Reviewerly/exHarmony huggingface dataset:
Hardening Interpretable Deep Learning Systems: Investigating Adversarial Threats and Defenses

Deep learning methods have gained increasing attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems. Interpretation models enable the understanding of the inner workings of deep learning models and offer a sense of security in detecting the misuse of artifacts in the input data. Similar to prediction models, interpretation models are also susceptible to adversarial inputs. This work introduces two attacks, AdvEdge and AdvEdge <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{+}$</tex-math></inline-formula> , which deceive both the target deep learning model and the coupled interpretation model. We assess the effectiveness of proposed attacks against four deep learning model architectures coupled with four interpretation models that represent different categories of interpretation models. Our experiments include the implementation of attacks using various attack frameworks. We also explore the attack resilience against three general defense mechanisms and potential countermeasures. Our analysis shows the effectiveness of our attacks in terms of deceiving the deep learning models and their interpreters, and highlights insights to improve and circumvent the attacks.
High-order Structural Relation Distillation Networks from LiDAR to Monocular Image 3D Detectors

3D object detection is a crucial and complex undertaking in the realm of 3D scene comprehension. Monocular-based 3D detectors, in comparison to LiDAR 3D detectors that utilize point clouds as input, often exhibit a significant performance gap. Incorporating guidance from LiDAR-based detectors has led to notable advancements in monocular 3D detection. Nevertheless, some current approaches focus solely on transferring feature or response knowledge from LiDAR data, neglecting the valuable geometric structural information present in the LiDAR data. As a result, there is untapped potential to fully exploit the geometric relationships within the LiDAR data to further improve monocular 3D detection performance. In this paper, we propose a High-order Structural Relation Distillation Networks, which establishes the geometric structure information in the BEV feature level and distills the relation from LiDAR Detectors to Monocular Image Detectors. Specifically, to better align the structural relation with the LiDAR BEV features, we propose the Image BEV Feature Enhancing Module, which can capture essential spatial depth context via explicitly modeling the interdependence between the depth channels of BEV features to improve the representations. In addition, instead of only distillation at feature level, our distillation model contains point, line, and surface relation distillation, which captures high-order structural relations from LiDAR BEV features and enhances the detection of the image-based student model, particularly for non-rigid object detection. Our method achieves state-of-the-art performance, especially in the case of non-rigid Cyclist class.
Cross-Video Contextual Knowledge Exploration and Exploitation for Ambiguity Reduction in Weakly Supervised Temporal Action Localization

Weakly supervised temporal action localization (WSTAL) aims to localize actions in untrimmed videos using video-level labels. Despite recent advances, existing approaches mainly follow a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">localization-by-classification</i> pipeline, generally processing each segment individually, thereby exploiting only limited contextual information. As a result, the model will lack a comprehensive understanding (e.g. appearance and temporal structure) of various action patterns, leading to ambiguity in classification learning and temporal localization. Our work addresses this from a novel perspective, by exploring and exploiting the cross-video contextual knowledge within the dataset to recover the dataset-level semantic structure of action instances via weak labels only, thereby indirectly improving the holistic understanding of fine-grained action patterns and alleviating the aforementioned ambiguities. Specifically, an end-to-end framework is proposed, including a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Robust Memory-Guided Contrastive Learning</i> (RMGCL) module and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Global Knowledge Summarization and Aggregation</i> (GKSA) module. First, the RMGCL module explores the contrast and consistency of cross-video action features, assisting in learning more structured and compact embedding space, thus reducing ambiguity in classification learning. Further, the GKSA module is used to efficiently summarize and propagate the cross-video representative action knowledge in a learnable manner to promote holistic action patterns understanding, which in turn allows the generation of high-confidence pseudo-labels for self-learning, thus alleviating ambiguity in temporal localization. Extensive experiments on THUMOS14, ActivityNet1.3, and FineAction demonstrate that our method outperforms the state-of-the-art methods, and can be easily plugged into other WSTAL methods.
TY-Net: Transforming YOLO for hand gesture recognition

Hand gesture recognition is a rapidly expanding field with diverse applications, and the use of skeleton-based methods is gaining popularity due to their potential for lightweight execution on embedded devices. However, ensuring robustness and accuracy in both gesture classification and temporal localization is critical for any gesture recognition system to be successful. In this paper, we propose a novel skeleton-based approach to online gesture recognition that draws inspiration from the YOLO object detection model. Specifically, we propose a transformer-based architecture for online gesture recognition that directly predicts both gesture classes and gesture boundaries from a sliding window input. The model is trained in an end-to-end manner using a proposed loss function that focuses on samples containing gesture centers and learns to predict boundaries in the temporal domain analogous to object centers and spatial bounding boxes in YOLO object detection. To evaluate the effectiveness of our method, we conduct experiments on two publicly available continuous hand gesture datasets: SHREC’22 and IPN Hand. Our results outperform the results of skeleton-based approaches from the SHREC’22 online gesture recognition contest. Moreover, our approach obtained competitive results compared to the approaches utilizing alternative input modalities on the IPN Hand dataset.
Improving Transferability and Immunity of Physical Layer Authentication by the Channel Time-Varying Pattern

Channel State Information (CSI)-based Physical Layer Authentication (PLA) is typically a promising strategy for wireless security. However, existing algorithms fail to transfer across various scenarios and immunize against attacks forging CSI. To improve the transferability and immunity of PLA, we propose a PLA enhancement framework to analyze, enhance, and assess authentication. Firstly, we provide a theoretical analysis method to discover the factors affecting the transferability and immunity of PLA. Secondly, inspired by the above discovery, an enhanced PLA algorithm is developed based on the channel time-varying pattern. Finally, we theoretically assess the scenario transferability and provide a closed-form expression for the bypassing condition of authentication. Furthermore, experimental results validate the practical applicability of our theoretical insights.
Private Read-Update-Write with Controllable Information Leakage for Storage-Efficient Federated Learning with Top <i>r</i> Sparsification

In federated learning (FL), a machine learning (ML) model is collectively trained by a large number of users, using their private data in their local devices. With top <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> sparsification in FL, the users only upload the most significant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> fraction of updates, and download only the most significant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> ’ fraction of parameters in order to reduce the communication cost. However, the values and the indices of the sparse updates and parameters leak information about the users’ private data. In this work, we consider an FL setting where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> non-colluding databases store the model to be trained, from which the users download and update sparse parameters privately, without revealing the values of the updates/parameters or their indices to the databases. We propose four schemes with different properties that are based on cross subspace alignment (CSA) and permutation techniques, to perform this task while achieving the minimum communication costs within the scope of CSA, and show that the information theoretic privacy of both the values and the positions of the sparse updates/parameters can be guaranteed. This is achieved at a considerable storage cost, though. To alleviate this, we generalize the schemes in such a way that the storage cost is reduced at the expense of a certain amount of information leakage, using a model segmentation mechanism. In general, we provide the trade-off between the communication cost, storage cost and information leakage in private FL with top <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> sparsification.
Distributed Semi-Supervised Sparse Statistical Inference

The debiased estimator is a crucial tool in statistical inference for high-dimensional model parameters. However, constructing such an estimator involves estimating the high-dimensional inverse Hessian matrix, incurring significant computational costs. This challenge becomes particularly acute in distributed setups, where traditional methods necessitate computing a debiased estimator on every machine. This becomes unwieldy, especially with a large number of machines. In this paper, we delve into semi-supervised sparse statistical inference in a distributed setup. An efficient multi-round distributed debiased estimator, which integrates both labeled and unlabelled data, is developed. We will show that the additional unlabeled data helps to improve the statistical rate of each round of iteration. Our approach offers tailored debiasing methods for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -estimation and generalized linear models according to the specific form of the loss function. Our method also applies to a non-smooth loss like absolute deviation loss. Furthermore, our algorithm is computationally efficient since it requires only one estimation of a high-dimensional inverse covariance matrix. We demonstrate the effectiveness of our method by presenting simulation studies and real data applications that highlight the benefits of incorporating unlabeled data.
Binary Classification Under ℓ<sub>0</sub> Attacks for General Noise Distribution

Adversarial examples have recently drawn considerable attention in the field of machine learning due to the fact that small perturbations in the data can result in major performance degradation. This phenomenon is usually modeled by a malicious adversary that can apply perturbations to the data in a constrained fashion, such as being bounded in a certain norm. In this paper, we study this problem when the adversary is constrained by the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm; i.e., it can perturb a certain number of coordinates in the input, but has no limit on how much it can perturb those coordinates. Due to the combinatorial nature of this setting, we need to go beyond the standard techniques in robust machine learning to address this problem. We consider a binary classification scenario where d noisy data samples of the true label are provided to us after adversarial perturbations. We introduce a classification method which employs a nonlinear component called truncation, and show in an asymptotic scenario, as long as the adversary is restricted to perturb no more than √d data samples, we can almost achieve the optimal classification error in the absence of the adversary, i.e. we can completely neutralize adversary’s effect. Surprisingly, we observe a phase transition in the sense that using a converse argument, we show that if the adversary can perturb more than √d coordinates, no classifier can do better than a random guess.
The Emergence of Nanoscale Neural Networks (N<sup>3</sup>) Systems: Moving Forward in the Years Ahead

Artificial Intelligence (Al) is highly pervasive in modern society. The advancement of Al is based on a diversity of models and paradigms; among them, Machine Learning (ML) has been widely advocated. Different schemes have been proposed for ML to accomplish aggressive goals in terms of high performance (accuracy and speed); among such schemes, nanoscale neural networks (NNNs) have been extensively investigated. However, it has also been widely reported that computation remains problematic due to the exorbitant and unsustainable complexity of training and running models on software in programmable platforms, such as GPUs, CPUs, and FPGAs. Hence, a renewed interest in hardware-based solutions (often using advanced feature technology nodes) has been reported in the academic and industry communities to overcome these challenges. This talk initially proposes and assesses new arithmetic techniques and ASIC-based hardware for implementing nano-scaled MAC (Multiply-ACcumulate) and FMA (Fused Multiply-Add) units for neuron design to improve accuracy and operating frequency by pipelining. Training and inference processes are discussed; considerations such as data format and arithmetic operations, are addressed. This talk focuses on single NNNs with multi-layer perceptions (MLPs) as well as multi-NNNs (Siamese and Triplet networks) for specific applicative domains, such as classification. Possible opportunistic directions for future research will also be outlined.
An Empirical Research of Non-core Argument Case Markers Under Semantic Events

Case markers play a crucial role in unveiling argument relationships within Chinese sentences, contributing significantly to a deeper comprehension of both the syntactic and semantic aspects of these sentences. This study adopts a perspective rooted in semantic events to analyze the distinctive syntactic environment that differs from prepositional phrases functioning as adverbials, which serves to introduce non-core arguments. It centres on the syntactic distribution environment where event attribute nouns are attached to the semantic main events as sentence subjects or objects, subsequently marking the environment for non-core arguments. Subsequently, case markers for non-core arguments were annotated in a multi-genre corpus, accumulating 643 sentences featuring annotated case markers for non-core arguments. Drawing from these annotated resources, this study empirically examines the syntactic and semantic characteristics of case markers for non-core arguments. Specifically, the research explores their role as syntactic constituents in introducing non-core arguments, their correlation with the main event, and their influence on selecting core predicate verbs within the sentences.
Sound

This chapter explores the use of sound as a material for representation in the service design process. It highlights how sound can be used as a complementary material for representing movements, dynamics, and temporality inherent to service contexts, and how it can facilitate performative forms of representation that have inclusive potential and influence creative experimentation. The chapter explains how sound can be described in terms of its physical properties and characterized by its loudness, pitch, timbre, and duration. It also provides examples of how sound characteristics can be used to recognize, analyze, and categorize different sounds in the service context, and how they can point to understated aspects of a servicescape. The chapter concludes that sound can be used as a tool for aesthetic and affective influence in the service design process and that it can be used in creating, using, and making sense of sound as material for representation.
Interlude 3: Data as material of services

This text discusses the role of data and related technologies as a key material in service design. The author argues that data is "the" material of service, and that all services are dependent upon data. The author identifies three types of service design occurring: traditional strategic service design, improving existing services, and a new approach that is more focused on working with the data itself to explore new opportunities. The author also describes four components of working with data, which they call "organs": collection strategy and mechanisms, modeling, rendering, and controlling. These organs are used internally in all projects and are important for different people with different skills to be brought in at different stages of forming the material.
A performer’s repertoire (the service designer’s material)

If Design is about forming materials, then what are the materials of Service Design? In this ground-breaking book, Johan Blomkvist, Simon Clatworthy and Stefan Holmlid explore this question by establishing a discourse around the materials of service design, discussing materials as a means to explore what service design is and could be.
To Callio Lab and beyond – update on the deep underground research centre in Finland.

Underground physics has been conducted at the Pyhäsalmi mine in Finland for over 20 years and it was one of the sites in FP7 LAGUNA and LAGUNA LBNO design studies. In 2015, the University of Oulu established the Callio Lab multidisciplinary research centre, which began coordinating scientific activities on-site. Since then, they have hosted and conducted research in disciplines ranging from particle physics and geosciences to underground food production and remote sensing. The operating environment would also suit studies in circular economy and space and planetary sciences, which are being explored by the Callio SpaceLab initiative. Together with the easy tunnel and elevator access, low seismicity, which has been observed to decrease dramatically since the conclusion of underground extraction, and the flat 1.43 km overburden (~ 4100 m.w.e.), the site is a potential candidate for future physics experiments. Callio Lab is a founding member of the European Underground Laboratories Association, a member of the DULIA network, and an EPOS and national FIN-EPOS research infrastructure. Underground mining ended in 2022 and re-purposing of the mine by the Pyhäjärvi town-owned CALLIO - Mine for Business concept is ongoing.
Vasily Milioti – a stage designer

The article tells about the formation of Vasily Milioti as a stage designer, about his first success, which made him part of artistic Moscow. The success was not long – only four years from 1905 to 1909. Successful cooperation with V.E. Meyerhold as a costume designer for Ibsen`s play “Hedda Gabler” in the St. Petersburg Theater V.F. Komissarzhevskaya was hardly logical, the following experience was assessed more modestly. But the first experience showed that Milioti is able to feel and understand beauty deeply.
Quantum-Inspired Genetic Algorithms for Combinatorial Optimization Problems

Quantum-Inspired Genetic Algorithms (QIGAs) are a trailblazing force in the ever-evolving field of optimization, combining traditional genetic algorithms with quantum concepts to solve challenging combinatorial problems. By contrasting QIGAs with traditional Genetic Algorithms (GAs) in the setting of the Traveling Salesman Problem (TSP), this study explores the potential of QIGAs. The research reveals the transformational potential of quantum-inspired techniques through a thorough investigation of convergence speed, solution quality, and scalability.&#x0D;
Intelligent Cross-axis Solar Power Tracking System

The sun is an abundant and powerful renewable energy resource that plays a crucial role in sustainable energy production. With efficient power generation in mind, here is a system designed to make intelligent judgements based on a condition so that the solar panel is always tilted towards the maximum sunlight. The hybrid intelligent cross-axis solar power tracking system is built using the microcontroller, light sensors, solar panel, and motors. Here, there are two methods used namely, the Sun Position Algorithm and the other is using the sensors. The algorithm uses the azimuth angle and the sun’s altitude to identify the position of the sun depending on the geo location. For maximum power generation from the solar panel, we use the sensor-based technique tocome into picture whenever the output voltage is less than the fixed threshold voltage using the Sun Position Algorithm.
STEM ПОДХОДЪТ В ОБУЧЕНИЕТО ПО РЕЛИГИЯ

In recent years, STEM education has become a leading trend in modern education. It offers integrated study of four subjects important for economic development – natural sciences, technology, engineering and mathematics, with the aim of solving real-world problems. The article emphasizes the benefits of applying the STEM approach and using appropriate STEM tools in religious education. Pedagogical experience from the preparation, organization and conduct of a STEM lesson on religion is presented, which was taught to students from the 3rd grade of Panayot Volov Primary School in Varna, who study in the school of Orthodox Christian Values and Traditions. The lesson integrates knowledge of religion, man and nature, mathematics and information technology.
КАК СОЗДАЕТСЯ „КАЧЕСТВЕННОЕ ВРЕМЯ” В СОВРЕМЕННОЙ МАССОВОЙ КУЛЬТУРЕ

The article deals with the phenomenon of “quality time” in the modern mass culture at the beginning of the 21st century. One of the most notable trends is the use of interactivity in creating cinematic works and computer games. Interactive storytelling allows the spectator/player to choose options for the development of the action and thus independently build the plot of the game film. Such involvement in creating a work of mass culture is an exciting new way to occupy the recipient’s time qualitatively. The variability and artistic potential of interactive cinema are discussed based on the example of one of the most popular computer games of recent years – “Detroit: Become Human” (2018) from the French studio “Quantic Dream”.
Adding Initial Models

In this chapter, we will add our first two Rails ActiveRecord models into our blog application. We will start with simple User and Page models and proceed to grow them, adding more functionality as we go along.