Reviewerly/exHarmony huggingface dataset:
A Deep Clustering via Automatic Feature Embedded Learning for Human Activity Recognition

Traditional clustering algorithms are widely used for building bag-of-words (BOW) models to aggregate spatio-temporal feature points extracted from a video for human activity recognition problems. Their performances are restricted by the computational complexity which limits the number of feature points being used. In contrast, deep clustering yields good clustering performance without the limit of the number of feature points. Therefore, this work proposes a dual stacked autoencoders features embedded clustering (DSAFEC) and a BOW construction method based on the DSAFEC (B-DSAFEC) to reduce the computational complexity and to remove the selection restriction. The DSAFEC first transforms feature points extracted from a video to a learned feature space and then probabilities of cluster assignment of feature points are predicted to build BOWs for human activity recognition. A soft clustering is used by assigning each feature point to multiple clusters yielding the largest probabilities instead of only one in hard clustering. Experimental results on three benchmark human activity datasets show that the B-DSAFEC yields better performance compared to five reference methods which are developed based on either traditional clustering methods or deep clustering methods.
Multi-Stroke Thai Finger-Spelling Sign Language Recognition System with Deep Learning

Sign language is a type of language for the hearing impaired that people in the general public commonly do not understand. A sign language recognition system, therefore, represents an intermediary between the two sides. As a communication tool, a multi-stroke Thai finger-spelling sign language (TFSL) recognition system featuring deep learning was developed in this study. This research uses a vision-based technique on a complex background with semantic segmentation performed with dilated convolution for hand segmentation, hand strokes separated using optical flow, and learning feature and classification done with convolution neural network (CNN). We then compared the five CNN structures that define the formats. The first format was used to set the number of filters to 64 and the size of the filter to 3 × 3 with 7 layers; the second format used 128 filters, each filter 3 × 3 in size with 7 layers; the third format used the number of filters in ascending order with 7 layers, all of which had an equal 3 × 3 filter size; the fourth format determined the number of filters in ascending order and the size of the filter based on a small size with 7 layers; the final format was a structure based on AlexNet. As a result, the average accuracy was 88.83%, 87.97%, 89.91%, 90.43%, and 92.03%, respectively. We implemented the CNN structure based on AlexNet to create models for multi-stroke TFSL recognition systems. The experiment was performed using an isolated video of 42 Thai alphabets, which are divided into three categories consisting of one stroke, two strokes, and three strokes. The results presented an 88.00% average accuracy for one stroke, 85.42% for two strokes, and 75.00% for three strokes.
Face alignment using two-stage cascaded pose regression and mirror error correction

Recently, a series of cascaded pose regression based facial landmark localization methods under occlusion have been proposed. However, partial occlusions and pose variations will break the entire structure of the face which poses obstacles to global regression. Moreover, there lack techniques to evaluate the reliability of the regression results during the regression process. In this paper, we propose a Two-Stage Cascaded Pose Regression(TSCPR) for facial landmark localization under occlusion. In the first stage, a global cascaded pose regression with robust initialization is performed to get localization results for the original face and its mirror image. The localization difference between the original image and the mirror image is used to determine whether the localization of each landmark is reliable, while unreliable localization can be adjusted. In the second stage, the global results are divided into multiple parts, which are further refined by local regressions. Finally, multiple refined local results are rated and adjusted to get the final output. We evaluated the proposed method on widely used datasets COFW, LFPW, HELEN, 300-W and Menpo-Semifrontal. The experimental results show that the proposed method can outperform the state-of-the-arts.
Reducing energy consumption of Neural Architecture Search: An inference latency prediction framework

Benefit from the success of NAS (Neural Architecture Search) in deep learning, humans are hopefully been released from the tremendous labor of manual tuning of structure and hyper-parameters. However, the success of NAS comes at the cost of much more computational resource consumption, thousands of times more computational power than ordinary training of manual-designed models, especially for the resource-aware multi-objective NAS, which must be serialized as a sequential loop of sampling, training, deployment, and inference. Recent research has shown that deep learning leads to huge energy consumption and CO2 emission (training of the namely Transformer can emit CO2 as much as five cars in their lifetimes Strubell et al. (2019)). Aiming to alleviate this issue, we propose the end-to-end inference latency prediction framework to empower the NAS process with a direct resource-aware efficiency indicator. Namely, we first propose the end-to-end latency prediction framework, which can predict latency quickly and accurately based on the dataset collected by ourselves. Eventually, we experimentally show that with the encoding scheme we designed, our proposed best model, LSTM-GBDT Latency Predictor(LGLP) achieves an excellent result of 0.9349 MSE, 0.5249 MAE, 0.9842 R2, and 0.9925 corrcoef. In other words, our limited dataset and encoding scheme already provide the precise knowledge representation of this large search space. By equipping NAS with the proposed framework, taking NEMO for example, it will save 1588 kWh⋅PUE energy, 1515 pounds CO2 emissions, and $3176 cloud compute cost of AWS. For NAS is now widely exploited in research or industry applications, this will bring incalculable benefits to society and the environment.
Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety

The concept of impact-minimisation has previously been proposed as an approach to addressing the safety concerns that can arise from utility-maximising agents. An impact-minimising agent takes into account the potential impact of its actions on the state of the environment when selecting actions, so as to avoid unacceptable side-effects. This paper proposes and empirically evaluates an implementation of impact-minimisation within the framework of multiobjective reinforcement learning. The key contributions are a novel potential-based approach to specifying a measure of impact, and an examination of a variety of non-linear action-selection operators so as to achieve an acceptable trade-off between achieving the agent’s primary task and minimising environmental impact. These experiments also highlight a previously unreported issue with noisy estimates for multiobjective agents using non-linear action-selection, which has broader implications for the application of multiobjective reinforcement learning.
Interactive multi-scale feature representation enhancement for small object detection

In the field of detection, there is a wide gap between the performance of small objects and that of medium, large objects. Some studies show that this gap is due to the contradiction between the classification-based backbone and localization. Although the reduction in the feature map size is beneficial for the extraction of abstract features, it will cause the loss of detailed features in the localization as traversing the backbone. Therefore, an interactive multi-scale feature representation enhancement strategy is proposed. This strategy includes two modules: first a multi-scale auxiliary enhancement network is proposed for feature interaction under multiple inputs. We scale the input to multiple scales corresponding to the prediction layers, and only passes through the lightweight extraction module to extract more detailed features for enhancing the original futures. Moreover, an adaptive interaction module is designed to aggregate the features of adjacent layers. This approach provides flexibility in achieving the improvement of small objects detection ability without changing the original network structure. Comprehensive experimental results based on PASCAL VOC and MS COCO datasets show the effectiveness of the proposed method.
The Effect of Mobile Learning on Student Success and Anxiety in Teaching Genital System Anatomy

The widespread use of smartphones has led to the emergence of new mobile learning tools. The aim of this study was to compare traditional methods to mobile learning applications, and their effect on the academic achievement and anxiety levels of students learning genital system anatomy. This research study was a randomized controlled study conducted with students who took anatomy between November and December 2018. The cohort consisted of 63 students who met the sampling criteria. Groups (control = 31, experimental = 32) were randomly selected using a simple number table. The mobile application developed for the experimental group was installed on the students' mobile devices with the extension "genitalsystem.apk." The anatomy of the genital system was taught to the control group using the standard curriculum and to the experimental group using the mobile application. After teaching the anatomy of the genital system, the state anxiety levels of the students in the control group were determined to be higher at 45.6 (±8.7) than the experimental group at 40.4 (±8.3) as measured by the 20-80 point STAI scale. The posttest examination average of the control group was 8.9 (±6.9) out of 22 or 40.4 (±6.9)% and the posttest average of the experimental group using mobile application was 14.9 (±5.5) or 67.7 (±5.5)%. State anxiety levels and examination grades showed a highly significant difference in favor of the experimental group. These results indicate that using mobile applications when teaching anatomy may be an effective method to enhance learning and reduce anxiety levels when compared to the traditional teaching methods.
Public Trust and Political Legitimacy in the Smart City: A Reckoning for Technocracy

The 2020 introduction by China’s central government of a national security law (NSL) in Hong Kong marked a watershed moment in the social and political history of the semiautonomous city. The law emerged after months of street protests that reflected declining public trust in Hong Kong’s government. Against this turbulent backdrop, Hong Kong’s policy projects moved forward, including smart city development. This article explores public trust in and political legitimacy of Hong Kong’s smart cities endeavors in the period leading up to the introduction of the NSL. At a theoretical level, the smart cities phenomenon invites critical reflection about tensions between technocracy and democracy, but this topic remains largely unexploited by empirical literature. Using survey data from 1,017 residents, this study identifies confidence in the benefits of smart cities but lesser trust in privacy and security and lesser satisfaction with participation opportunities in related policymaking. Probing these dynamics, the study finds that trust in smart city mechanics and governance associate positively with support for smart cities, controlling for ideology and issue awareness. Illuminating a theoretical and practical puzzle, these findings contribute empirically to discussions about the political legitimacy of scientific, technological, and technocratic undertakings in the public sector.
Towards IoT Security Automation and Orchestration

The massive boom of Internet of Things (IoT) has led to the explosion of smart IoT devices and the emergence of various applications such as smart cities, smart grids, smart mining, connected health, and more. While the proliferation of IoT systems promises many benefits for different sectors, it also exposes a large attack surface, raising an imperative need to put security in the first place. It is impractical to heavily rely on manual operations to deal with security of massive IoT devices and applications. Hence, there is a strong need for securing IoT systems with minimum human intervention. In light of this situation, in this paper, we envision security automation and orchestration for IoT systems. After conducting a comprehensive evaluation of the literature and having conversations with industry partners, we envision a framework integrating key elements towards this goal. For each element, we investigate the existing landscapes, discuss the current challenges, and identify future directions. We hope that this paper will bring the attention of the academic and industrial community towards solving challenges related to security automation and orchestration for IoT systems.
Svelto: High-Level Synthesis of Multi-Threaded Accelerators for Graph Analytics

Graph analytics are an emerging class of irregular applications. Operating on very large datasets, they present unique behaviors, such as fine-grained, unpredictable memory accesses, and highly unbalanced task level parallelism, that make existing high-performance general-purpose processors or accelerators (e.g., GPUs) suboptimal. To address these issues, research and industry are developing a variety of custom accelerator designs for this application area, including solutions based on reconfigurable devices (Field Programmable Gate Arrays). These new approaches often employ High-Level Synthesis (HLS) to Speed up the development of the accelerators. In this paper, we propose a novel architecture template for the automatic generation of accelerators for graph analytics and irregular applications. The architecture template includes a dynamic task scheduling mechanism, a parallel array of accelerators that enables supporting task-level parallelism with context switching, and a related multi-channel memory interface that decouples communication from computation and provides support for fine-grained atomic memory operations. We discuss the integration of the architectural template in an HLS flow, presenting the necessary modifications to enable automatic generation of the custom architectures starting from OpenMP annotated code. We evaluate our approach first by synthesizing and exploring triangle counting, a common graph algorithm, and then by synthesizing custom designs for a set of graph database benchmark queries, representing series of graph pattern matching routines. We compare the synthesized accelerators with previous state-of-the-art methodologies for the synthesis of parallel architectures, showing that the proposed approach allows reducing resource usage by optimizing the number of accelerators replicas without any performance penalty.
One-Class Classification Based Bug Triage System to Assign a Newly Added Developer

Bug triage is a software engineering problem in which a developer is assigned to a bug report. The existing methods use social network analysis, topic modeling, mining repositories, machine learning, and deep learning. Deep learning methods have shown promising results. However, these methods can not assign a newly added developer to the bug report. In this paper, we proposed a one-class SVM based method that trains a separate classifier for each developer. If a new developer is added to the project, then he or she can be considered by the triage system by training a classifier on his history. The results show an acceptable accuracy score. We believe that our preliminary study can pave the way to address this challenging problem.
Semi-Supervised Partial Multi-label Learning

Partial multi-label learning (PML) deals with problems where each instance is associated with a candidate label set, which contains multiple relevant labels and some noisy labels. In many real-world scenarios, it is impractical to annotate all examples for a huge-size dataset. Instead, a more common case is that only a small set of the data are annotated with partial labels, while most data are unlabeled. In this paper, we formalize such problems as a new learning framework called Semi-Supervised Partial Multi-label Learning (SSPML). To solve the SSPML problem, a latent label variable is introduced for each example as the low-dimensional embedding of the feature space. On one hand, label variables are recovered by encouraging consistent similarity measurement between the feature space and the label space; on the other hand, the similarities are adaptively updated based on the feedback from the label space. Meanwhile, the multi-label classifier is jointly trained under the supervision of label variables. Extensive experiments on multiple datasets from various real-world tasks validate the effectiveness of the proposed approach.
Frequency-Tuned Universal Adversarial Perturbations

The predictions of a convolutional neural network (CNN) for an image set can be severely altered by one single image-agnostic perturbation, or universal perturbation, even when the perturbation is small to restrict its perceptibility. Such universal perturbations are typically generated and added to an image in the spatial domain. However, it is well known that human perception is affected by local visual frequency characteristics. Based on this, we propose a frequency-tuned universal attack method to compute universal perturbations in the frequency domain. We show that our method can realize a good balance between perceptibility and effectiveness in terms of fooling rate by adapting the perturbations to the local frequency content. Compared with existing universal adversarial attack techniques, our frequency-tuned attack method can achieve cutting-edge quantitative results. We demonstrate that our approach can significantly improve the performance of the baseline on both white-box and black-box attacks.
Exploiting Scene-Specific Features for Object Goal Navigation

Can the intrinsic relation between an object and the room in which it is usually located help agents in the Visual Navigation Task? We study this question in the context of Object Navigation, a problem in which an agent has to reach an object of a specific class while moving in a complex domestic environment. In this paper, we introduce a new reduced dataset that speeds up the training of navigation models, a notoriously complex task. Our proposed dataset permits the training of models that do not exploit online-built maps in reasonable times even without the use of huge computational resources. Therefore, this reduced dataset guarantees a significant benchmark and it can be used to identify promising models that could be then tried on bigger and more challenging datasets. Subsequently, we propose the SMTSC model, an attention-based model capable of exploiting the correlation between scenes and objects contained in them, highlighting quantitatively how the idea is correct.
Gammadion binary pattern of Shearlet coefficients (GBPSC): An illumination-invariant heterogeneous face descriptor

This paper presents a novel face image descriptor called Gammadion Binary Pattern of Shearlet Coefficients (GBPSC) for illumination and noise invariant, homogeneous and heterogeneous face recognition. Exploiting the energy concentration property of the Digital Shearlet Transform, an efficient illumination and noise invariant feature extractor has been devised. Finally, inspired by the Gammadion structure, a robust multi-directional local binary pattern named Gammadion Binary Pattern (GBP) has been proposed. GBP is applied on the previously extracted illumination and noise invariant feature map to generate the GBPSC images. Recognition results on Extended Yale B and TUFTS dataset indicate the primacy of the proposed scheme in terms of common feature representation under varying illumination, and modality. Furthermore, the merger of the proposed GBPSC and Convolutional Neural Network (CNN) consistently outperforms other state-of-the art methods.
Detecting Adversarial Samples for Deep Learning Models: A Comparative Study

Deep learning techniques such as convolutional neural networks (CNNs) have been used in a wide range of fields due to their superior performance, e.g., image classification, autonomous driving and natural language processing. However, recent progress shows that deep learning models are vulnerable to adversarial samples, which are crafted by adding small perturbations on normal samples that are imperceptible to human beings but can mislead the deep learning models to output incorrect results. Many adversarial attack models are proposed and many adversarial detection methods are developed to detect adversarial samples generated by these attack models. However, the evaluations of these detection methods are fragmented and scatter in separate literature, and the community still lacks a comprehensive understanding of the ability and performance of existing adversarial detection methods when facing different attack models on different datasets. In this paper, by using image classification as the example application scenario, we conduct a comprehensive study on the performance of five mainstream adversarial detection methods against five major attack models on four widely used benchmark datasets. We find that the detection accuracy of different methods interleaves for different attack models and dataset. Moreover, besides detection accuracy, we also evaluate the time efficiency of different detection methods. The findings reported in this paper can provide useful insights when designing systems to detect adversarial samples and act as a guideline to design new methods to detect adversarial samples.
Does growth enhancement support scheme (GESS) contribute to youth development in informal farm entrepreneurship? Evidence from rural communities in Nigeria

Purpose The purpose of this paper is to critically examine the impact of a growth enhancement support scheme (GESS) on youth development in informal farm entrepreneurship in Nigeria. Its special focus is to investigate the impact of the GESS on rural youths’ adoption of new technologies needed to sustainably increase food security in Nigeria. Design/methodology/approach This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 800 rural youths were sampled across the six geopolitical zones of Nigeria. Findings The result from the use of a bivariate probit model indicates that the GESS has a significant impact on rural youths’ innovations in farming. Practical implications This suggests that information and communication technology could provide new opportunities for making farming more interesting and enterprising for rural young people. Social implications It implies that while old male and female farmers are less likely to adopt the new farming technologies needed to achieve Nigeria’s agricultural transformation agenda (ATA), a younger generation can help introduce new technologies, while also learning from traditional methods. Originality/value This research adds to the literature on informal farm entrepreneurship and rural communities’ debate in developing countries. It concludes that engaging youths in GESS should form the foundation of the ATA in Nigeria, which, in turn, would offer adequate combination of new and traditional solution to address the challenges of food insecurity in Sub-Saharan Africa.