Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Data Encryption Techniques for Data Security in the IoT Device

The Internet of things (IoT) has been widely used around the world and the use of IoT applications has been increasing. Because of its accessibility with the existing communication and computing capacity, the IoT application domain has been widening. But, as it has been the issue of security and privacy in other communication-based application, the IoT device-based applications have also been under the threat of attacks. There have been risks of both physical damaging on the devices and digital cyber attacks. To apply the security on the digital IoT system, lightweight cryptography techniques have been used as an alternative to minimize any potential threats and hence make the system more secure. Over the years, different encryption algorithms such as Advanced Encryption Standard (AES), Data Encryption Standard (DES), Triple Data Encryption Standard (3DES), Blowfish, and many others have been implemented in different IoT applications. However, selecting the right algorithm has always been challenging. In this paper, a comparative analysis in terms of the encryption process and throughput has been analyzed for AES, DES, 3DES, and Blowfish algorithms. As an IoT application, Raspberry Pi 3B+ based system for sensing humidity and temperature has been designed for the experimental comparison among these algorithms. In the application, the sensor data has been transmitted to the server using different encryption algorithms having varying key lengths. The comparative analysis of the encryption process time and throughput has been analysed to evaluate the strength and weaknesses of each encryption algorithm in our IoT application. The experimental results have shown that DES and 3DES algorithm has the minimum time requirement for encryption whereas Blowfish has the highest throughput.
Comparing the accuracy and effectiveness of Wordvice AI Proofreader to two automated editing tools and human editors

Purpose: Wordvice AI Proofreader is a recently developed web-based artificial intelligence-driven text processor that provides real-time automated proofreading and editing of user-input text. It aims to compare its accuracy and effectiveness to expert proofreading by human editors and two other popular proofreading applications—automated writing analysis tools of Google Docs, and Microsoft Word. Because this tool was primarily designed for use by academic authors to proofread their manuscript drafts, the comparison of this tool’s efficacy to other tools was intended to establish the usefulness of this particular field for these authors.Methods: We performed a comparative analysis of proofreading completed by the Wordvice AI Proofreader, by experienced human academic editors, and by two other popular proofreading applications. The number of errors accurately reported and the overall usefulness of the vocabulary suggestions was measured using a General Language Evaluation Understanding metric and open dataset comparisons.Results: In the majority of texts analyzed, the Wordvice AI Proofreader achieved performance levels at or near that of the human editors, identifying similar errors and offering comparable suggestions in the majority of sample passages. The Wordvice AI Proofreader also had higher performance and greater consistency than that of the other two proofreading applications evaluated.Conclusion: We found that the overall functionality of the Wordvice artificial intelligence proofreading tool is comparable to that of a human proofreader and equal or superior to that of two other programs with built-in automated writing evaluation proofreaders used by tens of millions of users: Google Docs and Microsoft Word.
Quantifying the Degradation of Optical Algorithms in Increasingly Turbid Mediums

The quality of stereo and monocular camera data is known to degrade as the density of suspended marine particulate - i.e., turbidity-increase. However, few studies have explored the degree to which common computer vision algorithms degrade in the presence of increasing turbidity. In this paper, we explore the quantitative tie between turbidity and the performance of several common computer vision algorithms. We focus on three computer vision operations: 1) the ability to detect and correspond feature points in a pair of images, 2) the ability to generate an accurate depth map from a stereo pair of images, and 3) the performance of deep learning detectors (Faster-RCNN and RetinaNet) to correctly identify an object in the environment. Additionally, for stereo camera feature detection and correspondence, we explore which common feature extractors perform best and how image enhancement impacts algorithm performance. Our results indicate that stereo algorithm performance does drop significantly in the presence of higher turbidity, as is expected. However, we find that optical data remains usable in low to medium turbidity environments. Additionally, we find that contrast limited adaptive histogram equalization (CLAHE) image enhancement has a positive impact on the ability to generate and correspond feature points, and that the introduction of small quantities of turbid data into a deep learning detector’s training step has a significant positive contribution to network performance.
Precise eye center localization in a practical environment

Eye center localization plays a crucial role in computer vision and pattern recognition applications such as face recognition, gaze estimation, driver drowsiness detection, etc. Accurate eye center localization is challenging because of face pose orientation, illumination variations, eye occlusion, and specular reflections. This paper proposes a precise eye center localization method to solve the problems stated above. In this paper, multi-iris shape features extract the number of possible eye locations in the upper half of the face region, reducing the non-eye regions and improving computational speed. Then, Convolutional neural networks (CNN) extract high-level features, and the support vector machine (SVM) classifier is used for classification. Experiments were performed on AR, CASPAEL, and BioID databases to test the performance of the proposed model under varying pose, occlusion, and illumination variations, etc. Extensive experiments suggest that the proposed method provides better performance as compared to other existing methods.
A spatial AR system for wide-area axis-aligned metric augmentation of planar scenes

Augmented reality (AR) promises to enable use cases in industrial settings that include the embedding of assembly instructions directly into the scene, potentially reducing or altogether obviating the need for workers to refer to such instructions in paper form or on a statically situated screen. Spatial AR, in turn, is a form of AR whereby the augmentation of the scene is carried out using a projector, with the advantage of rendering the augmentation visible to all onlookers simultaneously without calling for any to hold a handheld device such as a tablet or for each to wear some form of head-mounted display. In carrying out spatial AR, however, care must be taken to appropriately warp the image to be projected such that it, when projected, appear free of projective distortions to the viewer. For planar scene geometry (such as a floor, wall, or table), a manual process called keystone correction can be used to carry out an appropriate corrective image warp, a process that can be cumbersome. Another drawback of conventional spatial AR relying only on a single projector is that it is capable of augmenting only the portion of the scene within the projector’s static field of view, thereby hindering its applicability to use cases calling for augmentation of wide areas such as a factory floorspace. We propose a spatial AR system for wide-area metric augmentation of planar scene surfaces that produces the effect of keystone correction analytically as a function of the relative geometry of the projector and scene plane, using a projector equipped with a steerable mirror to direct the projection across varying target locations and a camera facing the scene plane in support of calibrating the system. Our system renders the placement of augmentations in the scene more intuitive than manual keystone correction in two ways. First, (i) the horizontal and vertical axes of the desired augmentations are set in accordance with the horizontal and vertical image axes of the camera, thereby making setting those axes as simple as appropriately rotating the camera relative to the scene plane, and thereby providing a consistent axial reference frame across all target locations. Second, (ii) the desired dimensions of the projected augmentations are specified in metric terms, thereby providing for consistent scaling, likewise across all target locations.
Research on Human Body Detection and Tracking Algorithm Based on Kinect

Human body detection and tracking technology as the main direction of the current field of computer vision research, not only for the overall social operation provides convenient, but also for the social and economic development to create more economic benefits. Therefore, based on the understanding of Azure Kinect DK device information, this paper analyzes the Kinect-based human detection and tracking algorithm according to the research direction of human detection and tracking computation in recent years.
Binary Self-Adaptive Salp Swarm Optimization-Based Dynamic Load Balancing in Cloud Computing

In the recent era of cloud computing, the huge demand for virtual resource provisioning requires mitigating the challenges of uniform load distribution as well as efficient resource utilization among the virtual machines in cloud datacenters. Salp swarm optimization is one of the simplest, yet efficient metaheuristic techniques to balance the load among the VMs. The proposed methodology has incorporated self-adaptive procedures to deal with the unpredictable population of the tasks being executed in cloud datacenters. Moreover, a sigmoid transfer function has been integrated to solve the discrete problem of tasks assigned to the appropriate VMs. Thus, the proposed algorithm binary self-adaptive salp swarm optimization has been simulated and compared with some of the recent metaheuristic approaches, like BSO, MPSO, and SSO for conflicting scheduling quality of service parameters. It has been observed from the result analysis that the proposed algorithm outperforms in terms of makespan, response time, and degree of load imbalance while maximizing the resource utilization.
B2-Tree: Page-Based String Indexing in Concurrent Environments

Abstract Recently proposed index structures, that combine trie-based and comparison-based search mechanisms, considerably improve retrieval throughput for in-memory database systems. However, most of these index structures allocate small memory chunks when required. This stands in contrast to block-based index structures, that are necessary for disk-accesses of beyond main-memory database systems such as Umbra. We therefore present the B 2 -tree. The outer structure is identical to that of an ordinary B+-tree. It still stores elements in a dense array in sorted order, enabling efficient range scan operations. However, B 2 -tree is composed of multiple trees, each page integrates another trie-based search tree, which is used to determine a small memory region where a sought entry may be found. An embedded tree thereby consists of decision nodes, which operate on a single byte at a time, and span nodes, which are used to store common prefixes. This architecture usually accesses fewer cache lines than a vanilla B+-tree as shown in our performance evaluation. As a result, the B 2 -tree answers point queries considerably faster. Concurrent access to B 2 -tree pages are managed by an optimistic locking protocol which results in high utilization of the available hardware resources. Our evaluation of read-write workloads attests more than competitive performance for the B 2 -tree compared to a traditional B+-tree.
The Potential of Using Smartphones in Teaching and Learning in Secondary School Education

This study aimed at exploring the potential of smartphone use in education with a particular focus on secondary schools. The literature review provided massive methods and applications of the smartphone in teaching and learning plus the challenges that surround it. The review made it possible to identify that there is a group of teachers who perceive the use of the devices as pedagogical tools, and another group of teachers that are against or have a reservation towards its inclusion in education. Students deemed the smartphones as highly necessary compared to teachers. From a literature perspective, it can be that some teachers are immigrants; therefore, they have difficulties in adopting or adapting to the trend. The study suggested that it is essential to try out some projects, such as teaching students with smartphones, especially in developing countries like those in sub-Saharan Africa to yield comparison against traditional teaching and learning.
Attention Guided Cosine Margin to Overcome Class-Imbalance in Few-Shot Road Object Detection

Few-Shot Object Detectors (FSOD) are tasked to localize and classify objects in an image given only a few data samples. Recent trends in FSOD research show the adoption of metric and meta-learning techniques, which are prone to catastrophic forgetting and class confusion. To overcome these pitfalls in metric learning based FSOD techniques, we introduce an Attention Guided Cosine Margin (AGCM) that facilitates the creation of tighter and well separated class-specific feature clusters in the classification head of the object detector. The Attentive Proposal Fusion (APF) module introduced in AGCM minimizes catastrophic forgetting by reducing the intra-class variance among co-occurring classes. At the same time, the Cosine Margin penalty in AGCM increases the angular margin between confusing classes to overcome the challenge of class confusion between already learned (base) and newly added (novel) classes. We conduct our experiments on the India Driving Dataset (IDD), which presents a real-world class-imbalanced setting alongside popular FSOD benchmark PASCAL-VOC. Our method outperforms existing approaches by up to 6.4 mAP points on the IDD-OS and up to 2.0 mAP points on the IDD-10 splits for the 10-shot setting. On the PASCAL-VOC dataset, we outperform existing approaches by up to 4.9 mAP points.
Multi-Precision Deep Neural Network Acceleration on FPGAs

Quantization is a promising approach to reduce the computational load of neural networks. The minimum bit-width that preserves the original accuracy varies significantly across different neural networks and even across different layers of a single neural network. Most existing designs over-provision neural network accelerators with sufficient bit-width to preserve the required accuracy across a wide range of neural networks. In this paper, we present mpDNN, a multi-precision multiplier with dynamically adjustable bit-width for deep neural network acceleration. The design supports run-time splitting an arithmetic operator into multiple independent operators with smaller bit-width, effectively increasing throughput when lower precision is required. The proposed architecture is designed for FPGAs, in that the multipliers and bit-width adjustment mechanism are optimized for the LUT-based structure of FPGAs. Experimental results show that by enabling run-time precision adjustment, mpDNN can offer 3-15x improvement in throughput.
Improving K-Means with Harris Hawks Optimization Algorithm

Data clustering aims at partitioning the data set into several disjoint segments, where the data inside the same segment are closely related, and the ones between two different segments are not. K-means is a popular data clustering algorithm, but it is easy to fall into the local optimum. To improve K-means’ performance, in this work, a Harris Hawks Optimization algorithm (HHO), which is a newly emerging Evolutionary Algorithm with strong global search capability, is utilized to optimize the central data set. In particular, we encode all the central data in one individual, and optimize them with HHO. In the experiment, multiple data sets are utilized to test our proposal’s performance. The experimental results show that our approach significantly outperforms the traditional K-means.
Online Model Adaptation of Autonomous Underwater Vehicles with LSTM Networks

This work addresses the online learning and adaptation of Autonomous Underwater vehicle (AUV) models. A framework is presented that employs long-short term memory (LSTM) networks which can be used to model the temporal dependencies in the AUV data. To reduce the effect of catastrophic forgetting, a memory efficient rehearsal method is developed with including and forgetting strategies to manage the butter of training samples. The proposed method is validated using experimental data proving its capability to adapt to real changes in the vehicles dynamics.
Video Forgery Detection Using Distance-based Features and Deep Convolutional Neural Network

Technological advancement of image and video processing tools made the tampering of videos faster and easier, and even a non-professional can make it simpler. Video tampering becomes a serious problem because it promotes fake news in social media and fake videos manifest in the court. Hence, the identification of forgery done in video is a difficult task. This paper introduces video forgery detection through Deep Convolutional Neural Network (DeepCNN). Initially, from the input video, the keyframe extraction is done using discrete cosine transform (DCT) and Euclidean distance. Then, the face objects are detected from the extracted keyframes using the Viola-Jones algorithm. After that, the light coefficients are calculated from the detected face object using 3D shape and texture models. Then, the features of the 3D face are extracted by employing distance measures. Finally, for video forgery detection the input video and the distance-based features are given to the DeepCNN. The devised method achieved maximal accuracy, True Positive Rate (TPR), True Negative Rate (TNR), and Receiver Operating Characteristics (ROC) of 91.02&#x0025;, 83.64&#x0025;, 94.89&#x0025;, and 95.56&#x0025;, respectively.
Online association by continuous-discrete appearance similarity measurement for multi-object tracking

Appearance similarity is of great importance for the association between objects and candidates. Recurrent models and similarity vector are two ways widely used by trackers for calculating similarities between objects and candidates. Recurrent models, like Long Short Term Memory network (LSTM), are capable of modeling the continuous change of object’s appearance in trajectory. But it is prone to identity (ID) switch when only employ recurrent models as appearance model. The similarity vector way is able to maintain correct IDs for objects when they reappear. But association fails easily when the object is partially occluded and similarity vector is used as the only appearance model. To obtain more accurate and robust appearance similarity, in this paper, we propose an online association by continuous-discrete appearance similarity measurement, OA-CDASM, for multi-object tracking. For continuous perspective, the concept of “smoothness” is proposed to explicitly model and use the continuous and smooth change of object’s appearance in trajectory. For discrete perspective, similarity vector is employed. By taking both continuous smoothness and discrete similarity vector into consideration, we can get the continuous-discrete appearance similarity measurement, CDASM, and further perform online association based on CDASM. Experimental results on three public benchmarks demonstrate the effectiveness of our work.
Fast Nonlinear Image Unblending

Nonlinear color blending, which is advanced blending indicated by blend modes such as "overlay" and "multiply," is extensively employed by digital creators to produce attractive visual effects. To enjoy such flexible editing modalities on existing bitmap images like photographs, however, creators need a fast nonlinear blending algorithm that decomposes an image into a set of semi-transparent layers. To address this issue, we propose a neural-network-based method for nonlinear decomposition of an input image into linear and nonlinear alpha layers that can be separately modified for editing purposes, based on the specified color palettes and blend modes. Experiments show that our proposed method achieves an inference speed 370 times faster than the state-of-the-art method of nonlinear image unblending, which uses computationally intensive iterative optimization. Furthermore, our reconstruction quality is higher or comparable than other methods, including linear blending models. In addition, we provide examples that apply our method to image editing with nonlinear blend modes.
Zoom Meetings vs Google Meet: Students' Experience

The COVID-19 pandemic has caused the closure of educational institutions and established a new normal for education systems by stopping face-to-face learning and requires online learning virtual education including Indonesia&#x0027;s education system has to follow these changes. To support online learning, Zoom Meetings and Google Meet were often utilized as virtual meeting room or online learning tool. The current study compares the students&#x0027; experience between Zoom Meetings and Google Meet. The number of samples taken was 100 respondents. The instruments used were the Indonesian UEQ that formulated and converted into a google form and distributed online. The result showed a significant difference in the Stimulation (0,0446) scale of the UEQ. This indicates that Zoom Meetings is more exciting and motivating to use than Google Meet. No significant difference for Attractiveness (0,8592), Efficiency (0,6320), Perspicuity (0,0876), Dependability (0,0686) and Novelty (0,1640) scales between Zoom Meetings and Google Meet. This indicates that students found both the Zoom Meetings and Google Meet attractive, enjoyable, pleasing, and easy to get familiar to, understand and use. Students could easily navigate, without unnecessary effort and their interaction with both systems was efficient and fast. Students felt in control of the interaction when using Zoom Meetings and Google Meet. Students also found both platforms innovative and creative capturing more their attention.