OpenRaiser/Intern-Atlas huggingface dataset:
SEM-O-RAN: Semantic and Flexible O-RAN Slicing for NextG Edge-Assisted Mobile Systems
Venue: arXiv cs.NI 2022
Year: 2022

5G and beyond cellular networks (NextG) will support the continuous execution of resource-expensive edge-assisted deep learning (DL) tasks. To this end, Radio Access Network (RAN) resources will need to be carefully "sliced" to satisfy heterogeneous application requirements while minimizing RAN usage. Existing slicing frameworks treat each DL task as equal and inflexibly define the resources to assign to each task, which leads to sub-optimal performance. In this paper, we propose SEM-O-RAN, the first semantic and flexible slicing framework for NextG Open RANs. Our key intuition is that different DL classifiers can tolerate different levels of image compression, due to the semantic nature of the target classes. Therefore, compression can be semantically applied so that the networking load can be minimized. Moreover, flexibility allows SEM-O-RAN to consider multiple edge allocations leading to the same task-related performance, which significantly improves system-wide performance as more tasks can be allocated. First, we mathematically formulate the Semantic Flexible Edge Slicing Problem (SF-ESP), demonstrate that it is NP-hard, and provide an approximation algorithm to solve it efficiently. Then, we evaluate the performance of SEM-O-RAN through extensive numerical analysis with state-of-the-art multi-object detection (YOLOX) and image segmentation (BiSeNet V2), as well as real-world experiments on the Colosseum testbed. Our results show that SEM-O-RAN improves the number of allocated tasks by up to 169% with respect to the state of the art.
Who Ate My Memory? Towards Attribution in Memory Management
Venue: arXiv cs.SE 2022
Year: 2022

To understand applications' memory usage details, engineers use instrumented builds and profiling tools. Both approaches are impractical for use in production environments or deployed mobile applications. As a result, developers can gather only high-level memory-related statistics for deployed software. In our experience, the lack of granular field data makes fixing performance and reliability-related defects complex and time-consuming. The software industry needs lightweight solutions to collect detailed data about applications' memory usage to increase developer productivity. Current research into memory attribution-related data structures, techniques, and tools is in the early stages and enables several new research avenues.
Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation
Venue: arXiv cs.AI 2022
Year: 2022

Conversational recommender systems (CRSs) often utilize external knowledge graphs (KGs) to introduce rich semantic information and recommend relevant items through natural language dialogues. However, original KGs employed in existing CRSs are often incomplete and sparse, which limits the reasoning capability in recommendation. Moreover, only few of existing studies exploit the dialogue context to dynamically refine knowledge from KGs for better recommendation. To address the above issues, we propose the Variational Reasoning over Incomplete KGs Conversational Recommender (VRICR). Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete KGs; and perform dynamic knowledge reasoning conditioned on the dialogue context. Specifically, we denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs refactor. We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context. Finally, we infuse the dialogue-specific subgraphs to decode the recommendation and responses. We conduct experiments on two benchmark CRSs datasets. Experimental results confirm the effectiveness of our proposed method.
Realizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges
Venue: arXiv cs.ET 2022
Year: 2022

Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various types of devices, we benefit from their use into energy-efficient algorithms for low powered devices. In this paper, we investigate a scale and medium that is far smaller than conventional devices as we move towards molecular systems that can be utilized to perform machine learning functions, i.e., Molecular Machine Learning (MML). Fundamental to the operation of MML is the transport, processing, and interpretation of information propagated by molecules through chemical reactions. We begin by reviewing the current approaches that have been developed for MML, before we move towards potential new directions that rely on gene regulatory networks inside biological organisms as well as their population interactions to create neural networks. We then investigate mechanisms for training machine learning structures in biological cells based on calcium signaling and demonstrate their application to build an Analog to Digital Converter (ADC). Lastly, we look at potential future directions as well as challenges that this area could solve.
BS-Patch: Constrained Bezier Parametric Patch
Venue: arXiv cs.GR 2022
Year: 2022

Bezier parametric patches are used in engineering practice quite often, especially in CAD/CAM systems oriented to mechanical design. In many cases quadrilateral meshes are used for tessellation of parameters domain. We propose a new modification of the Bezier cubic rectangular patch, the BS-patch, which is based on the requirement that diagonal curves must be of degree 3 instead of degree 6 as it is in the case of the Bezier patch. Theoretical derivation of conditions is presented and some experimental results as well. The BS-Patch is convenient for applications where for different tessellation of the u-v domain different degrees of diagonal curves are not acceptable.
Graph Learning with Localized Neighborhood Fairness
Venue: arXiv cs.SI 2022
Year: 2022

Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node. In this work, we formally introduce the notion of neighborhood fairness and develop a computational framework for learning such locally fair embeddings. We argue that the notion of neighborhood fairness is more appropriate since GNN-based models operate at the local neighborhood level of a node. Our neighborhood fairness framework has two main components that are flexible for learning fair graph representations from arbitrary data: the first aims to construct fair neighborhoods for any arbitrary node in a graph and the second enables adaption of these fair neighborhoods to better capture certain application or data-dependent constraints, such as allowing neighborhoods to be more biased towards certain attributes or neighbors in the graph.Furthermore, while link prediction has been extensively studied, we are the first to investigate the graph representation learning task of fair link classification. We demonstrate the effectiveness of the proposed neighborhood fairness framework for a variety of graph machine learning tasks including fair link prediction, link classification, and learning fair graph embeddings. Notably, our approach achieves not only better fairness but also increases the accuracy in the majority of cases across a wide variety of graphs, problem settings, and metrics.
Pushing the performances of ASR models on English and Spanish accents
Venue: arXiv cs.CL 2022
Year: 2022

Speech to text models tend to be trained and evaluated against a single target accent. This is especially true for English for which native speakers from the United States became the main benchmark. In this work, we are going to show how two simple methods: pre-trained embeddings and auxiliary classification losses can improve the performance of ASR systems. We are looking for upgrades as universal as possible and therefore we will explore their impact on several models architectures and several languages.
Semantically-consistent Landsat 8 image to Sentinel-2 image translation for alpine areas
Venue: arXiv cs.CV 2022
Year: 2022

The availability of frequent and cost-free satellite images is in growing demand in the research world. Such satellite constellations as Landsat 8 and Sentinel-2 provide a massive amount of valuable data daily. However, the discrepancy in the sensors' characteristics of these satellites makes it senseless to use a segmentation model trained on either dataset and applied to another, which is why domain adaptation techniques have recently become an active research area in remote sensing. In this paper, an experiment of domain adaptation through style-transferring is conducted using the HRSemI2I model to narrow the sensor discrepancy between Landsat 8 and Sentinel-2. This paper's main contribution is analyzing the expediency of that approach by comparing the results of segmentation using domain-adapted images with those without adaptation. The HRSemI2I model, adjusted to work with 6-band imagery, shows significant intersection-over-union performance improvement for both mean and per class metrics. A second contribution is providing different schemes of generalization between two label schemes - NALCMS 2015 and CORINE. The first scheme is standardization through higher-level land cover classes, and the second is through harmonization validation in the field.
Sense of Embodiment Inducement for People with Reduced Lower-body Mobility and Sensations with Partial-Visuomotor Stimulation
Venue: arXiv cs.HC 2022
Year: 2022

To induce the Sense of Embodiment~(SoE) on the virtual 3D avatar during a Virtual Reality~(VR) walking scenario, VR interfaces have employed the visuotactile or visuomotor approaches. However, people with reduced lower-body mobility and sensation~(PRLMS) who are incapable of feeling or moving their legs would find this task extremely challenging. Here, we propose an upper-body motion tracking-based partial-visuomotor technique to induce SoE and positive feedback for PRLMS patients. We design partial-visuomotor stimulation consisting of two distinctive inputs~(\textit{Button Control} \& \textit{Upper Motion tracking}) and outputs~(\textit{wheelchair motion} \& \textit{Gait Motion}). The preliminary user study was conducted to explore subjective preference with qualitative feedback. From the qualitative study result, we observed the positive response on the partial-visuomotor regarding SoE in the asynchronous VR experience for PRLMS.
Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal
Venue: arXiv cs.LG 2022
Year: 2022

Real estate appraisal is a crucial issue for urban applications, which aims to value the properties on the market. Traditional methods perform appraisal based on the domain knowledge, but suffer from the efforts of hand-crafted design. Recently, several methods have been developed to automatize the valuation process by taking the property trading transaction into account when estimating the property value. However, existing methods only consider the real estate itself, ignoring the relation between the properties. Moreover, naively aggregating the information of neighbors fails to model the relationships between the transactions. To tackle these limitations, we propose a novel Neighbor Relation Graph Learning Framework (ReGram) by incorporating the relation between target transaction and surrounding neighbors with the attention mechanism. To model the influence between communities, we integrate the environmental information and the past price of each transaction from other communities. Moreover, since the target transactions in different regions share some similarities and differences of characteristics, we introduce a dynamic adapter to model the different distributions of the target transactions based on the input-related kernel weights. Extensive experiments on the real-world dataset with various scenarios demonstrate that ReGram robustly outperforms the state-of-the-art methods. Furthermore, comprehensive ablation studies were conducted to examine the effectiveness of each component in ReGram.
Investigation of reinforcement learning for shape optimization of profile extrusion dies
Venue: arXiv cs.CE 2022
Year: 2022

Profile extrusion is a continuous production process for manufacturing plastic profiles from molten polymer. Especially interesting is the design of the die, through which the melt is pressed to attain the desired shape. However, due to an inhomogeneous velocity distribution at the die exit or residual stresses inside the extrudate, the final shape of the manufactured part often deviates from the desired one. To avoid these deviations, the shape of the die can be computationally optimized, which has already been investigated in the literature using classical optimization approaches. A new approach in the field of shape optimization is the utilization of Reinforcement Learning (RL) as a learning-based optimization algorithm. RL is based on trial-and-error interactions of an agent with an environment. For each action, the agent is rewarded and informed about the subsequent state of the environment. While not necessarily superior to classical, e.g., gradient-based or evolutionary, optimization algorithms for one single problem, RL techniques are expected to perform especially well when similar optimization tasks are repeated since the agent learns a more general strategy for generating optimal shapes instead of concentrating on just one single problem. In this work, we investigate this approach by applying it to two 2D test cases. The flow-channel geometry can be modified by the RL agent using so-called Free-Form Deformation, a method where the computational mesh is embedded into a transformation spline, which is then manipulated based on the control-point positions. In particular, we investigate the impact of utilizing different agents on the training progress and the potential of wall time saving by utilizing multiple environments during training.
A comparison, analysis, and provision of methods in identifying types of malware and means of malware detection and protection against them
Venue: arXiv cs.CR 2022
Year: 2022

In this research paper, our intent is to outline different types of malware, their means of operation, and how they are detected in order to protect yourself against such attacks. Varied permission, and limited technical resources mean that detecting malware and such attacks becomes more difficult. With the normal user being limited to the UI, their ability to see what happens in the background is virtually limited to none. Many do not have control on how they distribute permission over the data the applications they use controls, or how that data is stored or distributed. They also do not receive any notification as to whether their data is protected against various attacks and if it has not been attacked already. In this paper, we present evidence on what malware is, how malware operates, different types of malware, and the general means of defence.
Defending against cybersecurity threats to the payments and banking system
Venue: arXiv cs.CR 2022
Year: 2022

Cyber security threats to the payment and banking system have become a worldwide menace. The phenomenon has forced financial institutions to take risks as part of their business model. Hence, deliberate investment in sophisticated technologies and security measures has become imperative to safeguard against heavy financial losses and information breaches that may occur due to cyber-attacks. The proliferation of cyber crimes is a huge concern for various stakeholders in the banking sector. Usually, cyber-attacks are carried out via software systems running on a computing system in cyberspace. As such, to prevent risks of cyber-attacks on software systems, entities operating within cyberspace must be identified and the threats to the application security isolated after analyzing the vulnerabilities and developing defense mechanisms. This paper will examine various approaches that identify assets in cyberspace, classify the cyber threats, provide security defenses and map security measures to control types and functionalities. Thus, adopting the right application to the security threats and defenses will aid IT professionals and users alike in making decisions for developing a strong defense-in-depth mechanism.
Capacity Studies for a Differential Growing Neural Gas
Venue: arXiv cs.NE 2022
Year: 2022

In 2019 Kerdels and Peters proposed a grid cell model (GCM) based on a Differential Growing Neural Gas (DGNG) network architecture as a computationally efficient way to model an Autoassociative Memory Cell (AMC) \cite{Kerdels_Peters_2019}. An important feature of the DGNG architecture with respect to possible applications in the field of computational neuroscience is its \textit{capacity} refering to its capability to process and uniquely distinguish input signals and therefore obtain a valid representation of the input space. This study evaluates the capacity of a two layered DGNG grid cell model on the Fashion-MNIST dataset. The focus on the study lies on the variation of layer sizes to improve the understanding of capacity properties in relation to network parameters as well as its scaling properties. Additionally, parameter discussions and a plausability check with a pixel/segment variation method are provided. It is concluded, that the DGNG model is able to obtain a meaningful and plausible representation of the input space and to cope with the complexity of the Fashion-MNIST dataset even at moderate layer sizes.
Rule Learning by Modularity
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with traditional methods in rule learning to provide efficient and scalable algorithms for the classification of vast data sets, while remaining explainable. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherungs-Aktiengesellschaft which is an insurance company offering diverse services in Germany.
Channel charting based beamforming
Venue: arXiv cs.NI 2022
Year: 2022

Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to discover a low-dimensional latent space charting the channel manifold. In this paper, this latent modeling vision is leveraged together with a recently proposed location-based beamforming (LBB) method to show that channel charting can be used for mapping channels in space or frequency. Combining CC and LBB yields a neural network resembling an autoencoder. The proposed method is empirically assessed on a channel mapping task whose objective is to predict downlink channels from uplink channels.
Technical Report: Automating Vehicle SOA Threat Analysis using a Model-Based Methodology
Venue: arXiv cs.LO 2022
Year: 2022

While the adoption of Service-Oriented Architectures (SOA) eases the implementation of features such as autonomous driving and over-the-air updates, it also increases the vehicle's exposure to attacks that may place road-users in harm. To address this problem, standards (ISO 21434/UNECE) expect manufacturers to produce security arguments and evidence by carrying out appropriate threat analysis. As key threat analysis steps, e.g., damage/threat scenario and attack path enumeration, are often carried out manually and not rigorously, security arguments lack precise guarantees, e.g., traceability w.r.t. safety goals, especially under system updates. This article proposes automated methods for threat analysis using a model-based engineering methodology that provides precise guarantees with respect to safety goals. This is accomplished by proposing an intruder model for automotive SOA which together with the system architecture and the loss scenarios identified by safety analysis are used as input for computing assets, impact rating, damage/threat scenarios, and attack paths. To validate the proposed methodology, we developed a faithful model of the autonomous driving functions of the Apollo framework, a widely used open-source autonomous driving stack. The proposed machinery automatically enumerates several attack paths on Apollo, including attack paths not reported in the literature.
Optimizing the half-gcd algorithm
Venue: arXiv cs.CC 2022
Year: 2022

In this paper, we propose a carefully optimized "half-gcd" algorithm for polynomials. We achieve a constant speed-up with respect to previous work for the asymptotic time complexity. We also discuss special optimizations that are possible when polynomial multiplication is done using radix two FFTs.
Image Classification with Small Datasets: Overview and Benchmark
Venue: arXiv cs.CV 2022
Year: 2022

Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier.