Reviewerly/exHarmony huggingface dataset:
Facial Expression Recognition with Skip-Connection to Leverage Low-Level Features

Deep convolutional neural networks (CNNs) have established their feet in the ground of computer vision and machine learning, used in various applications. In this work, an attempt is made to learn a CNN for a task of facial expression recognition (FER). Our network has convolutional layers linked with an FC layer with a skip-connection to the classification layer. Motivation behind this design is that lower layers of a CNN are responsible for lower level features, and facial expressions can be mainly encoded in low-to-mid level features. Hence, in order to leverage the responses from lower layers, all convo-lutional layers are integrated via FC layers. Moreover, a network with shared parameters is used to extract landmark motion trajectory features. These visual and landmark features are fused to improve the performance. Our method is evaluated on the CK+ and Oulu-CASIA facial expression datasets.
Towards Hardware Implementation of Neural Network-based Communication Algorithms

There is a recent interest in neural network (NN)-based communication algorithms which have shown to achieve (beyond) state-of-the-art performance for a variety of problems or lead to reduced implementation complexity. However, most work on this topic is simulation based and implementation on specialized hardware for fast inference, such as field-programmable gate arrays (FPGAs), is widely ignored. In particular for practical uses, NN weights should be quantized and inference carried out by a fixed-point instead of floating-point system, widely used in consumer class computers and graphics processing units (GPUs). Moving to such representations enables higher inference rates and complexity reductions, at the cost of precision loss. We demonstrate that it is possible to implement NN-based algorithms in fixed-point arithmetic with quantized weights at negligible performance loss and with hardware complexity compatible with practical systems, such as FPGAs and application-specific integrated circuits (ASICs).
A New Unsupervised Predictive-Model Self-Assessment Approach That SCALEs

Evaluating the degradation of predictive models over time has always been a difficult task, also considering that new unseen data might not fit the training distribution. This is a well-known problem in real-world use cases, where collecting the historical training set for all possible prediction labels may be very hard, too expensive or completely unfeasible. To solve this issue, we present a new unsupervised approach to detect and evaluate the degradation of classification and prediction models, based on a scalable variant of the Silhouette index, named Descriptor Silhouette, specifically designed to advance current Big Data state-of-the-art solutions. The newly proposed strategy has been tested and validated over both synthetic and real-world industrial use cases. To this aim, it has been included in a framework named SCALE and resulted to be efficient and more effective in assessing the degradation of prediction performance than current state-of-the-art best solutions.
Deep Feature Factorization for Content-Based Image Retrieval and Localization

State of the art content-based image retrieval algorithms owe their excellent performance to the rich semantics encoded in the deep activations of a convolutional neural network. The difference between these algorithms lies mostly in how activations are combined into a compact global image descriptor. In this paper, we propose to use deep feature factorization to achieve this goal. By factorizing CNN activations, we decompose an input image into semantic regions, represented by both spatial saliency heatmaps and basis vectors serving as descriptors for those regions. When combined to form a global image descriptor, our experiments show that DFF surpasses the state of the art in both image retrieval and localization of the region of interest within the set of retrieved images.
Referring Expression Generation Using Entity Profiles

Referring Expression Generation (REG) is the task of generating contextually appropriate references to entities.A limitation of existing REG systems is that they rely on entityspecific supervised training, which means that they cannot handle entities not seen during training.In this study, we address this in two ways.First, we propose task setups in which we specifically test a REG system's ability to generalize to entities not seen during training.Second, we propose a profile-based deep neural network model, PROFILEREG, which encodes both the local context and an external profile of the entity to generate reference realizations.Our model generates tokens by learning to choose between generating pronouns, generating from a fixed vocabulary, or copying a word from the profile.We evaluate our model on three different splits of the WebNLG dataset, and show that it outperforms competitive baselines in all settings according to automatic and human evaluations.
Learning Performance Assessment for Culture Environment Learning and Custom Experience with an AR Navigation System

Culturally sustainable environmental education is a crucial issue that is inseparable from our society and environment; and closely linked to our life. Taiwan's cultural environment and family activities are characterized by religious beliefs and temple architectures. Incorporating digital technology into learning about the cultural environment of Taiwan can be an effective way to achieve sustainability. To have a deeper understanding of Taiwan's culture and aesthetics, this study uses AR (augmented reality) technology to bring interactive experiences to the temple, so that the public can interactively experience arts in the temple. Through the AR navigation system, people can ascertain cultural connotations and understand the power of Taiwan's beliefs. This study will take the Wumiao (martial temple) in Yanshui District, Tainan, Taiwan, as the research field to examine the learning experiences and performance with an AR navigation system. The empirical method is used to collect data of users’ behaviors and feelings; the statistics approach is also used to testify to the AR usability that is if with AR technology people can have a better understanding and learning performance on the culture aspect. Research finding proves one can have a better learning experience with AR tech with a positive result. According to the correlation coefficient analysis, only perceived enjoyment and perceived ease of use are moderately correlated, the other differences are highly correlated. It is thus clear that AR navigation systems should be applied for having better folk beliefs, cultural etiquette learning experiences, and learning performance, more, for not merely having faith, but also loyalty as well.
LASSO‐based false‐positive selection for class‐imbalanced data in metabolomics

Abstract Feature selection and rebalancing can be seen as two preprocessing ways in class‐imbalanced learning. Recently, there have been many research achievements and applications on LASSO‐type feature selection, whereas most of them are not directly designed for addressing class‐imbalanced data. In this study, we proposed a LASSO‐based stable feature selection algorithm for class‐imbalanced data analysis, and false‐positive selection (FPS) under balanced and imbalanced situations was calculated via selection frequency of each predictor in doing stable selection. The results on simulation studies and real data examples show that class imbalance contributes to avoid overselection caused by LASSO when the data are highly correlated and a lower FPS can be obtained with class‐imbalanced data than balanced one in most of cases in the same settings. A statistical explanation was given for this phenomenon. In addition, it does not need to rebalance the class‐imbalanced data for performing such LASSO‐based feature selection with a stable strategy, and to some degree, intentionally disequilibrating the balanced data could be an alternative strategy to weaken overselection and to perform biomarker identification for finding a few of most important biomarkers.
Decision Support System on Selection of Lecturer Research Grant Proposals using Preferences Selection Index

Abstract The Institute for Research and Community Service is a forum for lecturers at every university/institution to develop their knowledge according to their respective disciplines through research fields. This study aims to determine the recipient of the best research grant at STIKOM Tunas Bangsa Pematangsiantar. Research data comes from relevant agencies. There are 9 names of lecturers that are used as alternatives and 5 criteria as the assessment of grantees, namely type of problem formulation (C1), research output opportunities (C2), research method (C3), literature review (C4) and research feasibility (C5). This study uses the Preference selection index (PSI) method. In this method does not require relative determination of attributes, so the researcher does not need to determine the weight criteria. Based on the results of the study obtained several alternatives with the highest preference value, A8 (index selection value 0.8819), A9 (index selection value 0.8627), A4 (index selection value 0.8447), A3 (index selection value 0.83123) and A5 (Index selection value 0.8184) are entitled to get an internal research grant at STIKOM Tunas Bangsa. With this system, it can be used as a comparison between reviewer evaluators and the PSI system so as to maintain the quality of assessment in determining the recipients of internal research grants objectively. It is hoped that this research can provide recommendations to the Institute for Research and Community Services in determining internal lecturer research proposal grants so that the results obtained are more objective.
APLNet: Attention-enhanced progressive learning network

Single-stage detectors depend on a simple regression network to predict category scores and regress box offsets for a fixed set of default boxes directly. The regression network needs to have high generalization capability, so as to accurately model the relationship between various object shapes and default boxes. Instead of complicating the regression network to increase generalization capability, we iteratively refine the default boxes to model this relationship sequentially. In this paper, we propose an Attention-Enhanced Progressive Learning Network (APLNet), which employs multiple stages for progressive detection to improve performance of single-stage detectors. Specifically, a progressive learning module is proposed to iteratively update the feature representation space and gradually regress the default boxes, which are pushed closer to the target objects progressively. In addition, since low-level features have less semantic information about objects, we design an attention enhancement module to generate the attention map applied to inject more semantically meaningful information into the low-level features. This module is supervised by boxes-induced segmentation annotations, i.e., no extra segmentation annotations are required. The multi-task loss function is used to train the whole network in an end-to-end way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets demonstrate the effectiveness of the proposed APLNet.
Workload-Aware Task Placement in Edge-Assisted Human Re-identification

This work is a cross-domain study by utilizing the most recent cloud and edge computing techniques in the human re-identification, which is a popular computer-vision application motivated by the demand of connecting and monitoring our world in the era of Internet of Things (IoT). We systematically study the real-time re-identification problem within a large-scale video surveillance network. Motivated by the system heterogeneity in terms of real-time workload and hardware configurations, we develop a workload-aware distributed system, which optimally allocates tasks across edge servers and cloud, for pursuing a user-controlled trade-off between system responsiveness & utility. We use an experiment-oriented approach to measure and model the edge heterogeneity. A two-phase task-placement algorithm is proposed which runs with the model built in the off-line phase, and driven by the dynamic real-time workload in runtime. We implement our entire system on a commercial cloud platform and use extensive simulations and experiments to validate its efficacy and responsiveness in practice.
NurseNet: Monitoring Elderly Levels of Activity with a Piezoelectric Floor

Monitoring the activity of elderly individuals in nursing homes is key, as it has been shown that physical activity leads to significant health improvement. In this work, we introduce NurseNet, a system that combines an unobtrusive, affordable, and robust piezoelectric floor sensor with a convolutional neural network algorithm, which aims at measuring elderly physical activity. Our algorithm is trained using signal embedding based on atoms of a pre-learned dictionary and focuses the network's attention on step-related signals. We show that NurseNet is able to avoid the main limitation of floor sensors by recognizing relevant signals (i.e., signals produced by patients) and ignoring events related to the medical staff, offering a new tool to monitor elderly activity in nursing homes efficiently.
Comparison of SMART and SAW Methods in Decision Making

Abstract In this study using a comparative analysis of SMART and SAW methods. The results of the SAW method calculation analysis have a faster processing time with a time of 0.40 s. While the SMART method calculation analysis has a longer processing time difference of 0.47 s. In this study the authors suggest using the SMART method. Where the process of calculating the SMART method has more varied results, because the criteria value between one candidate and another candidate influences each other. While the calculation of the SAW method results are less varied, because the criteria value between one candidate and another candidate does not affect each other. Weighting is done without an algorithm. In this study the authors suggest using the SMART method because there is a final value of &lt;60 is not feasible given the provision of venture capital.
Efficient Truncated Statistics with Unknown Truncation

We study the problem of estimating the parameters of a Gaussian distribution when samples are only shown if they fall in some (unknown) set. This core problem in truncated statistics has long history going back to Galton, Lee, Pearson and Fisher. Recent work by Daskalakis et al. (FOCS'18), provides the first efficient algorithm that works for arbitrary sets in high dimension when the set is known, but leaves as an open problem the more challenging and relevant case of unknown truncation set. Our main result is a computationally and sample efficient algorithm for estimating the parameters of the Gaussian under arbitrary unknown truncation sets whose performance decays with a natural measure of complexity of the set, namely its Gaussian surface area. Notably, this algorithm works for large families of sets including intersections of halfspaces, polynomial threshold functions and general convex sets. We show that our algorithm closely captures the tradeoff between the complexity of the set and the number of samples needed to learn the parameters by exhibiting a set with small Gaussian surface area for which it is information theoretically impossible to learn the true Gaussian with few samples.
Understanding the Impact of Dynamic Power Capping on Application Progress

Electrical power has become an important design constraint in high-performance computing (HPC) systems. On future HPC machines, power is likely to be a budgeted resource and thus managed dynamically. Power management software needs to reliably measure application performance at runtime in order to respond effectively to changes in application behavior. Execution time tells us little about how the science in the application is progressing toward an application-defined end goal. To the best of our knowledge, no study has defined or categorized online application progress in the context of power management. Based on semi-structured interviews with HPC application-specialists, we define an online notion of progress-an application-specific metric that can be monitored at runtime to provide a sense of the rate at which application science is being performed. Using instrumentation, we characterize and categorize the progress of various production scientific applications and benchmarks. We propose a model of the impact of dynamic power capping on application progress. By experimental evaluation, we show that our model accurately captures the general behavior of the progress of different classes of applications under a power cap. We believe that such a model is an important first step toward the design of more dynamic power management policies for HPC systems.
“TwitterSpamDetector”

Twitter is the most popular microblogging platform which lets users post status messages called tweets. This popularity and the advanced API provided by Twitter to read and write Twitter data programmatically attracts the attention of spammers as well as legitimate users. Since Twitter has some unique characteristics, the traditional spam detecting methods cannot be directly used to detect spam on Twitter. Therefore, a spam detection framework which is specially designed for Twitter namely TwitterSpamDetector is proposed in this paper. TwitterSpamDetector uses Twitter-specific features to detect spam on Twitter. 77,033 tweets which are posted by 50,490 users collected using the API provided by Twitter. Naive Bayes is used to train TwitterSpamDetector using the selected features of Twitter which clearly classify the spammers from legitimate users. According to the evaluation result, TwitterSpamDetector's accuracy and sensitivity are calculated as 0.943 and 0.913, respectively.
Automatic femur length measurement for fetal ultrasound image using localizing region-based active contour method

Abstract This research aims to apply the localizing region-based active contour (LRAC) method to acquire the femur length in an ultrasound image automatically and to determine the effect of noise removal on the segmentation accuracy. The automatic femur length measurement system includes three main steps. The first step is the denoising process to reduce speckle noise in the ultrasound image. Afterwards, the LRAC method is applied to detect and segment a local region. The segmentation process with a certain number of iterations and a weight of the smoothing terms is started at the selected initial pixel. At the final step, the femur length is measured to estimate the gestational age. The experiment results show that the accuracy of the estimated gestational age increases significantly when the noise reduction technique is employed.
An approach for extracting curve profiles based on scanned point cloud

To control product quality, it is essential to conduct surface detection and curve detection. The existing approaches are sufficiently developed in detecting surfaces while are limited in detecting curves. It is essential to extract the curve profiles from the product at first. An approach of extracting curve profiles from scanned point cloud is proposed. Firstly, a slice set is constructed to extract section line points from scanned point cloud. Secondly, based on normal angle of adjacent points, three pattern vectors are defined to distinguish fold-line section line, small-arc section line and large-arc section line. Thirdly, three methods are proposed to identify the curve-profile points on the three kinds of section lines. Qualification experiments are conducted on seven circle profiles and a rectangular profile. The results show that extracted curve-profile points deviate from reference data within ±0.1 mm with standard deviations smaller than 0.07 mm.
Analysis of Complex Networks for Security Issues using Attack Graph

Organizations perform security analysis for assessing network health and safe-guarding their growing networks through Vulnerability Assessments (AKA VA Scans). The output of VA scans is reports on individual hosts and its vulnerabilities, which, are of little use as the origin of the attack can't be located from these. Attack Graphs, generated without an in-depth analysis of the VA reports, are used to fill in these gaps, but only provide cursory information. This study presents an effective model of depicting the devices and the data flow that efficiently identifies the weakest nodes along with the concerned vulnerability's origin.The complexity of the attach graph using MulVal has been greatly reduced using the proposed approach of using the risk and CVSS base score as evaluation criteria. This makes it easier for the user to interpret the attack graphs and thus reduce the time taken needed to identify the attack paths and where the attack originates from.
Contrastive Explanations to Classification Systems Using Sparse Dictionaries

Providing algorithmic explanations for the decisions of machine learning systems to end users, data protection officers, and other stakeholders in the design, production, commercialisation and use of machine learning systems pipeline is an important and challenging research problem. Much work in this area focuses on image classification, where the required explanations can be given in terms of images, therefore making explanations relatively easy to communicate to end-users. For a classification problem, a contrastive explanation tries to understand why the classifier has not answered a particular class, say B, instead of the returned class A. Sparse dictionaries have been recently used to identify local image properties as main ingredients for a system producing humanly understandable explanations for the decisions of a classifier developed based on machine learning methods. In this paper, we show how the system mentioned above can be extended to produce contrastive explanations.
Hybrid Evolutionary Algorithm for Synthesized Optimal Control Problem for Group of Interacting Robots

This paper considers the optimal control problem for a group of interacting robots. To solve the problem, a hybrid evolutionary algorithm is applied that consists of two popular evolutionary algorithms: particle swarm optimization and gray wolf optimizer. When solving the problem, we use the approach of synthesized optimal control. Initially, we make controlled objects stable in the state space with respect to a certain point. Then we look for coordinates of the stabilization points using the hybrid algorithm. Points should be located so that when switching stabilization points after each fixed time interval, the objects reach the control goal without violating the phase constraints with the optimal value of the quality criterion.