Reviewerly/exHarmony huggingface dataset:
Information Security Practices in Small-to-Medium Sized Businesses

Small to medium-sized enterprises (SMEs) in North America do not always adequately address security. Based on responses from 232 SME owners and managers, the authors found that the adoption of security recommendations made by experts appear to be significantly influenced by the decisions of other local SMEs. A hot-spot analysis of information security practices suggested that local trends lead to prioritizing certain security practices and not adopting others. Follow-up interviews with business owners and Chamber of Commerce directors provided insights on how security hotspots developed or not. The study identified both hot spot and cold spot communities, and sought to assess how local business networking conduits like chambers of commerce help promote best security practices
Data Classification Model of Visual Art Works Based on Sparse Coding

The vision system is more perfect than any artificial digital image signal processing system. Bionics is a good way to improve the technology of digital image processing. Sparse coding is an artificial neural network method that simulates simple cell receptive fields in the main visual cortex of the mammalian primary visual system. It can effectively extract the internal features of the image and also obtain the maximum amount of information in the image. Based on the sparse coding algorithm, this paper designs a style understanding and style classification model for dead artworks.
Compliance With Information Systems Security Controls in Higher Education in South Africa

The surge in information security breaches in higher learning institutions has prompted researchers to identify factors that need to be considered to ensure compliance with information systems security controls in place. This chapter investigated factors that influence compliance with information systems security by adopting a conceptual framework that combines the protection motivation theory (PMT) and the skills theory as the guiding framework. Data was collected from a convenient sample size of 85 respondents from a higher learning institution in South Africa. Data analysis was carried out using the partial least square structural equation modelling (PLS-SEM) with the aid of SMARTPLS 3.2.9 software. The results revealed that perceived vulnerability, perceived severity, perceived rewards, response efficacy, response costs, and awareness explained 42.2% of the variance in compliance with information systems security controls. Thus, this study suggests that practitioners need to ensure that students recognise information systems security threats of their universities.
Automated Detection of Breast Cancer Metastases

For women, breast cancer is the most commonly diagnosed cancer, which brings a heavy workload to pathologists. Because this diagnostic procedure is now prone to being time-consuming and sometimes misinterpreting. In order to solve these problems, techniques related to deep learning and machine learning have been applied to the diagnostic process of breast cancer. However, some problems have been found in application of these technologies, such as imbalanced data sets. This paper proposes a data augmentation technique based on generative adversarial networks (GAN) which can solve the problem of data imbalance, then uses ResNet to evaluate the impact of different data augmentation techniques on the experimental results.
Chinese Traffic Sign Text Detection Method Based on Improved EAST

As an important content in the research of unmanned driving and traffic systems, traffic sign recognition has great theoretical value and application prospects. Especially text-based traffic signs, which contain rich high-level semantic information, can provide extremely rich road information. In order to realize the automatic detection of Chinese traffic sign text, an improved EAST text detection method is proposed. This method introduces BLSTM between the output layer and the feature merging layer to enhance the context relationship of the feature vector and improve the receptive field of the network. At the same time, in order to improve the detection accuracy of the algorithm for night images, an automatic image enhancement module is added at the head end of the network. It can detect the light of the input image and automatically improve the contrast of the image that is judged to be poorly lit. Effectively improve the image detection effect of the model in low-light environments. The experimental results show that the improved method achieves satisfactory results on the CTST-1600 data set.
A Look-Ahead Based Meta-heuristics for Optimizing Continuous Optimization Problems

In this paper, the famous kernighan-Lin algorithm is adjusted and embedded into the simulated annealing algorithm and the genetic algorithm for continuous optimization problems. The performance of the different algorithms are evaluated using a set of well known optimization test functions.
Measuring the Impact of an IoT Temperature Sensor Framework for Tracking Contagious Diseases

Due to the COVID-19 pandemic, much computer science research has been dedicated to utilizing sensor readings for medical purposes. Throughout this period, the need for virus symptom tracking has become a promising area for remotely deployed sensor networks and platforms. Our research goal is to prove that the temperature readings from these sensor network platforms can be statistically linked to public record, medical case study data. The expected outcome of our project is to prove the correlation between sensor network tracking of remote human temperature data and medical records for COVID cases. The results of this study will prove that tracking human temperature can assist in tracking disease outbreaks in various populations. Our framework platform is comprised of four main modules: (1) Temperature Collection, (2) Internal Data Validation (3) Internal-External data merger, (4) Data Analytics. The temperature data are collected from internal databases, mobile sensing devices and medical health professionals. After collection, the internal data are validated by our software, TAU-FIVE, a multi-tier data quality validation system, then merged with external data sources into a data analytic based data warehouse. The data mart queries are designed to compare the location and date of temperature sensor data with known data sets from government officials. Once blended into a fully operational data warehouse, these data marts produce high quality data analysis linking remotely sensed human temperature readings to sources of disease outbreaks.
An Efficient Embedded Processing System for Remote Sensing Image Compression based on Heterogeneous Programmable MPSoC

The growth of remote sensing (RS) image data fuels the interest in embedded system energy-efficient processing. To reduce the storage and communication requirements, image compression is widely popular with the RS domain. However, RS image compression is a computationally intensive task. How to balance compression performance and efficiency is a challenging problem in embedded systems with limited resources. In this article, an efficient heterogeneous programmable multi-processor system on chip (MPSoC) embedded processing system for RS image compression is proposed. Firstly, a RS image compression method is proposed which is based on the region of interest extraction and implemented method on programmable logic. Then, a processing architecture is designed based on MPSoC and an embedded processing module is developed. Finally, the experimental results show that the proposed method can improve the compression ratio while ensuring the quality of the target information. The efficient RS image compression task could be achieved by using the embedded processing system.
Two-layer Clustering Method for Mammogram Segmentation Based on Pixel Blurriness

Segmentation plays a significant role in various applications. The clustering algorithm is a general way to realize image segmentation. However, the parameter setting is a challenge, the improper parameter results in over-segmentation. In this paper, we propose a two-layer clustering method to segment mammograms based on pixel blurriness. In the first layer of operation, the proposed method implements K-means, principal component analysis (PCA), and watershed segmentation. Subsequently, we employ the self-organizing map (SOM) to cluster segmented areas in the second layer. The experiment results demonstrate that our method achieves better performance than the compared approaches in segmentation.
Gradient Reversal Domain Adaptation Pipeline in Advanced Driver Assistance Systems

In the latest generation cars, the driver assistance systems (ADAS i.e. Advanced Driving Assistance Systems) contributed to significantly reduce road accidents due to the driver's inexperience or unexpected events. One of the aspects that characterizes ADAS technologies is related to the intelligent monitoring of the driving scenario. Recently, estimation of the visual saliency i.e. the part of the visual scene in which the subject pays more attention (specifically the part whose gaze is focused), has received significant research interests. This work makes further contributions to video saliency research with application on the automotive field. Anyway, often the absence of labeled data for a certain task in automotive field requires the usage of such domain adaptation methods. We propose a new approach to domain adaptation in deep architectures for automotive applications based on the implementation of Gradient Reversal Layer. More in detail, the proposed pipeline enables an intelligent identification and segmentation of the motion salient objects, reconstructing the motion dynamics and the correlated level of driving risk in different scenarios and domains. The performed test results confirmed the effectiveness of the overall proposed pipeline.
A Lightweight Object Detection Model Based on Neural Architecture Search

Object detection is an important subject in the field of computer vision and also an essential component part of aerospace intelligent system. Aiming at the problem that the object detection models are too large in aerospace scenarios, a new lightweight feature extraction network structure was obtained based on the backbone network of YOLOv5s object detection model by using neural architecture search algorithm. Compared with the original YOLOv5s, the improved network model has a more simplified network structure without loss of accuracy, and its requirement for computing is also reduced. And it can meet the task requirements of object detection on embedded devices and mobile terminals. At the same time, the degree of human participation in model design is greatly reduced, and the automation level of model generation is improved.
From None to Severe: Predicting Severity in Movie Scripts

In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script.We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Substance consumption, and Frightening scenes.The problem is handled using a Siamese network-based multitask framework which concurrently improves the interpretability of the predictions.The experimental results show that our method outperforms the previous state-of-the-art model and provides useful information to interpret model predictions.The proposed dataset and source code are publicly available at our GitHub repository 1 .
A Smart Traffic Controlling System Using Auxiliary Sensors with Deep Learning and Its Simulation

The traffic jam is a severe problem in modern cities, and many researchers tried to reduce the pain by optimizing the traffic system and traffic participants. This paper introduces a hardware system to optimize the time management of the crossings to improve the overall efficiency of the city's transportation. After installing this system, redundant seconds will be eliminated, and more time will be given to pathways that are in congestion. We separate our research into two parts. The aim of conducting the auxiliary sensors of light gates is to measure the accurate speed of vehicles passing by and counting the cars passing by for one lane for the calibration of the camera identification, in the form of model simulation. The detector algorithm uses Faster-RCNN as the base with real-time image processing to detect the existence of cars. The algorithm provides the numerical results of the amounts of vehicles. And the auxiliary light gate measures accurately the speed of the vehicles. The method is sufficient for a traffic lighting system with also elementary principles that reduces the cost of massively producing the system integrated chips. With the implementation of this system, the efficiency of a city will be promoted. Finally, we use the simulation method to get the total time of the traffic flow with and without modifying the time interval and periods of green lights, and the result shows the modification has a positive effect of reducing the total passing time of the cars.
A Web-based Mobile Medical Monitoring System

A hospitalwide mobile medical monitoring system is described. Telemetry devices are attached to patients to acquire, store and process continuous data about their state of health. Medical staff may examine realtime graphical information and make comparisons with historical data. Parameters may be set to cause an automatic alert to portable devices held by appropriate staff if a patient requires urgent observation. The system uses the services provided by Internet and Intranet to allow remote supervision and consultation. A hardware/software prototype has been constructed to demonstrate realtime data acquisition, wireless transmission/reception and connection to the Worldwide Web. The realtime, supervisory and remote teaching aspects of the software system are being designed and are in the process of implementation.
Toward Effective Moving Target Defense Against Adversarial AI

Deep learning (DL) models have been shown to be vulnerable to adversarial attacks. DL model security against adversarial attacks is critical to using DL-trained models in forward deployed systems, e.g. facial recognition, document characterization, or object detection. We provide results and lessons learned applying a moving target defense (MTD) strategy against iterative, gradient-based adversarial attacks. Our strategy involves (1) training a diverse ensemble of DL models, (2) applying randomized affine input transformations to inputs, and (3) randomizing output decisions. We report a primary lesson that this strategy is ineffective against a white-box adversary, which could completely circumvent output randomization using a deterministic surrogate. We reveal how our ensemble models lacked the diversity necessary for effective MTD. We also evaluate our MTD strategy against a black-box adversary employing an ensemble surrogate model. We conclude that an MTD strategy against black-box adversarial attacks crucially depends on lack of transferability between models.
Efficient Proposal Generation with U-shaped Network for Temporal Sentence Grounding

Temporal Sentence Grounding aims to localize the relevant temporal region in a given video according to the query sentence. It is a challenging task due to the semantic gap between different modalities and diversity of the event duration. Proposal generation plays an important role in previous mainstream methods. However, previous proposal generation methods apply the same feature extraction without considering the diversity of event duration. In this paper, we propose a novel temporal sentence grounding model with an U-shaped Network for efficient proposal generation (UN-TSG), which utilizes U-shaped structure to encode proposals of different lengths hierarchically. Experiments on two benchmark datasets demonstrate that with more efficient proposal generation method, our model can achieve the state-of-the-art grounding performance in faster speed and with less computation cost.
HfGCN: Hierarchical fused GCN for Joint Entity and Relation Extraction

The extraction of entities and relations from un-structured text is crucial for information extraction. The joint models have been proposed to solve both tasks simultaneously. However, previous work only focuses on the linear representation of sentences and neglects hierarchical grammatical knowledge, which can be injected into the word representations to improve the accuracy of joint extraction. In this paper, we propose a joint model, integrating the fused syntax graph information into a hierarchical graph convolution model for joint extraction. Specifically, we use the attention mechanism to mine the implicit relations between words and dynamically fuse it with explicit dependency syntax graph to obtain fused graph, and through hierarchical graph convolution network to obtain the fused features of different granularity. Experimental results show that our model yields a significant performance compared with prior strong baselines in two datasets.
Exploring the usefulness of Object Affordances through a Knowledge based Reinforcement Learning Agent

The conceptual understanding of human about object usage in executing high level activities is phenomenal. Embodiment of such understanding within a computational agent holds promise in building intelligent household robots for performing such activities. This paper examines the role of object affordances in realizing the high level indoor activities. To this end, we have studied the temporal constraints imposed by object affordances in such temporally extended activities. A novel knowledge based reinforcement learning agent is designed by considering Linear Temporal Logic (LTL). Experimental evaluation demonstrates the usefulness of this agent in performing different activities in grid world environments.
Recognition of Video Time Watermark Based on Water-Wave Connected Domain Segmentation Algorithm

Character segmentation has always been a difficult task in the field of text recognition. The common connected domain segmentation algorithm not only repeatedly traverses the pixels, but also does not solve the problem of character adhesion segmentation. In this work, targeted at these problems, we proposed a time watermark recognition method, which can quickly realize the time watermark recognition only by labeling a few sample pictures. It is very suitable for the scene where time watermark needs to be recognized quickly. Compared with the traditional connected domain segmentation algorithm, the water wave connected domain segmentation algorithm segmented characters more accurately, and it can also handle the situation of light adhesion. The segmentation method proposed in this paper avoids the repeated stacking and popping or recursive operations of the traditional connected domain segmentation algorithm mostly, and provides a new idea for character segmentation. Based on the water wave connected domain segmentation algorithm proposed in this article, four different image processing algorithms are compared and studied: template matching [1], Hamming distance calculation [2], Euclidean distance calculation [3], and pixel comparison calculation. The recognition method proposed in this paper is compared with the CRNN [4] recognition method under the same conditions, and the results show that when a small number of samples are used, the recognition rate of the proposed method reaches 92.3&#x0025;, and the CRNN recognition rate is 0. Compared with CRNN, the recognition method proposed in this article has achieved good results on very few samples.
A Framework to Design and Test Tracking Architectures

Multi-target tracking is at the heart of surveillance and autonomous systems. In multi-sensor systems, there are several options for the system-level tracking architectures that span a range of centralization levels. Therefore, the engineers responsible for building the system require a framework that enables the design, testing, and evaluation of various architectures. In previous works, we discussed a set of sensor fusion and tracking algorithms. In this work, we describe an extension to this set that supports the design of tracking system-of-systems. We provide examples that demonstrate the framework in action.
Architecting the Human Intranet

Equipping us humans with the necessary tools to interact with, survive, and prosper in a rapidly changing world may require us to intimately adopt some of the same technologies that are causing some of these changes. Various wearable devices have been or are being developed to do just that. To be effective, functionality cannot be centralized and needs to be distributed to capture the right information at the right place. This requires a human intranet, a platform that allows multiple distributed input/output and information processing functions to coalesce and form a single application. How to effectively do so in light of the many challenges from an efficiency, usability, and effectiveness perspective is the focus of this paper.
Elevating Software Quality Role Using Angle Methodology

Software Quality is an essential part of modern research, and up till now there are insufficient mechanisms in the intellectual environment to acknowledge, measure and to enhance quality parameters. In this paper, considering agile methodologies might produce software faster as well as boost software quality so that they complete quality necessities of the product. Choosing and adaptation of any methodology depends on the type and people involved in the project. Employee behavior, their mutual relations and enthusiasm is one of factors which can critically affect success of the method implementation. So, these factors also measured all through the version of agile methodology. There is no doubt that the foundation of agile techniques is people. The motive of this paper is to introduce a best method for implementation of the agile methodology. Finally In this paper, every issue affecting software quality assurance has been explored, and all fixes that are feasible have been provided. Practitioners may find the conclusions of this study useful in assessing specific measuring tools and application quality characteristics.
Study of Flood Impact Handling in Pekalongan District

Abstract The flood disaster that occurred in Pekalongan district in February 2021 was caused by the high intensity of rain for one week with rainfall &gt;50 mm/day and the ebb and flow of sea water due to tides reaching 0.9-1.1 meters. Other problems due to flooding in Pekalongan district are change in land use, land subsidence, waste, erosion-sedimentation, as well as operation and maintenance factors. Losses due to floods cause material and building losses and traffic conditions are paralyzed. The method used in this research is primary and secondary data collection, descriptive qualitative data processing by describing understanding, research approach with field observations. Flood handling that has been carried out is by adding an emergency sliding door to the drainage, procuring a mobile water pump with a capacity of 250 liters/second, a CCSP (Corrugated Concrete Sheet Pile) embankment, rehabilitation of the Bremi River floodgate, construction of a Mrican pump house with a capacity of 8×2 m 3 /second, and construction of a 2,200 meter long Silempeng earthen embankment. The emergency handling that was carried out during the floods of February 2021 was the opening of the Long Storage Silempeng-Sengkarang and the preparation of sandbags and large sacks for emergency handling.