Reviewerly/exHarmony huggingface dataset:
TI-former: A Time-Interval Prediction Transformer for Timestamped Sequences

The Transformer is a widely used neural network architecture for natural language processing. Recently, it has been applied to time series prediction tasks. However, the vanilla transformer has a critical limitation in that it cannot predict the time intervals between elements. To overcome this limitation, we propose a new model architecture called TI-former (Time Interval Transformers) that predicts both the sequence elements and the time intervals between them. To incorporate the elements’ sequential order and temporal interval information, first we propose a new positional encoding method. Second, we modify the output layer to predict both the next sequence element and the time interval simultaneously. Lastly, we suggest a new loss function for timestamped sequences, namely Time soft-DTW, which measures similarity between sequences considering timestamps. We present experimental results based on synthetic sequence data. The experimental results show that our proposed model outperforms than vanilla transformer model in various sequence lengths, sequence numbers, and element occurrence time ranges.
Floor Water Detection Technology Based on Improved Resnet

With the rapid development of artificial intelligence and the arrival of an aging population, the need for intelligent service robots is increasing. Older adults and the blind are susceptible to slipping due to weak vision, especially when they cannot see water on indoor floors. We use computer vision technology to solve this problem. However, it is challenging to detect floor water using existing target detection algorithms due to uncertainty about its shape and size. This paper proposes a floor water detection technology based on improved Resnet, which can be deployed on the intelligent service robot to remind the elderly and the blind to be careful When the service robot detects water in the ground. Our proposed topics and methods can significantly reduce the probability of the elderly and blind people slipping. The method proposed in this paper is 3.6% higher than the original Resnet18 and 8.1% higher than Mobilenetv2; the number of parameters in our method is only 8.5 percent of VGG16_bn and yet achieves similar performance to VGG16_bn. This paper suggests a new trajectory for intelligent service robots by detecting water on the floor, and it has demonstrated promising results in accuracy and speed. It is hoped that this paper will arouse more scholars’ interest in the detection technology of floor water.
A Statistical Method for API Usage Learning and API Misuse Violation Finding

A large corpus of software repositories enables an opportunity for using machine learning (ML) approaches to create new software engineering tools. In this paper, we propose a novel technique which leverages ML approaches for automating software engineering tasks and thus improves software quality. Our concrete goal is to (1) explore the abundance of predictable repetitive regularities of such a massive codebase, (2) develop an ML approach for training a statistical model to identify common patterns in software corpora, and then (3) use these patterns to statistically detect anomalous, likely buggy, program behavior that significantly deviates from these typical patterns. These internal regularities and repetitive properties of software can be captured as patterns to detect violations of these common patterns. Such violations have a critical impact on program behavior such as bugs, security vulnerabilities, or even program crashes. Our approach focuses on usage patterns of application programming interfaces (APIs). API usage patterns are commonly recurring, representative examples of how real-world applications use APIs in software corpora. These desirable patterns of API usage are learnable to validate or improve developers' implementations. This paper shows preliminary results that we use standard cross-entropy and perplexity to measure how surprising a test subject application is to a statistical model estimated from a software corpus. We continue to develop our approach and evaluate the effectiveness to focus on the following research questions. Are our ML models effectively trainable on large code corpora to learn desirable API usage patterns? How does the performance of our ML-based approach compare to state-of-the-art language models for software when learning API usage for detecting API misuse violations?
Neural Abstractive Summarization: A Brief Survey

Due to the development of neural networks, abstractive summarization has received more attention than extractive one, and has gained significant progress in generating fluent and human-like summaries with novel expressions. Seq2seq has become the primary framework for abstractive summarization, employing encoder-decoder architecture based on RNNs or CNNs, and Transformers. In this paper, we focus on reviewing the neural models that are based on seq2seq framework for abstractive summarization. Moreover, we discuss some of the most effective techniques for improving seq2seq models and provide two challenging directions, i.e. generating query-based abstractive summaries and incorporating commonsense knowledge, for in-depth investigation.
Compare with the Traditional Heterogeneous Solution: Accelerate Neural Network Algorithm through Heterogeneous Integrated CPU+NPU Chip on Server

The increasing popularity of artificial intelligence (AI) requires the ability to process intensive data and efficient heterogeneous computing power. As a result, a heterogeneous integration scheme involving both central processing units (CPUs) and neural processing units (NPUs) has become increasingly prevalent in various edge terminals, such as mobile phones. Compared with traditional separated heterogeneous solutions, the integration scheme can effectively reduce the distance and number of data transmissions, thereby accelerating deep neural network (DNN) models and improving energy efficiency. Due to the low power requirements of cloud computing, heterogeneous integration solutions are beginning to be used in the design of processor architectures for servers. The TF16110 integrates NPUs into server CPUs, creating an efficient parallel computing solution for servers that lack GPUs or other AI acceleration devices. In this paper, we evaluate and analyze commonly used DNN models. Compared with NVIDIA’s TX2 GPU, the heterogeneous integrated CPU+NPU design can provide similar computational power and achieve 5x higher energy efficiency and 10x cost-effectiveness under the premise of ensuring accuracy
Co-Simulation Strategy for Photovoltaic Power Prediction and Validation of Digital Twin

The growing energy demand, driven by expanding population and industrial sectors, has created a need to integrate low-cost renewable energy sources (RESs) into the power network. Solar energy is considered a viable means of generating electricity among RESs. However, the inherent intermittency of such energy sources presents various challenges to the power system. Therefore it requires accurate forecasting methods to ensure seamless power flow in energy networks. This paper investigates a data-driven digital twin of a PV plant, validated by incorporating a TRNSYS simulation model of PV using time-ahead forecasted weather data. Various PV power prediction models have been evaluated based on particular key performance characteristics, and a normalized boosting ensemble approach has been chosen to generate a potential digital twin of a PV plant.
An Effective Online Stream Feature Selection Auxiliary Method for High-Dimensional Unbalanced Data

In the area of feature selection from highdimensional data, online streaming feature selection methods have received extensive attention in the past few decades due to their online selection abilities. Existing online stream feature selection methods perform well on many balanced datasets, But the real datasets are usually high-dimensional and unbalanced. For example, in medical examination data, the proportion of the sick people is much smaller than that of the healthy people. In the face of unbalanced data, traditional stream feature selection algorithms confront problems such as few selected features and low classification accuracy. Therefore, how to perform online stream feature selection under high-dimensional and unbalanced conditions is a challenge. In this paper, a general and easy-toimplement auxiliary algorithm is proposed, which can supplement the existing stream feature selection methods and dig out feature subsets effectively. Finally, the experiments are carried out on seven high-dimensional and unbalanced datasets and the results show that the auxiliary method can improve the traditional online stream feature selection methods and enable the classifiers to achieve better classification performance.
SAKP: A Korean Sentiment Analysis Model via Knowledge Base and Prompt Tuning

With the help of pre-trained language models, tasks such as sentiment analysis and text classification have achieved good results. With the advent of prompt tuning, especially previous studies have shown that in the case of few data, the prompt tuning method has significant advantages over the general tuning method of additional classifiers. At present, there are relatively few studies on sentiment analysis of Korean Chinese texts.This paper proposes a low resource sentiment classification method based on pre-trained language models (PLMs) combined with prompt tuning. In this work, we chose to use the pre-trained language model KLUE and elaborated a Korean prompt template with an expanded knowledge base and filtering in the verbalizer section. We focus on collecting external knowledge and integrating it into the utterance to form a prompt tuning of knowledge to improve and stabilize the prompt tuning. Specifically, we use the K-means clustering algorithm to construct the label wordspace of the external knowledge base (kb) extended language, and use PLM itself to refine the extended labeled wordspace before using the extended labeled wordspace for prediction. A large number of experiments on the few-shot emotion classification task prove the effectiveness of knowledge prompt tuning.
Nighttime Vehicle Classification based on Thermal Images

Each Department of Transportation in the United States must provide to the Federal Highway Administration on annual basis the number and types of vehicles traveled on its state-maintained roads. These data are fed into the Highway Performance Monitoring System used to assess the nation’s highway system performance. Classifying vehicles (i.e., identifying their types, e.g., passenger cars, trucks, etc.) during nighttime is quite challenging due to limited lighting. This study designed and evaluated three Convolutional Neural Network (CNN) models to classify vehicles using their thermal images. These three models have architectures that differ in the number of layers and, in the case of the third model, the addition of an inception layer. Of these, the second model achieves the best performance, achieving mean accuracy scores of greater than 97% for each of the three vehicle classes and f1 scores of greater than 98%. We proposed two training-test methods based on data augmentation to avoid over-fitting and to improve performance. The experimental results demonstrated that a data augmentation training-test method improves model performance further with regard to both accuracy and f1-score.
Balance-aware Cost-efficient Routing in the Payment Channel Network

Payment Channel Networks (PCNs) have been introduced as a viable solution to the scalability problem of the popular blockchain. In PCNs, a payment channel allows its end nodes to pay each other without publishing every transaction to the blockchain. A transaction can be routed in the network if there is a path of channels with sufficient funds, and the intermediate routing nodes can ask the transaction sender for a compensatory fee. However, a channel may eventually become depleted and cannot support further payments in a certain direction, as transaction flows from that direction is heavier than flows from the other direction. In this paper, we discuss a PCN node’s possible roles and objectives, and analyze the strategies nodes should take under different roles by considering nodes’ benefits and the network’s performance. Then, we examine two basic network structures (ring and chord) and determine the constraints under which they constitute a Nash equilibrium. Based on the theoretical results, we propose a balance-aware fee-incentivized routing algorithm to guarantee cost-efficient routing, fair fee charging, and the network’s long lasting good performance in general PCNs. Testbed-based evaluation is conducted to validate our theoretical results and to show the feasibility of our proposed approach.
3 B (Block Byte Bit) Cipher Algorithm for Secure Socket Layer

This paper proposes an ECSSL(Elliptic Curve Secure Socket Layer) protocol that provides more securities and faster processing speed than an existing SSL(Secure Socket Layer) protocol. An ECSSL protocol consists of ECC(Elliptic Curve Cryptography), ThreeB(Block Byte Bit Cipher) algorithm to prevent from being eavesdropped, HMAC(Hash Message Authentication Code) algorithm to create digital signature using a shared secret key. In particular, as ThreeB uses byte exchange by using random technique. and bit-xor operation, compared with DES using fixed index table, the security and processing time of it are improved much better.
Analyzing the Effects of CI/CD on Open Source Repositories in GitHub and GitLab

Numerous articles emphasize the benefits of implementing Continuous Integration and Delivery (CI/CD) pipelines in software development. These pipelines are expected to improve a project’s reputation and decrease the number of commits and issues in the repository. Although CI/CD adoption may be slow initially, it is believed to accelerate service delivery and deployment in the long run. This study aims to investigate the impact of CI/CD on commit velocity and issue counts in two open-source repositories, GitLab and GitHub. By analyzing more than 12,000 repositories and recording every commit and issue, it was discovered that CI/CD enhances commit velocity by 141.19% but also increases the number of issues by 321.21%.
FPGA-Based Acceleration of a Neural Network for Symbol Classification with Hardware-Optimised Multiplier Implementation

Symbol classification is a crucial task in many applications such as handwriting recognition, computer vision, Optical Character Recognition (OCR), etc. However, multiclass classification is an intensive task that requires high computational power and memory resources. In this paper Field-Programmable Gate Array (FPGA) based acceleration of a neural network for symbol classification has been implemented with hardware-optimised multiplication techniques. The approach involves using FPGA to perform faster multiplications and accumulation operations with reduced resource usage. The neural network is trained using MATLAB and then implemented in VHDL on Quartus Prime Lite. The multiplication is compared to the Karatsuba Multiplication technique and the Iterative Logarithmic technique. Based on the comparative study the Iterative Logarithmic technique proved to be the best to bring down the overall hardware usage. The final optimised design has been validated using ModelSim and then deployed on an Altera Cyclone® V SE FPGA device.
Documentation Practices in Agile Software Development: A Systematic Literature Review

Context: Agile development methodologies in the software industry have increased significantly over the past decade. Although one of the main aspects of agile software development (ASD) is less documentation, there have always been conflicting opinions about what to document in ASD. Objective: This study aims to systematically identify what to document in ASD, which documentation tools and methods are in use, and how those tools can overcome documentation challenges. Method: We performed a systematic literature review of the studies published between 2010 and June 2021 that discusses agile documentation. Then, we systematically selected a pool of 74 studies using particular inclusion and exclusion criteria. After that, we conducted a quantitative and qualitative analysis using the data extracted from these studies. Results: We found nine primary vital factors to add to agile documentation from our pool of studies. Our analysis shows that agile practitioners have primarily developed their documentation tools and methods focusing on these factors. The results suggest that the tools and techniques in agile documentation are not in sync, and they separately solve different challenges. Conclusions: Based on our results and discussion, researchers and practitioners will better understand how current agile documentation tools and practices perform. In addition, investigation of the synchronization of these tools will be helpful in future research and development.
LSTM-AE for Anomaly Detection on Multivariate Telemetry Data

Organizations and companies that collect data generated by sales, transactions, client/server communications, IoT nodes, devices, engines, or any other data generating/exchanging source, need to analyze this data to reveal insights about the running activities on their systems. Since streaming data has multivariate variables bearing dependencies among each other that extend temporally (to previous time steps).Long-Short Term Memory (LSTM) is a variant of the Recurrent Neural Networks capable of learning long-term dependencies using previous timesteps of sequence-shape data. The LSTM model is a valid option to apply to our data for offline anomaly detection and help foresee future system incidents. Anything that negatively affects the system and the services provided via this system is considered an incident.Moreover, the raw input data might be noisy and improper for the model, leading to misleading predictions. A wiser choice is to use an LSTM Autoencoder (LSTM-AE) specialized for extracting meaningful features of the examined data and looking back several steps to preserve temporal dependencies.In our work, we developed two LSTM-AE models. We evaluated them in an industrial setup at Koçfinans (a finance company operating in Turkey), where they have a distributed system of several nodes running dozens of microservices. The outcome of this study shows that our trained LSTM-AE models succeeded in identifying the atypical behavior of offline data with high accuracies. Furthermore, after deploying the models, we identified the system failing at the exact times for the previous two reported failures. While after deployment, it launched cautions preceding the actual failure by a week, proving efficiency on online data. Our models achieved 99.7% accuracy and 89.1% as F1-score. Moreover, it shows potential in finding the proper LSTM-AE model architecture when time series data with temporal dependency property is fed to the model.
Study on the Assessment of Chinese Sentence Difficulty for Second Language Teaching

Sentence is an important factor affecting reading comprehension, and it is also the core and focus of language learning. This paper discusses the features and assessment methods of Chinese sentences difficulty, so as to provide learning materials with appropriate difficulty for learners. Firstly, the influencing factors of sentence difficulty are analyzed from three aspects of characters, words and sentences, and a total of 35 kinds of 101 features are extracted. On this basis, a sentence difficulty assessment method is designed by integrating principal component analysis, term weighting, equal proportional division and discriminant analysis. The experimental results show that the method proposed in this paper is more reasonable when the sentence difficulty is divided into three levels. Moreover, the method is simple and easy to use without further processing of sentences.
Mobile Augmented Reality System for Emergency Response

There are a wide variety of mobile phone emergency response applications exist for both indoor and outdoor environments. However, outdoor applications mostly provide accident and navigation information to users, and indoor applications are limited to the unavailability of GPS positioning and WiFi access problems. This paper describes the proposed mobile augmented reality system (MARS) that allows both outdoor and indoor users to retrieve and manage information for emergency response and navigation that is spatially registered with the real world. The proposed MARS utilizes feature extraction for location sensing in indoor environments as during emergencies GPS and WiFi systems might not work. This paper describes the implementation of this MARS deployed on tablets and smartphones for building evacuation purposes. The MARS delivers critical evacuation information to smartphone users in the indoor environment and navigation information in the outdoor environments. A limited user study was conducted to test the effectiveness of the proposed MARS using the mobile phone usability questionnaire (MPUQ) framework. The results show that AR features were well integrated into the MARS and it will help identify the nearest exit in the building during the emergency evacuation.
Human Activity Recognition Based on Transformer via Smart-phone Sensors

Capturing the spatial and temporal relationships of time-series signals is a significant obstacle for human activity recognition based on wearable devices. Traditional artificial intelligence algorithms cannot handle it well, with convolution-based models focusing on local feature extraction and recurrent networks lacking consideration of the spatial domain. This paper offers a deep learning architecture based on transformer to address the aforementioned issue with data collected from smart-phones embedded with three-axis accelerometers. The transformer model, as a deep learning network mainly applied to natural language processing (NLP), is good at processing time-series information, where the self-attention mechanism captures the dependencies of perceptual signals in the temporal and spatial domains, improving the overall comprehensibility. We implement convolutional neural networks (CNN) and long and short-term memory networks (LSTM) for evaluation while our proposed model achieves an average classification accuracy of 94.84%, which is an improvement compared to the traditional model.
Towards Imbalanced Large Scale Multi-label Classification with Partially Annotated Labels

Multi-label classification is a widely encountered problem in daily life, where an instance can be associated with multiple classes. In theory, this is a supervised learning method that requires a large amount of labeling. However, annotating data is time-consuming and may be infeasible for huge labeling spaces. In addition, label imbalance can limit the performance of multi-label classifiers, especially when some labels are missing. Therefore, it is meaningful to study how to train neural networks using partial labels. In this work, we address the issue of label imbalance and investigate how to train classifiers using partial labels in large labeling spaces. First, we introduce the pseudo-labeling technique, which allows commonly adopted networks to be applied in partially labeled settings without the need for additional complex structures. Then, we propose a novel loss function that leverages statistical information from existing datasets to effectively alleviate the label imbalance problem. In addition, we design a dynamic training scheme to reduce the dimension of the labeling space and further mitigate the imbalance. Finally, we conduct extensive experiments on some publicly available multi-label datasets such as COCO, NUS-WIDE, CUB, and Open Images to demonstrate the effectiveness of the proposed approach. The results show that our approach outperforms several state-of-the-art methods, and surprisingly, in some partial labeling settings, our approach even exceeds the methods trained with full labels.