Reviewerly/exHarmony huggingface dataset:
A Robust Watermarking Using RDWT and Slant Transform Using Hybrid Firefly and Differential Evolution Optimization Algorithm

In this work, an optimized watermarking framework is proposed with the hybrid combination of two metaheuristic algorithms-firefly optimization and differential evolution, namely HFADE. The cover image is partitioned into 4 $$ \times $$ 4 subblocks, and the watermark is concealed in the slant domain using Quantized Index Modulation (QIM). The optimized thresholds obtained with HFADE used in quantization to improve imperceptibility and robustness. Peak Signal to noise ratio(PSNR) and Normalized Cross Correlation (NCC) are used for evaluation of the proposed watermarking scheme. The fitness function for HFADE is taken as the reciprocal of mean square error between the watermarked and cover image. Simulation outcomes convey that the proposed scheme maintains improved imperceptibility, and the watermark extracted from a seriously distorted image.
ON DEMAND: Maximizing Engagement in Live Virtual Activities

Have you ever assigned a task or activity for your learners to complete individually or in a virtual breakout room? Maybe you assigned a series of discussion questions that were supposed to lead to a deliverable or a task that required using a virtual collaboration space and everyone ended up confused. As an instructor or trainer, you cannot be everywhere at once—particularly in virtual environments. So, how do you maximize activity engagement and avoid confusion during live virtual classes and trainings when you cannot be in every breakout room at the same time or reasonably field questions from numerous individuals or groups? A simple solution can be ensuring effective modeling of the activity before you start. Sometimes clear instructions are simply not enough. Effective modeling ensures that the assigned live virtual activity runs smoothly and does not leave you scrambling or your learners confused.In this video, participants will be shown various ways to virtually model an activity prior to starting it. Strategies will also be shared on how to check for understanding of live virtual activities before they begin, and how to evaluate their success upon completion. Lastly, participants will be encouraged to apply what they have learned to their role as an instructor, trainer, or anyone leading a virtual meeting.
MPA-Net: Multi-scale Pyramid Attention network for Liver Tumor Segmentation

Liver tumor is one of the most deadly cancers around the world. Since the traditional liver tumor segmentation method is not only time-consuming but also low-precision, a precise and automatic liver tumor segmentation model is urgently needed in clinical practice. To address these above, we present a novel Multi-scale Pyramid Attention network for liver tumor segmentation. This proposed method replaces the traditional convolution network into pyramid convolution, which simultaneously can process the input data and capture different levels of detail features. Moreover, the Squeeze-and-Excitation (SE) block is introduced to obtain the channel relevance between feature maps. We conduct extensive experiments to validate MPA-Net on the MICCAI 2017 Liver Tumor Segmentation Challenge dataset. A large number of experiments demonstrate that the MPA-Net achieves better performance than other state-of-the-art methods. The dice per case score and dice global score of liver tumor segmentation are 0.771 and 0.826.
Limitations in the Use of Social Network Analysis Methods in Marketing Project Management

One of the trends of the labor market is project management skills. In such conditions, the professional education of a project manager becomes especially relevant since it is often impossible to completely replace it with work experience.&#x0D; One of the most vital areas for project success is communication management. Nevertheless, in this area, unlike many others, tools that allow objectively analyzing the quality of communications are not used. Relatively recently, a method of social network analysis (SNA) has appeared, which enables, among other things, analyzing communications in projects. Researchers wrote about the applicability of this methodology in project management. This article will identify some of the limitations in the use of SNA in project management.
Video saliency detection based on temporal difference and pixel gradient

Even though temporal information matters for the quality of video saliency detection, many problems such as bad performance in time-space coherence and edge continuity still face present network frameworks. In response to these problems, this paper designs a full convolutional neural network, which integrates temporal differential and pixel gradient to fine tune the edges of targets. Meanwhile, the changes of pixel gradients of original images are used to recursively improve the continuity of target edges and details of central areas. The method presented in the paper has been tested with two available public datasets and its effectiveness been proved after it being compared with 6 other widely accepted methods.
A Control Model for Following Target Inclination Angle in Lateral Direction for Omni-Directional Low-Floor Mobility

The purpose of this research is to develop an omnidirectional low-floor mobility. The mobile platform and the interface have already been developed. The height of the mobile platform is 37 mm from ground to the boarding surface. Omnidirectional movement is realized by using four mecanum wheels. This mobility utilizes sway of the center of gravity of the users. In order to operate this mobility, the users voluntarily tilt their bodies. This body inclination is estimated from the load distribution information of eight load cells installed on the boarding surface of this mobility. This enables forward-backward, lateral, and turning movements. However, the motion of this mobility is oscillatory, because inertia of this mobility causes unintended human body movements, which are input to the interface. In this research, in order to solve this problem, the authors introduce not only an inverted pendulum model but also a human postural control model. In order to change the body inclination voluntarily to operate this mobility, it is indispensable for a human postural control model to be able to follow an arbitrary target inclination angle, not just an upright state. The authors have now achieved forward-backward movement. In this paper, the authors propose a control model for following target inclination angle in lateral direction. First, a control model for maintaining an upright posture is introduced. Next, the authors conduct a body postural maintenance experiment in lateral inclination. A model is identified as a control model for following target inclination angle based on the result of this experiment.
ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures

The superiority of convolutional neural networks (CNNs) largely relies on their architectures that are usually manually crafted with extensive human expertise. Unfortunately, such kind of domain knowledge is not necessarily owned by every interested user. Data mining on existing CNNs can discover useful patterns and fundamental comments from their architectures, providing researchers with strong prior knowledge to design effective CNN architectures when they have no expertise in CNNs. There are various state-of-the-art data mining algorithms at hand, while there is only rare work on mining CNN architectures. One of the main reasons is the gap between CNN architectures and data mining algorithms. Specifically, the current CNN architecture descriptions cannot be exactly vectorized to feed to a data mining algorithm. In this article, we propose a unified approach, named ArcText, to describing CNN architectures based on text. Particularly, four different units and an ordering method have been elaborately designed in ArcText, to uniquely describe the same CNN architecture with sufficient information. Also, the resulted description can be exactly converted back to the corresponding CNN architecture. ArcText bridges the gap between CNN architectures and data mining researchers, and has the potential to be utilized to wider scenarios.
Design and Implementation a New Approach for Enhancing Encryption and Decryption Mechanisms

In cryptography, encryption is the process of transforming information to make it unreadable to anyone except those possessing special knowledge, usually referred to as a key. The result of the process is encrypted information. This research is intended to introduce a new approach for encryption is the design and implement Kurdish and English languages cipher system. It uses Kurdish letters and their diacritics for encrypting English messages and vice versa. A pseudo random generator is used to generate integer numbers to represent each character in Kurdish language. The same numbers are used again after sorting them to represent the English characters. The conclusions that are extracted indicate the efficiency of KAELCS system according to security and time performance.
Analysis of Intelligent Information Security Risk Assessment Based on Decision Tree

In order to optimize problem of information security risks faced by the current social development, scientific research scholar assessed using machine learning algorithm is put forward the corresponding countermeasures, so can not only solve the previous evaluation subjectivity is strong, classification accuracy is too low, can reasonable use qualitative quantitative way to step by step a grading factors of information security risk assessment. Therefore, on the basis of understanding the application of machine learning algorithm, this paper comprehensively discusses how to do a good job of intelligent information security risk assessment according to the basic nature of decision tree.
Python GUI for Language Identification in Real-Time Using FFNN and MFCC Features

Automatic and real-time language identification is a crucial task in several automated applications such as call centre, online robotic tele-medicine counselling, etc. With the help of advancement in the machine learning technique, automatic language identification problems can be solved very easily. In this work, an online language identification system has been developed with the help of open-source software Python and its libraries like numpy, scipy, and tkinter. In this proposed system, Mel Frequency Cepstral Coefficients (MFCC) has been used as feature vector. Further, Artificial Neural Network (ANN) with multiple layer architecture has been used as a classifier in order to classify the input speech signal into different languages. Python graphical user interface (GUI) was also developed in order to make the classification and sound recording task easier for the end-user.
Chapter 14. On adverbial perfect participial clauses in Portuguese varieties and British English

The purpose of this paper is twofold: (i) to discuss the validity of Lobo’s proposal (2003) of distinguishing two types of adverbial perfect participial clauses (APC) in European Portuguese; and (ii) to ascertain the key factors behind their temporal interpretation. To achieve these aims, we compare and contrast patterns displayed by APC in European, Brazilian, Mozambican and Angolan Portuguese and British English across a corpus built from newspapers. Our research reveals that the data do not reflect the bipartite division argued for by Lobo and that, for Portuguese varieties, the position of APC in the sentence and the combinations of some aspectual classes are important to infer temporal relations, whereas, for British English, the anterior orientation/perfect aspect of the perfect participle imposes for most cases an anteriority temporal relation, surpassing the influence of any other factor.
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation

While agents trained by Reinforcement Learning (RL) can solve increasingly challenging tasks directly from visual observations, generalizing learned skills to novel environments remains very challenging. Extensive use of data augmentation is a promising technique for improving generalization in RL, but it is often found to decrease sample efficiency and can even lead to divergence. In this paper, we investigate causes of instability when using data augmentation in common off-policy RL algorithms. We identify two problems, both rooted in high-variance Q-targets. Based on our findings, we propose a simple yet effective technique for stabilizing this class of algorithms under augmentation. We perform extensive empirical evaluation of image-based RL using both ConvNets and Vision Transformers (ViT) on a family of benchmarks based on DeepMind Control Suite, as well as in robotic manipulation tasks. Our method greatly improves stability and sample efficiency of ConvNets under augmentation, and achieves generalization results competitive with state-of-the-art methods for image-based RL. We further show that our method scales to RL with ViT-based architectures, and that data augmentation may be especially important in this setting.
Violence Detection of Sinhala Image Posts with Autoencoders

Social media has become one of the main sources of information with an increased number of users. Unlike other media, control over the contents in social media is minimal. Due to the less controllability, futile, offensive, and racist contents that trigger hatred and violence can be created and distributed among the community. Image posts are one of the mechanisms in social media to spread an idea, an emotion, or information. Image posts contain both text and visual, hence it has become a powerful mechanism of spreading information. Although some of the existing systems focus on English contents, less research focuses on other languages. This research explains a mechanism to detect Sinhala image posts that trigger hatred and violence using a mechanism in deep learning called autoencoders. The results show that autoencoders can identify offensive and violent content in image posts to a certain extent.
Textual Data Science with R

Textual Data Science With R targets an important and relatively understudied area of data science: the statistical analysis of largely unstructured data in the form of natural language text. Using ...
Vision Dynamics: Environment Modelling, Path Planning and Control Based on Semantic Segmentation

Autonomous driving has been a hot topic in robotics for several years. Two main approaches have been proposed in the scope of solving the autonomous driving challenge: model-based methods relying on trajectory tracking and control and model-free learning based methods, usually directly mapping input signals to control actions. In this paper, we propose a vision-dynamics approach as an enhancement of the classical model-based perception-planning-control architecture, based on a semantic segmentation learning algorithm. We use the output of the learning algorithm in the planning module of our setup as to generate better local trajectories for our controller to track. We deploy our algorithm in a real-world setup, using a Pioneer 3-DX robot that navigates the innards of a park.
Primitives Enhancing GPU Runtime Support for Improved DNN Performance

Deep neural networks (DNNs) are increasingly used for real-time inference, requiring low latency, but require significant computational power as they continue to increase in complexity. Edge clouds promise to offer lower latency due to their proximity to end users and having powerful accelerators like GPUs to provide the computation power needed for DNNs. But it is also important to ensure that the edge-cloud resources are utilized well. For this, multiplexing several DNN models through spatial sharing of the GPU can substantially improve edge-cloud resource usage. Typical GPU runtime environments have significant interactions with the CPU, to transfer data to the GPU, for CPU-GPU synchronization on inference task completions, etc. These result in overheads. We present a DNN inference framework with a set of software primitives that reduce the overhead for DNN inference, increase GPU utilization and improve performance, with lower latency and higher throughput. Our first primitive uses the GPU DMA effectively, reducing the CPU cycles spent to transfer the data to the GPU. A second primitive uses asynchronous ‘events' for faster task completion notification. GPU runtimes typically preclude fine-grained user control on GPU resources, causing long GPU downtimes when adjusting resources. Our third primitive supports overlapping of model-loading and execution, thus allowing GPU resource re-allocation with very little GPU idle time. Our other primitives increase inference throughput by improving scheduling and processing more requests. Overall, our primitives decrease inference latency by more than 35% and increase DNN throughput by 2-3x.
Target Association Method Based on Fuzzy Membership and Hungary Algorithm

In order to ensure the accuracy and rationality of information processing in the process of multi-sensor multi-target tracking and multi-sensor information fusion, and to establish the correct correspondence between multi-sensor measurement data and interested targets, a target association method based on fuzzy membership function and Hungarian algorithm is proposed. This method adopts fuzzy inference technology to perform fuzzy membership analysis on the sensor measurement data, and establishes a fuzzy association matrix as a basis for target association through membership function. With the minimization of membership as a constraint, the Hungarian algorithm is used to obtain the optimal matching scheme for multiple target associations. Simulation test results show that the method is simple and effective and suitable for engineering practice.
Medical Ultrasound Image Segmentation Based on Improved MultiResUNet Network

An improved MultiResUNet is proposed to recognize tissue from clinical ultrasonic images. The designed anti-aliased convolution and MultiRes module is sensitive to high frequency semantics such as tissue boundaries, while the ResPath module reinforces the communication between encoding blocks and decoding blocks and reduces semantic discrepancies. During training process, a hybrid loss function is employed to focus on irregular or discontinuous boundaries. The result shows that proposed network outperforms conventional methods with similar parameter numbers and faster convergence speed.
EdgePS: Selective Parameter Aggregation for Distributed Machine Learning in Edge Computing

In this paper, we propose EdgePS, an advanced parameter server approach for distributed machine learning in edge computing scenarios. Different from the Conventional Parameter Server (CPS) approach, which performs parameter aggregation after every local training epoch, EdgePS synchronizes the parameters of all workers only when the local training cannot improve the global model performance. We first analyze how the local training will impact the performance of the global model, and then design algorithms to determine when the best time is to perform the parameter aggregation. Both real testbed experiments and extensive large scale simulations demonstrate that EdgePS can train a practical machine learning model, e.g., VGG-16, with up to 59.28% less time compared with the CPS approach. With the same training time, EdgePS can improve model accuracy by up to 30.19 % compared with the state-of-the-art distributed machine learning algorithm designed for edge computing scenarios.
Path Planning of Manipulator Based on Improved Informed-RRT* Algorithm

To solve the problems of low efficiency and slow convergence of traditional RRT algorithm and RRT* algorithms, an improved informed-RRT* algorithm is proposed in this paper. The algorithm keeps the probability completeness and path optimality of RRT algorithm, improves the speed of iterative convergence and the quality of the generated path. After the final path is obtained, the problem of sharp and burr in the generated trajectory is solved by trajectory smoothing strategy. Finally, the comparison experiment shows that the performance of the proposed algorithm in three-dimensional space is better than RRT* algorithm, and the algorithm is applied to real manipulator, which verifies the feasibility of the algorithm.
Optimization of water treatment process performance of Duren Seribu Water Treatment Plant in Depok City: water quality and design parameters

Abstract Duren Seribu Water Treatment Plant (WTP) plays a role in fulfilling water needs due to the increase in population growth. This study aims to evaluate the performance of the WTP and the potential for capacity uprating from the aspect of conformity to water quality standards, removal efficiency, and design parameters. Data collection was carried out by observation, measurement, and sampling. The result showed that raw water quality complies with the quality standards and is influenced by seasons. Meanwhile, the quality of drinking water produced for the parameters of pH, turbidity, TDS, organic matter, iron, and total coliform has complied with the quality standards. The efficiency removal of turbidity, TDS, organic matter, and iron occurred in conventional processing units were 94.6%, 70.5%, and 90.9%, and the total coliform removal efficiency was 1.8-log (67.05%). Based on the assessment of the design criteria, there are design parameters of the unit that require technical improvement such as detention time of flocculation unit, velocity and detention time in tube settler sedimentation unit, and the capacity of Duren Seribu WTP could be increased by 10% or up to 110 liters/second.
An Improved Multi-label k-Nearest Neighbour Algorithm with Prototype Selection using DENCLUE

Prototype Selection (PS) techniques can be implemented very efficiently to allow the nearest neighbor classification to be faster by using a simple method to preserve the most prominent data for the Multi-Label k-Nearest Neighbour (ML-kNN) training. The most often used algorithm for classifying multi-labeled data is ML-kNN data which is adopted through the use of the well-known kNN algorithm. The use of Prototype Selection often minimizes the accuracy, and hence the performance of the algorithm. To solve the problem of reducing the accuracy of the ML-kNN, we proposed a novel solution that reduces data by using the well-known clustering algorithm-DENCLUE. Data that belongs to one of the clusters is considered prominent, while data that does not belong to any of the clusters is considered noisy and will not be included in the training process. In terms of noisy data reduction, the findings demonstrate a significant improvement over the ML-kNN.
Estimating the Software Project Effort using Regression based Techniques

Software cost and effort estimation has an important role in the software industry. It leads any project to success or failure due to their variations during the project implementations. There are different techniques that are used for estimating the project. Analysis of Variance (ANOVA) and Linear Regression perform in different ways but the results they both generate are almost the same. The aim of this paper is to estimate the effort using the Linear regression and then verify it using the ANOVA and provide the relation between Linear Regression and ANOVA while estimating the software cost.