Reviewerly/exHarmony huggingface dataset:
Digital Forensics Education: Challenges and Future Opportunities

Digital technologies are becoming more engrained in our daily life and society (e.g., smart city and smart nation), and these technologies can be both the target of and tool used to facilitate a broad range of malicious cyber activities. This reinforces the importance of disciplines such as digital forensics. Digital forensics is a relatively new, multidisciplinary field with roots in traditional forensic sciences, as well as combining elements of technology, legal, social science, political science, criminal justice, and various other disciplines. Compared to the more established fields of computer science, information security, and cybersecurity, digital forensics is somewhat understudied. Though interest in the field is growing and the industry is in need of trained professionals, there are a multitude of challenges to overcome as it relates to education. There is a glaring lack of standardization and structure both in existing educational programs and for those developing new digital forensics programs. Existing programs are scattered among various colleges and departments, and there are significant gaps in the materials covered and relevant emerging technologies. Despite these challenges, the digital forensics field and its variety of subfields such as Internet of Things, mobile, cloud, network, and vehicle forensics have been steadily gaining academic interest and attention. Finding solutions and developing robust higher educational programs is a necessary step to improve the quality of digital forensics education and produce highly trained professionals with the skills required to detect, investigate and prosecute malicious cyber activities in civil litigations (e.g., corporate espionage), criminal investigations and national security investigations.
Enhancement Of Downlink Chaos MIMO Performance Using Initial Chaos Keys In Selected Magnitude Ranges

Over the past few years, wireless traffic is increasing rapidly, as more number of smartphones and tablets are used. So there is a need for higher transmission rates and higher security in wireless communication systems. To meet these needs, chaos modulation, where initial chaos keys are necessary, is employed here. Then, we propose magnitude ranges for initial chaos keys. This is implemented in two methods of chaos modulation. In the first method, we implement a downlink chaos multiple–input multiple–output (C–MIMO) scheme with a single initial chaos key for each user, whereas in the second method, ten initial chaos keys are allotted to each user. By evaluating the numerical results, we show that both methods are superior to conventional MIMO scheme. Two methods show an enhanced bit error rate (BER) performance than the C–MIMO scheme with initial chaos keys not in the proposed magnitude ranges. However, second method provides a greater physical layer security than the first method.
Analysis of Differences in College Students' Independent Learning Based on Information Technologies

In the information era, cultivation of independent learning has become the top priority of education reform. The paper sample surveyed factors affecting the independent learning of college students based on big data and online questionnaires, and constructed a multivariate logistics regression model where SPSS was used to analyze differences in the independent learning of college students. The paper concluded several distinctive factors affecting their independent learning skills: taking potion in the student union, education background, personality and online learning.
Particle filter algorithm based on geometric center and likelihood estimation

The improved particle filter (PF) based on the geometric center and likelihood estimation is proposed in order to solve the problem of particle dilution and degradation. In the resampling stage, the geometric center is used to resample the particles. The particles are filtered according to the distance between the particles and the geometric center, and then the particles are resampled. The resampled particles are composed of newborn particles and non-resampled particles. The former can help alleviate the degradation problem, while the latter can keep the diversity of the particle set. In order to ensure effectiveness of the PF, the positioning error threshold of the particle filter is introduced. In the phase of particle weighting calculation, in view of the problem of low accuracy and divergence of PF state estimation caused by non-stationary and non-Gaussian noise, it adopts non-Gaussian noise parameter estimation based on likelihood to approximately estimate the measurement noise instead of the Gaussian density function. The proposed model is applied to particle weight calculation to avoid particle degradation caused by Gaussian density function approximation. The simulation results show that, after the improved algorithm, the root-mean-square error is reduced to 0.085, the variance is reduced to 0.014, and the running time is shortened by 14.8% compared with the polynomial resampling algorithm, which can effectively alleviate particle degradation and dilution in the traditional PF algorithm, and the positioning accuracy is also improved.
Faculty Development Needs of young teachers in Local Universities in China —Empirical analysis based on the survey of 1008 teachers in 8 provinces

This study explored the development needs, prospects and opportunities of young teachers in local Universities and consequently proposed measures need to be taken for them. Using random sampling, 1008 full time young faculty members from 8 provinces in Chinese local Universities were selected as respondents. Based on SPSS22.0 Statistical analysis, the following conclusions were arrived by the researcher: 1) The young faculty member respondents need these competencies to a great extent: instructional development, research professional development, organizational development and personal development. 2) There is no significant difference in the extent of the young faculty members' needs when their profiles are taken as factors. 3) Local universities provide human resources, logistics, budget and policy opportunities for young faculty member, which are insufficient. Therefore, it was suggested that Local Universities should increase the development of teachers' human resources, innovate the mechanism, expand financial revenue, establish the support system to promote the development of young teachers.
Video Classification using SlowFast Network via Fuzzy rule

Anomalous events occur rarely and are challenging to model. Therefore, automatic recognition of abnormal activities in surveillance videos is a non-trivial task. Though with the availability of video datasets of abnormal activities, there has been some progress, recognition of abnormal activities in real-time with high confidence remains unsolved. Existing video-based anomaly detection techniques using traditional machine learning and deep-learning are compute-intensive and give low recognition accuracy. This paper presents a robust and computationally efficient deep learning-based framework to recognize different real-world anomalies from the video. The proposed scheme uses a Fuzzy rule to summarize the video to scale the problem into fewer frames and the slow-fast neural network for classification. Intuitively, the designed pipeline aims to solve two significant problems that arise with video classification; one is to reduce the redundant frames and avoid the computation of optical flow for a video that has a substantial computational requirement. The proposed scheme tested on the UCF-crime dataset and has achieved recognition accuracy of 53%.
Automatic Analysis of Basketball Shooting Based on Machine Learning

In order to help basketball players to carry out training more efficiently and reasonably, and obtain the technical specifications of their shootings timely in training, this paper constructs a shooting detection algorithm to assist basketball players in training. Firstly, Hough transform algorithm is applied to the shooting video to detect the trajectory of the basketball in the shooting. By analyzing the basketball trajectory, it is found that the best shooting angle should be 49° to 51°. Then, the basic principles of HOG feature and SVM are studied, and the HOG feature of basketball hit and miss is extracted to train the SVM classifier. Finally, the training model was tested, and the classification accuracy of basketball hit or miss was 91.75%. This study will help the basketball players in their training and also could be applied to other ball games.
Visualizing and Characterizing the Parameter Configuration Landscape of Particle Swarm Optimization using Physical Landform Classification

When designing effective parameter tuning and/or self-adaptive mechanisms for meta-heuristic optimizers, any insights about the configuration process and its associated landscape are of great benefit. Recently, the parameter configuration landscape (PCL) was proposed as a mechanism to formally study and characterize the landscape induced by the control parameter values of meta-heuristic search techniques. As an extension, the use of geomorphon landform types to further characterize and visualize the PCL was recently proposed. This study adopts the geomorphon classification scheme and applies it to particle swarm optimization (PSO). The methodology is applied on 20 minimization benchmark problems with various problem dimensions and swarm sizes, thereby providing deep insights into the PCL associated with PSO.
An Improved Global State Fusion Filter for Track Estimation

According to the characteristics of target motion and composite guidance, this paper establishes the state motion equation, designs the global state feedback model based on Kalman filter method and proposes an improved track fusion estimation method of the moving target on the sea by introducing the scalar weighting optimal state fusion. The method avoids the matrix inverse operation, so it significantly reduces the computational amount and effectively improves the timeliness in multi-mode composite guidance engineering. Simulation results of active and passive radar composite guidance show that this method can effectively reduce the computation amount and meet the time limitation requirement of composite guidance under conditions of ensuring the precision of track estimation.
The Role of Education on Technology Inclusion and the Performance of Indonesian Human Resources

This study examines the relationship between technology inclusion and educational investment made by the Indonesian government on economic growth in Indonesia. This study uses secondary data from world banks and processed regression using the moving average autoregression method. We found that education and technology inclusion were positively related to economic growth. This indicates that education and technology inclusion in Indonesia contribute to economic growth in Indonesia.
Relationships Among Online Teaching Design, Experience, and Perception of College Teachers During the Pandemic

The outbreak of COVID-19 forced many higher education institutions worldwide to transfer from dominantly off-line teaching to online teaching mode in a short time. Instructors’ online teaching design is critical to learning achievements of undergraduate students during and even after the pandemic. The current study investigates the relationships between college teachers’ online teaching design, online teaching experience, and perceptions. A total of 1, 318 instructors (645 females, 673 males) from a Southeastern university in China completed an online survey and statistical analyses were conducted to compare their online teaching design across experiences and perceptions of online teaching. Results showed that college teachers designed online teaching differently across genders and ages. Additionally, teachers’ perception and prior experience of online teaching might also affect their online teaching design . The implications and suggestions for improving teachers’ online teaching design practice from the institutional perspectives are discussed.
Impact of Augmented Reality and Virtual Reality in the Transformation of Virtual Customer Relationship Management Sector

The integration of virtual reality and artificial intelligence into the business environment takes human-machine interaction to an advanced level. The inception of these technologies along with deep learning approaches has transformed the business environment completely, especially customer relationship management. Additionally, augmented reality has changed the business production environment with interactive designs and machines learning approaches. Augmented reality is expected to grow by $814.7B in 2025 which will bring a huge transformation in the businesses. Though leveraging technologies have been introduced to transform customer experience, with an ever-accelerating tidal wave of advancing technology, the adoption rate of these technologies in business is not satisfactory. This is because of unawareness of the experience transformation rate of augmented reality on business. Many applications related to Artificial Intelligence and Virtual Reality experience were found but no success rate was observed because of unfamiliarity with the key features to the technology. There is a need to introduce the power of technology by engaging with it. A systematic approach to identify the key aspects of augmented reality and virtual reality for the successful adoption of business in customer relationship management sector has been used for the present study. A case study of Amazon is considered to analyze the impact of the implementation of Virtual Reality on product sales.
Unsupervised Domain Adaptive Object Detection Based on Channel Recalibration and Ancillary Net

In the field of object detection, it is generally hoped that the model trained in the label-rich environment can be well applied to other various and label-agnostic environment. However, due to the existence of domain discrepancy between the training environment and the application environment, the performance of conventional object detection models inevitably encounters a sharp drop. Aiming at the limitations of the object detection application environment and the time-consuming of image labeling, this paper proposes an unsupervised domain adaptive object detection model based on Faster R-CNN. We integrate SENets into the feature extraction network to capture channel-wise correlations between features and strengthen the representations produced by CNNs, and add ancillary nets at the image level and the instance level respectively to prevent data distribution distortion caused by adversarial training. The experiments show that our proposed approach not only enhances the robustness of object detection under different environmental conditions, but also improves the detection accuracy of objects.
A Novel Fully Transparent Photoluminescent Display

A fully transparent RGB emissive screen is developed, by combining novel RGB emissive nanomaterials that can be selectively excited by digital projection of 3 violet-blue wavebands. The novel photoluminescent display supports a scalable and freeform missive display onto any surfaces, including glass windows and high contrast black screens, with high emissive display quality and superior optical clarity.
An Evolutionary Neuron Model with Dendritic Computation for Classification and Prediction

Advances in the understanding of dendrites promote the development of dendritic computation. For decades, the researchers are committed to proposing an appropriate neural model, which may feedback the research on neurons. This paper aims to employ an effective metaheuristic optimization algorithm as the learning algorithms to train the dendritic neuron model (DNM). The powerful ability of the backpropagation (BP) algorithm to train artificial neural networks led us to employ it as a learning algorithm for a conventional DNM, but this also inevitably causes the DNM to suffer from the drawbacks of the algorithm. Therefore, a metaheuristic optimization algorithm, named the firefly algorithm (FA) is adopted to train the DNM (FADNM). Experiments on twelve datasets involving classification and prediction are performed to evaluate the performance. The experimental results and corresponding statistical analysis show that the learning algorithm plays a decisive role in the performance of the DNM. It is worth emphasizing that the FADNM incorporates an invaluable neural pruning scheme to eliminate superfluous synapses and dendrites, simplifying its structure and forming a unique morphology. This simplified morphology can be implemented in hardware through logic circuits, which approximately has no effect on the accuracy of the original model. The hardwareization enables the FADNM to efficiently process high-speed data streams for large-scale data, which leads us to believe that it might be a promising technology to deal with big data.
Learning human-object interactions by attention aggregation

Recent years, deep neural networks have achieved impressive progress in object detection. However, detecting the interactions between objects is still challenging. Many researchers pay attention to human-object interaction (HOI) detection as a basic task in detailed scene understanding. Most conventional HOI detectors are in a two-stage manner and usually slow in inference. One-stage methods for direct parallel detection of HOI triples breaks through the limitation of object detection, but the extracted features are still insufficient. To overcome these drawbacks above, we propose an improved one-stage HOI detection approach, in which attention aggregation module and dynamic point matching strategy play key roles. The attention aggregation enhances the semantic expression ability of interaction points explicitly by aggregating contextually important information, while the matching strategy can filter the negative HOI pairs effectively in the inference stage. Extensive experiments on two challenging HOI detection benchmarks: VCOCO and HICO-DET show that our method achieves considerable performance compared to state-of-the-art performance without any additional human pose and language features.
Effective Filter Pruning Method Using Additional Downsampled Image for Global Pooling Applied CNN

Deep Convolutional Neural Networks (CNNs) show remarkable performance in many areas. However, most of the applications require huge computational costs and massive memory, which are hard to obtain in devices with a relatively weak performance like embedded devices. To reduce the computational cost, and meantime, to preserve the performance of the trained deep CNN, we propose a new filter pruning method using an additional dataset derived by downsampling the original dataset. Our method takes advantage of the fact that information in high-resolution images is lost in the downsampling process. Each trained convolutional filter reacts differently to this information loss. Based on this, the importance of the filter is evaluated by comparing the gradient obtained from two different resolution images. We validate the superiority of our filter evaluation method using a VGG-16 model trained on CIFAR-10 and CUB-200-2011 datasets. The pruned network with our method shows an average of 2.66% higher accuracy in the latter dataset, compared to existing pruning methods when about 75% of the parameters are removed.
Object Position Estimation from a Single Moving Camera

This paper deals with the position estimation of a road sign from a single camera attached to a vehicle. We developed, implemented, and tested two mathematical approaches based on triangulation when the object annotation in the form of a bounding box is provided. We created a synthetic dataset (a simulation of a car passing by a road sign) to test the methods in a controlled environment. Additionally, the real dataset was created by recording a car trip within a town. Results on the synthetic dataset showed that the position could be estimated within 1 m accuracy. In the case of the real dataset, we measured the accuracy to be up to 4.3 m depending on the distance from the object. We performed experiments with artificial noise on synthetic data to evaluate the impact of different types of noise. Our contribution consists of two computationally inexpensive methods for object position estimation that are easy to use as they do not require calibration of parameters.
A Middleware for Implicit Human-Computer Interaction Across IoT Platforms

The Internet of Things is increasingly becoming a part of our daily life. With popular task automation or IoT platforms such as If This Then That (IFTTT), users are able to define rules to enable interactions between smart devices in their environment and thereby improve their daily lives. However, rules authored by users on these popular task automation or IoT platforms are often tied to the platform or even to the specific devices for which they have been configured, implying a vendor lock-in for users. Therefore, switching to a different task automation or IoT platform or the introduction of new devices might demand for a re-creation of the rules that are specific to the new platform and devices. In order to address this problem, we need human-computer interaction that works across IoT platforms, in particular with the proliferation of IoT services in domains such as smart health where a user’s quality of life might depend on an uninterrupted service offered by a platform. In this paper, we propose an architecture to enable implicit human-computer interaction across IoT platforms by introducing the necessary concepts providing users ownership and control over their IoT data and rules.
Multi-mode Composite Track Estimation Method Based on Distributed Measurement Fusion

In the multi-mode track fusion algorithm, the commonly used method based on state fusion is relatively small in calculation, but it is not the optimal estimation. The centralized filter fusion method based on measurement is the optimal, but the measurement equation has high dimension, and the calculation is large. In this paper, the extended measurement method is used to unify the dimensionality of distributed measurement data, and a distributed measurement fusion algorithm adapted to track estimation is designed, which can effectively reduce the computation cost, improve the reliability and fault-tolerant ability, and achieve the optimal estimation of state data. The simulation results of active and passive radar composite guidance show that the algorithm is simple in calculation and fast in convergence, and can guarantee the high precision optimal estimation of target trajectory.
Cek Similarity Journal VIPERARTS: Visual Aesthetics Understanding of Elementary Students in Creating the Artworks

This research has motivated by the author's desire to know deeply about elementary students' understanding of visual aesthetics through the objectification process. The purpose of this research is to explore the ability of elementary school students to interpret the beauty and visual arts through practical activities in making three-dimensional artworks. The author using qualitative research methods with data collection techniques are observation, interviews, and document reviews. The subjects are students in Grade 6 at SD Negeri Bhayangkara Yogyakarta. The data analysis technique used is narration. The results are obtaining students taking the stages in making artworks with a visual aesthetic approach and the objectification process towards natural objects. Visual aesthetic understanding for elementary students comes from how we can invite them doing practicing at outclass and let them look for reference from many sources. Creativity reveals from combining person, product, process, and environment. The involvement of the lecturer and tutor teacher in the learning process is beneficial for students in representing ideas and ideas. Besides, the process of mentoring during practice can control the attitudes of those who tend to play around still.Keywords: Visual Aesthetics, Creativity, Visual Art Education