Reviewerly/exHarmony huggingface dataset:
FPGA-based Edge Inferencing for Fall Detection

In the geriatric population, physical injuries sustained by an unintentional or an unpredictable fall on a hard surface is the leading cause of injury related morbidity and sometimes mortality. Each year, close to 30% of adults around the age group of 65 fall down at least once. In the year 2015, close to 2.9 million falls were reported, resulting in 33,000 deaths. As much as 61% of elderly nursing home residents fell at some point during their first year of residence.These falls may aggravate the situation leading to bone fracture, concussion, internal bleeding or traumatic brain injury when immediate medical attention is not offered to the person. Delay in course of the event may sometimes lead to death as well. Recently, many studies have come up with wearable devices. These devices that are now commercially available in the market are small, compact, wireless, battery operated and power efficient. This study discusses the findings that the optimal location for a Fall Detection Sensor on the human body is in front of the Shin bone. This is based on the 183 features collected from Inertial Measurement Unit (IMU) sensors placed on 16 human body locations and trained-tested using Convolutional Neural Networks (CNN) machine learning paradigm. The ultimate goal is to develop a mobile, wireless, wearable, low-power medical device that uses a small Lattice iCE40 Field Programmable Gate Array (FPGA) integrated with gyro and accelerometer sensors which detects whether the device wearer has fallen or not. This FPGA is capable of realizing the Neural Network model implemented in it. This Insitu or Edge inferencing wearable device is capable of providing real-time classifications without any Transmitting or Receiving capabilities over a wireless communication channel.
The Task of Improving the University Ranking Based on the Statistical Analysis Methods

The task of ranking the university in international rating systems is urgent. An approach to solving the problem is proposed to ensure the required values of the basic indicators of the university’s activity in the international institutional ranking QS using models developed on the basis of methods of correlation-regression analysis and factor analysis. Estimates of basic indicators and ratings were obtained based on the methods of correlation and regression analysis. A comparative analysis of the results obtained for universities in the reference group Interpretation of the results of factor analysis revealed a set of latent factors that have a significant impact on the baseline indicators. It is shown that measures to achieve the specified indicators must be carried out considering the identified correlation dependences of latent exponential factors and basic indicators, as well as the results of interpretation of the developed factor model. The novelty of the developed proposals lies in the assessment of the significance of latent factors affecting the basic indicators of the analysis of the university’s activities, based on the use of correlation-regression methods and methods of factor analysis. The developed factorial model made it possible to group and structure the obtained data, as well as to reduce the dimension of the problem being solved. The results obtained allow us to solve the problem of identifying latent factors and to substantiate the conditions for achieving the required indicators of the university ranking in the international financial ranking QS.
The Weighted Average Ensemble Learning Based on Polar Bear Algorithm with T-Distribution Parameters

Ensemble learning is a supervised classification algorithm in the field of machine learning and artificial intelligence. Its main idea is to combine the weak classifiers into strong classifiers according to a certain strategy, so that ensemble learning becomes a linear weighting function. In this paper, the weighted average ensemble algorithm is used for dichotomy. However, due to the limitation of loss function, the traditional gradient descent method and the random gradient descent method are not available. Therefore, the polar bear algorithm in the swarm intelligence algorithm that does not depend on the gradient information is adopted in this paper to obtain the optimal weight, which overcomes the original shortcomings.
An Intuitionistic Fuzzy Set Driven Stochastic Active Contour Model with Uncertainty Analysis

Image segmentation is a process that densely classifies image pixels into different regions corresponding to real world objects. However, this correspondence is not always exact in images since there are many uncertainty factors, e.g., recognition hesitation, imaging equipment, condition, and atmosphere environment. To achieve the segmentation result with low uncertainty and reduce the influence on the subsequent procedures, e.g., image parsing and image understanding, we propose a novel stochastic active contour model based on intuitionistic fuzzy set, in which the hesitation degree is leveraged to model the recognition uncertainty in image segmentation. The advantages of our model are as follows. (1) Supported by fuzzy partition, our model is robust against image noise and inhomogeneity. (2) Benefiting from the stochastic process, our model easily crosses saddle points of energy functional. (3) Our model realizes image segmentation with low uncertainty and co-produces the quantitative uncertainty degree to the segmentation results, which is helpful to improve reliability of intelligent image systems. The associated experiments suggested that our model could obtain competitive segmentation results compared to the relevant state-of-the-art active contour models and could provide segmentation with a pixel-wise uncertainty degree.
Instant and Accurate Instance Segmentation Equipped with Path Aggregation and Attention Gate

With the development of GPU and deep learning, there has been great advances in the field of object detection and segmentation. Instance segmentation is one of the most important tasks used in many areas including autonomous vehicles and video surveillance because such areas require both high frames per second (FPS) and high accuracy. In this paper, we propose a method of attaching path aggregation network and attention gate based on real-time instance segmentation model, YOLACT, to increase the accuracy of instance segmentation. As a result of applying the proposed method to the YOLACT framework, the processing speed drops slightly by 2.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , but the accuracy increases significantly up to 1.4AP, while still maintaining realtime processing of 32.6FPS.
Promises of Anthropomorphism in Virtual Coaches

This position paper aims to initiate a dialogue among HCI researchers about the current research and future directions on anthropomorphism in implementing health virtual coaches. We summarize key research trends on anthropomorphized virtual coaches and present three future directions on this area that expand on and address gaps from previous studies. Our ultimate goal is to implicate future creation of virtual coaches in health that can effectively improve the well-being of users.
Revealing stable and unstable modes of denoisers through nonlinear eigenvalue analysis

In this paper, we propose to analyze stable and unstable modes of black-box image denoisers through nonlinear eigenvalue analysis. We aim to find input images for which the denoiser output is proportional to the input. We treat this as a generalized nonlinear eigenproblem. Potential implications are wide, as most image processing algorithms can be viewed as black-box operators. We introduce a generalized nonlinear power-method to solve eigenproblems for such operators. This allows us to reveal stable modes of the denoiser: optimal inputs, achieving superior PSNR in noise removal. Analogously to the linear case, such stable modes show coarse structures and correspond to large eigenvalues. We also provide a method to generate unstable modes, which the denoiser suppresses strongly, which are textural with small eigenvalues. We validate the method using total-variation (TV) and demonstrate it on the EPLL (Zoran–Weiss) and the Non-local means denoisers. Finally, we suggest an encryption–decryption application.
Design and Development of AR-PlaSys: Augmented Reality Based Plant Monitoring System

This paper presents the design of the Augmented Reality Plant System (AR-PlaSys). AR-PlaSys is designed by merging Augmented Reality (AR) and the Internet of Things (IoT). A proficient observing framework is required for easy and long-term plant monitoring in industries such as nurseries and at domestic level. AR-PlaSys is an innovative and viable solution for the same. The humidity and temperature data from plant is collected by a DHT11 sensor and uploaded to Particle Photon cloud. The screen to be augmented is built in Unity 3D which is a real-time 3D development platform and is connected to the Particle Cloud. An android application file (APK) is developed which can be installed in all android cell phones. Whenever the camera detects the target image data is retrieved from the cloud and augmented on the screen. This system is unique, compact, cost and energy-efficient which can be deployed for domestic and industrial purposes.
On a correspondence between maximal cliques in Paley graphs of square order

Let q be an odd prime power. Denote by r(q) the value of q modulo 4. In this paper, we establish a linear fractional correspondence between two types of maximal cliques of size q+r(q)2 in the Paley graph of order q2.
Maintaining Privacy and Utility in IoT System Analytics

Privacy risks can hinder the future of IoT applications. Users should know and trust that they are only sharing or exposing specific portions of their data that will be used for data analysis on cloud services. However, this is not a trivial problem, given that any IoT sensor can potentially capture much more information than the original purpose. This data can potentially contain unknown micro-signatures of sensitive information that can be mined by attackers using highly available and powerful AI tools. Indiscriminately sharing all data for remote storage or processing is risky. In this paper, we share our vision of how IoT systems can be built to preserve both privacy and utility. We show that some unique open challenges and opportunities arise in designing utility-preserving privacy techniques in resource-constrained IoT devices.
Loss of fidelity in scanned digital images compared to glass slides of brain tumors resected using cavitron ultrasonic surgical aspirator

Conversion of glass slides to digital images is necessary to capitalize on advances in computational pathology and could potentially transform our approach to primary diagnosis, research, and medical education. Most slide scanners have a limited maximum scannable area and utilize proprietary tissue detection algorithms to selectively scan regions that contain tissue, allowing for increased scanning speed and reduced file size compared to scanning the entire slide at high resolution. However, very small and faintly stained tissue fragments may not be recognized by these algorithms, leading to loss of fidelity in the digital image compared to the glass slides. Cavitron ultrasonic surgical aspirator (CUSA) is frequently used in brain tumor resections, resulting in highly fragmented specimens that are used for primary diagnosis. Here we evaluated the rate of loss of fidelity in 296 digital images from 40 CUSA-resected brain tumors scanned using a Philips Ultra Fast Scanner. Overall, 54% of the slides (at least one from every case) showed loss of fidelity, with at least one tissue fragment not scanned at high resolution. The majority of the missed tissue fragments were small (<0.5 mm), but rare slides were missing fragments greater than 5 mm in greatest dimension. In addition, 19% of the slides with missing tissue showed no indication of loss of fidelity in the digital image itself; the missing tissue could only be appreciated upon review of the glass slides. These results highlight a potential liability in the use of digital images for primary diagnosis in CUSA-resected brain tumor specimens.
The Impact of Elliptic Curves Name Selection to Session Initiation Protocol Server

Voice over Internet Protocol (VoIP) has gained much attention in couple decade ago, and it uses Session Initiation Protocol (SIP) as a signaling protocol to handle voice calls setup. SIP likes other technologies is vulnerable to security attacks and for that it utilizes cryptography algorithms in order to secure its data transmission. Recently, Elliptic Curve Cryptography (ECC) is relatively newer form of public key cryptography that ensures high level security with acceptable performance. However, ECC developed with various curve names and key size which in turn impact SIP server performance accordingly. This paper aims to evaluate SIP server performance under different curve names such as brainpool, prime, secp, sect, and etc. The experiment has been carried out in different scenarios with heavy SIP traffic load and torture test applied. Results come out with recommended selective curves name to conclude that prime(256) curve utilizes low CPU usage to hit only 1.49% of all CPU system which is suitable choice for resource-limited devices whereas secp(521) is a preferable curve name selected for real-time communication as it spends 20ms response time.
Auto-Tiler: Variable-Dimension Autoencoder with Tiling for Compressing Intermediate Feature Space of Deep Neural Networks for Internet of Things

Due to limited resources of the Internet of Things (IoT) edge devices, deep neural network (DNN) inference requires collaboration with cloud server platforms, where DNN inference is partitioned and offloaded to high-performance servers to reduce end-to-end latency. As data-intensive intermediate feature space at the partitioned layer should be transmitted to the servers, efficient compression of the feature space is imperative for high-throughput inference. However, the feature space at deeper layers has different characteristics than natural images, limiting the compression performance by conventional preprocessing and encoding techniques. To tackle this limitation, we introduce a new method for compressing DNN intermediate feature space using a specialized autoencoder, called auto-tiler. The proposed auto-tiler is designed to include the tiling process and provide multiple input/output dimensions to support various partitioned layers and compression ratios. The results show that auto-tiler achieves 18% to 67% higher percent point accuracy compared to the existing methods at the same bitrate while reducing the process latency by 73% to 81%. The dimension variability of an auto-tiler also reduces the storage overhead by 62% with negligible accuracy loss.
Detecting Crime Related Twitter Posts using Artificial Neural Networks based Approach

Crime is defined as an act harmful not only to the individual involved but also to the community as a whole. We can identify the crime patterns and predict the crimes by detecting and analyzing the historical data. However, some crimes are unregistered and unsolved due to a lack of evidence. Thus, detecting crimes is a still challenging task. Recently, Social Networks are becoming a great environment for sharing news. Thus, we can use social media like twitter to detect crimes related activities. Because Twitter users sometimes convey messages related to his or her surrounding environment via twitter. In this paper, we proposed a machine learning approach to detect the crimes and the location of the crimes. As the first step, we fetch the twitter posts using predefined keywords relating to the crimes. Then, we constructed an Artificial Neural Network (ANN) model to classify the twitter posts. Then in the final stage, we get the geolocation and crime type. The empirical study of our prototyping system has proved the effectiveness of our proposed crime detection approach.
Automating the Evaluation of Education Apps With App Store Data

With the vast number of apps and the complexity of their features, it is becoming challenging for teachers to select a suitable learning app for their courses. Several evaluation frameworks have been proposed in the literature to assist teachers with this selection. The iPAC framework is a well-established mobile learning framework highlighting the learners' experience of personalization, authenticity, and collaboration (iPAC). In this article, we introduce an approach to automate the identification and comparison of iPAC relevant apps. We experiment with natural language processing and machine learning techniques, using data from the app description and app reviews publicly available in app stores. We further empirically validate the keyword base of the iPAC framework based on the app users' language in app reviews. Our approach automatically identifies iPAC relevant apps with promising results (F1 score ~ 72%) and evaluates them similarly as domain experts (Spearman's rank correlation 0.54). We discuss how our findings can be useful for teachers, students, and app vendors.
Preliminary Applications of Blockchain Technology in Electricity Information Acquisition System

In order to improve the security of electricity information acquisition system and reduce the risk of being damaged by attacks, combined with the technical advantages of blockchain such as decentralization, non-tampering, and traceability, this paper proposes an electricity information acquisition system based on blockchain technology. Firstly, the paper introduces the basic principles and technical advantages of blockchain, and then describes the network architecture of blockchain-based acquisition system in detail. According to the characteristics of electricity information acquisition system, the formulation of smart contracts in typical application scenarios is discussed. This article provides a reference for the application and practice of blockchain technology in the electricity information acquisition system.
Quantifying Cloud Misbehavior

Clouds have gained popularity over the years as they provide on-demand resources without associated long-term costs. Cloud users often gain superuser access to cloud machines, which is necessary to customize them to user needs. But superuser access to a vast amount of resources, without support or oversight of experienced system administrators, can create fertile ground for accidental or intentional misuse. Attackers can rent cloud machines or hijack them from cloud users, and leverage them to generate unwanted traffic, such as spam and phishing, denial of service, vulnerability scans, drive-by downloads, etc. In this paper, we analyze 13 datasets, containing various types of unwanted traffic, to quantify cloud misbehavior and identify clouds that most often and most aggressively generate unwanted traffic. We find that although clouds own only 5.4% of the routable IPv4 address space (with 94.6% going to non-clouds), they often generate similar amounts of scans as non-clouds, and contribute to 22-96% of entries on blocklists. Among /24 prefixes that send vulnerability scans, a cloud's /24 prefix is 20-100 times more aggressive than a non-cloud's. Among /24 prefixes whose addresses appear on blocklists, a cloud's /24 prefix is almost twice as likely to have its address listed, compared to a non-cloud's /24 prefix. Misbehavior is heavy-tailed among both clouds and non-clouds. There are 25 clouds that contribute 90% of all the cloud scans, and 10 clouds contribute more than 20% of blocklist entries from clouds.
Exploring and Detecting Opinion Spam on Social Media

In recent years, microblogging service such as Twitter and Weibo attracts a large number of users. Unlike traditional media by which user can only accept information, social media allows users to share their opinions. Among the social media users, there exist a group of users called opinion spam. They are well organized and post a large number of purposed comments to misdirect the public opinion. In this way, they significantly magnify the impact of their employers. We conduct quantitative analysis to study and understand the characteristics of opinion spam. We analyze the psycholinguistic styles of opinion spam, explore their behavior patterns and network structure. Finally, based on the analysis, context based collective classification is proposed to detect opinion spam and the model can achieve 91% F1 score.
Domain-Driven Architecture Modeling and Rapid Prototyping with Context Mapper

Strategic Domain-driven Design (DDD) has become an established practice for system decomposition and service identification in recent years. The trend towards microservices increased the popularity of DDD patterns such as Subdomain, Bounded Context, Aggregate and Context Map. In our previous work, we presented a Domain-Specific Language (DSL) providing a clear and concise interpretation of the DDD patterns and their combinations. As a machine-readable description of DDD, the DSL establishes a foundation for systematic service decomposition and DDD-based architecture descriptions that can be refactored and refined by model transformations. The DSL and supporting tools are implemented in the open source project Context Mapper. In this extended version of our previous paper we enhance the DSL grammar to allow domain-driven designers to prototype applications rapidly: they can specify user stories and/or use cases in the DSL, and model transformations can then derive Subdomains and Bounded Contexts automatically. The Context Mapper tool chain supports the continuous, iterative specification and evolution of Context Maps and other service design artifacts. Our validation activities included prototyping, action research, and case studies. This paper illustrates such a transformation chain on the basis of one of our case studies.
SRI-XDFM: A Service Reliability Inference Method Based on Deep Neural Network

With the vigorous development of the Internet industry and the iterative updating of web service technologies, there are increasing web services with the same or similar functions in the ocean of platforms on the Internet. The issue of selecting the most reliable web service for users has received considerable critical attention. Aiming to solve this task, we propose a service reliability inference method based on deep neural network (SRI-XDFM) in this article. First, according to the pattern of the raw data in our scenario, we improve the performance of embedding by extracting self-correlated information with the help of character encoding and a CNN. Second, the original sum pooling method in xDeepFM is improved with an adaptive pooling method for reducing the information loss of the pooling operations when learning linear information. Finally, an inter-attention mechanism is applied in the DNN to learn the relationship between the user and the service data when learning nonlinear information. Experiments that were conducted on a public real-world web service data set confirm the effectiveness and superiority of the SRI-XDFM.