Reviewerly/exHarmony huggingface dataset:
A FACT-based Approach: Making Machine Learning Collective Autotuning Feasible on Exascale Systems

According to recent performance analyses, MPI collective operations make up a quarter of the execution time on production systems. Machine learning (ML) autotuners use supervised learning to select collective algorithms, significantly improving collective performance. However, we observe two barriers preventing their adoption over the default heuristic-based autotuners. First, a user may find it difficult to compare autotuners because we lack a methodology to quantify their performance. We call this the performance quantification challenge. Second, to obtain the advertised performance, ML model training requires benchmark data from a vast majority of the feature space. Collecting such data regularly on large scale systems consumes far too much time and resources, and this will only get worse with exascale systems. We refer to this as the training data collection challenge. To address these challenges, we contribute (1) a performance evaluation framework to compare and improve collective au-totuner designs and (2) the Feature scaling, Active learning, Converge, Tune hyperparameters (FACT) approach, a three-part methodology to minimize the training data collection time (and thus maximize practicality at larger scale) without sacrificing accuracy. In the methodology, we first preprocess feature and output values based on domain knowledge. Then, we use active learning to iteratively collect only necessary training data points. Lastly, we perform hyperparameter tuning to further improve model accuracy without any additional data. On a production scale system, our methodology produces a model of equal accuracy using 6.88x less training data collection time.
Citation entity recognition method using multi‐feature semantic fusion based on deep learning

Abstract The effective entity recognition method can quickly and accurately identify the citation entity to facilitate citation comparison, thereby reducing the occurrence of academic fraud and other behaviors. But there is no very effective way to solve this problem till now. In recent years, neural network models for named entity recognition (NER) have shown better performances on general domain datasets. After the multi‐feature citation dataset is created, the article proposes contextual multi‐feature embedding (CMFE) method for word embedding which use multi‐feature to enhance semantic and use CNN to get multi‐level feature. Based on CMFE, a multi‐feature semantic fusion model (MFSFM) is proposed. It designs the multi‐convolution kernel mixed residual CNN module to obtain local attention information and enhance the sensitivity of the entity boundary information. The BiLSTM and LSTM is used for timing learning. The experimental results of Chinese citation datasets and Chinese–English mixed citation datasets show that CMFE can better represent semantics, and MFSFM can perform citation entity recognition well. Finally, the experimental results of CONLL2003 dataset show that it is general on NER.
Spoonful: Mobile Application for Reducing Household Food Waste using Fogg Behavior Model (FBM)

Indonesia is the second largest food waste producer country in the world after Saudi Arabia. This condition is very worrying because food waste could impact on many aspects of people’s life including food security, economy and environment. One of the approaches to solve this problem is to make mobile application to change human consumption behavior. It was also found that majority of interaction designs for sustainable food consumption practices are not yet fully using behavior change theory (BCT) as a basis design decision making so that it cannot be ascertained the resulting design intervention can influence user behavior. Fogg Behavior Model (FBM) Theory provides an explanation regarding what factors can be used to change human behavior. This project aims to apply FBM theory to the development of application interaction design for reducing household food waste with user-centered design (UCD) approach that has been adapted. Low-fidelity and high-fidelity prototyping are also used in solution design stage of UCD. Output from this project is high-fidelity prototype of Spoonful with usability goals effective to use, easy to learn, and user experience goals helpful. Each of goals are measured with usability testing using task completion rate, Single Ease Question (SEQ) and Intrinsic Motivation Inventory (IMI) value/usefulness subscale. System Usability Scale (SUS) and self-evaluation of the theory of the Transtheoretical Model (TTM) are also used for testing on overall usability and behavior change. The results show that the prototype is effective to use with success rate 94.3%, easy to learn with SEQ score 6.12 out of 7, helpful with IMI score for the value/usefulness subscale score of 6.59 out of 7, grade C acceptable with SUS score 74.58 out of 100 and help people to move to action stage from preparation stage with self-evaluation of TTM theory score 71.83 out of 80. FBM theory is very helpful and proven to provide better design justification for behavior change.
Towards efficient network compression via Few-Shot Slimming

While previous network compression methods achieve great success, most of them rely on the abundant training data which is, unfortunately, often unavailable in practice due to some reasons, e.g., privacy issues, storage constraints, and transmission limitations. A promising way to solve this problem is to perform compression with a few unlabeled data. Proceeding along this way, we propose a novel few-shot network compression framework named Few-Shot Slimming (FSS). FSS follows the student/teacher paradigm, and contains two steps: (1) construct the student by inheriting principal feature maps from the teacher; (2) refine the student feature representation by knowledge distillation with an enhanced mixing data augmentation method called GridMix. Specifically, in the first step, we employ normalized cross correlation to perform the principal feature analysis, and then theoretically construct a new indicator to select the most informative feature maps from the teacher for the student. The indicator is based on the variances of feature maps which can efficiently quantitate the information richness of the input feature maps in a feature-agnostic manner. In the second step, we perform the knowledge distillation for the initialized student in first step with a novel grid-based mixing data augmentation technique which greatly extends the limited sample dataset. In this way, the student is able to refine its feature representation and achieves a better result. Extensive experiments on multiple benchmarks demonstrate the state-of-the-art performance of FSS. For example, by using 0.2% label-free data of full training set, FSS yields a 60% FLOPs reduction for DenseNet-40 on CIFAR-10 with only a loss of 0.8% in top-1 accuracy, achieving a result on par with that obtained by the conventional full-data methods.
Semantic Attribute Enriched Storytelling from a Sequence of Images

Visual storytelling (VST) pertains to the task of generating story-based sentences from an ordered sequence of images.Contemporary techniques suffer from several limitations such as inadequate encapsulation of visual variance and context capturing among the input sequence.Consequently, generated story from such techniques often lacks coherence, context and semantic information.In this research, we devise a 'Semantic Attribute Enriched Storytelling' (SAES) framework to mitigate these issues.To that end, we first extract the visual features of input image sequence and the noun entities present in the visual input by employing an off-the-shelf object detector.The two features are concatenated to encapsulate the visual variance of the input sequence.The features are then passed through a Bidirectional-LSTM sequence encoder to capture the past and future context of the input image sequence followed by attention mechanism to enhance the discriminality of the input to language model i.e., mogrifier-LSTM.Additionally, we incorporate semantic attributes e.g., nouns to complement the semantic context in the generated story.Detailed experimental and human evaluations are performed to establish competitive performance of proposed technique.We achieve up 1.4% improvement on BLEU metric over the recent state-of-art methods.
Network Traffic Prediction Using Online-Sequential Extreme Learning Machine

For years, it has been a great challenge for Internet Service Providers (ISP) to predict traffic load or future demand, since each bit of traffic is an economic cost to operators. Additionally, more and more users are adopting the different telecommunication services as well as the ISP must provide more bandwidth and reliability. In this sense, the study focuses on forecasting with neural network techniques, specifically Long Short-Term Memory (LSTM) and Online-Sequential Extreme Learning Machine (OS-ELM), the real traffic of a Chile ISP. The results show that OS-ELM outperforms LSTM in terms of computational cost by a factor of 2300, and in terms of network prediction, OS-ELM effectively competes with LSTM.
Vehicle Augmented Reality Head-up Display information visualization enhancement algorithm and system

In this paper, we propose an augmented reality head-up display (ARHUD) algorithm and system that can enhance the information visualization for drivers. Recently, Head-up Display (HUD) getting popular in the automotive industry. It can serve the car and other information to the driver efficiently. Therefore, the drivers do not have to move the perspectives to the car dashboard. Finally, it can reduce the accident rate effectively. On the other hand, HUD serves only 2D information, and it is challenging to provide suitable 3D information according to road conditions. To overcome this problem, ARHUD has been developed. ARHUD can generate 3D information and can serve adaptive information for drivers by using a car camera. However, the conventional ARHUD defines content color without considering the road condition. It can cause a critical visualization problem. For example, if the information color and the car color are the same in an actual traffic situation, it is not easy to visualize the content information. In addition, to recognize the road situation in real-time, an image processing machine is needed. However, the conventional machine consumes much electricity, and it can cause vehicle discharge. To solve these problems, we propose an effective processing algorithm and system which can define the content color and recognize the object with high speed without consumes high electricity.
Using Experience to Improve Constrained Planning on Foliations for Multi-Modal Problems

Many robotic manipulation problems are multi-modal—they consist of a discrete set of mode families (e.g., whether an object is grasped or placed) each with a continuum of parameters (e.g., where exactly an object is grasped). Core to these problems is solving single-mode motion plans, i.e., given a mode from a mode family (e.g., a specific grasp), find a feasible motion to transition to the next desired mode. Many planners for such problems have been proposed, but complex manipulation plans may require prohibitively long computation times due to the difficulty of solving these underlying single-mode problems. It has been shown that using experience from similar planning queries can significantly improve the efficiency of motion planning. However, even though modes from the same family are similar, they impose different constraints on the planning problem, and thus experience gained in one mode cannot be directly applied to another. We present a new experience-based framework, ALEF, for such multi-modal planning problems. ALEF learns using paths from single-mode problems from a mode family, and applies this experience to novel modes from the same family. We evaluate ALEF on a variety of challenging problems and show a significant improvement in the efficiency of sampling-based planners both in isolation and within a multi-modal manipulation planner.
CoDEC: Connected Data For Road Infrastructure Asset Management

Abstract Road infrastructure asset management is rapidly transforming into a digital environment where data accessibility, effective integration and collaboration and accessibility from different sources and assets are key. However, current asset management processes are not yet fully integrated or linked, and there are incompatibilities between various systems and platforms that limit the ability to integrate asset management with BIM. The CoDEC project has sought to understand the current status of information management for assets, including inventory, condition and new data sources such as sensors and scanning systems, to identify the challenges and needs for linking and integrating different data sets to support effective asset management. As a result, CoDEC has developed a data dictionary framework to help link/integrate static and dynamic data for the “key” infrastructure assets (road pavements, bridges, tunnels). This will enable BIM and Asset Management Systems (AMS) to exchange data and help optimise and integrate data management across systems and throughout the different asset lifecycle phases, from build to operation. This work will be followed up with three pilot projects to demonstrate the feasibility of integrating asset data from various sources through linked data/semantic web technology to build the connection between AMS and BIM platforms.
Exploring synergy in a mobile learning model for piano playing ornaments exercise with local musical heritage

This study addresses the potential of a mobile learning model that integrates local Malaysian musical heritage in piano ornaments finger exercise. An asynchronous model adapted from the concept of karaoke was designed aiming at a synergy of piano exercise, edutainment, and heritage education with visual and auditory accompaniments. We aimed to raise interest and motivation for piano playing ornaments exercise among learners during practice while raising awareness of local musical heritage. A total of 30 piano students aged 13 to 15 years participated in this intervention study; they were equally divided into a control group and an experimental group. The pre- and post-intervention assessments revealed an increase in the levels of motivation, awareness of local musical heritage, and perceived satisfaction factors for the new mobile learning model.
VM Migration Scheduling as Moving Target Defense against Memory DoS Attacks: An Empirical Study

Memory Denial of Service (DoS) attacks are easy-to-launch, hard to detect, and significantly impact their targets. In memory DoS, the attacker targets the memory of his Virtual Machine (VM) and, due to hardware isolation issues, the attack affects the co-resident VMs. Theoretically, we can deploy VM migration as Moving Target Defense (MTD) against memory DoS. However, the current literature lacks empirical evidence supporting this hypothesis. Moreover, there is a need to evaluate how the VM migration timing impacts the potential MTD protection. This practical experience report presents an experiment on VM migration-based MTD against memory DoS. We evaluate the impact of memory DoS attacks in the context of two applications running in co-hosted VMs: machine learning and OLTP. The results highlight that the memory DoS attacks lead to more than 70% reduction in the applications' performance. Nevertheless, timely VM migrations can significantly mitigate the attack effects in both considered applications.
Robust total variation-based destriping model via sparse representation learning for business infrared imaging systems

• An infrared image destriping model with total variation and nonnegative constraint is proposed. • The bidirection total variation is proposed to preserve the infrared image edge and texture details. • Tthe proposed TVDM method is superior to the existed destriping algorithms. Infrared images are often degraded by the issue of random and stripe noises in the business infrared focal plane imaging process. In this study, we proposed a novel infrared image destriping model with unidirectional and bidirectional total variation regularization. From the destriped infrared images, the stripe noise only exists in unidirection. The nature of unidirectional noise is leveraged to converted the total variation regularization and constructed the proposed model. For the random noise, the bidirectional total variation is introduced, which can preserve the image edge and texture details. Furthermore, the alternating direction method of multiplier algorithm is introduced to solve the infrared image destriping model. The proposed model is executed on several public infrared image datasets, thus outperforming the traditional destriping models in the qualitative evaluation index.
SQL Injection Detection Using Machine Learning Techniques

SQL Injection is one of the most popular cyberattacks in which hackers can gain unauthorized access to sensitive data such as customer information, trade secrets, etc. SQL Injection works by injecting malicious SQL code into user input fields in order to illegitimately read, update, or delete database contents. Detecting SQL Injection is a binary classification problem in which it is required to decide whether a user input is malicious (i.e., includes an injected SQL code) or benign. SQL Injection can be detected using rule-based, machine learning, or deep learning techniques. This paper focuses on machine learning techniques for SQL Injection detection. The paper defines 13 relevant features that can be extracted from user inputs, and it presents and evaluates 6 different machine learning algorithms. A feature selection process is also conducted, and the models achieve an accuracy up to 99.6%. The presented models show clear abilities to generalize to unseen data and perform well with a limited training set size. Overall, Logistic Regression is concluded to be the most performing model.
Data Augmentation for Small Face Datasets and Face Verification by Generative Adversarial Networks Inversion

One of the most challenging issues in the utilisation of machine learning in face datasets is the lack of data, especially when there is inadequate collection of datasets. On one hand, the cost of collecting new face images could be very costly and it depend heavily on the resources and the availability of the data collection. On the other hand, insufficient face datasets could lead to over-fitting issues in any deep learning models especially in the face verification tasks as it requires adequate amount of face dataset. Nevertheless, Generative Adversarial Networks (GANs) offers a better way to augment the data by generating synthetic face images based on the close-distributed pixels of real images. With this intention, GAN inversion was introduced to produce better performance comparing to the previous GAN concepts; by inverting a given face image back into the latent space of a pretrained GAN model with low loss transmissions. This paper demonstrates the feasibility of GAN inversion during the face verification process. We will also illustrate the comparison between previous GAN models, and traditional machine learning augmentation methods in face images generation.
Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining, Natural language processing, Image processing, and Information retrieval etc. Word embedding has been applied by many researchers for Information retrieval tasks. In this paper word embedding-based skip-gram model has been developed for the query expansion task. Vocabulary terms are obtained from the top “k” initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user query. The performance of the model based on mean average precision is 0.3176. The proposed model compares with other existing models. An improvement of 6.61%, 6.93%, and 9.07% on MAP value is observed compare to the Original query, BM25 model, and query expansion with the Chi-Square model respectively. The proposed model also retrieves 84, 25, and 81 additional relevant documents compare to the original query, query expansion with Chi-Square model, and BM25 model respectively and thus improves the recall value also. The per query analysis reveals that the proposed model performs well in 30, 36, and 30 queries compare to the original query, query expansion with Chi-square model, and BM25 model respectively.
Negative controlled Fredkin gate circuits with mirrors

Data loss is a major problem in conventional circuits. But it cannot happen in reversible circuits. So energy dissipation per bit loss does not happen in this circuit. Also, recovery of input data can be possible in this circuit. Negative controlled Fredkin gate (NCF) is a self-invertible reversible gate. It performs SWAP operation if its control input is logic zero. In this paper, a mechanically controlled mirror-based NCF gate is designed, where the operation is based on the movement of mirrors. To move the mirror from its position electrical signal can be used. Optical signals are used in computational operations. Some complex reversible circuits viz. some logical operations (XOR-XNOR, AND-NAND, OR-NOR), DE-MUX/MUX, switching, and data shifting circuits are also shown in this paper. Quantum realization of the circuit was performed. Also, the matrix calculation was done to find out the operation of the circuit. Optical delay, optical cost, quantum delay, and quantum cost of every proposed design were calculated.
The pedagogical and user interface usability evaluation of a mobile app that can be used as a resource to guide university students to live sustainably

The most important way to promote sustainable living is through education and awareness-raising by disseminating information through different media. International organizations and NGOs have proposed several solutions. One of these is the AWorld, a mobile app that provides clear and accurate information from reliable sources. This app seeks to change its users’ habits and mindset to save the planet through its multiple functionalities. However, could be considered AWorld an educational mobile app for promoting sustainable living in university students? This paper reports the results of the pedagogical and user interface usability evaluation of the AWorld. Twenty-five experts conducted the assessment. They evaluated the app through a questionnaire to value the quality of mobile learning apps. Results indicate that the AWorld app could be considered a potential resource to learn and commit to leading sustainable, healthy lifestyles and address problems such as climate change in higher education environments.