Reviewerly/exHarmony huggingface dataset:
A Novel Systolic Parallel Hardware Architecture for the FPGA Acceleration of Feedforward Neural Networks

New chips for machine learning applications appear, they are tuned for a specific topology, being efficient by using highly parallel designs at the cost of high power or large complex devices. However, the computational demands of deep neural networks require flexible and efficient hardware architectures able to fit different applications, neural network types, number of inputs, outputs, layers, and units in each layer, making the migration from software to hardware easy. This paper describes novel hardware implementing any feedforward neural network (FFNN): multilayer perceptron, autoencoder, and logistic regression. The architecture admits an arbitrary input and output number, units in layers, and a number of layers. The hardware combines matrix algebra concepts with serial-parallel computation. It is based on a systolic ring of neural processing elements (NPE), only requiring as many NPEs as neuron units in the largest layer, no matter the number of layers. The use of resources grows linearly with the number of NPEs. This versatile architecture serves as an accelerator in real-time applications and its size does not affect the system clock frequency. Unlike most approaches, a single activation function block (AFB) for the whole FFNN is required. Performance, resource usage, and accuracy for several network topologies and activation functions are evaluated. The architecture reaches 550 MHz clock speed in a Virtex7 FPGA. The proposed implementation uses 18-bit fixed point achieving similar classification performance to a floating point approach. A reduced weight bit size does not affect the accuracy, allowing more weights in the same memory. Different FFNN for Iris and MNIST datasets were evaluated and, for a real-time application of abnormal cardiac detection, a ${\hspace{-0.112em}\times \hspace{-0.112em}}256$ acceleration was achieved. The proposed architecture can perform up to 1980 Giga operations per second (GOPS), implementing the multilayer FFNN of up to 3600 neurons per layer in a single chip. The architecture can be extended to bigger capacity devices or multi-chip by the simple NPE ring extension.
Feature-Level Frankenstein: Eliminating Variations for Discriminative Recognition

Recent successes of deep learning-based recognition rely on maintaining the content related to the main-task label. However, how to explicitly dispel the noisy signals for better generalization remains an open issue. We systematically summarize the detrimental factors as task-relevant/irrelevant semantic variations and unspecified latent variation. In this paper, we cast these problems as an adversarial minimax game in the latent space. Specifically, we propose equipping an end-to-end conditional adversarial network with the ability to decompose an input sample into three complementary parts. The discriminative representation inherits the desired invariance property guided by prior knowledge of the task, which is marginally independent to the task-relevant/irrelevant semantic and latent variations. Our proposed framework achieves top performance on a serial of tasks, including digits recognition, lighting, makeup, disguise-tolerant face recognition, and facial attributes recognition.
Towards a definition of “mathematical digital competency”

Due to the embeddedness of digital technologies in mathematics education of today, we often see examples of students simultaneously using their mathematical competencies and digital competencies. In relevant literature, however, these are not seen as a connected whole. Based on reviewing existing competency frameworks, both mathematical and digital, and by exploring an empirical example, this article addresses the question of how to think about and understand a combined “mathematical digital competency” (MDC). In doing so, the article relies on the two theoretical frameworks of the instrumental approach and conceptual fields to “bridge” the mathematical and digital competency descriptions.
Combining graph edit distance and triplet networks for offline signature verification

Offline signature verification is a challenging pattern recognition task where a writer model is inferred using only a small number of genuine signatures. A combination of complementary writer models can make it more difficult for an attacker to deceive the verification system. In this work, we propose to combine a recent structural approach based on graph edit distance with a statistical approach based on deep triplet networks. The combination of the structural and statistical models achieve significant improvements in performance on four publicly available benchmark datasets, highlighting their complementary perspectives.
An unconstrained ordination- and GIS-based approach for identifying anthropogenic sources of heavy metal pollution in marine sediments

A new method consisting of enrichment factor (EF) determination, nonmetric multidimensional scaling (NMS), and the geographic information system (GIS) technique was firstly developed to identify anthropogenic heavy metal sources in marine sediments of Hong Kong. Firstly, the EF was determined to differentiate between heavy metals originating from human and natural sources. Subsequently, NMS was applied to identify various source patterns of heavy metals, and the NMS score was calculated and spatially interpolated using GIS technology to evaluate the spatial influences of anthropogenic impacts in different areas. The concentrations of heavy metals in sediments of Hong Kong substantially exceeded their background values, demonstrating anthropogenic pollution. Two different types of human sources could be identified via NMS, one representing the industrial pollution discharges in the period from the 1960s to the 1980s before pollution control was introduced and one representing sewage discharge before the Tolo Harbour Action Plan in the mid-1980s.
On using eXtreme Gradient Boosting (XGBoost) Machine Learning algorithm for Home Network Traffic Classification

Traffic classification (TC) is a fundamental task of network management and monitoring operations. Previous works relying on selected packet header fields (e.g. port numbers) or application layer protocol decoding techniques are becoming increasingly difficult and inefficient when facing encrypted traffic and peer-to-peer flows. In this paper, we address the problem of flow based TC using machine learning (ML) algorithms. Our work considers a supervised approach, namely eXtreme Gradient Boosting (XGBoost) algorithm, which has never been investigated for TC. Performance evaluation results show that we obtain 99.5% accuracy on a dataset containing real flows. Additionally, compared to other ML algorithms, XGBoost is the most accurate one.
The number needed to benefit: estimating the value of predictive analytics in healthcare

Abstract Predictive analytics in health care has generated increasing enthusiasm recently, as reflected in a rapidly growing body of predictive models reported in literature and in real-time embedded models using electronic health record data. However, estimating the benefit of applying any single model to a specific clinical problem remains challenging today. Developing a shared framework for estimating model value is therefore critical to facilitate the effective, safe, and sustainable use of predictive tools into the future. We highlight key concepts within the prediction-action dyad that together are expected to impact model benefit. These include factors relevant to model prediction (including the number needed to screen) as well as those relevant to the subsequent action (number needed to treat). In the simplest terms, a number needed to benefit contextualizes the numbers needed to screen and treat, offering an opportunity to estimate the value of a clinical predictive model in action.
Migrant inventors and the technological advantage of nations

We investigate the relationship between the presence of migrant inventors and the dynamics of innovation in the migrants’ receiving countries. We find that countries are 25 to 60% more likely to gain advantage in patenting in certain technologies given a twofold increase in the number of foreign inventors from other nations that specialize in those same technologies. For the average country in our sample, this number corresponds to only 25 inventors and a standard deviation of 135. We deal with endogeneity concerns by using historical migration networks to instrument for stocks of migrant inventors. Our results generalize the evidence of previous studies that show how migrant inventors “import” knowledge from their home countries, which translates into higher patenting in the receiving countries. We interpret these results as tangible evidence of migrants facilitating the technology-specific diffusion of knowledge across nations.
Multiple Relational Attention Network for Multi-task Learning

Multi-task learning is a successful machine learning framework which improves the performance of prediction models by leveraging knowledge among tasks, e.g., the relationships between different tasks. Most of existing multi-task learning methods focus on guiding learning process by predefined task relationships. In fact, these methods have not fully exploited the associated relationships during the learning process. On the one hand, replacing predefined task relationships by adaptively learned ones may result in higher prediction accuracy as it can avoid the risk of misguiding caused by improperly predefined relationships. On the other hand, apart from the task relationships, feature-task dependence and feature-feature interactions could also be employed to guide the learning process. Along this line, we propose aMultiple Relational Attention Network (MRAN) framework for multi-task learning, in which three types of relationships are considered. Correspondingly, MRAN consists of three attention-based relationship learning modules: 1) a task-task relationship learning module which captures the relationships among tasks automatically and controls the positive and negative knowledge transfer adaptively; 2) a feature-feature interaction learning module that handles the complicated interactions among features; 3) a task-feature dependence learning module, which can associate the related features with target tasks separately. To evaluate the effectiveness of the proposed MARN, experiments are conducted on two public datasets and a real-world dataset crawled from a review hosting site. Experimental results demonstrate the superiority of our method over both classical and the state-of-the-art multi-task learning methods.
Survey on Intelligent Chatbots: State-of-the-Art and Future Research Directions

Human-computer interaction (HCI) is an area of interest which plays a major role in understanding the interaction between humans and machines. Dialogue systems or conversational systems including chatbots, voice control interfaces and personal assistants are examples of HCI application that have been developed to interact with users using natural language. Chatbots can help customers find useful information for their needs. Thus, numerous organizations are using chatbots to automate their customer service. Thus, the needs for using artificial intelligence has been increasing due to the needs of automated services. However, devolving smart bots that can respond at the human level is challenging. In this paper, we survey the state-of-art chatbot approaches from based on the ability to generate appropriate responses perspective. After summarizing the review from this aspect, we identify the research issues and challenges in chatbots. The findings of this research will highlight directions for future work.
A Simple and Efficient Network for Small Target Detection

Target detection based on deep learning is developing rapidly. However, small target detection is still a challenge. In this paper, a simple and efficient network for small target detection is proposed. We put forward to improve the detection performance of the small targets in three aspects. First, as the contextual information is important to detect the small targets, we proposed to use “dilated module” to expand the receptive field without loss of resolution or coverage. Second, we applied feature fusion in different dilated modules to improve the ability of the network in detecting small targets. Finally, we used “passthrough module” to get the finer-grained information from the earlier layer and combined it with the semantic information from the deeper layer. To improve the detection speed of the network, it is proposed to use <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 1$ </tex-math></inline-formula> convolution to reduce the dimension of the network. We composed small vehicle dataset based on VEDAI dataset and DOTA dataset, respectively, and also analyzed the distribution of the small targets in each dataset. To evaluate the performance of the proposed network, we trained the model on the dataset above and compared with the state-of-the-art target detection algorithms, our approach achieved 80.16% average precision (AP) on VEDAI dataset and 88.63% AP on DOTA dataset and the frames per second (FPS) is 75.4. The AP of our network is much better than the result of the tiny YOLO V3 and is nearly the same as the result of the YOLO V3. However, the FPS of our network is almost the same as that of the tiny YOLO V3.
Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack

Power side-channel analysis (SCA) has been of immense interest to most embedded designers to evaluate the physical security of the system. This work presents profiling-based cross-device power SCA attacks using deep learning techniques on 8-bit AVR microcontroller devices running AES-128. Firstly, we show the practical issues that arise in these profiling-based cross-device attacks due to significant device-to-device variations. Secondly, we show that utilizing Principal Component Analysis (PCA) based pre-processing and multi-device training, a Multi-Layer Perceptron (MLP) based 256-class classifier can achieve an average accuracy of 99.43% in recovering the first key byte from all the 30 devices in our data set, even in the presence of significant inter-device variations. Results show that the designed MLP with PCA-based pre-processing outperforms a Convolutional Neural Network (CNN) with 4-device training by ~20%in terms of the average test accuracy of cross-device attack for the aligned traces captured using the ChipWhisperer hardware.Finally, to extend the practicality of these cross-device attacks, another pre-processing step, namely, Dynamic Time Warping (DTW) has been utilized to remove any misalignment among the traces, before performing PCA. DTW along with PCA followed by the 256-class MLP classifier provides >=10.97% higher accuracy than the CNN based approach for cross-device attack even in the presence of up to 50 time-sample misalignments between the traces.
Sorry to Bother You: Designing Bots for Effective Recommendations

Bots have been proposed as a way to encourage developer actions and support software development activities. Many bots make recommendations to users, however humans may find these recommendations ineffective or problematic. In this paper, we argue that while bots can help automate many tasks, ultimately bots still need to find ways to interact with humans and handle all of the associated social and cognitive problems entailed. To illustrate this problem, we performed a small study where we generated 52 pull requests making tool recommendation to developers. As a result, we only convinced two developers to accept the pull request, while receiving several forms of feedback on why the pull request was ineffective. We summarize this feedback and suggest design principles for bot recommendations, including how psychology frameworks, such as nudge theory, can be used to improve human-bot interactions.
CROSS

Social e-commerce, as a new concept of e-commerce, uses social media as a new prevalent platform for online shopping. Users are now able to view, add to cart, and buy products within a single social media app. In this paper, we address the problem of cross-platform recommendation for social e-commerce, i.e., recommending products to users when they are shopping through social media. To the best of our knowledge, this is a new and important problem for all e-commerce companies (e.g. Amazon, Alibaba), but has never been studied before.
Exploratory Study of Slack Q&amp;A Chats as a Mining Source for Software Engineering Tools

Modern software development communities are increasingly social. Popular chat platforms such as Slack host public chat communities that focus on specific development topics such as Python or Ruby-on-Rails. Conversations in these public chats often follow a Q&A format, with someone seeking information and others providing answers in chat form. In this paper, we describe an exploratory study into the potential use-fulness and challenges of mining developer Q&A conversations for supporting software maintenance and evolution tools. We designed the study to investigate the availability of information that has been successfully mined from other developer communications, particularly Stack Overflow. We also analyze characteristics of chat conversations that might inhibit accurate automated analysis. Our results indicate the prevalence of useful information, including API mentions and code snippets with descriptions, and several hurdles that need to be overcome to automate mining that information.
Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network

It has been widely accepted that Long Short-Term Memory (LSTM) network, coupled with attention mechanism and memory module, is useful for aspect-level sentiment classification. However, existing approaches largely rely on the modelling of semantic relatedness of an aspect with its context words, while to some extent ignore their syntactic dependencies within sentences. Consequently, this may lead to an undesirable result that the aspect attends on contextual words that are descriptive of other aspects. In this paper, we propose a proximity-weighted convolution network to offer an aspect-specific syntax-aware representation of contexts. In particular, two ways of determining proximity weight are explored, namely position proximity and dependency proximity. The representation is primarily abstracted by a bidirectional LSTM architecture and further enhanced by a proximity-weighted convolution. Experiments conducted on the SemEval 2014 benchmark demonstrate the effectiveness of our proposed approach compared with a range of state-of-the-art models is available at https://github.com/GeneZC/PWCN.
An Efficient Hand Gesture Recognition System Based on Deep CNN

The goal of this paper is to use a webcam to instantly track the region of interest (ROI), namely, the hand region, in the image range and identify hand gestures for home appliance control (in order to create smart homes) or human-computer interaction fields. Firstly, we use skin color detection and morphology to remove unnecessary background information from the image, and then use background subtraction to detect the ROI. Next, to avoid background influences on objects or noise affecting the ROI, we use the kernelized correlation filters (KCF) algorithm to track the detected ROI. The image size of the ROI is then resized to 100x120 and then entered into the deep convolutional neural network (CNN), in order to identify multiple hand gestures. Two deep CNN architectures are developed in this study that are modified from AlexNet and VGGNet, respectively. Then, the above process of tracking and recognition is repeated to achieve an instant effect, and the system's execution continues until the hand leaves the camera range. Finally, the training data set can reach a recognition rate of 99.90%, and the test data set has a recognition rate of 95.61%, which represents the feasibility of the practical application.
Design Assessment in Virtual and Mixed Reality Environments: Comparison of Novices and Experts

The construction industry is facing a severe shortage of skilled workforce. Higher education is challenged to develop innovative strategies to help college students develop career-specific competency and accelerate the transition from novice to expert. Technology innovations such as virtual reality (VR) and mixed reality (MR) have been successfully integrated into learning and training programs to create authentic learning experiences within simulated virtual learning environments to facilitate tacit knowledge acquisition and workplace expertise development, which traditionally takes years of empirical experience and apprenticeship training. This study aims to explore potential VR and MR interventions in construction education and workforce development. It is directed at enhancing understanding of key differences between novices and experts and how VR and MR may facilitate tacit knowledge acquisition and expertise development to address the current skills gap in the construction industry. A simulation of accessibility design review and assessment for a tiny house was conducted via VR and MR mock-ups with the participation of both student novices and professional experts to collect behavioral and perceptual data using instruments that included a think-aloud protocol, a pair of pre- and postsurvey questionnaires, and audio/video recordings. Comparative analyses were conducted, and the results indicated that student novices, despite their lack of expertise, demonstrated comparable patterns of behaviors and achieved design review outcomes similar to those of professional experts with the VR and MR mock-ups. The findings of this study contribute to the body of knowledge by providing preliminary evidence of learning affordances of VR and MR in bridging experience-related gaps and suggesting opportunities for accelerating workplace expertise development among college students via technology intervention. These findings also have the potential to inform instructional design and pedagogical approaches that integrate VR and MR technology in undergraduate construction and engineering curricula.
Robust Video Stabilization by Optimization in CNN Weight Space

We propose a novel robust video stabilization method. Unlike traditional video stabilization techniques that involve complex motion models, we directly model the appearance change of the frames as the dense optical flow field of consecutive frames. We introduce a new formulation of the video stabilization task based on first principles, which leads to a large scale non-convex problem. This problem is hard to solve, so previous optical flow based approaches have resorted to heuristics. In this paper, we propose a novel optimization routine that transfers this problem into the convolutional neural network parameter domain. While we exploit the general benefits of CNNs, including standard gradient-based optimization techniques, our method is a new approach to using CNNs purely as an optimizer rather than learning from data.Our method trains the CNN from scratch on each specific input example, and intentionally overfits the CNN parameters to produce the best result on the input example. By solving the problem in the CNN weight space rather than directly for image pixels, we make it a viable formulation for video stabilization. Our method produces both visually and quantitatively better results than previous work, and is robust in situations acknowledged as limitations in current state-of-the-art methods.