Reviewerly/exHarmony huggingface dataset:
SwiftDir: Secure Cache Coherence without Overprotection

Cache coherence states have recently been exploited to leak secrets through timing-channel attacks. The root cause lies in the fact that shared data in state Exclusive (E) and state Shared (S) are served from different cache layers. The state-of-the-art countermeasure—S-MESI—serves both E- and S-state shared data from the last-level cache (LLC) by explicitly synchronizing the Modified (M) state across private caches and the LLC. This has to sacrifice the silent upgrade feature that MESI introduces for speedup. Moreover, it enforces protection to not only exploitable shared data but also unshared data. This further slows down performance, especially for write-after-read intensive applications. In this paper, we propose SwiftDir to efficiently secure cache coherence against cover-channel attacks without overprotection. SwiftDir fundamentally narrows down the protection scope to write-protected data. Such exploitable shared data can be uniquely identified with the write-protection permission in the memory management unit (MMU) and do not necessarily transit to state M. We validate this idea through tracing system calls of shared libraries on Linux. We then investigate all three commercial cache architectures (i.e., PIPT, VIPT, and VIVT) and find it feasible to hitchhike the address translation process to transmit the write-protection information from the MMU to the coherence controller. Then SwiftDir enforces protection over only write-protected data by serving all requests toward them directly from the LLC with a constant latency. This not only simplifies how MESI handles write-protected data but also avoids how S-MESI overprotects them. Meanwhile, SwiftDir still preserves silent upgrade for efficient handling of unshared data. Extensive experiments demonstrate that our SwiftDir can secure cache coherence while outperforming not only secure SMESI but also unprotected MESI.
Mint: An Accelerator For Mining Temporal Motifs

A variety of complex systems, including social and communication networks, financial markets, biology, and neuroscience are modeled using temporal graphs that contain a set of nodes and directed timestamped edges. Temporal motifs in temporal graphs are generalized from subgraph patterns in static graphs in that they also account for edge ordering and time duration, in addition to the graph structure. Mining temporal motifs is a fundamental problem used in several application domains. However, existing software frameworks offer suboptimal performance due to high algorithmic complexity and irregular memory accesses of temporal motif mining.This paper presents $\mathsf{Mint}$—a novel accelerator architecture and a programming model for mining temporal motifs efficiently. We first divide this workload into three fundamental tasks: search, book-keeping, and backtracking. Based on this, we propose a task-centric programming model that enables decoupled, asynchronous execution. This model unlocks massive opportunities for parallelism, and allows storing task context information on-chip. To best utilize the proposed programming model, we design a domain-specific hardware accelerator using its data path and memory subsystem design to cater to the unique workload characteristics of temporal motif mining. To further improve performance, we propose a novel optimization called search index memoization that significantly reduces memory traffic. We comprehensively compare the performance of $\mathsf{Mint}$ with state-of-the-art temporal motif mining software frameworks (both approximate and exact) running on both CPU and GPU, and show $9\times-2576\times$ benefit in performance.
DiVa: An Accelerator for Differentially Private Machine Learning

The widespread deployment of machine learning (ML) is raising serious concerns on protecting the privacy of users who contributed to the collection of training data. Differential privacy (DP) is rapidly gaining momentum in the industry as a practical standard for privacy protection. Despite DP’s importance, however, little has been explored within the computer systems community regarding the implication of this emerging ML algorithm on system designs. In this work, we conduct a detailed workload characterization on a state-of-the-art differentially private ML training algorithm named DPSGD. We uncover several unique properties of DP-SGD (e.g., its high memory capacity and computation requirements vs. non-private ML), root-causing its key bottlenecks. Based on our analysis, we propose an accelerator for differentially private ML named DiVa, which provides a significant improvement in compute utilization, leading to 2.6× higher energy-efficiency vs. conventional systolic arrays.
Developing a Singlish Neural Language Model using ELECTRA

We develop and benchmark a Singlish pretrained neural language model. To this end, we build a novel 3 GB Singlish freetext dataset collected through various Singaporean websites. Then, we leverage ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) to train a transformer-based Singlish language model. ELECTRA is chosen due to its resource-efficiency to better ensure reproducibility. We further build two text classification datasets in Singlish: sentiment analysis and language identification. We use the two datasets to fine-tune our ELECTRA model and benchmark the results against other available pretrained models in English and Singlish. Our experiments show that our Singlish ELECTRA model is competitive against the best open-source models we found despite being pretrained within a significantly less amount of time. We publicly release the benchmarking dataset.
AgilePkgC: An Agile System Idle State Architecture for Energy Proportional Datacenter Servers

Modern user-facing applications deployed in datacenters use a distributed system architecture that exacerbates the latency requirements of their constituent microservices (30-250$\mu$s). Existing CPU power-saving techniques degrade the performance of these applications due to the long transition latency (order of 100$\mu$s) to wake up from a deep CPU idle state (C-state). For this reason, server vendors recommend only enabling shallow core C-states (e.g., CC1) for idle CPU cores, thus preventing the system from entering deep package C-states (e.g., PC6) when all CPU cores are idle. This choice, however, impairs server energy proportionality since power-hungry resources (e.g., IOs, uncore, DRAM) remain active even when there is no active core to use them. As we show, it is common for all cores to be idle due to the low average utilization (e.g., 5-20%) of datacenter servers running user-facing applications. We propose to reap this opportunity with AgilePkgC (APC), a new package C-state architecture that improves the energy proportionality of server processors running latency-critical applications. APC implements PC 1A (package C l agile), a new deep package C-state that a system can enter once all cores are in a shallow C-state (i.e., CC1) and has a nanosecond-scale transition latency. PC 1A is based on four key techniques. First, a hardware-based agile power management unit (APMU) rapidly detects when all cores enter a shallow core C-state (CC1) and triggers the system-level power savings control flow. Second, an IO Standby Mode (IOSM) places IO interfaces (e.g., PCIe, DMI, UPI, DRAM) in shallow (nanosecond-scale transition latency) low-power modes. Third, a CLM Retention (CLMR) mode rapidly reduces the CLM (Cache-and-home-agent, Last-level-cache, and Mesh network-on-chip) domain’s voltage to its retention level, drastically reducing its power consumption. Fourth, APC keeps all system PLLs active in PC 1A to allow nanosecond-scale exit latency by avoiding PLL re-locking overhead. Combining these techniques enables significant power savings while requiring less than 200ns transition latency, $\gt250\times$ faster than existing deep package C-states (e.g., PC6), making PC 1A practical for datacenter servers. Our evaluation based on an Intel Skylake-based server shows that APC reduces the energy consumption of Memcached by up to 41% (25% on average) with <0.1% performance degradation. APC provides similar benefits for other representative workloads.
Instantaneous Ego-Motion Estimation Using a Coherent Radar Network

This paper presents an approach to estimate the ego-motion of a vehicle equipped with a network of two or more coherently working bistatic or multistatic MIMO radars. The multi-perspective view of this setup allows for an immediate determination of the radar targets' relative velocity vector. The ego-motion is then derived from the inverted vectorial velocity of all static targets that can be obtained by clustering the velocity vectors. For this, it can be assumed that the majority of the detected targets are static, thus generating the largest cluster. The novel approach was tested with a setup consisting of two 77 GHz front-looking, long-range radars that only exhibit a narrow field of view with a small azimuth variation. The functionality of the proposed algorithm was evaluated and confirmed in a highway test drive.
Design and Development of Object Detection System in Augmented Reality Based Indoor Navigation Application (Case Study: Faculty of Mathematics and Sciences Building, Syiah Kuala University)

The Faculty of Mathematics and Natural Sciences building in Syiah Kuala University is a three-story building which consists of many rooms. The number of rooms causes the process of finding a room will take a lot of time. Currently, the informatics team in Syiah Kuala University is developing an application which implements Indoor Positioning System. One of the features is an indoor navigation feature based on Augmented Reality (AR). However, the route from one point to another is not always straightforward. Therefore, this research will design and develop an application which aims to detect obstacles present in the route from starting point to ending point. Obstacles that can be detected in this application are 3D objects, classified as human, bag, chair, potted plant and table or desk. In the application, TensorFlow Lite is used to detect obstacles in the form of 3D objects, where the distance is calculated using a triangle similarity method. Once an object is identified, the application will provide sound/voice notification, vibration as well as augmented reality image on the screen to inform the user about the obstacle. This is done by having ARCore Software Development Kit. To evaluate the system, tests were carried out which included walking speed limit testing, and usability testing. In the walking speed limit test, the maximum speed when using the application is in the range of 1.27 m/s to 1.35 m/s. Finally, the results of usability testing using Post-Study System Usability Questionnaire (PSSUQ) also show that the application can run well and can be accepted by users with a usability test score of 5.87.
Raspberry Pi Based Voice Controlled Smart Wheelchair

Voice signals are the most important means of communication among human. Every conversation and interaction is done employing voice signals. Voice signals can be converted into electrical signals using a microphone. This electronic document represents a project which aims to design and construct a wheelchair that can be controlled by human voice. This is for paralyzed people that are severely injured and can’t even use their hands to control the joystick wheelchair. Physical disability can occur due to multiple reasons like injuries from an accident, age-related & health problems. A wheelchair is used to provide a mode of transportation for disabled people with impairments in hands and legs. This project will provide smooth self-drive indoors and outdoors. It can detect objects, stairs, or any constraint and can stop autonomously. The prototype of the wheelchair is constructed using a microcontroller, chosen for versatility and performance in operations and communication with other electronic devices. This project is based on the basic principle of motor controlling system and voice processing using a microcontroller. The system has been designed in a cost-effective way so that it can be commercialized and needy users may benefit from it.
Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection

Time series anomaly detection has been recognized as of critical importance for the reliable and efficient operation of real-world systems. Many anomaly detection methods have been developed based on various assumptions on anomaly characteristics. However, due to the complex nature of real-world data, different anomalies within a time series usually have diverse profiles supporting different anomaly assumptions. This makes it difficult to find a single anomaly detector that can consistently outperform other models. In this work, to harness the benefits of different base models, we propose a reinforcement learning-based model selection framework. Specifically, we first learn a pool of different anomaly detection models, and then utilize reinforcement learning to dynamically select a candidate model from these base models. Experiments on real-world data have demonstrated that the proposed strategy can indeed outplay all baseline models in terms of overall performance.
Application of Grayscale Co-occurrence Matrix (GLCM) Method for Classification of Quality Type of Guava Leaves as Traditional Medicine Using Neural Network Algorithm

Currently, there are still many people who use traditional medicine such as the use of guava leaves as anti-diarrhea medicine. But the types of guava leaves have different qualities for traditional medicine, some types of guava leaves have different leaf shape characteristics, and it will be difficult to distinguish the quality of the leaves. The purpose of this study was to classify the quality of guava leaf species with the classification of "good" quality and "bad" quality. The method used in this study starts with data collection, then the next process is Pre-Processing. After doing the Pre-Processing, the GLCM method will be applied with the Matlab application, the results of the application of the GLCM method will produce a data matrix which will later be used for the process of implementing the neural network algorithm on RapidMiner for the classification process and will produce an accuracy value. The results of this study produce several attributes, namely Angular Second Moment (ASM) as data attribute 1, contrast as data attribute 2, Inverse Different Moment (IDM) as data attribute 3, entropy as data attribute 4, and correlation as data attribute 5 in the sample type. Guava was tested, then the final result of the application of the neural network algorithm in this study resulted in an accuracy of 95%.
Pure Versus Hybrid Transformers For Multi-Modal Brain Tumor Segmentation: A Comparative Study

Vision Transformers (ViT)-based models are witnessing an exponential growth in the medical imaging community. Among desirable properties, ViTs provide a powerful modeling of long-range pixel relationships, contrary to inherently local convolutional neural networks (CNN). These emerging models can be categorized either as hybrid-based when used in conjunction with CNN layers (CNN-ViT) or purely Transformers-based. In this work, we conduct a comparative quantitative analysis to study the differences between a range of available Transformers-based models using controlled brain tumor segmentation experiments. We also investigate to what extent such models could benefit from modality interaction schemes in a multi-modal setting. Results on the publicly-available BraTS2021 dataset show that hybrid-based pipelines generally tend to outperform simple Transformers-based models. In these experiments, no particular improvement using multi-modal interaction schemes was observed.
Study of Limiting Factors in the Selection of a Learning Sample of Images for Automatic Tracking of Changes in Weather Conditions

In our previous work on using images from stationary outdoor cameras for tracking the loss of visibility, we analysed a series of videos taken from different localizations. The results showed the need of big data processing, and also the presence of situations that would lead to mis-identification of loss of visibility without its availability. The material shows the cases of such situations and the need of consideration of the camera localization, its orientation and the current position of the sun.
Serverless Computing and FaaS for Airport Meteorology

Serverless computing and Function-as-a-Service are programming paradigms that have many advantages for modern, distributed and highly modular applications. However, the situation for older applications with more monolithic architectures is more complex. It may be questionable whether the obvious advantages received from their porting into cloud and FaaS can outweigh the effort and resources spent on the transformation itself. In this paper, we present our initial effort aimed at transforming one such application, successfully being used for several years for visibility determination at airports - a significant component of air traffic safety. We have chosen to modularize the application, divide it into parts that can be implemented as functions in the FaaS paradigm, and implement simple cloud-based data management. The tools that we are using in the initial stage are OpenWhisk for FaaS and Airflow for workflow management (events production for OpenWhisk, which has an event-driven programming model), while we are also exploring the application of OpenFaaS and OSCAR. The work is still in initial stages and only a small part of the application has been implemented as OpenWhisk actions (FaaS functions) so far.
Reinforcement Learning Made Affordable for Hardware Verification Engineers

Constrained random stimulus generation is no longer sufficient to fully simulate the functionality of a digital design. The increasing complexity of today's hardware devices must be supported by powerful development and simulation environments, powerful computational mechanisms, and appropriate software to exploit them. Reinforcement learning, a powerful technique belonging to the field of artificial intelligence, provides the means to efficiently exploit computational resources to find even the least obvious correlations between configuration parameters, stimuli applied to digital design inputs, and their functional states. This paper, in which a novel software system is used to simplify the analysis of simulation outputs and the generation of input stimuli through reinforcement learning methods, provides important details about the setup of the proposed method to automate the verification process. By understanding how to properly configure a reinforcement algorithm to fit the specifics of a digital design, verification engineers can more quickly adopt this automated and efficient stimulus generation method (compared with classical verification) to bring the digital design to a desired functional state. The results obtained are most promising, with even 52 times fewer steps needed to reach a target state using reinforcement learning than when constrained random stimulus generation was used.
A survey of multi-class imbalanced data classification methods

In reality, the data generated in many fields are often imbalanced, such as fraud detection, network intrusion detection and disease diagnosis. The class with fewer instances in the data is called the minority class, and the minority class in some applications contains the significant information. So far, many classification methods and strategies for binary imbalanced data have been proposed, but there are still many problems and challenges in multi-class imbalanced data that need to be solved urgently. The classification methods for multi-class imbalanced data are analyzed and summarized in terms of data preprocessing methods and algorithm-level classification methods, and the performance of the algorithms using the same dataset is compared separately. In the data preprocessing methods, the methods of oversampling, under-sampling, hybrid sampling and feature selection are mainly introduced. Algorithm-level classification methods are comprehensively introduced in four aspects: ensemble learning, neural network, support vector machine and multi-class decomposition technique. At the same time, all data preprocessing methods and algorithm-level classification methods are analyzed in detail in terms of the techniques used, comparison algorithms, pros and cons, respectively. Moreover, the evaluation metrics commonly used for multi-class imbalanced data classification methods are described comprehensively. Finally, the future directions of multi-class imbalanced data classification are given.
Research on Application of Federated Learning of Blockchain Technology System under Computer Networking Technology

This paper proposes an edge computing privacy protection method based on blockchain and federated learning to address the efficiency problem of privacy data sharing on the Internet of Vehicles. This paper uses blockchain to endow edge computing with anti-tampering and single-point-of-failure attacks and integrates gradient verification and incentive mechanisms into the consensus protocol. This encourages more local devices to contribute computing power and data honestly to federated learning. In this paper, the distributed model is safely shared by building a master-slave chain architecture between vehicles, roadside units, and base stations. At the same time, the paper proposes an asynchronous federated learning algorithm based on an incentive mechanism. The research results show that the proposed mechanism can improve the efficiency of data sharing and has sure scalability.
MedCV: An Interactive Visualization System for Patient Cohort Identification from Medical Claim Data

Healthcare providers generate a medical claim after every patient visit. A medical claim consists of a list of medical codes describing the diagnosis and any treatment provided during the visit. Medical claims have been popular in medical research as a data source for retrospective cohort studies. This paper introduces a medical claim visualization system (MedCV) that supports cohort selection from medical claim data. MedCV was developed as part of a design study in collaboration with clinical researchers and statisticians. It helps a researcher to define inclusion rules for cohort selection by revealing relationships between medical codes and visualizing medical claims and patient timelines. Evaluation of our system through a user study indicates that MedCV enables domain experts to define high-quality inclusion rules in a time-efficient manner.