Reviewerly/exHarmony huggingface dataset:
MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems

Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly. As a promising solution achieving high scalability and low manufacturing cost, multi-accelerator systems widely exist in data centers, cloud platforms, and SoCs. Thus, a challenging problem arises in multi-accelerator systems: selecting a proper combination of accelerators from available designs and searching for efficient DNN mapping strategies. To this end, we propose MARS, a novel mapping framework that can perform computation-aware accelerator selection, and apply communication-aware sharding strategies to maximize parallelism. Experimental results show that MARS can achieve 32.2% latency reduction on average for typical DNN workloads compared to the baseline, and 59.4% latency reduction on heterogeneous models compared to the corresponding state-of-the-art method.
Fast Adversarial Training with Dynamic Batch-level Attack Control

Despite the fact that adversarial training provides an effective protection against adversarial attacks, it suffers from a huge computational overhead. To mitigate the overhead, we propose DBAC, a fast adversarial training with dynamic batch-level attack control. Based on a prior study where attack strength should gradually grow throughout the training, we control the number of samples attacked per batch for better throughput. Additionally, we collect samples from multiple batches to form a pseudo-batch and attack them simultaneously for higher GPU utilization. We implement DBAC using PyTorch to show its superior throughput with similar robust accuracy compared to the prior art.
Development of Software Cost Estimation and Resource Allocation Using Natural Language Processing, Cosine Similarity and Function Point

Cost estimation is the first step of software development that calculate costs and resources required. The budgeting process involves project analysis and factors such as absence of price calculations used as a basic reference. Major rely on prior works, and allocating experts needs a proper calculation basis for assigning experts to job, which impacts completion time and financial losses due to miscalculations. This research uses combination of methods such as text summarization word2vec for sentence analysis and weighting, catalog extraction to identify all SRS files detected as system features, including features had ambiguity, and cosine similarity to determine closeness of weighted values between sentences tested and function point method as counter to processing results of values generated from cosine similarity to produce new model in calculation and confirm that SRS is feasible to be applied as a calculation variable based on its functionality details. The results of this research apply new modeling techniques to produce basic price reference system, determining number of experts in software project budgeting that is accurate and efficient. Thus, it can be a tool to calculate software project budgeting in the future so that budgeting is too low or high and determine right number of experts.
Robust Audio Anti-Spoofing System Based on Low-Frequency Sub-Band Information

The current audio anti-spoofing systems usually have a computationally complex architecture without providing the fundamental discriminative factors for the detection judgments. The state-of-the-arts also highly depend on voice information to develop detector systems, which may become vulnerable when the spoofing algorithms have further improved the quality of fake speech. Therefore, we conduct a series of experiments on different frequency sub-bands to investigate the underlying discriminative features. We find the lowest frequency sub-band in the range from 0 to 1600Hz contains the most critical features that distinguish between Deepfake and real speech. We also focus on forensic evidence and identify that the basis of detectors’ judgment exists in non-speech parts in audio samples. Based on the findings, our single detection system, with only 57K parameters and utilizing a one-tenth segment of the entire spectrogram as input, demonstrates its robustness by outperforming all official baselines of the ASVspoof2021 DF track. Our lightweight system can be easily applied in practical use cases, such as automated Deepfake screening or protecting voice-able devices.
A Novel Class-Specific Deep Convolutional Feature Selection Method for Image Representation

Image representation is a critical problem in computer vision, which can improve the result of image classification, object detection, visual tracking, and so on. The image feature can be divided into two categories, the handcrafted feature and the deep convolutional feature (DCF). Compared with the handcrafted feature, the DCF based on deep convolutional neural networks is more effective and efficient. Therefore, more and more researchers utilized DCF in various computer vision tasks. However, most existing works directly use or simply reduce the dimension of DCF, which may induce performance degradation in practical applications due to its high degree of redundancy. To tackle this problem, we propose a novel DCF selection method named class-specific deep convolutional feature selection (CS-DCFS) for effective image representation. As various classes are distinguished from others based on different feature subsets, the CS-DCFS selects a specific feature subset for each class from the DCF candidate set. A modified genetic algorithm that uses one of the two mutation operations is proposed to select the feature, two designed mutation operations can increase the generating speed of the population. Furthermore, class-separability is adopted to measure the selected feature subset, which is a powerful and effective measurement of feature discrimination. Experimental results have shown the effectiveness and efficiency of the proposed method on the image classification task in four challenging benchmark datasets.
A Survey on Blockchain Security and Its Impact Analysis

The blockchain is an emerging and potential technology with consideration of its security control mechanism, immutability, transparency, traceability, and reconciliation process which already has created hype in the different sectors like healthcare, education, supply chain, and especially more focused on cross-border payment in the financial sector. However, most of the blockchain applications which presently exist in the market, are still facing security issues like scalability, network security, data privacy, private and public key management system, and smart contract vulnerabilities. To measure these security issues, a detailed survey was conducted among the blockchain application users and developers with 37 questionaries which are highlighted in section III, Table III. Based on the survey data, this study also found that 15.85% of users were happy with the existing security system, 9.15% of users gave their opinion with no comments, and the remaining 72% of users highly recommended the enhancement of the blockchain security system. There is also a details impact analysis that has been done on financial, production, brand image, intellectual property rights, and regulatory penalties which is severe. However, in this paper, a new concept about the blockchain security survey and impact analysis has been used which will help to build a secured blockchain application system as well as it will help the researchers to continue and enhance the blockchain security system.
DBPS: Dynamic Block Size and Precision Scaling for Efficient DNN Training Supported by RISC-V ISA Extensions

Over the past decade, it has been found that deep neural networks (DNNs) perform better on visual perception and language understanding tasks as their size increases. However, this comes at the cost of high energy consumption and large memory requirement to train such large models. As the training DNNs necessitates a wide dynamic range in representing tensors, floating point formats are normally used. In this work, we utilize a block floating point (BFP) format that significantly reduces the size of tensors and the power consumption of arithmetic units. Unfortunately, prior work on BFP-based DNN training empirically selects the block size and the precision that maintain the training accuracy. To make the BFP-based training more feasible, we propose dynamic block size and precision scaling (DBPS) for highly efficient DNN training. We also present a hardware accelerator, called DBPS core, which supports the DBPS control by configuring arithmetic units with custom instructions extended in a RISC-V processor. As a result, the training time and energy consumption reduce by 67.1% and 72.0%, respectively, without hurting the training accuracy.
Sustainable Business Using Code-switching Strategy in HMNS Digital Platform

Nowadays, a number of businesses uses digital platforms to promote their products and to maintain their relationships with customers. Language can be used as one of powerful tools. This study investigates how HMNS Perfumery promote their products in one of their digital platforms, Instagram, using code-switching. The purpose of this study is to identify HMNS’ use of code-switching and to find out the customers’ opinion regarding the use of code-switching in HMNS business account Instagram. The data are gathered from HMNS Instagram contents and through a questionnaire distributed to HMNS customers. The results show that in promoting HMNS products in their Instagram account, code-switching is used quite frequently - 32 in total from 15 data. In addition, it is indicated that the customers have positive views toward HMNS’ use of code-switching in their Instagram. In other words, they support HMNS using code-switching in promoting the products through the brand’s digital platform. Thus, it can be said that code-switching can be used as one sustainable business strategy to develop positive interaction between the brand and the customers.
VulDet: Smart Contract Vulnerability Detection Based on Graph Attention Networks

Due to the rapid development of blockchain, security issues caused by smart contract vulnerabilities are receiving increasingly widespread attention. Unfortunately, traditional smart contract vulnerability detection methods rely heavily on expert knowledge and elaborate rules, while neural network-based vulnerability detection methods have not yet achieved satisfactory accuracy either. In this paper, we propose a novel vulnerability detection method for smart contracts called VULDET. We first construct a contract graph based on the structure of the smart contract source code and combine security domain knowledge to attach additional features to nodes in the graph that are closely associated with vulnerabilities to highlight key nodes, and finally use graph attention networks for contract vulnerability detection. We apply VULDET to reentrancy vulnerability as well as timestamp dependency vulnerability detection and conduct extensive experiments, and the results show that our approach has significant advantages over existing methods.
WinoTrain: Winograd-Aware Training for Accurate Full 8-bit Convolution Acceleration

Efficient inference is critical in realizing a low-power, real-time implementation of convolutional neural networks (CNNs) on compute and memory-constrained embedded platforms. Using quantization techniques and fast convolutional algorithms like Winograd, CNN inference can achieve benefits in latency and in energy consumption. Performing Winograd convolution involves (1) transforming the weights and activations to the Winograd domain, (2) performing element-wise multiplication on the transformed tensors, and (3) transforming the results back to the conventional spatial domain. Combining Winograd with quantization of all its steps results in severe accuracy degradation due to numerical instability. In this paper we propose a simple quantization-aware training technique, which quantizes all three steps of the Winograd convolution, while using a minimal number of scaling factors. Additionally, we propose an FPGA accelerator employing tiling and unrolling methods to highlight the performance benefits of using the full 8-bit quantized Winograd algorithm. We achieve 2× reduction in inference time compared to standard convolution on ResNet-18 for the ImageNet dataset, while improving the Top-1 accuracy by 55.7 p.p. compared to a standard post-training quantized Winograd variant of the network.
The Culinary Student Perspective in Using Augmented Reality for Practical Class Activity

One of the pride symbols that is frequently emphasized in promoting Indonesia is Indonesian food. Indonesian food should be taught at hospitality or tourism schools because it utilizes different culinary techniques than European food. In the era of globalization, numerous types of foreign cuisine are eligible to access Indonesia, and if this occurs quickly, many young people do not recognize the peculiarities of Indonesian food itself. On site learning implementation has slowed significantly during the Covid-19 pandemic. Schools and campuses were closed, and learning was shifted to online learning. This is done to make sure that students do not get behind in their study particularly practical course. Augmented Reality technology is being used to carry out innovation. As a conceptual framework, the technology acceptance Model (TAM) is used in this study. Data were collected by the use of purpose samples. The survey questionnaire was delivered to 150 culinary students. The data was then analyzed using multiple regression analyses. And according to conclusions of this study, augmented reality may be utilized for distance learning as an addition or as a replacement.
On the Unpredictability of SPICE Simulations for Side-Channel Leakage Verification of Masked Cryptographic Circuits

Circuits for cryptography are vulnerable to side-channel (SC) attacks. Masking is a countermeasure which splits secrets into random shares. It is provable secure under the assumption that physical leakage of each share is independent of each other. For a secure implementation of masked circuits, this independency assumption must be satisfied after layout. A transistor-level simulator such as SPICE produces analog waveforms that are sufficiently trustworthy to verify timing accuracy. Due to this accuracy, SPICE is expected to be useful for SC leakage verification after layout. However, we demonstrate that the statistical variation of the power noise amplitude in SPICE simulation is not always correct and varies a lot for SC evaluation. We believe it results from the internal time-step creation optimized for efficiency. It causes false-positives in the verification of security order. A small nonlinear function with a domain-oriented masking scheme is used to demonstrate these SPICE-simulation anomalies.
AccShield: a New Trusted Execution Environment with Machine-Learning Accelerators

Machine learning accelerators such as the Tensor Processing Unit (TPU) are already being deployed in the hybrid cloud, and we foresee such accelerators proliferating in the future. In such scenarios, secure access to the acceleration service and trustworthiness of the underlying accelerators become a concern. In this work, we present AccShield, a new method to extend trusted execution environments (TEEs) to cloud accelerators which takes both isolation and multi-tenancy into security consideration. We demonstrate the feasibility of accelerator TEEs by a proof of concept on an FPGA board. Experiments with our prototype implementation also provide concrete results and insights for different design choices related to link encryption, isolation using partitioning and memory encryption.
Implementation and remote monitoring of a photovoltaic test bench with and without solar tracking in equatorial zones

Photovoltaic systems have been positioning themselves as an alternative with great potential for harnessing the sun’s energy, but their use has not yet reached the desired levels. The use of solar tracking systems is an option to improve the solar energy collection capacity, compared to a static system, provided that the atmospheric conditions are favorable, as confirmed by the tests carried out in this project. To know the influence of solar rays, tests were carried out in two cities near the coasts of Ecuador, Machala, and Guayaquil, which have different geographical and environmental characteristics. To obtain data on the different electrical and position parameters of the systems with and without tracking, remote monitoring of 2 test benches located in the aforementioned cities was carried out through the Internet. At the end of the tests, a comparative analysis was performed, where it was determined that, due to the predominance of cloudy weather in Machala, the static photovoltaic system had an advantage of about 53%. On the other hand, according to the analysis of the data in the city of Guayaquil, the photovoltaic system with solar tracking had an advantage of 33% over the static system.
IOTA-MSS: A Pay-per-Play Music Streaming System based on IOTA

In recent years, music streaming has emerged as the primary method for users to enjoy their favorite songs.Music Streaming Services (MSS) have risen to become influential entities in the music industry, exemplified by Spotify's impressive 406 million monthly active users by the end of 2021.However, many artists express concerns that the current MSS model fails to adequately compensate them for their music, raising questions about fairness.To tackle this issue, this paper proposes the implementation of IOTA-MSS, a Pay-per-Play Music Streaming System that leverages the secure and scalable IOTA distributed ledger technology (DLT).By harnessing the capabilities of IOTA's DLT, IOTA-MSS offers a platform that enables seamless microtransactions.This innovative system empowers users to play and distribute music in real-time while ensuring that rights holders receive corresponding payments.Through the utilization of IOTA's DLT, IOTA-MSS addresses the challenges faced by artists, providing a more equitable solution for music compensation.By adopting a Pay-per-Play model, this system aligns artist remuneration with the actual usage and popularity of their music.Furthermore, IOTA-MSS enhances the user experience, offering a secure and efficient platform for music enthusiasts to engage with their favorite tunes.
A digital 3D TCAM accelerator for the inference phase of Random Forest

Random forest is a popular ensemble machine-learning algorithm for classification and regression tasks. However, the irregular tree shapes and non-deterministic memory access patterns make it hard for the current von Neumann architecture to handle random forest efficiently. This paper proposes a digital 3D TCAM-based accelerator for the random forest, adopting the idea of processing-in-memory (PIM) to reduce data movement. By utilizing this accelerator, we propose a TCAM-based approach to provide real-time inference with low energy consumption, making it suitable for edge or embedded environments. In the experiments, the proposed approach achieves an average of 3.13 times higher throughput with 22 times more energy saving than the GPU approach.
Late Breaking Results: An Efficient Bridge-based Compression Algorithm for Topologically Quantum Error Corrected Circuits

Topological quantum error correction (TQEC) is a promising method for fault-tolerant quantum circuits. A TQEC circuit can be visualized as the defect movement along the time axis and modeled as a 3D space-time volume to estimate the required resource. A quantum algorithm must minimize the space-time volume for a feasible physical qubit number and computational time, especially for large-scale designs. Previous work presents quadratic-time simultaneous primal and dual bridge compression for a TQEC circuit, which is infeasible for large-scale problems. This paper presents an efficient divide-and-conquer approach to primal and dual bridge compression, which can support different code distances between qubits. Compared with the previous work, experimental results show that our algorithm is effective and efficient even for large-scale problems.
Late Breaking Results: Weight Decay is ALL You Need for Neural Network Sparsification

The heuristic iterative pruning strategy has been widely used for neural network sparsification. However, it is challenging to identify the right connections to remove at each pruning iteration with only a one-shot evaluation of weight magnitude, especially at the early pruning stage. The erroneously removed connections, unfortunately, can hardly be recovered. In this work, we propose a weight decay strategy as a substitute for pruning, which let the "insignificant" weights moderately decay instead of being directly clamped to zero. At the end of the training, the vast majority of redundant weights will naturally become close to zero, making it easier to identify which connections could be removed safely. Experimental results show that the proposed weight decay method can achieve an ultra-high sparsity of 99%. Compared to the current pruning strategy, the model size is further reduced by 34%, improving the compression rate from 69× to 106× at the same accuracy.
EagleRec: Edge-Scale Recommendation System Acceleration with Inter-Stage Parallelism Optimization on GPUs

Recommendation systems suggest items to users by predicting their preferences based on historical data. The industry traditionally handles large-scale recommendation requests by scaling the number of devices without much concern for a single device’s performance. However, there is a trend for recommendation systems to gradually move from a centralized service to an edge device. The edge-scale recommendation systems have distinct features that are different from traditional large-scale deployments, which poses different challenges to the acceleration of the recommendation system. In this paper, we focus on the edge-scale recommendation system and propose an inter-stage parallelism optimization method deployed on a single GPU. Experiments show that our framework could improve recommendation system throughput by 1.89×~2.2× for different datasets on the GPU.
PIMA-LPN: Processing-in-memory Acceleration for Efficient LPN-based Post-Quantum Cryptography

Learning parity with noise (LPN) is under intensive research in building advanced cryptography suites and protocols. However, in LPN-based cryptography, the transmission of the large matrices between the memory and the processor units generally incurs a significant latency overhead. In this work, we propose PIMA-LPN, a processing-in-memory (PIM) accelerator for LPN cryptography. Specifically, our PIM architecture can carry out the entire computations of LPN in memory. In this experiment, we demonstrate that PIMA-LPN can be 20.86× ~ 216.8× faster than existing CPU and FPGA implementations of LPN cryptography. Furthermore, we show that using PIMA-LPN, LPN cryptography can achieve similar computational efficiency compared to the post-quantum cryptography standard (i.e., CRYSTALS-Kyber) with 15.4x fewer memory units.