupervised learning as a state-space search problem. We introduce three different heuristic functions and perform extensive experiments on datasets with tens, hundreds, and thousands of features. Namely, we test different search algorithms using the heuristic functions we introduce. Our results show that the heuristic search approach for feature selection in unsupervised learning problems can be far superior than traditional baselines such as PCA and random projections.
Towards a Reformulation Based Approach for Efficient Numeric Planning: Numeric Outer Entanglements

Restricting the search space has shown to be an effective approach for improving the performance of automated planning systems. A planner-independent technique for pruning the search space is domain and problem reformulation. Recently, Outer Entanglements, which are relations between planning operators and initial or goal predicates, have been introduced as a reformulation technique for eliminating potential undesirable instances of planning operators, and thus restricting the search space. Reformulation techniques, however, have been mainly applied in classical planning, although many real-world planning applications require to deal with numerical information. In this paper, we investigate the usefulness of reformulation approaches in planning with numerical fluents. In particular, we propose and extension of the notion of outer entanglements for handling numeric fluents. An empirical evaluation, which involves 150 instances from 5 domains, shows promising results.
Applying DTGolog to Large-scale Domains

While decision theoretic planning (DTP) offers great potential benefits to elicit purposeful behavior of the agent operating in uncertain environments, state-based approaches to DTP are known to be computationally intractable in large-scale domains. DTGolog is a decision-theoretic extension of a logic-based high level programming language Golog that completes a given partial Golog program using a form of directed value iteration. DTGolog has been proposed to alleviate some of the computational difficulties associated with DTP. The main advantages of DTGolog are that a DTP problem can be formulated using a logical representation to avoid explicit state enumeration, and the programmer can encode domain-specific knowledge in terms of high-level procedural templates to partially specify behavior of an agent. These templates constrain the search space to manageable size. Despite these clear advantages, there are few studies that investigate the applicability of DTGolog to very large-scale practical domains. In this thesis, we conduct two studies. First, we apply DTGolog to the well known case-study of the London Ambulance Service to demonstrate advantages and potentials of DTGolog as a quantitative evaluation tool for designing decision making agents. Second, we develop a software interface that allows to control the well-known Sony's AIBO robotics platform using DTGolog. We show that DTGolog can be used on this platform with a minimal amount of software customization. We run experiments to test functionality of our interface. The main contribution of this thesis is demonstration of applicability of DTGolog to two different large scale domains that are both practical and interesting.
Robustness and Flexibility of GHOST

GHOST is a framework to help game developers to model and implement their own optimization problems, or to simply instantiate a problem already encoded in GHOST. Previous works show that GHOST leads to high-quality solutions in some tens of milliseconds for three RTS-related problems: build order, wall-in placement and target selection. In this paper, we show the robustness of the framework, having very good results for a problem it is not designed for (pathfinding), as well as its flexibility, where it is easy to propose different models of the same problem (resource allocation problem). The goal of the paper is not to improve the state-of-the-art on these problems, but to use them as benchmarks to test GHOST properties.
Converting the Words of God: An experimental evaluation of stylistic choices in the new Dutch Bible translation

In an experiment stylistic choices in the new Dutch Bible translation were evaluated. 185 participants evaluated 4 fragments which differed in type of variation (lexical or syntactic), source of the fragment (Bible - classical text) and the way the variations were applied (single - mixed). Also the religious¬ness of the evaluator was taken into account. Examined is whether lexical modernization and syntactic simplification are evaluated the same, whether single variations show a better insight into the evaluations and if the evaluation is influenced by the source of the text or the religiousness of the evaluator. The results showed that there is a difference in evaluation between syntactic and lexical variation, that single variation gives better insight into the evaluations and that source of the text and religiousness do not influence the evaluations. This study demonstrates that translators must involve readers at an early stage in their discussions on a required or appropriate register.
Friends, Strangers, and the Value of Ego Networks for Recommendation

Two main approaches to using social network information in recommendation have emerged: augmenting collaborative filtering with social data and algorithms that use only ego-centric data. We compare the two approaches using movie and music data from Facebook, and hashtag data from Twitter. We find that recommendation algorithms based only on friends perform no worse than those based on the full network, even though they require much less data and computational resources. Further, our evidence suggests that locality of preference, or the non-random distribution of item preferences in a social network, is a driving force behind the value of incorporating social network information into recommender algorithms. When locality is high, as in Twitter data, simple k-nn recommenders do better based only on friends than they do if they draw from the entire network. These results help us understand when, and why, social network information is likely to support recommendation systems, and show that systems that see ego-centric slices of a complete network (such as websites that use Facebook logins) or have computational limitations (such as mobile devices) may profitably use ego-centric recommendation algorithms.
Stochastic Plan Optimization in Real-Time Strategy Games

We present a domain independent off-line adaptation technique called Stochastic Plan Optimization for finding and improving plans in real-time strategy games. Our method is based on ideas from genetic algorithms but we utilize a different representation for our plans and an alternate initialization procedure for our search process. The key to our technique is the use of expert plans to initialize our search in the most relevant parts of plan space. Our experiments validate this approach using our existing case based reasoning system Darmok in the real-time strategy game Wargus, a clone of Warcraft II.