Reviewerly/exHarmony huggingface dataset:
EVAX: Towards a Practical, Pro-active &amp; Adaptive Architecture for High Performance &amp; Security

This paper provides an end-to-end solution to defend against known microarchitectural attacks such as speculative execution attacks, fault-injection attacks, covert and side channel attacks, and unknown or evasive versions of these attacks. Current defenses are attack specific and can have unacceptably high performance overhead. We propose an approach that reduces the overhead of state-of-art defenses by over 95%, by applying defenses only when attacks are detected. Many current proposed mitigations are not practical for deployment; for example, InvisiSpec has 27% overhead and Fencing has 74% overhead while protecting against only Spectre attacks. Other mitigations carry similar performance penalties. We reduce the overhead for InvisiSpec to 1.26% and for Fencing to 3.45% offering performance and security for not only spectre attacks but other known transient attacks as well, including the dangerous class of LVI and Rowhammer attacks, as well as covering a large set of future evasive and zero-day attacks. Critical to our approach is an accurate detector that is not fooled by evasive attacks and that can generalize to novel zero-day attacks. We use a novel Generative framework, Evasion Vaccination (EVAX) for training ML models and engineering new security-centric performance counters. EVAX significantly increases sensitivity to detect and classify attacks in time for mitigation to be deployed with low false positives (4 FPs in every 1M instructions in our experiments). Such performance enables efficient and timely mitigations, enabling the processor to automatically switch between performance and security as needed.
Aplikasi Program HEC-RAS 5.0.7 untuk Pemodelan Banjir di Sub-sub DAS Martapura Kabupaten Banjar

Rainfall data is one of cause factor for flooding in some areas in Indonesia. An extreme rainfall event occurred in the end of 2020 until early 2021 have resulted a severe flooding in West Martapura District, Banjar Regency, South Kalimantan Province. As a tributary to Barito River, Martapura River experienced high water level that gave negative impact to the surrounding areas. HEC-RAS (Hydrologic Engineering Center's River Analysis System) 5.0.7 software combined with ArcGIS 10.8 were applied to model flooding spatially. Mononobe method was used to calculate rainfall intensity for 5-year, 50-year, and 100-year return periods. From the results of the flood modeling, the maximum flood water level elevation was obtained as deep as 3.8 m (5-year return period), 5.2 m (50-year return period), and 9.6 m (100-year return period) with a maximum flood discharge of 34.84 m3/s (5-year return period), 81.68 m3/s (50-year return period), and 142.64 m3/s (100-year return period), respectively.
An Encouraging Design for Data Owners to Join Multiple Co-existing Federated Learning

Federated learning is a distributed learning system that addresses the distributed difficulty such as communication overhead and private information in machine learning while maintaining high performance. However, the distributed learners have to dedicate their resources to improving the global model, which is not likely to happen voluntarily. This motivated us to design an incentive mechanism for users (data owners) to actively participate in the FL processes. In this paper, we consider multiple co-existing FL service providers (FLSPs) with the need to train their models and multiple data owners (DOs) that can offer that service. In the system, DO, and FLSP will submit their cost and valuation values to the cloud platform. Based on this information, we formulate an optimization problem that aims to maximize the social welfare under the nonnegative utility constraint and maximum gain of FLSPs. Then, we propose a heuristic algorithm, Binary Whale Optimization Algorithm (B-WOA), that can solve our formulated NP-hard problem in polynomial time. Finally, numerical results are shown to demonstrate the effectiveness of our proposed algorithm. Moreover, we also compare the performance of our proposed algorithm with Hungarian and greedy algorithms.
EDP: An eBPF-based Dynamic Perimeter for SDP in Data Center

In recent years, the concept of Zero Trust Networks (ZTN) has been proposed to overcome unrealistic security assumptions, e.g., what lies in private networks (such as data centers) is always trusted and safe. In ZTN, no device or user is assumed to be secure, instead all connections have to be authenticated and authorized before being established. Software Defined Perimeter (SDP) is one of the most promising solution for ZTN, where the gateway allows clients to access services only after receiving legitimate Single Packet Authorization (SPA) data. However, existing SDP solutions either (1) need to decouple the SPA from the connection request, resulting in redundant communication processes and impersonation attacks; or (2) need to copy the SPA data to the user space from sniffers, causing the packets to enter the protocol stack repeatedly. Due to the large number of short-lived streams in the data center, inefficiency and insecurity of the SPA process lead to severe connection delays and network attacks (e.g., DDoS). To this end, we propose an eBPF-based Dynamic Perimeter (EDP) to enhance the security and performance of SDP. By using EDP, authentication data can be efficiently embedded into every packet and checked before entering the receiver's protocol stack. Experimental results show that the connection delay of EDP is 80% less than that of the existing state-of-the-art solutions.
Using Pre-Trained Models for Sentiment Analysis in Indonesian Tweets

Sentiment analysis from Twitter can provide useful information about public opinion to public perceptions of a topic, product, and service provided by industry players to government. This information can be useful to be the basis for consideration in making strategies, policies, and decisions making. One of the approach methods used for sentiment analysis is supervised learning. However, the construction of the scratch form classification model requires considerable effort and time. Pre-trained model libraries for sentiment analysis namely Vader, Textblob, and Flair can be used to reduce this burden. In addition, this pre-trained model can be used for the process of labeling training data in the supervised learning method approach. In this study, the challenges of sentiment prediction by the three pre-trained models were discussed on Indonesian text data. As a result, pre-trained models outperform classifier built from scratch. In the comparison of the accuracy levels of the three pre-trained models, the sentiment predictions made by Vader were more accurate than those of Textblob and Flair.
Towards a Methodology for the Characterization of IoT Data Sets of the Smart Building Sector

The long-term objective of the paper aims to provide decision aid support to a technical smart buildings manager to potentially reduce the emission of data produced by sensors inside a building and, more generally, to acquire knowledge on the data produced in the facility. As the first step, the paper proposes to characterize the smart-building ecosystem's Internet-of-things (IoT) data sets. The description and the construction of learning models over data sets are crucial in engineering studies to advance critical analysis and serve diverse researchers' communities, such as architects or data scientists. We examine two data sets deployed in one location in the Grenoble area in France. We assume that the building is an autonomic computing system. Thus, the underlying model we deal with is the well-known MAPE-K methodology introduced by IBM. The paper mainly addresses the analysis component and the adjacent connector component of the MAPE-K model. The content of this layer, and its organization, constitutes the methodological point we put forward. Consequently, we automatically provide a complete set of practices and methods to pass to the planning component of the MAPE-K model. We also sketch a semi-automatic way of reducing the number of measures done by sensors. In the background of our study, we aim to reduce the operational cost of making measures with a much more sober approach than the current one. We also discuss in profound the main findings of our work. Finally, we provide insights and open questions for future outcomes based on our experience.
Participatory, location-based systems in community place-making

The last few years there has been a proliferation of location-based systems beyond the commonplace use of digital maps for navigation. Such uses range from Geographic Information Systems (GIS) for neighbourhood planning, to mapping selforganised civic initiatives and location-based Facebook events. Responding to such developments, the HCI community and research has been active in exploring the potential of such systems for a more meaningful participation of citizens in place-making. Building on such research, this paper explores the design of GIS to support location-based civic participation through undertaking expert interviews, local community workshops and deploying a bespoke GIS system in the context of Athens, Greece. In our findings, we highlight the diversity of opinions and needs of different stakeholders in such technology-enabled and location-based projects.
All Aboard: One Year of Accessible and Inclusive Remote School Trips

The rapid spread of the CoViD-19 pandemic in early 2020 and the adoption of social distancing and "stay-at-home" orders gave rise to the sudden need for schools to be able to provide effective remote teaching. In Italy and elsewhere, the introduction of distance learning technologies has significantly impacted the education of students and limited their social interaction and activities, among which are school trips and other scholastic experiences. To support the constrained educational possibilities of Italian schools, we developed "ActiveViewer", a technological platform that enables massive and interactive remote events, and performed a year-long large-scale experiment, providing a series of massive remote school trips to several Italian cities. We designed a new school trip format, named "CodyTrip" that merges coding and computational thinking with elements of traditional school outings, and provides an engaging experience for participants. In this paper, we present the format and five online trips performed during the school year 2020/2021. The results based on user feedback support the adoption of the tool not only as a stopgap measure for school outings throughout the pandemic but as an accessible and inclusive tool to widen the possibilities of cultural tourism.
Stitching Weight-Shared Deep Neural Networks for Efficient Multitask Inference on GPU

Intelligent personal and home applications demand multiple deep neural networks (DNNs) running on resourceconstrained platforms for compound inference tasks, known as multitask inference. To fit multiple DNNs into low-resource devices, emerging techniques resort to weight sharing among DNNs to reduce their storage. However, such reduction in storage fails to translate into efficient execution on common accelerators such as GPUs. Most DNN graph rewriters are blind for multi-DNN optimization, while GPU vendors provide inefficient APIs for parallel multi-DNN execution at runtime. A few prior graph rewriters suggest cross-model graph fusion for low-latency multi-DNN execution. Yet they request duplication of the shared weights, erasing the memory saving of weight-shared DNNs. In this paper, we propose MTS, a novel graph rewriter for efficient multitask inference with weight-shared DNNs. MTS adopts a model stitching algorithm which outputs a single computational graph for weight-shared DNNs without duplicating any shared weight. MTS also utilizes a model grouping strategy to avoid overwhelming the GPU when co-running tens of DNNs. Extensive experiments show that MTS accelerates multitask inference by up to 6.0× compared to sequentially executing multiple weightshared DNNs. MTS also yields up to 2.5× lower latency and 3.7× less memory usage compared with NETFUSE, a state-of-the-art multi-DNN graph rewriter.
About ML Approach for Role Mining Problem Solving

Role-based access control (RBAC) is a clear and agile method to manage user access permissions. It is being used widely the last few years by companies of any size. Transition of non-RBAC model to the RBAC one may impact business because of its complexity and confusion. Role mining problem is often presented as complexity reduction maximization by minimal number of informative roles. A lot of role mining algorithms have been developed in a past few years, but none of them give the result close enough to ideal. In this article, we will describe another one of these algorithms that is based on machine learning technique – clustering, which will be paired with outlier processing algorithm. The main goal of work is to explore, develop, evaluate ML-based approach for role mining problem solving and compare it with another role-mining solutions. Tests will be performed on different size and density real-world datasets that include all real-world data noise, human errors and other difficulties.
DARPA: Deployment of UAVs for Polygonal Sizable Object Surveillance

Unmanned aerial vehicle (UAV) has attracted much attention due to its excellent ability to collect visual information of surroundings. In this paper, we investigate a new monitoring model to focus on sizes and shapes of objects, and occlusion between objects, and then study the placement of a set of UAVs to monitor polygonal sizable objects. Our aim is to maximize the overall monitoring utility of all objects by determining the positions and orientations of UAVs, given a set of polygonal sizable objects with fixed coordinates and shapes on a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\mathbf{D}$</tex> plane. We study two typical scenarios of the problem: the former stipulates that a line segment is effectively monitored only when it is completely monitored by a single UAV, and the latter allows multiple UAVs to cooperatively monitor a line segment and then integrate their image information. The problem is proved to be NP-hard with infinite continuous solution space. For the first scenario, we propose a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1-1/e)$</tex> -approximation algorithm. For the second one, we first propose a 1/2-approximation algorithm to address its simple version, and then propose a heuristic solution. Numerical evaluations validate the effectiveness of our proposed algorithms.
Residual Time-restricted Self-Attentive TDNN Speaker Embedding for Noisy and Far-field Conditions

One of the emerging challenges in automatic speaker recognition is the development of systems that are robust to noisy and far-field conditions. The current standard for x-vector speaker embedding is based on a time-delay neural network (TDNN) and is less robust than systems based on a residual network (ResNet) and other baseline systems that use signal enhancement preprocessing in presence of these conditions. In this study, we improve the performance of TDNN-based embedding by integrating a residual block with a time-restricted self-attention option (AttResBlock) into the TDNN frame level. Experiments using the Voices Obscured in Complex Environmental Settings (VOiCES) corpus are carried out to evaluate the proposed speaker embedding extractor (AttResBlock-TDNN). The experimental results show that AttResBlock-TDNN outperforms state-of-the-art systems under many adverse conditions. For instance, the proposed AttResBlock-TDNN produces relative improvements in the minDCF and EER of 11.4% and 15.5%, respectively, over the original TDNN-based encoder.
Label-free Monitoring of Self-Supervised Learning Progress

Self-supervised learning (SSL) is an effective method for exploiting unlabelled data to learn a high-level embedding space that can be used for various downstream tasks. However, existing methods to monitor the quality of the encoder — either during training for one model or to compare several trained models — still rely on access to annotated data. When SSL methodologies are applied to new data domains, a sufficiently large labelled dataset may not always be available. In this study, we propose several evaluation metrics which can be applied on the embeddings of unlabelled data and investigate their viability by comparing them to linear probe accuracy (a common metric which utilizes an annotated dataset). In particular, we apply k-means clustering and measure the clustering quality with the silhouette score and clustering agreement. We also measure the entropy of the embedding distribution. We find that while the clusters did correspond better to the ground truth annotations as training of the network progressed, label-free clustering metrics correlated with the linear probe accuracy only when training with SSL methods SimCLR and MoCo-v2, but not with SimSiam. Additionally, although entropy did not always have strong correlations with LP accuracy, this appears to be due to instability arising from early training, with the metric stabilizing and becoming more reliable at later stages of learning. Furthermore, while entropy generally decreases as learning progresses, this trend reverses for SimSiam. More research is required to establish the cause for this unexpected behaviour. Lastly, we find that while clustering based approaches are likely only viable for same-architecture comparisons, entropy may be architecture-independent.
Semi-Instance Normalization Network for Turbulence Degraded Image Restoration

In computer vision tasks, a variety of normalization methods are widely used. Compared with other normalization methods, Instance Normalization (IN) performs better in turbulence degraded image restoration. However, the simple application of IN to a degraded image restoration network can be suboptimal. In this paper, we present a novel block named Semi Instance Normalization Block (SIN Block), which can improve the performance of the image restoration network. SIN Block incorporates original features in the normalization layer, which can preserve contextual information. Furthermore, we designed a semi-instance normalization Network (SINet) consisting of a series of the SIN Block for restoring turbulence degraded images. Extensive experiment on simulation dataset demonstrates that SINet can effectively restore details of the turbulence degraded image and sharpen its edges.
TERA-Scheduler for a Dependency-based Orchestration of Microservices

Due to the increase use of Microservices and the adoption of containers as the best choice for their deployment. Orchestration tools are continuously updating their auto-scaling and scheduling strategies in order to offer better options and capabilities to users. Despite the proposed solutions, there are still many unaddressed issues and optimization is still needed from different perspectives. Therefore, we present TERA-scheduler, with a new scheduling strategy, that we implement using Kubernetes, to reduce both the scheduling duration and the traffic between pods, by considering dependencies between Microservices. Experimentation results show that TERA-Scheduler fasten the response time by 39% compared to Kubernetes (K8s for short) default scheduler which make it a better choice especially for latency-aware systems.
Probability and Attack Graph models in Contextual Risk Scoring System

The interest of attackers is expanding in response to the on-growing enthusiasm for rapid development, so seeking ways to quantify and manage the risks has become a priority. However, in the current context, there is no consistent and effective strategy for evaluating computer network security. Therefore, we propose a comprehensive software solution that collects, processes, and evaluates data from devices inside a network, in order to identify the key security issues and measure the level of risk using a probability-based model. The final result is represented by the network score, which is a measure of the risk to which it is exposed.
Scalable Deployments for Real-Time AI Video Stream Processing

Real-time stream processing is becoming more prevalent today due to huge chunks of data needing to be processed upon arrival. In video streaming the need for real-time management is both important and challenging because video frames come at high frequency. AI advances have made it possible to understand video feeds at a high level in real-time, making them a valuable source of information in regards to human behavior, trends, surveillance and much more. As a consequence of these reasons, there is a need for a highly performant and deployable system in terms of latency, scalability, accuracy of results and computing power, which leverages the cloud as a service. A design for a low-latency and highly scalable system is much needed as video stream processing or video analytics has uses in areas such as surveillance, real-time video analytics, criminality and autonomous vehicles, which require fast and accurate analysis of data. Such a system should be able to employ not only streaming but also different processing actions including machine learning models which can be applied to frames resulting in data analysis or further data pipelines. We developed a scalable real-time video processing system which consumes video frames, processes them using deep learning models, renders them and stores the resulting semantic information in a database for further downstream processing. We show that our proposed processing architectures are a suitable solution for modern video analytics systems, which can be scaled both vertically and horizontally, and achieves real-time latency within maximum of 1 second for frame rates ranging from 10 to 60 fps.
Bootstrapping Road Sign Detection for Self-Driving Cars using Weakly-Supervised Learning

Given the escalating trend in the number of cars on the public roads, advanced autonomous driver-assistance systems become more accessible to the masses in order to keep safe traffic participants. This work addresses the problem of traffic signs detection constrained by running in a minimal embedded platform. Our solution consists of the generation of a synthetic object detection dataset using CARLA Simulator, a popular self-driving car virtual environment, enhancing it with image augmentation policies and bootstrapping the model performance by using knowledge distillation from a model ensemble. We make use of modern weakly supervised techniques to minimize labelling noise and achieve a fast, predictive, high-precision model that performs well in real-life scenarios, having a mean average precision of over 53%. The model is integrated with ease into real-time applications achieving 19 FPS on our embedded platform that uses a small size Coral Edge TPU USB Accelerator. Our proposed computer vision solution that powered a scaled self-driving car enabled the team to rank third in the Bosch Future Mobility Challenge 2022, an IEEE ITSS certified contest that encourages the development of complete autonomous driving solutions for scaled vehicles in controlled real-life scenarios.
Macroscopic dynamics prediction by reservoir computing

Nowadays, the problem of forecasting complex signals is significant and has many applications in real life. One of such applications is the prediction of neurophysiological signals, like EEG. Such signals are macroscopic signals of a group of neurons, and the connections between them adapt in time. Here, we investigate the possibility of forecasting the dynamics of the modulated adaptive network, which topology changes in time, using Reservoir Computing (RC). We show that the dynamics of the signal is chaotic, and RC cannot predict it, but reconstruction of the phase space by adding the delays improves the quality of the signal’s prediction.