Reviewerly/exHarmony huggingface dataset:
VFH Based Local Path Planning for Mobile Robot

Vector Field Histogram(VFH) and its variants are effective obstacles avoidance method for mobile robots. Under the more consideration of realistic, we proposed a new local path planning method for mobile robot in this paper. The proposed method takes global path point generated by the A* algorithm as temporary target point and adopts the basic concepts of the VFH* algorithm, the difference is mainly lies in the representation of vector magnitude of the obstacles, the determination of the threshold, the selection of the candidate direction, and the definition of the node cost function in search tree. Besides, according to the optimal obstacle avoidance direction obtained in the above steps, we also give a speed control method to control the movement of the omnidirectional mobile robot to the target point. Simulation and experiment results shows that the proposed method is suited for mobile robot moving in realistic situation.
Object Detection Techniques: Overview and Performance Comparison

Object detection algorithms are improving by the minute. There are many common libraries or application program interfaces (APIs) to use. The most two common ones are Microsoft Azure Cloud object detection and Google Tensorflow object detection. The first is an online-network based API, while the second is an offline machine-based API. Both have their advantages and disadvantages. A direct comparison between the most common object detection methods helps in finding the best solution for advance system integration. This paper will discuss both methods and compare them in terms of accuracy, complexity and practicality. It will show advantages and also limitations of each method, and possibilities for improvement.
Augmented Reality: Creating and Implementing Digital Classroom Supports

Augmented reality (AR) is an emerging technology that can help educators enhance their classrooms. With one-to-one initiatives and application (app) development creating affordable technology, teachers have more access to innovative supports for their students with disabilities’ academic, social, emotional, and behavioral skill development. Using AR, teachers can provide multimedia support while utilizing the student’s own environment. There are multiple apps available for teacher usage, which are free across multiple platforms and/or devices. This article describes exactly what AR is and how it can be used to support students with disabilities. An app is introduced to get started in the classroom with additional ways to utilize AR for learning.
Multi‐instance multi‐label learning for surgical image annotation

Abstract Background Various techniques have been proposed in the literature for phase and tool recognition from laparoscopic videos. In comparison, research in multilabel annotation of still frames is limited. Methods We describe a framework for multilabel annotation of images extracted from laparoscopic cholecystectomy (LC) videos based on multi‐instance multiple‐label learning. The image is considered as a bag of features extracted from local regions after coarse segmentation. A method based on variational Bayesian gaussian mixture models (VBGMM) is proposed for bag representation. Three techniques based on different feature extraction and bag representation models are employed for comparison. Results Four anatomical structures (abdominal wall, gallbladder, fat, and liver bed) and a tool‐like object (specimen bag) were annotated in 482 images. Our method achieved the best performance on single label accuracy: 0.87 (highest) and 0.69 (lowest). Moreover, the performance was &gt;20% higher in terms of four multilabel classification error metrics (one‐error, ranking‐loss, hamming‐loss, and coverage). Conclusions Our approach provides an accurate and efficient image representation for multilabel classification of still images captured in LC.
Monitoring patients with fragilities in the context of de-hospitalization services: An Ambient Assisted Living Healthcare Framework for e-Health applications

This article focuses on e-Health solutions for monitoring patients with fragilities in the context of de-hospitalization. Assistive technologies clearly have a direct and positive impact on the quality of life of patients, but they also improve the overall management of the organizational processes. In the first steps of “La Casa nel Parco” project funded by Regione Piemonte, we introduce here a general Ambient Assisted Living Healthcare framework to include business process analysis, telehealth and telemedicine applications. In particular, we define modeling and simulation of hospital services as a base to investigate the role of technological innovations. The project further investigates the impact of a telehealth solution with a wrist-worn device, as well as a telemedicine application based on augmented reality applied to pharmaceutical products. Our framework allows to explore the positive impact both on patient well-being as well as on business process management perspective.
Multi-variable estimation-based safe screening rule for small sphere and large margin support vector machine

Small Sphere and Large Margin (SSLM) SVM is one of the most competitive methods for Novelty Detection. However, the existing solvers for SSLM cannot deal with large data due to the expensive time cost. Although recently emerged safe screening methods can effectively enhance the computational speed, it is not available for SSLM because SSLM has multiple variables which cannot be represented explicitly by the linear combination of training samples. In this work, we construct a new safe screening rule for SSLM (MVE-SSR-SSLM) by integrating the ν-property, KKT conditions and variational inequalities. It is the first safe screening rule for a family of hypersphere support vector machine with multiple variables. The inactive samples are removed before actually solving the problem to accelerate the solving procedure without any loss of safety. Numerical experiments on fifteen benchmark datasets and Chinese wine dataset are conducted to show the validity and stability of the proposed MVE-SSR-SSLM.
Growing the FAIR Community at the Intersection of the Geosciences and Pure and Applied Chemistry

The geoscience and chemistry communities have numerous common practices and dependency on data standards. Recent efforts from the International Union on Pure and Applied Chemistry (IUPAC) and the American Geophysical Union (AGU) are to explore and collaborate on approaches and sharing lessons learned on efforts to implement the FAIR Guiding Principles as they apply to data in their respective communities. This paper summarizes their efforts-to-date highlighting the importance of existing communities, Scientific Unions, standards bodies and societies in taking deliberate steps to move and encourage researcher adoption of the FAIR tenets.
Industry Specific Word Embedding and its Application in Log Classification

Word, sentence and document embeddings have become the cornerstone of most natural language processing-based solutions. The training of an effective embedding depends on a large corpus of relevant documents. However, such corpus is not always available, especially for specialized heavy industries such as oil, mining, or steel. To address the problem, this paper proposes a semi-supervised learning framework to create document corpus and embedding starting from an industry taxonomy, along with a very limited set of relevant positive and negative documents. Our solution organizes candidate documents into a graph and adopts different explore and exploit strategies to iteratively create the corpus and its embedding. At each iteration, two metrics, called Coverage and Context Similarity, are used as proxy to measure the quality of the results. Our experiments demonstrate how an embedding created by our solution is more effective than the one created by processing thousands of industry-specific document pages. We also explore using our embedding in downstream tasks, such as building an industry specific classification model given labeled training data, as well as classifying unlabeled documents according to industry taxonomy terms.
Autonomous Robot Navigation Using Moth-Flame-Based Neuroevolution

Determining the best set of weights and biases for training neural networks (NN) using gradient descent techniques is a computationally challenging task. On the other hand, training of gradient descent algorithms suffers from being trapped in local optima and slow convergence speed in the last iterations. The moth-flame optimization (MFO) is a novel evolutionary method based on navigation paths of moths in nature. This algorithm showed its effectiveness in many real-world optimization problems. In this chapter, MFO is employed for training multilayer perceptron (MLP) to overcome the problems of gradient descent algorithms. This algorithm also investigates the application of the MFO for tackling the navigation of autonomous mobile robots. The results are compared with four powerful evolutionary algorithms including gray wolf optimizer (GWO), cuckoo search (CS), multiverse optimizer algorithm (MVO), and particle swarm optimization (PSO). Moreover, the results are compared to two gradient-based training MLP algorithms including Levenberg–Marquardt (LM) and back-propagation (BP). The evaluation metrics used in this book chapter are accuracy and area under the curve (AUC). The experimental results show that MFO-based MLP algorithm outperforms other algorithms and showed its capabilities effectively.
"Parar-daktar Understands My Problems Better"

This paper discusses the issues that arise while designing better wellbeing support for a low-income rural population in Bangladesh. Through a four-month long ethnographic study, we explored how people in 13villages in southwestern Bangladesh accessed healthcare. Over the course of our fieldwork, we asked the participants about the existing healthcare services available to them and how they interacted with different ICT-based wellbeing support systems. Our findings show that insufficient resources, schedules, and the distant location of government-supported healthcare facilities were major challenges for the villagers. We also found that villagers' limited knowledge and mistrust of care-providing infrastructure block them from the benefits of available ICT-based supports and resources. Drawing on our findings from the field, we discuss possible alternative design directions for improving wellbeing support for rural Bangladeshis.
Adaptive Iterative Pruning for Accelerating Deep Neural Networks

The new adaptive iterative pruning (AIP) approach is proposed. It differs from the standard iterative pruning- retraining scheme by the adaptively changing hyperparameters by linear, multiplicative, or adaptive laws (batch size, number of epochs, a minimal number of channels per layer) that compensate the loss of accuracy due to excessive retraining of the survived weights. It was shown, for example of classification task by VGG-16 model on MNIST dataset, that there is some domain of values where the loss of accuracy could be not higher than 2% with the inference speedup up to ×17 and the loss of accuracy could be not higher than 4% with the inference speedup up to ×30. In the more general sense, AIP can be widely applied for any deep learning model containing the convolutional layers, i.e. for any convolutional neural networks (CNNs), with the similar speedup of inference time and potentially faster convergence during training.
Information literacy education in WeChat environment at academic libraries in China

This paper investigates the practice of information literacy education in the mobile environment in China. The research chose 39 university libraries of Project 985 as the objects of study, and especially focused on the practice of the East China Normal University Library for in-depth discussions. The data and examples were collected through surveys on both Internet and mobile Internet from September 1, 2016 to March 31, 2019. Based on the discussion of survey results and specific examination of practice of information literacy education through WeChat at the East China Normal University, this paper offers some experience, feasible strategies, and suggestions on how to improve the mobile information literacy education by using WeChat platform.
A Qualitative Investigation of Insecure Code Propagation from Online Forums

Research demonstrates that code snippets listed on programming-oriented online forums (e.g., Stack Overflow) – including snippets containing security mistakes – make their way into production code. Prior work also shows that software developers who reference Stack Overflow in their development cycle produce less secure code. While there are many plausible explanations for why developers propagate insecure code in this manner, there is little or no empirical evidence. To address this question, we identify Stack Overflow code snippets that contain security errors and find clones of these snippets in open source GitHub repositories. We then survey (n=133) and interview (n=15) the authors of these GitHub repositories to explore how and why these errors were introduced. We find that some developers (perhaps mistakenly) trust their security skills to validate the code they import, but the majority admit they would need to learn more about security before they could properly perform such validation. Further, although some prioritize functionality over security, others believe that ensuring security is not, or should not be, their responsibility. Our results have implications for attempts to ameliorate the propagation of this insecure code.
Video deraining via nonlocal low-rank regularization

Outdoor videos captured in rainy weather may be significantly corrupted by the undesired rain streaks, which severely affect the video processing tasks in outdoor computer vision systems. In this paper, we propose a tensor-based video rain streaks removal method using the nonlocal low-rank regularization. Specifically, we first divide videos into overlapped spatial–temporal patches. Then for each patch, we group its nonlocal similar spatial–temporal patches to form a third-order tensor. To model the clean videos, we characterize the wealth redundancy by adopting the tensor nuclear norm to regularize the low-rankness of the third-order tensors formed by similar spatial–temporal patches of clean videos. We also consider the piecewise smoothness and the temporal continuity of clean videos and utilize the unidirectional total variation to enhance the smoothness and continuity. Moreover, as rain streaks are sparse and smooth along the rain direction, we model the rain streaks by employing an ℓ1 norm and the unidirectional total variation penalty to boost the sparsity and directional smoothness, respectively. We develop an efficient alternating direction method of multipliers to solve the proposed model. Experimental results on both synthetic and real rainy videos show that our method outperforms the state-of-the-art methods quantitatively and qualitatively.
Peak Clock

Clock glitches are an inexpensive method to attack embedded systems. Usually the intention is to alter the program flow or to extract cryptographic keys. However, the wide-spread use of Phase Locked Loops (PLLs) prohibits the direct reach-through on the internal clock. Hence, the commonly applied procedure to induce glitches on the external clock does not have any effect on these systems. In this paper, we show by means of two different ARM Cortex-M microcontrollers, that despite the fact that the system clock is derived from the external clock signal by a PLL, fault injection by manipulation of the external clock signal is yet feasible. Even though the process of fault injection is impeded, our results indicate that the risk from this attack vector cannot be eliminated by the use of PLLs. We demonstrate this in practice by successfully performing a differential fault attack on an AES implementation.
Poster

The interplay between machine learning and security is becoming more prominent. New applications using machine learning also bring new security risks. Here, we show it is possible to reverse-engineer the inputs to a neural network with only a single-shot side-channel measurement assuming the attacker knows the neural network architecture being used.
Significance of Pitch-Based Spectral Normalization for Children's Speech Recognition

It is well known from the literature that due to several acoustic mismatches, the recognition performances of children's speech using adult-trained-acoustic models get deteriorated. The differences in pitch and speaking rate are the two major factors that cause the acoustic mismatch between two groups of speakers. This work proposes to incorporate pitch information into an automatic speech recognition (ASR) system by exploiting the correlation between pitch and formants. By using the pitch-based spectrum normalization module in the front-end feature extraction process, the performance of mismatch ASR system is improved for children of different age groups. Further, fuzzy-based time scale modification is applied to study the effect of speaking-rate normalization on the proposed feature. The proposed feature results in relative improvement of 30% and 33% on DLSTM-based ASR system over the MFCC baseline without and with speaking-rate normalization, respectively.
Scalable 360 Video Streaming using HTTP/2

360-degree video is the main content type of Virtual Reality, providing users with immersive viewing experience. In this paper, we propose a novel adaptation method for 360-degree video streaming over HTTP/2, which can provide high viewing experience to users under time-varying network conditions and time-varying user head movements. The proposed method utilizes Scalable Video Coding to solve the trade-off between network adaptivity and user adaptivity. An optimal tile layer selection algorithm is provided. To cope with sudden throughput drops, the delivery of late layers is terminated using HTTP/2's stream termination feature. Also, a tile layer updating scheme is proposed to deal with viewport estimation errors. Experimental results show that the proposed method can improve the average bitrate of viewport by 16-17% compared to a reference method.
ME-TLS: Middlebox-Enhanced TLS for Internet-of-Things Devices

In-network middleboxes are vital for the Internet-of-Things (IoT) system security, but the widely adopted transport layer security (TLS) protocol blinds application-level middleboxes due to the encryption of traffic data. To resolve this problem, many solutions have been proposed to date. Among them, SplitTLS is widely adopted in the industry by proxy manufacturers. It requires TLS client to install customized root certificates and incurs additional security flaws, e.g., disabling server authentication and using weak cipher suites. Another approach is to customize the TLS protocol where middleboxes are enabled via either performing handshake directly with TLS endpoints or receiving session key materials in an out-of-band manner. Overall, current solutions would either jeopardize the original TLS handshake procedure or incur additional overheads on the endpoints. In this article, we design a new middlebox-enhanced TLS (ME-TLS), which enables endpoints to introduce authenticated middleboxes into a TLS session while control middleboxes' access permission and processing order of traffic data. Particularly, in our proposed ME-TLS, the handshake structure of TLS keeps unchanged and middleboxes work in a passive manner. That is, middleboxes in the ME-TLS could recover session key materials by monitoring handshake messages passively instead of interacting with endpoints; secondary secure channels for key transmission are also not needed in our ME-TLS. We implement our ME-TLS based on the TLS 1.3 protocol and evaluate its performances. The experimental results demonstrate that our proposal is practical and deployable for real-world IoT scenarios.
Multi-task based Sales Predictions for Online Promotions

The e-commerce era is witnessing a rapid development of various annual online promotions, such as Black Friday, Cyber Monday, and Alibaba's 11.11, etc. S ales P redictions for O nline P romotions (SPOP) are a set of sales related forecasts for the promotion day, including gross merchandise volume, sales volume, best selling products, etc. SPOP is highly important for e-commerce platforms to efficiently organize merchandise and maximize business values. However, sales patterns during the promotions are varied according to different scenarios, each model of which is designed with different features, static or dynamic, for one task in particular. Therefore, several models are proposed with part of features that are possibly beneficial to other tasks, which indicates the universal representation for the items needs to be learned across different promotion scenarios. To address this problem, this paper proposes a D eep I tem N etwork for O nline P romotions (DINOP). In DINOP, we design a novel T arget U sers C ontrolled G ated R ecurrent U nit (TUC-GRU) structure for dynamic features, and provide a new attention mechanism introducing static users profiles. In contrast to traditional prediction models, the network we proposed can effectively and efficiently learn universal item representation by incorporating users' properties as controllers. Furthermore, it can successfully discover the static and dynamic features guided by the multi-task learning, and is easily extended to other sales related prediction problems without retraining. Empirical results show that performance of DINOP in the real data set of Alibaba's Global Shopping Festival is superior to other state-of-the-arts practical methodologies in terms of the convergence rate and prediction accuracy.
Are we there yet?

Geoparsing is an important task in geographic information retrieval. A geoparsing system, known as a geoparser, takes some texts as the input and outputs the recognized place mentions and their location coordinates. In June 2019, a geoparsing competition, Toponym Resolution in Scientific Papers, was held as one of the SemEval 2019 tasks. The winning teams developed neural network based geoparsers that achieved outstanding performances (over 90% precision, recall, and F1 score for toponym recognition). This exciting result brings the question "are we there yet?", namely have we achieved high enough performances to possibly consider the problem of geoparsing as solved? One limitation of this competition is that the developed geoparsers were tested on only one dataset which has 45 research articles collected from the particular domain of Bio-medicine. It is known that the same geoparser can have very different performances on different datasets. Thus, this work performs a systematic evaluation of these state-of-the-art geoparsers using our recently developed benchmarking platform EUPEG that has eight annotated datasets, nine baseline geoparsers, and eight performance metrics. The evaluation result suggests that these new geoparsers indeed improve the performances of geoparsing on multiple datasets although some challenges remain.
Open-Source FPGA Implementation of Post-Quantum Cryptographic Hardware Primitives

The following topics are dealt with: field programmable gate arrays; learning (artificial intelligence); convolutional neural nets; logic design; system-on-chip; power aware computing; cryptography; cloud computing; reconfigurable architectures; neural nets.