Reviewerly/exHarmony huggingface dataset:
Open Government Data as a Digital Infrastructure for Public Information Services: A Case Study of the City of Denpasar

Over the past few years, there has been a growing interest in leveraging open data for the government domain. Open data is defined as data that is accessible and free for anyone to use. This brings more transparency and encourages public participation in the government. In this research, we conduct a case study to the open data implementation by the city of Denpasar. The implementation is part of the effort to comply with the Freedom of Information Act (UU Keterbukaan Informasi Publik) that has been passed in 2008 by the Indonesian government. The form of the implementation is as a data bank, that is, a web-based platform for data publication and consumption. We report on how such a data bank is initialized and implemented in Denpasar. We also analyze the benefits and challenges of the implementation for both the government and public of Denpasar, and discuss lessons learned as well as replication opportunities by other governments.
Active Tampering Detection for Image Authentication using Hadamard Tracing

Image processing software that is growing rapidly makes it easier for someone to process an image for the better. However, this convenience also has a bad impact, especially if it is used in plagiarism or manipulation activities. Image authentication methods, especially those with active detection are widely developed to overcome these problems. One of the most popular methods is the SVD-based because it can generate important properties from an image. Nonetheless, most SVD-based methods only make use of the singular matrix S and ignore the other two orthogonal matrices. Therefore, this study proposes the use of Hadamard blocks to generate a new S matrix through a simpler operation. The experimental results show that the use of Hadamard blocks has the same detection capabilities and image quality as the previous method. In terms of computational time, the proposed method has a faster computational time with the highest speed improvement of 21% and 15% in the embedding and authentication process respectively. The proposed method can work faster while maintaining the level of imperceptibility and detection.
A Distributed Memetic Algorithm with a semi-greedy operator for the Traveling Salesman Problem

In this work, we propose a distributed Memetic Algorithm for the traveling salesman problem focusing on small and medium-sized instances. The algorithm employs a new crossover operator that favors the fitter of the two parents and develops varying progeny from the same parents to evade premature convergence. In the implementation, we use the High-Level Architecture (HLA) to distribute laborious tasks and lower the lead time of the heuristic. Results show that our proposed approach significantly outperforms other algorithms when tested on instances from the TSPLIB benchmark.
Parameter Tuning of Linear Programming Solvers

Linear programming solvers include various options that can be used to control algorithmic aspects and considerably impact the solver performance. As it is obvious, manually finding optimal parameters is a very difficult task and sometimes impossible. For this reason, it is necessary to implement smart techniques that will automate this process. Other works have utilized derivative-free optimization solvers to tune solver parameters. In this work, eight open-source derivative-free optimization solvers are utilized for finding (near) optimal tuning parameters of state-of-the-art linear programming solvers. We investigate how sensitive linear programming solvers are to a parameter tuning process. Extensive computational results are presented on tuning four linear programming solvers (CLP, CPLEX, GUROBI, and XPRESS) over a set of 70 benchmark problems. We find better parameters for all linear programming solvers, achieving a reduction in execution time over their default parameters up to 26%. We conclude that several derivative-free optimization solvers outperform others on finding optimal optimal tuning parameters for linear programming solvers.
User Experience and Multimodal Usability of Navigation Systems - Evaluation of Effectiveness and Efficiency

Every new generation of navigational systems provides new features and extended functionality, with added and/or upgraded functions that can oftentimes confound the primary information on the system's functionality. To analyze such dif-ferences, this paper focuses on few aspects regarding the functionality and modality. The conducted experiment analyses and observes available versions of navigation systems such as Garmin Drive 50 and/or Tom Tom Go, which are offered with certain advantages on new features. Our study presents the selected aspects regarding the implementation, design, environment, re-cruitment, tests, and evaluation of these navigation systems, with the objective of finding differences between audio and visual mode.
Determining the ABEP under the Influence of K -µ Fading and CCI with SC combining at L-branch Receiver Using Moment Generating Function

In this work, the useful signal suffering disappearance of small scale fading described by k-µ distribution, as well as k-µ co-channel disturbance, will be observed. The moment generating function (MGF)-based calculation of average bit error probability (ABEP) of L-branch selection combining (SC) receiver under the influence of these disturbances will be made. The results will be shown graphically in order to analyze influence of the fading and co-channel interference parameters. Then, a classification-based machine learning approach in order to estimate Quality of Service (QoS) making use of the previously derived ABEP value as one of the inputs is proposed. For implementation, we rely on PyTorch framework for neural networks in synergy with ZenML for machine learning pipeline automation.
Blockchain Redaction in Self-Sovereign Identity

The development of blockchain has allowed for the development of new concepts and ideas. A completely immutable ledger might not be appropriate for all new applications that are being envisaged for the blockchain. One of them is self-sovereign identity. The aim of this paper is to analyze the possible use cases for blockchain redaction in SSI. Main concepts of redaction and a summary of the current research progress are given. Use cases for redaction in SSI are categorized and described alongside their existing solutions. Detailed proposal for possible use cases is given and comparison is drawn between this solution and existing solution. Future challenges are introduced.
ScreenInformer: Whispering Secret Information via an LCD Screen

In this paper, we observe an acoustic covert channel by modulating capacitor squeal on the monitor's power supply unit, and then present ScreenInformer to build a covert communication within a physically isolated network system. Unlike traditional electromagnetic side channels, capacitor squeal is usually not covered by safety shielding measures. To precisely modulate the capacitor squeal, we reveal the relationships among the displayed content on the screen, voltage variation on the power unit, and the frequency of acoustic leakage. In order to improve the demodulating ability for acoustic side-channel leakage with a low signal-to-noise ratio (SNR), we design a cross-correlation demodulation algorithm for the rich harmonics of leakage. An off-the-shelf mobile phone can support ScreenIn-former for exfiltrating sensitive information under ambient noise of up to 55 dB. Our various real-world experimental results show that ScreenInformer can achieve a communication distance of up to 130 cm and a maximum throughput of 170 bps. In addition, our observed capacitor squeal is widespread, which has the potential to enable common electronic devices to have communication capability.
Universal Timestamping with Ambient Sensing

Universal time is critical for coordinating function-alities among co-located as well as geographically distributed IoT devices. Current time alignment approaches for IoT devices rely on radio-based communication that puts an extra burden on the already resource-constrained devices. Other timing approaches depend either on customized hardware frontends or on fixed networking capabilities. Intermittent network connectivity fur-ther deteriorates timing performance for these devices. These constraints motivate us to create a new design that actively embeds time information into the surroundings of IoT devices that can harvest timing signals with off-the-shelf sensing capabil-ities. Our design decouples clock performance from the network uncertainties, introduce resource efficiency and extensibility, and requires no modification in existing devices. A unique property of our design is that it leverages the ubiquitous Electric Network Frequency (ENF) fluctuations as global time reference for the sensing devices, and takes on a variety of challenges at the in-tersection of sensing and signal processing to provide a universal sense of time without custom hardware frontends and network dependability. We evaluate the extensibility of our design in remote setups and show its robustness in real world settings.
MagDefender: Detecting Eavesdropping on Mobile Devices using the Built-in Magnetometer

This study reveals that on-board hardware modules leak electromagnetic (EM) emissions whenever audio or camera data is accessed, and proposes Magdefender scheme that explores the possibility of using the magnetometer built into mobile devices to detect eavesdropping instances by malicious apps and even the unscrupulous phone vendors. However, the target EM signals generated by accessing multimedia data is weak and tends to be buried beneath other noisy EM signals from apps running in the foreground. It is also subject to the external interference from geomagnetic signals generated by the device movement. To cope with the challenges, we adopt a generative adversarial networks (GAN) based model to facilitate the extraction of target EM signals indicating the occurrence of eavesdropping from the overall magnetometer readings. We also develop a neural network-based classifier with triplet loss embedding to identify the EM signals from the camera and/or microphones. Empirical results demonstrate the efficacy of MagDefenderin recognizing instances of eavesdropping on cameras/microphones data, with average accuracy of 97.3% when applied to the trained devices, and average 91.5% on unseen mobile devices.
Fine-Grained Electromagnetic Side-Channel Analysis Resilient Secure AES Core with Stacked Voltage Domains and Spatio-temporally Randomized Circuit Blocks

In this work, we demonstrate a novel unique design approach specifically targeting improved resilience against the fine-grained electromagnetic (EM) side-channel analysis (SCA) attacks. Fine-grained EM SCA captures high SNR EM signature with tiny probes (1mm diameter) compared to coarse-grained EM (∼10mm diameter), making it more potent and leading to a higher threat. The EM-SCA critical circuit blocks are voltage-stacked to share a current loop, and a push-pull voltage regulator (VR) balances the mismatch current between the two stacked blocks. Dataflow and current loops are spatially and temporally randomized to obscure the EM side-channel signatures. Measurement results from a 128-bit Parallel Advanced Encryption Standard (AES) core fabricated in 65nm CMOS shows MTD improvement of 1507X for fine-grained EM SCA. Coarse-grained EM and Power SCA MTDs also show an improvement of 122X and 657X respectively, which may be improved further by combining prior reported SCA resilient techniques.
Fast Graph Algorithms for Superpixel Segmentation

We introduce the novel graph-based algorithm SLAM (simultaneous local assortative mixing) for fast and high-quality superpixel segmentation of any large color image. Super-pixels are compact semantic image elements; superpixel segmen-tation is fundamental to a broad range of vision tasks in existing and emerging applications, especially, to safety-critical and time-critical applications. SLAM leverages a graph representation of the image, which encodes the pixel features and similarities, for its rich potential in implicit feature transformation and extra means for feature differentiation and association at multiple resolution scales. We demonstrate, with our experimental results on 500 benchmark images, that SLAM outperforms the state-of-art algorithms in superpixel quality, by multiple measures, within the same time frame. The contributions are at least two-fold: SLAM breaks down the long-standing speed barriers in graph-based algorithms for superpixel segmentation; it lifts the fundamental limitations in the feature-point-based algorithms.
Evaluating the Performance of the Eclipse OpenJ9 JVM JIT Compiler on AArch64

The embedded computing market, which includes Internet-of-Things (IoT) and mobile computing devices, is a non-traditional computing market where computation resources are limited. Therefore, software, particularly the managed runtime, is required to be more compact and efficient than in a cloud/desktop-based environment. This paper focuses on porting the Eclipse OpenJ9 runtime, a Java Virtual Machine (JVM), built on top of Eclipse OMR, to a new environment while continuing to provide a generic runtime environment. The low-power AArch64 (ARMv8-A) platform is becoming the answer for resource constrained environments of embedded systems. We evaluate and validate the AArch64 implementation of OpenJ9’s Just-in-Time (JIT) compiler against more mature architectures currently available, namely x86-64. The evaluation reveals performance discrepancies and necessary improvements, beyond those that are already known. Our work is an effort to template new architectural support and allow others to follow our model. We provide a baseline for future research on OpenJ9, OMR and the JIT on the AArch64 platform and outline some improvements as future work.
DASH: Scheduling Deep Learning Workloads on Multi-Generational GPU-Accelerated Clusters

Two notable characteristics of modern GPU-accelerated HPC clusters are: (1) they increasingly run deep learning (DL) model-training workloads, and (2) they consist of multiple generations of GPUs, i.e., they are heterogeneous. However, existing works in GPU cluster scheduling for DL workloads have not addressed the GPU multi-generation problem. We propose DASH, a GPU cluster scheduler designed to optimally make a match between different DL workloads and GPU types in a multi-generational GPU environment. By lever-aging execution characteristics of co-scheduled DL workloads, DASH can improve the average job runtime by 17% and the average job completion time by 14 % compared to the traditional heterogeneity-unaware job scheduler.
Real-Time Software Architecture for EM-Based Radar Signal Processing and Tracking

While a radar tracks the kinematic state (position, velocity, and acceleration) of the target, an optimal signal processing requires knowledge of the target's range rate and radial acceleration that are derived from the tracking function in real time. High precision tracks are achieved through precise range and angle measurements whose precision are determined by the signal-to-noise ratio (SNR) of the received signal. The SNR is maximized by minimizing the matched filter loss due to uncertainties in the radial velocity and acceleration of the target. In this paper, the Expectation-Maximization (EM) algorithm is proposed as an iterative signal processing scheme for maximizing the SNR by executing enhanced range walk compensation i.e., correction for errors in the radial velocity and acceleration) in the real-time control loop software architecture. Maintaining a stringent timeline and adhering to latency requirements are essential for real-time sensor signal processing. This research aims to examine existing methods and explore new approaches and technologies to mitigate the harmful effects of range walk in tracking radar systems with an EM-Based iterative algorithm and implement the new control loop steering methods in a real-time computing environment.
DESITA: E-Service Optimization of Student Final Project Management Based on Website Technology

This research aims to implement a new model of e-business technology to optimize the three higher education principles. A strategic step that can be taken is to apply digitalization to increase the selling value of universities in the education industry and increase the usefulness of the satisfaction of lecturers and students. The research approach is exploratory qualitative with data collection techniques through interviews, observations, and literature studies. The sampling method in this study used non-probability sampling (purposive sampling). It can conclude that several supporting factors that influence optimization of the three principles of higher education are flexibility of workplace and time, as well as the use of information technology which is easier to use and has high accessibility. The urgency of this research is to support university programs in implementing smart university so that the three higher education principles become more comprehensive. The stuff that needs to be prepared is integration in implementing the program, ideally to be replicated more easily by the dean in each faculty.
Infrared Image Generation Algorithm Based on GAN and contrastive learning

For the task of converting dimly lit, low luminance nighttime visible to infrared images, we propose a Contrastive Visible-Infrared Image Translation Network (CVIIT). To better distinguish and translate objects such as pedestrians and vehicles, we introduce an attention module based on class activation map in the generator and discriminator of the Generative Adversarial Network (GAN), which captures richer context information in the images. In addition, we introduce contrastive learning to align the generated images with the visible images in terms of content. Qualitative and quantitative experiments on a publicly available visible-Infrared image pairing dataset (LLVIP) show that the proposed method generates infrared images of significantly higher quality than other state-of-the-art image-to-image translation (I2IT) methods.
Gradient-guided GAN for dynamic scene deblurring

Dynamic scene blur, mainly caused by camera shake and motions, is one of the most common causes of image degradation. Recent GAN-based strategies have performance on deblurring tasks. To further improve the performance of GAN-based approaches on deblurring tasks, we propose Gradient-guided GAN for dynamic scene deblurring, it includes image restoration branch and gradient branch, which uses the gradient as a guide to supervise the restoration process. In particular, perform an attention fusion of feature image generated by restoration branch and gradient feature image generated by gradient branch, which using gradient information to guide the network to fully learn the deep feature information. Extensive experiments on GOPRO dataset show that our method achieve state-of-the-art performance in dynamic scene deblurring.
XGBoost: Interpretable Machine Learning Approach in Medicine

This research tries to establish a communication bridge between the decision making of a practicing physician and a machine learning model. Machine learning has long been used to assist in diagnosing patients through deep learning and medical image analysis. However, in medical science, it is possible to have multiple right answers when treating patients. This leaves the practitioners room to exercise their judgement on what would be best for the patient. The machine learning model can be a communication tool to assist the practitioners in reach the final decision. In this paper, the machine learning model used for this experiment is XGBoost, a gradient boosted decision tree. Through the experiment with the real patient data, we show how decision tree constructions are easily readable for medical determination.
Design of Full Coverage Path Algorithm for exploration truck

The survey work for urban underground pipelines is mostly carried out outdoors, and the randomness of its environment brings a lot of difficulties to the full-coverage path planning work of intelligent exploration vehicles. To adapt to the complex outdoor environment and improve the performance and efficiency of the crossing, this article constructs the exploration vehicle model and improves the traditional DFS algorithm. Changing its backtracking strategy based on the original algorithm and introducing a node cost function to linearly constrain the number of turns of the exploration vehicle. Through simulation experiments, the improved DFS algorithm can efficiently complete the full-coverage exploration task for pipeline exploration sites and compared with some classical algorithms, the improved algorithm has a significant reduction in the map traversal repetition rate, and also shows obvious advantages in solving the full-coverage path planning problem in complex environments, which can complete the related work more efficiently.
SSQEM: Semi-Supervised Quantum Error Mitigation

One of the fundamental obstacles for quantum computation (especially in noisy intermediate-scale quantum (NISQ) era) to be a near-term reality is the manufacturing gate/measurement technologies that make the system state quite fragile due to decoherence. As the world we live in is quite far away from the ideal, complex particle-level material imperfections due to interactions with the environment are an inevitable part of the computation process. Hence keeping the accurate state of the particles involved in the computation becomes almost impossible. In this study, we posit that any physical quantum computer sys-tem manifests more multiple error source processes as the number of qubits as well as depth of the circuit increase. Accordingly, we propose a semi-supervised quantum error mitigation technique consisting of two separate stages each based on an unsupervised and a supervised machine learning model, respectively. The proposed scheme initially learns the error types/processes and then compensates the error due to data processing and the projective measurement all in the computational basis.
Change Detection in Moving-Camera Videos Using a Shift-Invariant Dissimilarity Metric

This work addresses the problem of identifying anomalous objects in a cluttered environment using footage captured with moving cameras. The proposed method consists in the application of an edge-like feature extractor on the input images and in the learning of the space of non-anomalous features through deep autoencoder networks. Interestingly, this process is equivalent to employing a shift-invariant dissimilarity metric as an optimization target to the autoencoders. By the nature of the change detection task, it is necessary to collect and annotate large, diverse sets of anomalous conditions in order to train deep models in a supervised manner. The models trained in this proposal rely solely on anomalous-free data for parameter training, facilitating its application in a real-world scenario. The developed method was trained on the VDAO dataset, a challenging dataset with recordings of varied illumination situations. Unlike previous works using the same set, the autoencoder does not require the computationally expensive step of matching and registering a known reference frame to the tested frame that potentially contains an anomaly. Requiring not much more than a single network inference step, this proposal allows for real-time execution, even in systems with modest computational power. When tested against the VDAO200 dataset, consisting of 56 short excerpts of recordings of the VDAO scenes, this proposal matches the evaluation figures of the state-of-the-art algorithms, with measured DIS (distance to upper-left ROC corner) of 0.29 and an average precision score of 0.91.