Reviewerly/exHarmony huggingface dataset:
Encryption with Automatic key Generation and Compression

Many applications rely on the transmission of digital images which contains confidential information. It is necessary to secure the confidential images from the eve’s droppers. Along with the attempts of providing confidentiality, it is also essential to reduce the amount of time taken for the encryption with the help of data compression techniques. To enhance the security, key should be changed frequently to repel the brute force attacks. The aim of this research is twofold. First, to propose a simple automatic key generation technique by using the modified logistic chaotic map and Chinese remainder theorem (CRT). Second, to improve the speed of encryption by using Integer wavelet-based compression. The qualitative analysis of the key generation is evaluated with the help of average hamming distance. The encryption quality is assessed with the help of statistical and sensitivity tests. Performance of the system is reported in terms of throughput.
Detection of Abnormal Activity Using Ensemble Deep Learning Model with Object Detection Network

The identification of violent or aberrant actions in surveillance domains is essential for preventing any casualties that could lead to social, economic, and ecological consequences. Importantly, automatic violence detection can effectively aid relevant agencies in taking swift action. Preventing future criminal activity requires a foundational process: detecting and predicting future criminal activity. Using deep learning models for crime detection is a fascinating field of study. In order to build a peaceful society, it is crucial that criminal actions be identified and reduced. In dangerous situations, video surveillance can help automate the process so that law enforcement can take prompt, effective action to ensure the protection of the public. Commercial and household security systems rely heavily on video and picture footage for prompt action, identification, and object detection. Detecting objects and people both call for distinct feature-based classifications from either stationary or moving camera data. In the training phase, existing approaches mainly rely on pre-trained weights from diverse Deep learning-based architectures for the visual illustration of video frames, which impacts the discrepancy determination of features like visual and temporal indicator distinction. In order to solve this issue and recognise the numerous human behaviours seen in movies, we combine an Object Detection Network (ODN) with a bidirectional long short-term memory (BiLSTM) to selectively focus on efficient information in the input frame. We may determine the most useful discriminative features from this complex network by the use of leftover blocks to boost the features that retain more information than a shallow layer. When the BiLSTM has learned the long-term dependencies, we utilize an attention strategy to boost presentation and extract additional high-level selective action-related designs and signals. The centre loss in conjunction with Softmax to further expand the loss function and get better performance in the real-time video-based action classification task. The suggested scheme outperformed state-of-the-art (SOTA) methods by 1%-3% when evaluated on publicly accessible data, resulting in a 95% recognition rate.
Bigger than they appear: Electronic architecture in recording studios and what that means to the musician and engineer

Technological advancements in electronic architecture (EA), and specific adaptations of that technology to the challenges of deploying it in small spaces, have made it an affordable application for recording studios. When combined with appropriate natural acoustics, this approach can achieve acoustical results not possible by treatments alone. The apparent volume, surface treatments, and resulting reverberant energy can be modified in real time beyond the constraints of the physical space, both for what the performer experiences within the space, and for what the engineer can capture in a recording. As a follow up to a paper presented in Seattle 2021, captured audio and video demonstrations from actual recording sessions of a variety of instrumentation and ensembles will illustrate EA’s practical use in the studio and what it means to the musician and the engineer.
Geometry-independent indoor motion planning using floor maps as prior information

In this study, we propose a route planning method using floor maps such as facility information maps as prior information. The floor map does not provide geometric information because the details are omitted and in some cases the ratio of height and width is inaccurate. Therefore, we extract the connection relations of the passages from the floor map as a node graph, and calculate the shortest path from the specified current location to the destination based on the node graph.We use LiDAR to detect the direction and branching of the corridor in the real environment, and then follow the calculated path.
Noise Ratio in Receivers

Fluctuation noise acting in receiver circuits can be external or internal in origin. The former include cosmic noises, atmospheric noises, quantum noises of the signal and background illumination, and the latter include the EMF and currents arising in the receiver elements due to the chaotic movement of various carriers of electric charges.
STUDENTS’ PERCEPTION OF SELF-AUTONOMY, MOTIVATION AND EFFECTIVE LEARNING IN THE ONLINE ENVIRONMENT

The onset of the Covid-19 pandemic changed the global society and generated mutations in all areas of activity, strongly impacting the education system as well. The solutions for the continuation of the educational process-imposed adaptations, changes, rethinking and restructuring of the way in which the instructive-educational approach was carried out until then. The traditional school imported methods, techniques, tools, resources and moved to the virtual space, on educational platforms. In this new context, the resilience, flexibility and creativity of teachers have proven to be fundamental skills for maintaining a satisfactory quality standard, conditioned, however, in many situations by teacher-independent factors: internet connection, appropriate devices or even students’ motivation for learning. Education continued, but the differences between the social categories of students deepened. In the online environment the teacher’s authority is lower, the efficiency of some methods is lost, communication is altered by contextual, environmental factors, students’ interest and motivation are difficult to maintain. Specialists in the educational field state that human interaction in the online environment is artificial, not authentic and does not have the same formative valences as in the physical space of the classroom. After two and a half years of pandemic, in which education took place in the online environment, alternating with periods when teachers and students returned to class, there are both obstacles and opportunities to integrate modern technology in the educational process. The pandemic has shown that today’s school needs to change, to be open to the outside world and to take on viable models and paradigms that meet the current and complex needs of students. In higher education, a number of advantages of online learning can be highlighted, as students generally have independently developed study skills, autonomy and the ability to organize their own training and development process.This investigative research aims to identify the students’ perception regarding the efficiency of teaching activities in the online environment, how their autonomy and motivation for learning has contributed to a quality educational process, despite the obstacles encountered, and to identify useful landmarks for teachers in achieving an education that integrates the online component effectively. The sample of the study included 83 higher education students from Valahia University of Targoviste, Romania.
Python Graphics with Tkinter

AbstractPython comes with a package called Tkinter (pronounced “tee-kay-inter”, and short for “Tk interface”), which provides access to a very rich graphics toolkit called Tk. Tk is a cross-platform toolkit and is the standard GUI (graphical user interface) for a number of programming languages including Python, Tcl and Perl. It provides us with all the common components (usually called widgets) that we are used to seeing—buttons, entry fields, check boxes and so on.
CĂTRE UN MODEL BAZAT PE SENZORI ÎN ANALIZA ÎNVĂȚĂRII

TOWARDS A MODEL FOR SENSOR BASED COMPONENT IN LEARNING ANALYTICS SYSTEM. In context of pandemic crisis, the emergency remote teaching (ERT) process raised a multitude of problems for institutions, teachers, and students, led the author to design a model for evaluating teaching and evaluation processes. In our previous studies, we developed a model for the evaluation system called the learning analytics and evaluation model (LAEM). We also validated a software instrument we designed called the EvalMathI system, which is to be used in the evaluation system and was developed and tested during the pandemic. For optimization of the evaluation process was accomplished by including and integrating the dashboard model in a responsive panel. In this paper, we analyse the possibilities to complete our learning analytics system with a sensor-supported component. Also, we identify which sensor technologies can be used in a more suited manner. Sensor technologies have been adapted for various domains, such as healthcare, and education. The proposed model component can be used to represent the capabilities of current learning analytic system and provide a basis for the future development.
IMEC: A Memory-Efficient Convolution Algorithm For Quantised Neural Network Accelerators

Quantised convolution neural networks (QCNNs) on FPGAs have shown tremendous potential for deploying deep learning on resource constrained devices closer to the data source or in embedded applications. An essential building block of (Q)CNNs are the convolutional layers. FPGA implementations use modified versions of convolution kernels to reduce the resource overheads using variations of the sliding kernel algorithm. While these alleviate resource consumption to a certain degree, they still incur considerable (distributed) memory resources, requiring the use of larger FPGA devices with sufficient on-chip memory elements to implement deep QCNNs. In this paper, we present the Inverse Memory Efficient Convolution (IMEC) algorithm, a novel strategy to lower the memory consumption of convolutional layers in QCNNs. IMEC lowers the footprint of intermediate matrix buffers incurred within the convolutional layers and the multiply-accumulate (MAC) operators required at each layer through a series of data organisation and computational optimisations. We evaluate IMEC by integrating it into the BNN-PYNQ framework that can compile high-level QCNN representations to the FPGA bitstream. Our results show that IMEC can optimise memory footprint and the overall resource overhead of the convolutional layers by ~33% and ~20% (LUT and FF count) respectively, across multiple quantisation levels (1-bit to 8-bit), while maintaining identical inference accuracy as the state-of-the-art QCNN implementations.
Development of Powerpoint-Based Interactive Media on Theme 8 Subtheme 1 in Elementary School

The use of media in learning in elementary schools is an important part that must receive attention from educators. The use of interactive media is needed by elementary schools so that the learning provided becomes more effective and efficient. This study aims to develop interactive media based on powerpoint on the theme of 8 sub-theme 1 in class III of elementary school during offline learning activities. This research is a development research and uses the ADDIE model. The types of data taken from this development are qualitative data and quantitative data. The population of this research is third grade students. The trial conducted was a limited-scale trial with a sample of 6 students with different learning score categories. Data collection techniques consist of two types, namely the first teacher and student response questionnaire, second validation sheet material experts, media experts and linguists. Based on the results of research from media experts with a total of 93.33%, material experts with a total of 90.83% and linguists with a total of 90.94% and the overall results of 91.1% concluded that it is very valid to be used as a learning medium. The results of the analysis of teacher responses with a total of 94% and students with a total of 88.33% and the total results of 91.16% concluded that practical media were used in the learning process activities took place.
Reflections on Lisa Zurk as an Educator

Lisa Zurk was my advisor and mentor through both my masters and doctoral degree programs. I had the pleasure of working with her beginning in 2008; first as her graduate student, then in my subsequent professional career while she was a program manager in the Strategic Technology Office at DARPA. I consider myself extremely fortunate to have been part of the Northwest Electromagnetics and Acoustics Research Laboratory (NEAR-Lab), which she founded at Portland State University in 2005. Under her guidance and leadership, the group became a diverse community of tight-knit students, faculty, and other researchers; united in our passion for research in terahertz imaging and spectroscopy, underwater acoustics, and physics-based signal processing. My experience there, and that of my fellow students, paved the way for our future careers in science and engineering in industry, academia, and everywhere in between. Today, NEAR-Lab alumni can be found at top tech companies such as Apple, Boeing, Intel, Tektronix, and Metron; nationally-renowned research organizations like The Johns Hopkins University Applied Physics Laboratory, MIT Lincoln Laboratory, and the Pacific Northwest National Laboratory; and even internationally at the University of Victoria, British Columbia, and JASCO Applied Sciences. The success of her students speaks volumes about her impact on the generation of innovators that followed her. This talk will reflect on Dr. Zurk as an educator and will present some of the work she coauthored and published with her students.
Research on Elastic Cloud Resource Management Strategies based on Kubernetes

Kubernetes’ native horizontal scaling strategy, horizontal pod autoscaler(HPA), suffers from a single monitoring metric that does not accurately measure workload. In addition, there are response delays in the expansion stage, and the deletion of redundant Pods in the capacity reduction phase may adversely affect load balancing. In order to improve the efficiency of computing resource usage, this paper designs an automatic horizontal scaling system architecture based on the Monitor-Analyse-Planning-Execution (MAPE) loop to address the above problems. The system also equips the horizontal scaling system with the resource deletion strategy RRS, which enables the system to handle bursty workloads better. The main contribution of this paper is to propose a passive strategy based on weighted metric thresholds and an active strategy based on ARIMA prediction. The results of multiple experiments show that the ARIMA-prediction-based strategy has the problem of decreasing prediction accuracy in the late stage of the experiment, demonstrating the limitations of the ARIMA model in the real-time scaling system. The weighted-metric-threshold-based strategy outperforms HPA in that the timing, number, and replica variation of Pod scaling better matches workload variation and improves response latency. This paper provides a reference for researchers, developers, and others using Kubernetes to optimize the performance of HPA.
Library automation in the context of digitalization: Advances and challenges

The evolution of library automation is reviewed. The term informatization is defined. Automation gives way to informatization that, in its turn, appears to be the precursor of digitalization. The terms digitization and digitalization are defined; the difference between them is revealed. The requirements to modern ALIS in the age of the 4-th industrial revolution are examined. The home-grown IRBIS ALIS is characterized.
STAKEHOLDER SATISFACTION AS A MEASURE OF ONLINE LEARNING QUALITY

The massive transition of universities to online learning in the context of the COVID-19 pandemic has revealed a number of challenges in achieving learning outcomes. The article presents the results of an analysis of the satisfaction of online learning participants undertaken by some organizations. A number of measures is proposed to improve the quality of the online learning process.
The Missing Margin: How Sample Corruption Affects Distance to the Boundary in ANNs

Classification margins are commonly used to estimate the generalization ability of machine learning models. We present an empirical study of these margins in artificial neural networks. A global estimate of margin size is usually used in the literature. In this work, we point out seldom considered nuances regarding classification margins. Notably, we demonstrate that some types of training samples are modelled with consistently small margins while affecting generalization in different ways. By showing a link with the minimum distance to a different-target sample and the remoteness of samples from one another, we provide a plausible explanation for this observation. We support our findings with an analysis of fully-connected networks trained on noise-corrupted MNIST data, as well as convolutional networks trained on noise-corrupted CIFAR10 data.
STRATIGRAPHIC FRAMEWORK OF THE HIGH-PRIORITY FALL ZONE AND MIDDLE SHORELINES PLACER FOCUS AREAS, DRAKE AND RED OAK QUADRANGLES, ATLANTIC COASTAL PLAIN, NORTH CAROLINA, USA

The NC Geological Survey acquired EARTH MRI funds to map detailed stratigraphy (24K scale) in Red Oak and Drake 7.5-minute quadrangles to identify placers with REE minerals in updip Atlantic Coastal Plain strata along-strike from existing placer mines. Red Oak, situated in the Fall Zone Placer focus area, is west of Drake, which staddles the boundary (the Kenly paleoshoreline) between the Fall Zone and Middle Shorelines focus areas. A 2021 airborne geophysical survey footprint that followed the VA-NC Fall Zone included both quads. A 24K map of bedrock exists for Red Oak, but not Drake. Elevations range from 92 m in Red Oak to 27 m in Drake. Relict Pliocene and Quaternary landforms are preserved here; hence 3D subsurface mapping strategies were applied. Geomorphology derived from 10 ft LiDAR DEMs helped plan the subsurface analysis. Cores to basement were collected along profiles chosen to transect specific geomorphic features. To date, 3,130 ft of core at 100 drill sites were collected to define the stratigraphy, mineral resource prospectivity, and depth to basement (bedrock and saprolitized metamorphic rocks and granitoids). Using 3D mapping methods, sedimentary facies formerly assigned to the Yorktown, Chowan River and Bacons Castle Formations are redefined and tracked relative to major paleoshorelines: the Kenly Scarp equivalent with a toe at ~50 m, and a second, more-landward scarp with several downstepping notches between 62 – 58 m. Both features are lower in elevation than the Coats/Orangeburg (84 m) and Wilson Mills (75 m) scarps as defined in NC. Stratigraphy here is complex: marine/estuarine terrace deposits occur as aprons around topographic highs of exposed saprolite or bedrock, but the highs may be capped by older Coastal Plain units. Between the two shorelines, Carolina Bays have prominent rims, and thin surficial gravels are common. Two marine units overlie the basement: 1) bluish-gray mollusk-rich facies, the ‘Yorktown Formation’, but may be the younger Chowan River, and 2) a yellow unit consisting of a set of upward coarsening parasequences with no macrofossils. Upward-fining surficial feldspathic units and other facies may overlie the marine units both west and east of the Kenly shoreline. Heavy mineral sands are distributed within these surficial deposits, including potential Cretaceous units at 92 m.
Putting away the Groceries with Precise Semantic Placements

We present a methodology for a service robot to put away the groceries in a precise and stable configuration that satisfies the desired semantic relationship of grocery and pantry objects. This task is invaluable to the motor impaired and increases the capability of contextualized service robots. We show that augmenting traditional geometric assessment with contextual knowledge of food categorization and a language association model, allows the robot to complete this task efficiently. We quantitatively validate our approach with a data set of 51 common grocery items, of which 11 objects are used for real world experiments. According to our evaluation, our method is able to successfully shelve objects next to semantically related objects 100% of the time when these relationships exist. We achieve this with an average placement precision of 3.2cm and a standard deviation of 1.1cm. We discuss remaining challenges and needed improvements for robots with these capabilities to be introduced to home environments.
Hilbert-Schmidt speed-based witness faithfully detects non-Markovianity

Hilbert-Schmidt speed (HSS)-based witness, a quantifier of quantum statistical speed having the advantage of avoiding the diagonalization of the evolved density matrix, has been introduced as a trustful witness of non-Markovianity in low-dimensional systems. We investigate the sensitivity of this HSS-based witness to detect non-Markovianity in high-dimensional and multipartite open quantum systems. We find that the time behaviors of the HSS-based witness are always in agreement with those of quantum negativity or quantum correlation measures. These results show that the HSS-based witness is a faithful identifier of the memory effects appearing in the quantum evolution of a high-dimensional system.
PENINGKATAN PEMBELAJARAN DARING MELALUI PENDAMPINGAN GURU DENGAN PEMBUATAN MEDIA PEMBELAJARAN VIDEO ASYNCHRONOUS DI PAUD INKLUSI SAYMARA

The implementation of social distancing in an effort to stop the spread of the Corona virus, a number of schools have eliminated teaching and learning activities, so a study from home policy has been issued by utilizing technology through e-learning to zoom. However, in practice, many complaints are experienced by both students and parents. The purpose of this activity is to provide knowledge and get to know about learning media that attracts students' attention by utilizing media in the form of videos with Power Point, OBS, and Google Meet. This training explores the creativity of PAUD teachers because the teacher plays an important role in attracting students. The benefits of this teacher assistance are inviting teachers and parents to motivate students in learning, the results of this activity itself are videos that have been made or compiled by teachers related to the learning themes that have been prepared according to the material and practice presented, 98% of the participants in the activity convey satisfaction in training events and according to current needs
The difficulties of implementing Agile and within it the Scrum framework

Agile is a relatively new area of project management schools and methodologies that many companies have adopted in recent years. However, it is not just a task structure, a documentation requirement, or a defined and formalized manifestation of processes, but the integration of a new way of thinking and approach into everyday work. When implementing Scrum, different Agile elements may be cumbersome for different professional levels and job positions for different reasons. The cumbersomeness and the causes are investigated in our primary research.