Reviewerly/exHarmony huggingface dataset:
Antique Photo Restoration and Colorization via Generative Model

In the past, many photographs of famous historical figures and moments were captured in back and white photos. Those captures are often distorted by the limitation of the old-style camera and the negative influence of the poor storing environment. It is obvious that the restoration and colorization of those images can make history lively. Since manually retouching images is time-consuming and hard to be done by people without aesthetic senses, many researchers have proposed models that automatically remove the artifacts in the old photos. However, these methods only solve either image restoration or colorization tasks which cannot fully address the task of image retouching. Consequently, in this work, we propose an effective end-to-end framework, named AIRC, for image retouching. Besides, previous works often use synthesized old photos for training but these pseudo datasets can not replicate exactly the real antique photo and prevent the trained model from being used in reality. To this end, we also introduce a new antique synthetic dataset, namely OldifiedScenes, that resembles real old photos by blending with paper and artifact textures. Quantitative and qualitative results are provided to demonstrate the effectiveness of our proposed method.
IndoKEPLER, IndoWiki, and IndoLAMA: A Knowledge-enhanced Language Model, Dataset, and Benchmark for the Indonesian Language

Pretrained language models posses an ability to learn the structural representation of a natural language by processing unstructured textual data. However, the current language model design lacks the ability to learn factual knowledge from knowledge graphs. Several attempts have been made to address this issue, such as the development of KEPLER. KEPLER combines the BERT language model and TransE knowledge embedding method to achieve a language model that can incorporate knowledge graphs as training data. Unfortunately, such knowledge enhanced language model is not yet available for the Indonesian language. In this experiment, we propose IndoKEPLER: a language model trained usingWikipedia Bahasa Indonesia andWikidata. We also create a new knowledge probing benchmark named IndoLAMA to test the ability of a language model to recall factual knowledge. The benchmark is based on LAMA, which is designed to test the suitability of our language model to be used as a knowledge base. IndoLAMA tests a language model by giving cloze style question and compare the prediction of the model to the factually correct answer. This experiment shows that IndoKEPLER increases the ability of a normal DistilBERT model to recall factual knowledge by 0.8%. Moreover, the most significant increase happens when dealing with many-to-one relationships, where IndoKEPLER outperforms it’s original text encoder model by 3%.
An efficient image compression hardware design architecture

With the explosion of industry 4.0 technologies, digital image compression technology has gradually played an important role in the fast communication and live processing of images over the internet. As a result, the demand for storing and transferring images is increasing day by day. Image compression is an application that compresses data on digital images, the main goal is to decrease the redundancy of image data so that it can be stored or transmitted in an efficient form. This paper proposes a hardware efficient architecture of the Microshift - an image compression algorithm. In this design, the image is first micro-shifted, then the sub-quantized values are further compressed. In decompression, we use the MRF model to recover the bit-depth by exploiting the spatial correlation of natural images. Experimental results on a field-programmable gate-array (FPGA) platform illustrate that the proposed architecture enables operation around 172 MHz and the peak signal to noise ratio (PSNR) of 31.51 dB with 1.5 bits per pixel(BPP) and structural similarity index measurement(SSIM) achieve 0.9. This design is synthesized on the Altera Cyclone V- 5CSEMA5F31C8.
A Framework for Evaluating Video Summary Approaches

Video summarization is a crucial task to solve the explosion of video data. The goal of video summarizing is to create a shortened version of the original video while retaining its essential and pertinent content. In general, a video summary system is composed of three primary modules: shot boundary detection, shot scoring, and shot selection. However, existing research focuses exclusively on a single module, necessitating a comprehensive assessment when methods are changed across modules. In this study, we provide a framework for evaluating alternative techniques for the video summary problem that permits multiple combinations in different modules to evaluate the significance of adding method stages in video summaries. The analysis and combination results of the framework reveal that the combination of Uniform and DSNet Anchor-free provides state-of-the-art performance on the SumMe dataset. We also provide the framework source code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for the community.
Eager Memory Cryptography in Caches

To protect memory values from adversaries with physical access to data centers, secure memory systems ensure memory confidentiality and integrity via memory encryption and verification. The corresponding cryptography calculations require a memory block’s write counter as input. As such, CPUs today cache counters in the memory controller (MC). Due to the large memory footprint and irregular access patterns of many real-world applications, MC’s counter cache is too small to achieve high hit rate. A promising solution is also caching counters in the much bigger Last Level cache (LLC). As such, many prior works use LLC as a second level cache for counters to back up the smaller counter cache in MC. Caching counters in LLC introduces a new problem, however. Modern server CPUs have a long LLC access latency that not only can diminish the benefit of caching counters in LLC, but also can sometimes significantly increase counter access latency compared to not caching counters in LLC. We note the problem lies with MC sitting behind LLC; due to its physical location, MC can only see LLC misses and, therefore, can only serially access and use counters after data miss in LLC has completed. However, prior designs without caching counters in LLC can access and use counters in parallel with accessing data. If a block’s counter misses in MC’s counter cache, MC can fetch the counter from DRAM in parallel with data; if the counter hits in MC’s counter cache, MC can use counters for cryptography calculation in parallel with data traveling from DRAM to MC. To parallelize the access and use of counters with data access while caching counters in LLC, we observe that in modern CPUs, L2 is typically the first place that caches data from DRAM (i.e., L2 and L3 are non-inclusive); as such, data from DRAM need not be decrypted and verified until they reach L2. So it is possible to offload some decryption and verification tasks from MC to L2. Since L2 sits before L3, L2 can access counter and data in parallel from L3; L2 can also use counters for cryptography calculation in parallel with data traveling from DRAM to L2, instead of just from DRAM to MC. As such, we propose caching and using counters directly in L2 and refer to this idea as Eager Memory Cryptography in Caches (EMCC). Our evaluation shows that when applied to the state-of-the-art baseline, EMCC improves performance of large and/or irregular workloads by 7%, on average.
Modification of YOLO with GhostNet to Reduce Parameters and Computing Resources for Detecting Acute Lymphoblastic Leukemia

The Detection of acute lymphoblastic leukemia (ALL) subtypes on multicellular microscopic images is significant for early diagnosis to support the treatment process. Recently, the object detection with a deep learning approach shows good accuracy and fast computation time in the medical field. Therefore, we propose using the You Only Look Once (YOLO) method, namely the Yolov4 and Yolov5 models, to detect the L1, L2, and L3 subtypes. However, both models still have high GFLOPS values and high number of parameters. This paper proposes a modification of Yolov4 and Yolov5 by replacing the standard backbone convolution module with the GhostNet convolution module. The GhostNet module can reduce the GFLOPS value and the number of parameters. Overall., the Yolo backbone modification model has comparable results with the original Yolo model with a slight difference with a value of 1.4 % in the Yolov4 backbone modification model and 2.4 % in the Yolov5 backbone modification model. The number of parameters and GFLOPS values of the two models modified was reduced by 35% and 40%, respectively.
Maturity Evaluation and Improvement Recommendation of Information Technology Governance with the Control Objective of Information and Related Technology 2019 Framework: A Case Study of Tax Court Secretariat

IT Governance is important for organizations that use information technology to support their business processes. Within the Ministry of Finance, IT governance is regulated by Minister of Finance Regulation Number 97/PMK.01/ 2017. Currently, there are several obstacles faced by the Secretariat of the Tax Court with regard to IT governance. This research was conducted to determine the maturity level of IT governance and provide recommendations for improvement aimed at improving the implementation of IT governance at the current Tax Court Secretariat. The research method uses mixed method with 4 stages, namely coding, data entry, data display and decision making. Primary data were obtained through interviews and focus group discussions of ten resource persons consisting of the Head of the ICT Operations and Maintenance Subdivision, functional officials, and IT staff. Maturity level measurement show that there are 5 processes managed configuration, managed problem, managed IT change, managed asset, and managed knowledge at the capability level 1 (performed) and 5 processes managed risk, managed service request, managed continuity, managed security, and managed requirement definition at the capability level 2 (managed). The benefit of this research is to provide recommendations to Secretariat of the Tax Court for improving IT governance based on the best activities provided by COBIT 2019
Work-in-Progress: SuperNAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation

We present SuperNAS, a fast multi-objective neural architecture search framework for semantic segmentation. SuperNAS subsamples the structure and pre-trained parameters of DeepLabV3+, without fine-tuning, dramatically reducing training time during search. To further reduce candidate evaluation time, we use a subset of the validation dataset during search. Only the final, Pareto-dominant, candidates are ultimately fine-tuned using the complete training set. We evaluate SuperNAS by searching for models that effectively trade accuracy and computational cost on the PASCAL VOC 2012 dataset. SuperNAS finds competitive designs quickly, e.g., taking just 0.5 GPU days to discover a DeepLabV3+ variant that reduces FLOPs and parameters by 10% and 20% respectively, for less than 3% increased error.
Stabilizing Deep Reinforcement Learning Model Training for Video Conferencing

While many studies have been conducted to apply reinforcement learning (RL) to real world problems beyond games such as Atari, video conferencing is also one of real world applications. In video conferencing, reinforcement learning is used to control the bitrate to improve the user's quality of experience (QoE). However, real world problems such as video conferencing have different characteristics compared to electronic games. Usually the rewards in real world problems are not clear or abstract, and this makes it difficult to design the RL model and training process of the model to maximize the cumulative reward. Therefore, in this paper, we present the method for stabilizing the training of the models that apply reinforcement learning to video conferencing. In addition, we established a simulation environment that can train deep RL models in 1-to-1 video conferencing. An evaluation is performed to analyze the difference between the baseline model and the models generated using the stabilization method in the simulation environment.
Temporal Forecasting System of Potential Catching Areas of Skipjack Tuna in Bone Sea Using Artificial Neural Network

Indonesia's potential in the field of capture fisheries is very supportive of the economy, one of which is large pelagic fish such as skipjack tuna. The distribution of skipjack tuna in the waters of Bone Regency, South Sulawesi, which tends to be dynamic, is very important to determine its distribution. Therefore, the purpose of this study is to forecast the potential point of distribution of skipjack tuna temporally by utilizing data mining and Artificial Neural Network algorithms. Feature extraction was used in the form of oceanographic data that affects the habitat of skipjack tuna in waters such as Sea Surface Temperature (SST) and chlorophyll-a for the last five years (2017-2021) with a total of 34,800 training data and 8,700 test data. The performance of the system was evaluated using the confusion matrix. The results showed an accuracy value of 94.89% and an F1 score of 92.09%. The application of this forecasting system is very useful for determining the skipjack fishing calendar temporally to increase the effectiveness and efficiency of costs, time, and effort.
Spatial Analysis of the Spread of Tuberculosis Cases Based on Socio-Economic Factors Using Distance-Based Algorithm

Tuberculosis is a contagious infectious disease that is one of the ten most deadly diseases in the world. Indonesia itself is the third highest country in the case of the spread of tuberculosis. Therefore, comprehensive supervision is needed by grouping tuberculosis cases by region. The aim is to obtain the results of the spatial analysis of tuberculosis mapping based on age, gender, and socio-economic factors by utilizing data mining and the K-Means clustering algorithm. Then, the spatial distribution pattern is visualized using a geographic information system (GIS). The tuberculosis case data used is secondary data obtained from the Makassar Community Lung Health Center (BBKPM) for the last three years (2018 - 2020). From the results of the analysis, it was found that the highest tuberculosis cases were dominated by elderly men. Meanwhile, the highest spread of tuberculosis was in the Panakukang sub-district which was dominated by adult males. The results of this study can be used to assist in making decisions about tuberculosis case-control planning in Makassar City.
LSTM and Evans model for next 24 hours solar photovoltaic power output forecasting

This paper presents the results of forecasting the power output of a photovoltaic array for the next 24 hours from irradiation and temperature data by combining a Long Short-Term Memory (LSTM) neural network and the Evans model. The LSTM network allowed the forecast of irradiation and temperature as time series. Then, thanks to the forecasted values, the Evans model was used to estimate the photovoltaic power for each pair of predicted values from the panel parameters. The data used were obtained from the NASA databank for a period from 1st January 2010 to 31st December 2021 for Benin City Abomey-Calavi. The forecasted data were then validated experimentally by comparing them to the measured values in the field. For the experimental validation, for irradiation, temperature and PV power respectively Mean Square Error (MSE) values of 4.9 $W/m^{2}$, $1.2^{\circ}C$, 1.76W were obtained and values of 0.98, 0.96, 0.97 were obtained for the regression coefficient (R2).
Augmented Reality and Internet of Things Trends in Education

Augmented Reality (AR) Technologies are gaining considerable attention due to their increased stability and reliability. Combined with the availability of smarter and cheaper devices, like smart-phones and tablets, AR technology greatly helps in enhancing the amount, relevance and ease of access of the available information. At the same pace, Internet of Things (IoT) technologies due to their pervasiveness, are paving the way towards a smarter interconnected world. Several industries, like entertainment, medicine and engineering, have developed and adapted IoT and AR technologies where the enrichment of the physical reality by the information and capabilities offered by AR proved useful for their business. The same approach should and could be the case in the education field. Nevertheless, the usage of IoT and AR technologies is still in its infancy, not following yet the path of many other industry fields. In this paper, after the presentation of contemporary AR and IoT technologies a proposal for utilizing these technologies to teach concepts that are required by the curriculum of the “Informatics Applications” course for the first class of senior high schools in Greece is presented.
HTC: Hybrid vertex-parallel and edge-parallel Triangle Counting

Graph algorithms (e.g., triangle counting) are widely used to find the deep association of data in various real-world applications such as friend recommendation and junk mail detection. However, even if using the massive parallelism of GPU, existing methods fail to run triangle counting queries efficiently on various large graphs. In this paper, we propose a fast hybrid algorithm HTC, which can utilize both vertex-parallel and edge-parallel paradigm and deliver much better performance on GPU. Different from current GPU implementations, HTC adaptively selects different parallel paradigm for different vertices. Also, bitwise-based intersection on segmented bitmap is proposed instead of naive binary search. Furthermore, preprocessing techniques like graph reordering and recursive clipping are adopted to optimize the graph structure. Extensive experiments show that HTC outperforms all state-of-the-art triangle counting implementations on GPU by 1.2x~42x.
Application of Liquid Rank Reputation System for Content Recommendation

An effective content recommendation on social media platforms should be able to benefit both creators to earn fair compensation and consumers to enjoy really relevant, interesting, and personalized content. In this paper, we propose a model to implement the liquid democracy principle for the content recommendation system. It uses a personalized content recommendation model based on reputation ranking system to encourage personal interests driven recommendation. Moreover, the personalization factors to an end users’ higher-order friends on the social network (initial input Twitter channels in our case study) to improve the accuracy and diversity of recommendation results. This paper analyzes the dataset based on cryptocurrency news on Twitter to find the opinion leader using the liquid rank reputation system. This paper deals with the tier-2 implementation of a liquid rank in a content recommendation model. This model can be also used as an additional layer in the other recommendation systems. The paper proposes the implementation, challenges, and future scope of the liquid rank reputation model.
Sparse Deep Neural Network Inference Using Different Programming Models

Sparse deep neural networks have gained increasing attention recently in achieving speedups on inference with reduced memory footprints. Real-world applications often have to deal with sparse data and irregularities in the computations, yet a wide variety of Deep Neural Network (DNN) tasks remain dense without exploiting the advantages of sparsity in networks. Recent works presented in MIT/IEEE/Amazon GraphChallenge have demonstrated significant speedups and various techniques. Still, we find that there is limited investigation of the impact of various Python and C/C++ based programming models to explore new opportunities for the general cases. In this work, we provide extensive quantitative evaluations of various contemporary GPGPU programming models such as Cupy, Cuda CUSPARSE, and Openmp in the context of Sparse Deep Neural Network (SPDNN) implementations (derived from the Graph Challenge reference serial code) on single and multiple GPUs from NVIDIA DGX-A100 40GB/80GB platforms.
Explicit Ordering Refinement for Accelerating Irregular Graph Analysis

Vertex reordering for efficient memory access in extreme-scale graph-based data analysis shows considerable improvement to the cache efficiency and runtimes of widely used graph analysis algorithms. Despite this, modern efficient ordering methods are often heuristic-based and do not directly optimize some given metrics. Thus, this paper conducts an experimental study into explicit metric-based vertex ordering optimization. We introduce a universal graph partitioning-inspired approach focusing on CPU shared-memory parallelism to the vertex ordering problem through the explicit refinement of low-degree vertices using the Linear Gap Arrangement and Log Gap Arrangement problems as comprehensive metrics for ordering improvement. This degree-based refinement method is evaluated upon a number of initial orderings with timing and cache efficiency results relative to three shared-memory graph analytic algorithms: PageRank, Louvain and the Multistep algorithm. Applying refinement, we observe runtime improvements of up to 15x on the ClueWeb09 graph and up to 4x improvements to cache efficiency on a variety of network types and initial orderings, demonstrating the feasibility of an optimization approach to the vertex ordering problem at a large scale.
Online Detection and Classification of State Transitions of Multivariate Shock and Vibration Data

The US Department of Energy's (DOE) Los Alamos National Laboratory (LANL) is interested in automatic anomaly detection and classification applied to highly instrumented flight shock and vibration data for the purpose of providing insight into operational safety. For example, the safe and secure transport of materials and devices during a variety of conditions is particularly of interest. In this work, we apply well-known Machine Learning (ML) techniques to a publicly available motor vibration data set that serves as a proxy to the actual LANL data. We successfully train a random forest to classify anomalous motor states using the signal data set, and use this model to simulate real-time anomaly detection and event classification on multi-variate time series data [1], [2]. Furthermore, we perform an extensive suite of computational studies on a large cluster computer to determine optimal parametric settings for our framework and evaluate the cost-benefit of these parameters.
Enhanced dialog for Human-Autonomy Teaming - A breakthrough approach

Considering the ever-growing, deeper interaction between humans and intelligent systems, it is clear that efforts need to be made to ensure a less demanding cognitive workload for the operator. The key to this is bringing the focus onto the Human relationship with machines, ensuring that this interaction is as effective as possible. Any difficulty in understanding at this point can have significant repercussions. Thus, our approach named COMBI, is an interpreter able to enhance communication between humans and Embedded Decision Aid Systems, which can be incredibly complex, to make it mutually intelligible for both parties. This means: (i)Ensuring machines receive in a suitable "language" the operational intention of the Human user; (ii)Clarifying the proposals from the system in a way that can be easily used by humans. In creating a shared and mutually understandable cognitive representation, COMBI contributes to bridging that ontological gap between the high-level abstraction employed by humans and low-level abstraction employed by machines.