Reviewerly/exHarmony huggingface dataset:
Proof-of-Authentication for Private Distributed Ledger

Over the last few years, blockchain-based technologies have flourished in many application areas.One of them is the creation of distributed ledgers where records of immutable objects are widely replicated for both transparency and availability.However, the Proof-of-Work (PoW) approach, a popular gating control that determines who can add new records into a ledger, is deemed infeasible for IoT devices with resource constraints.In this paper, we present the design of DLedger, a private distributed ledger system designed for an experimental solar network developed by Operant Networks.DLedger records both individual customers' solar energy production/consumption as well as all other noteworthy system events, such as certificate issuance and revocations.Compared to today's centralized record keeping solutions, DLedger brings the benefits of information transparency and availability to both customers and the system vendor.Operant's solar network uses the Named Data Networking (NDN) protocol, based on which DLedger controls the addition of new records using a lightweight Proof-of-Authentication (PoA).PoA leverages the properties of NDN where (i) every entity in the system possesses a name and a digital certificate, and (ii) they share the same trust anchor and thus can authenticate each other.DLedger further leverages NDN's data-centric design to keep the ledger synchronized in a truly distributed and efficient manner.
Stakeholders' perceptions of integrating mobile devices in teaching and learning

The purpose of this study is to investigate the potential impact of using iPads in teaching and learning and to capture stakeholders' perceptions of using iPads in an international school in the Middle East. The study uses a case study methodology and collects both quantitative and qualitative data. The results show that all stakeholders reported positive perceptions about using the iPad. IPad use may have increased students' chances to communicate and ask for help from peers. Teachers reported they still need more help to plan for using iPads in their teaching. Some parents felt alienated from the types of learning their children used the iPad for. The qualitative results explain the stakeholders' experience in terms of technology access, ownership, integration, connectivity and institutional support. Based on the study results, specific recommendations are provided to improve integrating mobile technology in schools.
Survey on Fractal image compression

The need for multimedia applications has increased over the years. Due to this issue, image and video compression becoming an essential problem in reducing time and data storage. Fractal Image Compression (FIC) is a compression technique which results in loss, and we can have large amounts of compression. This scheme works by classifying an image into blocks and uses Contractive Mapping. Some of FICs advantages can be expressed as follows: comparing with other standard compression techniques, it has a good interchange between PNSR and compression ratio and it zooms the image without reducing the quality due to its resolution independent nature. The main FICs drawback, is its complexity in encoding and lack of speed.
A Heuristic Ranking of Different Characteristic Mining Based Mathematical Formulae Retrieval Models

The significant difficulty in the present circumstances is how to classify the math related keywords from a given text file and group them in one math file. Through this article a heuristic ranking model was developed and was evaluated on different mathematical formulae retrieval algorithms based on Characteristic mining. Our proposed heuristic ranking model was developed using the performance metrics of exiting retrieval algorithms such as NMF clustering, Levenstein distance, Sequence matcher, Fuzzy-wuzzy and Tensorflow. Performance metrics such as sensitivity, specificity, efficiency, accuracy and retrieval time were used in developing our heuristic ranking model.
Assessment of the Quality and Reliability of the Information on Bone Tumor on Youtube

Objective: Our aim is to assess the content quality and reliability of Youtube videos on bone tumors. Method:We searched Youtube using the keyword "bone tumors, " and included the first 50 videos listed in our study.Two orthopedic surgeons analyzed the videos, and then, we examined them in terms of length, number of views and likes, and source.To evaluate their content quality, the Global Quality score (GQS) (0-4), Journal of the American Medical Association (JAMA) (0-5), and DISCERN (15-75) scoring systems were used; based on these, the obtained data were statistically analyzed.Results: Of the 50 videos analyzed, four (8%) included animation, while 46 (92%) did not; 12 (24%) were uploaded by doctors, 23 (46%) by health channels, 10 (20%) by universities, three (6%) by clinics, and one (2%) each by a hospital and a trainer.The average video length was 16 minutes and 18 seconds (38-6.088seconds), average number of views was 14,856.24,and average number of likes was 130.50 (1-1448).The mean scores of GQS, JAMA, and DISCERN were 2.22 (1-4), 2.12 (1-3), and 33.48 (15-75), respectively.There was no statistical significance in the scores and video length, or the number of likes and views between the two researchers. Conclusion:Youtube videos on bone tumors have low content quality.Improvement of the same will help dispense correct information to patients, so that they can continue their treatment.We believe that patient treatment compliance can be increased by accelerating the patient's preparation and adaptation process for treatment with accurate information.
Effective Data Analytics on Opinion Mining

Over the extent of the most recent couple of years, in setting on the augmentation being utilized Internet, bit of client's opinion has stretched out that has led to the growth in motivation towards opinion mining. Opinion Mining is fundamental for the two people and affiliations. People like to see the opinions given by different clients about a specific thing or alliance. Affiliations need to break down the duty of its clients so as to improve its future choices. Concentrates on the Internet about a specific subject can be in millions that makes it premonition to comprehend a client's opinion and necessities. End examination enables us to center audits and present the structure that could be useful for certified investigating and thing improvement. In this paper, we complete an examination of Opinion Mining, covering different procedures in doubt examination and difficulties that show up in this field.
A Real Time Malaysian Sign Language Detection Algorithm Based on YOLOv3

Sign language is a language that involves a movement of hand gestures. It is a medium for the hearing impaired person (deaf or mute) to communicate with others. However, in order to communicate with the hearing impaired person, the communicator has to have knowledge in sign language. This is to ensure that the message delivered by the hearing impaired person is understood. This project proposes a real time Malaysian sign language detection based on the Convolutional Neural Network (CNN) technique utilizing the You Only Look Once version 3 (YOLOv3) algorithm. Sign language images from web sources and recorded sign language videos by frames were collected. The images were labelled either alphabets or movements. Once the preprocessing phase was completed, the system was trained and tested on the Darknet framework. The system achieved 63 percent accuracy with learning saturation (overfitting) at 7000 iterations. Once it is successfully conducted, this model will be integrated with other platform in the future such as mobile application.
High Capacity Steganography Protected using Shamir’s Threshold Scheme and Permutation Framework

This paper presents a framework that conceal large volume of secret information using distributed file system that permit implantation of decomposed secret images across multiple-cover images. The strong security is imposed utilizing Shamir’s threshold scheme and permutation generator framework. Three layers of security is being applied to protect the secret information, in first, secret image is decomposed into equal size of smaller sub-images and generate a framework of permutations from an integer for distributing and reassembling the circulated broken secret sub images among the intended participants. At the time of embedding purpose of permutation generator is to arrange the sub-images in unknown order for outsider. During the discloser stage only inverse of permutation can rearrange the distributed sub-images to reassemble into original image by authorized contributors. In second, Shamir’s threshold scheme is designed for authentication of shared associated stego-cover images before starting the extraction process. This process provides an extremely secured construction of shared secret information. In third, image is divided in 2x2 blocks of pixels and traverses it in zig-zag manner; the pixel value difference is computed for all Red, Green, and Blue (RGB) components between non-overlapping pixels of selected diagonal path with in targeted block. Secret bits are concealed inside RGB color pixels of cover image by utilizing proposed novel pixel-value differencing (PVD) scheme, furthermore varying embedding capacity may be obtained by controlling the selection of number of 2x2 block. Exploratory result displays that the proposed approach provides productive algorithms in term multilayer unbreakable security and higher payload of embedded information.
Cross-Modal Learning Filters for RGB-Neuromorphic Wormhole Learning

Robots that need to act in an uncertain, populated, and varied world need heterogeneous sensors to be able to perceive and act robustly.For example, self-driving cars currently on the road are equipped with dozens of sensors of several types (lidar, radar, sonar, cameras, . . .).All of this existing and emerging complexity opens up many interesting questions regarding how to deal with multi-modal perception and learning.The recently developed technique of "wormhole learning" shows that even temporary access to a different sensor with complementary invariance characteristics can be used to enlarge the operating domain of an existing object detector without the use of additional training data.For example, an RGB object detector trained with daytime data can be updated to function at night time by using a "wormhole" jump through a different modality that is more illumination invariant, such as an IR camera.It turns out that having an additional sensor improves performance, even if you subsequently lose it.In this work we extend wormhole learning to allow it to cope with sensors that are radically different, such as RGB cameras and event-based neuromorphic sensors.Their profound differences imply that we need a more careful selection of which samples to transfer, thus we design "cross-modal learning filters".We will walk in a relatively unexplored territory of multi-modal observability that is not usually considered in machine learning.We show that wormhole learning increases performance even though the intermediate neuromorphic modality is on average much worse at the task.These results suggest that multi-modal learning for perception is still an early field and there might be many opportunities to improve the perception performance by accessing a rich set of heterogeneous sensors (even if some are not actually deployed on the robot).
A Survey on Content Based Image Retrieval Using Convolutional Neural Networks

A smart image retrieval technique has been an increasing demand by the advancements in the field of computer networks and mobile computing.Generally, uploaded images have textual information associated with the owner's tags.When an input image is given as a search query, its text content is processed to identify the semantics of the image, and the output will be based on the labels stored in it.Traditional hashing techniques are most commonly used to provide high quality search results for labeled images.In this case, hashing codes are processed based on the semantic preserving information obtained from the features of input images using supervised learning methods.But the delivery of high quality pictures without human interaction, using automated annotation among very large scale image databases, is still a continuous research process.This paper focuses on comparing various methods for reducing the semantic gap between low-dimensional and highdimensional features.It also focuses on content based image retrieval technique (CBIR), with an unsupervised learning method using convolutional Neural Networks (CNN).
Secret Image Sharing and Steganography Using Haar Wavelet Transform

Image steganography enables secure communication whether even intimating the enemy regarding the occurrence of communication.Steganography and cryptography play a vast role in rendering effective security for the secret data.However, when the presence of the secret message is revealed then, the secret message is disclosed, which is the major drawback of the existing strategies.Thus, the paper proposes an effective image sharing and steganography method using the Haar wavelet.There are two phases in this research: encoding and decoding phase.In the first phase, the encoding phase, where the secret message is embedded in the input image using the proposed Haar wavelet-based steganography, while in the decoding phase, the secret message is uncovered.In the decoding phase, the Lagrange's interpolation is applied that decodes the secret message from the stego or embedded image.The great significance of the method is that the greater degree of security is rendered against the security attacks and is a robust strategy for combining the secret sharing of messages and steganography.The analysis of the proposed method with respect to the existing methods is enabled using the 500 stego images acquired from the UCID database.The comparative analysis of the proposed method based on the metrics, such as Peak Signal-To-Noise Ratio (PSNR) and Structural Similarity (SSIM) index reveals that the proposed method acquired a maximal PSNR and SSIM of 57.142 dB and 0.9991, respectively.
Development of Interactive E-Module Based on Human Digestive System Material Inquiry on Theme 3 About Healthy Foods for Fifth Grade Elementary School

Currently, conventional teaching materials and learning media have not met the learning demands in the 4.0 evolutionary era. It results in many students who do not understand the subject matter, so that the learning objectives have not been achieved optimally. This study aims to produce an inquiry-based interactive electronic module to support learning. This type of research is development research using the ADDIE model, which includes the analysis, design, development, implementation, and evaluation stages. The techniques used in collecting data are observation, interviews, and questionnaires. The instrument used to collect data is a questionnaire. The analysis used is qualitative descriptive analysis technique and quantitative descriptive analysis. The research results review subject matter experts who get very good qualifications (100%). The results of the learning design expert review are very good (90%). The results of the learning media expert review are good (86.7%). The individual test results are very good (97,3%). The small group test results are very good (95.7%). It can be concluded that the inquiry-based interactive e-module is very valid and feasible to use in learning. This research implies that teachers can use the developed media to help students learn.
Peran Orang Tua dalam Meningkatkan Motivasi Belajar Anak Dengan Sistem Daring pada Masa Pandemi di Desa Depokrejo, Kebumen

This research is motivated by the results of observations and research as one of the Depokrejo village communities. This is to find out how the role of parents in developing learning abilities for early childhood during the Covid-19 pandemic. The subjects of this study were parents and their children. In this study, researchers used data collection methods in the form of interviews. The results of research on the role of parents teaching children in the midst of the Covid-19 pandemic, show that there are parents who pay less attention to children's learning activities at home and are busy with their respective jobs, but there are also parents who want to direct their children to study at home and mutually communicate and guide children to learn together.&#x0D; The conclusion of this study is that the role of parents who have high enthusiasm for learning motivation and developing children's learning abilities will have a good impact on children, especially in the current Covid-19 pandemic, children really need the role of parents to foster interest in learning. them through learning that will be taught at home.
Secure RSA Variant System to Avoid Factorization Attack using Phony Modules and Phony Public Key Exponent

In cryptography Public key cryptography plays a significant job in the field of data communication. Public key uses two distinctive keys where keys are related so those, the public key can use to encode the information and private key is utilized to decode. RSA is considered as one of the effective algorithm in public key cryptography. Effectiveness of RSA Algorithm for the most part relies upon how adequately public key segments is shared i.e. common modulus n and public key exponent e. If these components compromised by the hacker using mathematical attacks, acquiring private key becomes easier task for the intruder. This paper present an upgraded RSA algorithm which is used to avoid the limitations of Integer factorization attack by improving the complexity of factorization process by utilizing fake/phony public key exponent type f rather than e and phony modulus X rather than n. Paper also gives comparative analysis of the proposed work using standard metrics.
I²R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation

In this paper, we present the Intra- and Inter-Human Relation Networks I²R-Net for Multi-Person Pose Estimation. It involves two basic modules. First, the Intra-Human Relation Module operates on a single person and aims to capture Intra-Human dependencies. Second, the Inter-Human Relation Module considers the relation between multiple instances and focuses on capturing Inter-Human interactions. The Inter-Human Relation Module can be designed very lightweight by reducing the resolution of feature map, yet learn useful relation information to significantly boost the performance of the Intra-Human Relation Module. Even without bells and whistles, our method can compete or outperform current competition winners. We conduct extensive experiments on COCO, CrowdPose, and OCHuman datasets. The results demonstrate that the proposed model surpasses all the state-of-the-art methods. Concretely, the proposed method achieves 77.4% AP on CrowPose dataset and 67.8% AP on OCHuman dataset respectively, outperforming existing methods by a large margin. Additionally, the ablation study and visualization analysis also prove the effectiveness of our model.
KG4Py: A toolkit for generating Python knowledge graph and code semantic search

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting

s highlight changes to a model’s input that alter its prediction in a particular way. s have gained considerable traction as a psychologically grounded solution for explainable artificial intelligence (XAI). Recent innovations introduce the notion of plausibility for automatically generated s, enhancing their robustness by exclusively creating plausible explanations. However, practical benefits of this constraint on user experience are yet unclear. In this study, we evaluate objective and subjective usability of plausible s in an iterative learning task. We rely on a game-like experimental design, revolving around an abstract scenario. Our results show that novice users benefit less from receiving plausible rather than closest s that induce minimal changes leading to the desired outcome. Responses in a post-game survey reveal no differences for subjective usability between both groups. Following the view of psychological plausibility as comparative similarity, users in the closest condition may experience their s as more psychologically plausible than the computationally plausible counterpart. In sum, our work highlights a little-considered divergence of definitions of computational plausibility and psychological plausibility, critically confirming the need to incorporate human behavior, preferences and mental models already at the design stages of XAI. All source code and data of the current study are available: https://github.com/ukuhl/PlausibleAlienZoo
On computational capabilities of Ising machines based on nonlinear oscillators

Dynamical Ising machines are actively investigated from the perspective of finding efficient heuristics for NP-hard optimization problems. However, the existing data demonstrate super-polynomial scaling of the running time with the system size, which is incompatible with large NP-hard problems. We show that oscillator networks implementing the Kuramoto model of synchronization are capable of demonstrating polynomial scaling. The dynamics of these networks is related to the semidefinite programming relaxation of the Ising model ground state problem. Consequently, such networks, as we numerically demonstrate, are capable of producing the best possible approximation in polynomial time. To reach such performance, however, the reconstruction of the binary Ising state (rounding) must be specially addressed. We demonstrate that commonly implemented forced collapse to a close-to-Ising state may diminish the computational capabilities up to their complete invalidation. Therefore, consistent treatment of rounding may cardinally improve various operation metrics of already existing and upcoming dynamical Ising machines.
BMPQ: Bit-Gradient Sensitivity-Driven Mixed-Precision Quantization of DNNs from Scratch

Large DNNs with mixed-precision quantization can achieve ultra-high compression while retaining high classification performance. However, because of the challenges in finding an accurate metric that can guide the optimization process, these methods either sacrifice significant performance compared to the 32-bit floating-point (FP-32) baseline or rely on a compute-expensive, iterative training policy that requires the availability of a pre-trained baseline. To address this issue, this paper presents BMPQ, a training method that uses bit gradients to analyze layer sensitivities and yield mixed-precision quantized models. BMPQ requires a single training iteration but does not need a pre-trained baseline. It uses an integer linear program (ILP) to dynamically adjust the precision of layers during training, subject to a fixed hardware budget. To evaluate the efficacy of BMPQ, we conduct extensive experiments with VGG16 and ResNet18 on CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets. Compared to the baseline FP-32 models, BMPQ can yield models that have 15.4x fewer parameter bits with negligible drop in accuracy. Compared to the SOTA “during training”, mixed-precision training scheme, our models are 2.1 x, 2.2x, and 2.9x smaller, on CIFAR-10, CIFAR-100, and Tiny-ImageNet, respectively, with an improved accuracy of up to 14.54%.