Reviewerly/exHarmony huggingface dataset:
SMU-DDI Cyber Autonomy Range

The Southern Methodist University-Darwin Deason Institute for Cybersecurity (SMU-DDI) Cyber Autonomy Range (CAR) addresses the incorporation of increased resiliency, reliability, and cyber security of the autonomous systems (AS) cyberinfrastructure; an issue with widespread concern and broad impact on society. The advances of data science and Machine Learning/Artificial Intelligence (ML/AI) methods coupled with their integration into autonomous subsystems is an enabling trend that supports AS maturity. Likewise, these same aspects of ML/AI present entirely new aspects of cyber security, many of which have only been analyzed in a preliminary sense or for special cases. The ML/AI aspects of cyber security are critically important, with significant ramifications in human safety and well-being. The CAR is a collaborative facility that supports the assessment of AS when faced with cyber threats by assessing their attack surface, vulnerability, and their degree of resistance to such threats. It is instrumented to simulate and/or emulate the external environment of an AS and can subject the AS to a variety of controlled cyber-attacks. Because the decision-making capabilities of many AS are based upon data-driven ML/AI-enabled technologies, the threat surface surrounding ML/AI subsystems is of particular concern. The CAR is especially configured to investigate and simulate (or emulate) cyber-attacks on ML/AI-equipped subsystems; particularly ML/AI subsystems that depend upon data sources derived from sensor suites or other data sources.
Star-Net: a Multi-Branch Convolutional Network for Multiple Source Image Segmentation

We present Star-Net, a multi-branch convolutional network architecture to deal with the multiple-source (multi-modal) image segmentation. It is composed of several satellite networks, one per source, connected in the corresponding layers through a central unit whose role is to calculate and assign the weights to the sources according to their relevance. In each layer of the network, the weights are different, case-specific and dynamically calculated. With this architecture, we reward the relevant sources, penalizing the less relevant ones. Star-Net takes into account the non-linear behaviour of the image interpretation, so as the active role of one source in a layer can be reduced in another, possibly growing up again in a following layer. When used in the field of multimodal Magnetic Resonance Imaging (MRI) segmentation, we have found that Star-Net is capable to speed up the training and improving the performance compared to traditional CNN architectures. Additionally and more importantly, it allows to perform case-specific analyses of network activation and increases network transparency.
An Approach to Real-Time Indian Sign Language Recognition and Braille Script Translation

There is a need to bridge the communication gap between the speech or vision impaired and the rest of society. The limitation associated with existing solutions is that they don’t perform well in the real-time scenario. Also, they yield a low level of accuracy. To mitigate these limitations, we aim to develop a system that recognizes the hand gestures for the English alphabet in Indian Sign Language (ISL) and then translates them into Braille Script in real time. The proposed mechanism can identify 26 hand poses for the 26 letters of the English alphabet as per the ISL. The different signs are captured from a camera and its frames are then used for recognition using Convolutional Neural Networks (CNN). In our work, we also utilize lookup maps for Braille Script Translation. The proposed method affirms the efficacy of our self-acquired dataset with an accuracy of 94.23% on the Sign-Language-Custom (SLC) model. Next, an accuracy score of 1 is obtained on the C-O and M-N models designed to eliminate the ambiguity in the SLC model for the above-mentioned two pairs of alphabets.
Case study of digital innovation process improvement, using BPMN, case-based reasoning, and text mining

Managers of digital products frequently receive feature requests from customers, end users, C-suite executives, and other company officials. They need to collect, process, and implement such requests to implement the most valuable features. Moreover, it is often difficult to decide which requests should be applied. The product backlog prioritization strategy is a well-known method widely utilized to enhance this process. However, it is time-consuming because of the large amount of data and is also subject to the human factor. Moreover, the current literature lacks models allowing automated recommendations to help speed up feature request processing, especially for digital startups with a lack of resources. In this paper, we used BPMN to illustrate a case study of redesigning an innovation process in a digital startup. Furthermore, based on the To-Be Process model, we created an approach to recommend new features using case-based reasoning and text mining.
QoE Estimation of WebRTC-based Audiovisual Conversations from Facial Expressions

Online videoconferencing applications have become more popular and occupy a significant portion of consumer Internet traffic. Thus, it is crucial to assess the Quality of Experience (QoE) for these applications to effectively implementing high-quality services. Although the major approach to assess the QoE is the conduction of a subjective quality assessment, this method relies on user’s feedback and it is not suitable for real-time management systems. For this reason, in this paper, we propose alternative QoE estimation models based on facial expression features extracted from the face of the users during audiovisual conversations with a WebRTC-based telemeeting service. We first conducted a subjective quality assessment to investigate the QoE of two-party audiovisual conversations impaired by poor network conditions. Then, we used the extracted facial expression features obtained by observing the test participants to train Machine Learning (ML) algorithms and a Fully Convolutional Network (FCN) with the aim to estimate the perceived QoE solely on the base of these features. Finally, we discuss and compare the QoE estimation performance of the proposed models in terms of accuracy, precision, recall, and F1-score metrics. In particular, the ML model (Support Vector Machine) and the FCN achieved the mean estimation accuracy of 0.78 and 0.70, respectively.
Towards Cost-Benefit-Aware Adaptive Monitoring for Cyber-Physical Systems

Cyber-Physical Systems (CPS) are becoming ubiquitous in many different domains, for example, in the form of Unmanned Aerial Vehicles (UAVs), (semi-)autonomous systems, or robotic applications. Given that CPS frequently operate in a safety-critical context, and interact and collaborate with humans, ensuring that these systems behave as intended and adhere to their specified security and safety requirements at runtime is essential. Providing automated support for monitoring is a fundamental part of collecting information about the system, and facilitating subsequent analysis and reasoning. However, while advances have been made, particularly in self-adaptation and self-management, the monitoring aspect is often neglected, resulting in suboptimal data collection that does not consider associated monitoring costs, changing environments, or varying benefits of the collected data. Such benefits range from accountability in the aftermath of a security incident, up to proactive defense against risks if connected to alarming. In this paper, we outline our initial concept for cost-benefit-aware adaptive runtime monitoring, for (but not limited to) safety and security requirements. As part of this work, we identify relevant monitoring aspects and create a Cost-Benefit-Aware Adaptive Model (CBAAM), conceptualizing the costs and benefits of adaptive monitoring. This is also especially relevant for security, saving resources, e.g., budget and computational. We further present an architecture for defining and executing these adaptations and discuss our research roadmap and next steps.
Design of a Mixed Reality-Based Immersive Virtual Environment System for Social Interaction and Behavioral Studies

The advancements in immersive technologies allow us to create more sophisticated environments designed to help engage users by merging the physical world with a digital or simulated reality.These can range from completely immersive virtual environments to mixed reality immersive environments, where the virtual world and real world collide.Virtual reality is a completely immersive environment where the users' reality is replaced with a simulated environment, and the hardware works to convince the user that they are in a different world.In contrast, augmented reality is a mixed type of reality, combining both virtual and the natural world by augmenting the real world with digital assets and components.While both types of experiences contribute to creating rich collaborative environments, a limitation, and sometimes inconvenience, is present with the requirement of wearing a head-mounted device (HMD), creating restriction that prevents users from having physical interactions with others.Rather than interacting in the virtual space, we propose a concept that provides the structure for a physical space where users can interact with the shared mixed reality environment; an environment projected to help create the collaborative aspect in this project, without any wearable devices.In this paper, we will present the developed system and implemented four dimensional interactions and demonstrate the feasibility of the structured experience we have created.
Optimal Combinatorial Neural Codes With Matched Metric δ<sub>r</sub>: Characterization and Constructions

Based on theoretical neuroscience, G. Cotardo and A. Ravagnani (2022) introduced a class of asymmetric binary codes called combinatorial neural codes (CN codes for short), with a “matched metric” <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta _{r}$ </tex-math></inline-formula> called asymmetric discrepancy, instead of the Hamming distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_{H}$ </tex-math></inline-formula> for usual error-correcting codes. They also presented the Hamming, Singleton and Plotkin bounds for CN codes with respect to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta _{r}$ </tex-math></inline-formula> and asked how to construct CN codes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal C}$ </tex-math></inline-formula> with large size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$| {\mathcal C}|$ </tex-math></inline-formula> and minimum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta _{r}({\mathcal C})$ </tex-math></inline-formula> . In this paper, we first show that a binary code <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal C}$ </tex-math></inline-formula> reaches one of the above bounds for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta _{r}({\mathcal C})$ </tex-math></inline-formula> if and only if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal C}$ </tex-math></inline-formula> reaches the corresponding bounds for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_{H}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> is sufficiently close to 1. This means that all optimal CN codes come from the usual optimal codes. Then, we present several constructions of CN codes with good and flexible parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n,K, \delta _{r}({\mathcal C}))$ </tex-math></inline-formula> by using bent functions.
On the Security of the One-and-a-Half-Class Classifier for SPAM Feature-Based Image Forensics

Combining multiple classifiers is a promising approach to hardening forensic detectors against adversarial evasion attacks. The key idea is that an attacker must fool all individual classifiers to evade detection. The 1.5C classifier is one of these multiple-classifier detectors that is attack-agnostic, and thus even increases the difficulty for an omniscient attacker. Recent work evaluated the 1.5C classifier with SPAM features for image manipulation detection. Despite showing promising results, their security analysis leaves several aspects unresolved. Surprisingly, the results reveal that fooling only one component is often sufficient to evade detection. Additionally, the authors evaluate classifier robustness with only a black-box attack because, currently, there is no white-box attack against SPAM feature-based classifiers. This paper addresses these shortcomings and complements the previous security analysis. First, we develop a novel white-box attack against SPAM feature-based detectors. The proposed attack produces adversarial images with lower distortion than the previous attack. Second, by analyzing the 1.5C classifier’s acceptance region, we identify three pitfalls that explain why the current 1.5C classifier is less robust than a binary classifier in some settings. Third, we illustrate how to mitigate these pitfalls with a simple axis-aligned split classifier. Our experimental evaluation demonstrates the increased robustness of the proposed detector for SPAM feature-based image manipulation detection.
Identifying Concepts in Software Projects

When working on a project, software developers must be familiar with computing concepts, standards, and technologies related to the project. We present a novel approach, called Scode, to automatically identify those concepts using the project's documentation. Scode combines entity linking and network analysis techniques specialized for the software development domain. In addition to concepts explicitly mentioned in the documentation, Scode can retrieve implicit concepts related to the project's domain. Concepts identified by Scode have a recognized meaning that is consistent across projects. We compared Scode to different baselines and found that it is more effective at mapping projects to a consistent concept space.
Effective Interpretable Policy Distillation via Critical Experience Point Identification

Interpretable policy distillation aims to imitate a deep reinforcement learning (DRL) policy into a self-explainable model. However, the distilled policy usually does not generalize well to complex tasks. To investigate this phenomenon, we examine the experience pools of DRL tasks and find that these interactive experience distributions are heavy tailed. However, this critical issue is largely ignored by existing approaches, and, thus, they do not fully unitize the less frequent but very critical experience points. To address this issue, we propose characterizing decision boundaries via the minimum experience retention to deal with the heavy-tailed experience distributions. Our method identifies critical experience points that are close to the model’s decision boundaries, and such experience points are more critical because they portray the prerequisite of a model to take an action. As a result, our method distills the DRL policy to a self-explainable structure without a neural structure and ambiguous intermediate parameters. Through experiments on six games, we show that our method outperforms the state-of-the-art baselines in cumulative rewards, stability, and faithfulness.
A Dual Reinforcement Learning Framework for Weakly Supervised Phrase Grounding

Weakly-supervised phrase grounding aims to localize a specific region in an image that corresponds to the given textual phrase, where the mapping between noun phrases and image regions is not available in the training stage. Previous methods typically exploit an additional proxy task (e.g., phrase reconstruction or image-phrase alignment) to provide supervision for training, since the lack of region-level annotations in the weakly-supervised setting. However, there exists a significant gap in optimization objectives between the proxy tasks and the target grounding task, which may result in low-efficient optimization for the target model. Therefore, in this paper, we propose a novel dual reinforcement learning framework to directly optimize the phrase grounding model. Specifically, we consider the duality of phrase grounding and phrase generation tasks. These two tasks form a closed loop that can provide quality feedback signals to measure the performance of each other. In this way, we can measure the correctness of the localized regions and thus be able to optimize the grounding model directly. We design two reward functions to quantify the feedback signals and train the models via reinforcement learning. In addition, to relieve the training difficulty of our framework, we present a heuristic algorithm to generate pseudo region-phrase pairs to warm-start our models. We perform experiments on two popular phrase grounding datasets: ReferItGame and Flickr30 K Entities, and the results demonstrate that our method outperforms the previous methods by a large margin.
Luminance and Detail Enhancement for HDR Images Based on Surround-Aware Perceptual Quantization Under Ambient Illumination

The lighting conditions of mobile devices could severely affect the viewing experience of the displayed image, such as loss of details and contrast, especially for high dynamic range (HDR) contents. In this paper, a content independent luminance and detail enhancement method is proposed for HDR images under ambient illumination. The perceptual luminance at the given lighting conditions is first quantified based on the human visual adaption. Combining the quantization curve and the display model, the mapping operator for the luminance channel could then be efficiently constructed. Both psychophysical evaluation and objective measurement indicate that the proposed method significantly improves the perceptual quality of HDR images under ambient illumination.
Persistent Memory Security Threats to Interprocess Isolation

Persistent memory object (PMO) is a general system abstraction for holding persistent data in persistent main memory, managed by an operating system. A PMO programming model breaks interprocess isolation as it results in the sharing of persistent data between two processes as they alternatively access the same PMO. In this article, we discuss security implications of a PMO model. We demonstrate that the model enables one process to affect execution of another process, even without sharing a PMO over time. This allows an adversary to launch inter-PMO security attacks if two processes are linked via other unshared PMOs. We present formalization of inter-PMO attacks, their examples, and potential strategies to defend against them.
Factorized Industrial Anomaly Detection and Localization

Convolutional neural networks trained on large datasets can generalize various down-streaming tasks, including industrial anomaly detection and localization, which is critical in modern large-scale industrial manufacturing. Whereas previous methods have demonstrated that the feature fusion strategy across multiple layers is effective for better performance on industrial anomaly detection and localization, they lack flexibility in intervening and manipulating the local and global information composition process. Through experiments, we demonstrate that the brute-force feature fusion strategy used in previous methods leads to sub-optimal performance in most industrial anomaly detection scenarios. To this end, we propose a novel feature factorization and reversion framework based on invertible neural networks, enabling the selective emphasis or suppression of distinct information in a continuous space by request to fit various preferences for detecting different abnormalities. The preferred local and global info-combination for detecting different defects on 15 objects is studied by experiments exhaustively on the popular benchmark MVTec-AD. Based on feature factorization and reversion, our method is able to outperform previous state-of-the-art methods by a noticeable margin, achieving an image-level anomaly detection AUROC score of up to 99.67% (previously 99.4%), pixel-level anomaly localization AUROC score of 98.61% (previously 98.5%), and AUPRO of 95.15% (previously 94.6%), which validates the effectiveness of the proposed method for industrial anomaly detection and localization.
GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression

The single-cell pseudotemporal trajectory inference is an important way to explore the process of developmental changes within a cell. Due to the uneven rate of cell growth, changes in gene expression depend less on the time of data collection and more on a cell's "internal clock". To overcome the challenges of gene analysis, and replicate biological developmental processes, several strategies have been put forth. However, due to the size of single-cell datasets, locating relevant signposts usually necessitate clustering analysis or a sizable amount of priori information. To this end, we propose a novel single-cell pseudotemporal trajectory inference technique: GCSTI method, which is based on graph compression and doesn't rely on a priori knowledge or clustering procedures, can handle the trajectory inference problem for a large network in a stable and efficient manner. Additionally, we simultaneously improve the pseudotime defining method currently employed in this study in order to obtain more trustworthy and beneficial outcomes for trajectory inference. Finally, we validate the efficacy and stability of the GCSTI method using datasets from human skeletal muscle myogenic cells and four simulated datasets.
Enhancing Bug Localization Using Phase-Based Approach

Software bug localization is an important step in the software maintenance process. Automatic bug localization can reduce the time consumed in the process of localization. Some techniques are applied in the bug localization process, but those techniques suffer from limitations in time and accuracy. This paper proposes a phase-based bug localization approach to overcome these limitations. The approach is composed of three main phases which are raw data preparation, package classification, and source code recommendation. The main input to our approach is a bug report and the source code of the past versions for the target system of interest. From the bug report, various information is utilized: the summary, the description, the stack traces, and the fixed source code files. The raw data preparation phase is used to restructure those inputs. The package classification phase aims to locate the package that would include the source code to be modified as a first step, hence reducing the time needed to locate the source code file due to the lexical mismatch between those files and the bug report data. Bidirectional Encoder Representations from Transformers (BERT), which is a sentence embedding technique, is utilized in the package classification and source code recommendation phases. The experimental results show that our approach outperforms existing approaches according to TOP-N rank and Mean Reciprocal Rank (MRR) evaluation metrics.