Reviewerly/exHarmony huggingface dataset:
The Importance of Using the Internet of Things in Education

The subject of the Internet of Things is very important, especially at present, which is why it has attracted the attention of researchers and scientists due to its importance in human life. Through it, a person can do several things easily, accurately, and in an organized manner. The research addressed important topics, the most important of which are the concept of the Internet of Things, the history of its emergence and development, the reasons for its interest and importance, and its most prominent advantages and characteristics. The research sheds light on the structure of the Internet of Things, its structural components, and its most important components. The research dealt with the most important search engines in the Internet of Things, the steps of designing Internet of things systems. Among the important topics included in the research are the use of Internet of things technology in the educational field, the role of the Internet of things in solving education problems, and the most important applications of the Internet of things in education and its importance for students and teachers. Also indicated the contribution of the Internet of Things to ensuring sustainable development, environmental protection and the most important areas of application of the Internet of Things. The research sample amounted to (152 teaching staff) from Iraqi Universities and their academic achievement (84 Ph.D., 68 Master’s, 42 Bachelor’s) from (17) different scientific specializations; and (42 students). The number of respondents who use the Internet in education is 191 and the number of those who do not use the Internet and teach in person is (3) from (194) person. Students and professors use smartphones in study and teaching at a rate of (97.42%) and computers and their necessities at a rate of (92.27%) are higher than electronic devices and equipment including the smart board (11,86%). Use of IoT technology is of great and medium importance in providing electronic lectures effectively, with a percentage of 96.91%, the use of it has a large and medium role in solving education problems in giving lectures in light of health crises, especially in light of the Coronavirus crisis. Availability at the local and global levels, whether with the appropriate content or the selection of professors anywhere in the world, at a rate of 98.45%.
Evaluating a virtual reality sensory room for adults with disabilities

Sensory processing difficulties can negatively impact wellbeing in adults with disabilities. A range of interventions to address sensory difficulties have been explored and virtual reality (VR) technology may offer a promising avenue for the provision of sensory interventions. In this study, preliminary evidence about the impact of Evenness, an immersive VR sensory room experience, for people with disabilities was investigated via a single intervention pre-post mixed methods design. Quantitative methodology included single intervention pre-post design (five month timeframe) with 31 adults with various developmental disabilities to determine the impact of use of aVR sensory room using a head mounted display (HMD) in relation to anxiety, depression, sensory processing, personal wellbeing and adaptive behaviour. Qualitative semi-structured interviews were also conducted with thirteen purposefully selected stakeholders following Evenness use. Results indicated significant improvements in anxiety, depression and sensory processing following Evenness use. Qualitative analysis corroborated the anxiety findings. No significant changes were observed in personal wellbeing or adaptive behaviour. Results are promising and indicate that a VR sensory room may have a positive impact on anxiety, depression and sensory processing for adults with disabilities. A longer study timeframe and a more rigorous experimental methodology is needed to confirm these findings.
Inductive Synthesis of Structurally Recursive Functional Programs from Non-recursive Expressions

We present a novel approach to synthesizing recursive functional programs from input-output examples. Synthesizing a recursive function is challenging because recursive subexpressions should be constructed while the target function has not been fully defined yet. We address this challenge by using a new technique we call block-based pruning. A block refers to a recursion- and conditional-free expression (i.e., straight-line code) that yields an output from a particular input. We first synthesize as many blocks as possible for each input-output example, and then we explore the space of recursive programs, pruning candidates that are inconsistent with the blocks. Our method is based on an efficient version space learning, thereby effectively dealing with a possibly enormous number of blocks. In addition, we present a method that uses sampled input-output behaviors of library functions to enable a goal-directed search for a recursive program using the library. We have implemented our approach in a system called Trio and evaluated it on synthesis tasks from prior work and on new tasks. Our experiments show that Trio outperforms prior work by synthesizing a solution to 98% of the benchmarks in our benchmark suite.
Classification Prediction of Breast Cancer Based on Machine Learning

Breast cancer is the most common and deadly type of cancer in the world. Based on machine learning algorithms such as XGBoost, random forest, logistic regression, and K-nearest neighbor, this paper establishes different models to classify and predict breast cancer, so as to provide a reference for the early diagnosis of breast cancer. Recall indicates the probability of detecting malignant cancer cells in medical diagnosis, which is of great significance for the classification of breast cancer, so this article takes recall as the primary evaluation index and considers the precision, accuracy, and F1-score evaluation indicators to evaluate and compare the prediction effect of each model. In order to eliminate the influence of different dimensional concepts on the effect of the model, the data are standardized. In order to find the optimal subset and improve the accuracy of the model, 15 features were screened out as input to the model through the Pearson correlation test. The K-nearest neighbor model uses the cross-validation method to select the optimal k value by using recall as an evaluation index. For the problem of positive and negative sample imbalance, the hierarchical sampling method is used to extract the training set and test set proportionally according to different categories. The experimental results show that under different dataset division (8 : 2 and 7 : 3), the prediction effect of the same model will have different changes. Comparative analysis shows that the XGBoost model established in this paper (which divides the training set and test set by 8 : 2) has better effects, and its recall, precision, accuracy, and F1-score are 1.00, 0.960, 0.974, and 0.980, respectively.
Optimization of Scalable Ion-Cavity Interfaces for Quantum Photonic Networks

In the design optimization of ion-cavity interfaces for quantum networking applications, difficulties occur due to the many competing figures of merit and highly interdependent design constraints, many of which present ``soft limits,'' which are amenable to improvement at the cost of engineering time. In this work, we present a systematic approach to this problem that offers a means to identify efficient and robust operating regimes and to elucidate the trade-offs involved in the design process, allowing engineering efforts to be focused on the most sensitive and critical parameters. We show that in many relevant cases it is possible to approximately separate the geometric aspects of the cooperativity from those associated with the atomic system and the mirror surfaces themselves, greatly simplifying the optimization procedure. Although our approach to optimization can be applied to most operating regimes, here we consider cavities suitable for typical ion-trapping experiments and with substantial transverse misalignment of the mirrors. We find that cavities with mirror misalignments of many micrometers can still offer very high photon extraction efficiencies, offering an appealing route to the scalable production of ion-cavity interfaces for large-scale quantum networks.
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?

Federated learning (FL) has attracted growing interest for enabling privacy-preserving machine learning on data stored at multiple users while avoiding moving the data off-device. However, while data never leaves users’ devices, privacy still cannot be guaranteed since significant computations on users’ training data are shared in the form of trained local models. These local models have recently been shown to pose a substantial privacy threat through different privacy attacks such as model inversion attacks. As a remedy, Secure Aggregation (SA) has been developed as a framework to preserve privacy in FL, by guaranteeing the server can only learn the global aggregated model update but not the individual model updates.While SA ensures no additional information is leaked about the individual model update beyond the aggregated model update, there are no formal guarantees on how much privacy FL with SA can actually offer; as information about the individual dataset can still potentially leak through the aggregated model computed at the server. In this work, we perform a first analysis of the formal privacy guarantees for FL with SA. Specifically, we use Mutual Information (MI) as a quantification metric and derive upper bounds on how much information about each user's dataset can leak through the aggregated model update. When using the FedSGD aggregation algorithm, our theoretical bounds show that the amount of privacy leakage reduces linearly with the number of users participating in FL with SA. To validate our theoretical bounds, we use an MI Neural Estimator to empirically evaluate the privacy leakage under different FL setups on both the MNIST and CIFAR10 datasets. Our experiments verify our theoretical bounds for FedSGD, which show a reduction in privacy leakage as the number of users and local batch size grow, and an increase in privacy leakage as the number of training rounds increases. We also observe similar dependencies for the FedAvg and FedProx protocol.
Analysing the Security Aspects of IoT using Blockchain and Cryptographic Algorithms

Technological advancement is a never-ending field that shows its evolution from time to time. In 1832, with the invention of the electromagnetic telegraph, the era of the Internet of Things (IoT) began. Within the time of 190 years, this technological domain has revolutionized IoT and made it omnipresent. However, with this evolved and omnipresent nature of IoT, many drawbacks, privacy, interoperability, and security issues have also been generated. These different concerns should be tackled with some newer technologies rather than the conventional ones as somehow, they are only the generator of those issues. Outdated Security could be an appropriate issue of IoT along with the centralized point of failure. It also possesses more concerns and challenges to tackle. On the other side, there is a visible solution to address the challenges of IoT in this developing domain of technology. The visible approach is Blockchain which acted as the backbone in securing Bitcoin in 2008, which was created by the pseudo group named Satoshi Nakamoto. Blockchain has evolved from Blockchain 1.0 to Blockchain 4.0 as the latest one depicts its amalgamation with another component of Industry 4.0 i.e., Artificial Intelligence (AI). AI will give the ability to think logically and like humans. In addition to this SMART solution, there is also an advanced cryptographical technique known as the Elliptic Curve Digital Signature Algorithm (ECDSA) which can enhance the security spectrum of IoT if applied appropriately. This paper produces a vision to enhance and optimize the security of IoT using a network peer-to-peer technology Blockchain along with advanced cryptography.
Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

Saudi Telecom Company (STC) is among the most popular companies in Saudi Arabia, with many customers. Yet, there is still a big room for improvement in users' satisfaction. Social media is the most robust platform to gauge users' satisfaction and determine their sentiments and critics. Twitter is among the most popular social media platform in this regard. STC customers prefer to use Twitter to write their feedback because it's a fast way to get responses due to the STC customer services account. One way to achieve customer demands and improve customer service is using the Sentiment Analysis tool. Sentiment Analysis on Twitter is highly used because of the significant number of tweets and the different opinions. Likewise, Deep learning is the best existing Sentiment Analysis method, and it has diverse models. Bidirectional Encoder Representations from Transformers (BERT) model is one of the deep learning models which have achieved excellent results in Sentiment Analysis for Natural Language Processing (NLP). NLP is mainly investigated in the English language. However, for Arabic, there is a significant gap to be filled. This study trained the proposed model using MARBERT and measured the performance using f1-score, precision, and recall metrics. We trained the model with an Arabic dataset of 24,513 tweets, including 1,437 positive, 13,828 negative, 5,694 neutral, 1,221 sarcasm, and 2,297 indeterminate tweets. The main goal is to analyze the tweets and get the sentiment to improve STC customer service. The proposed scheme is promising in terms of accuracy in contrast to existing techniques in the literature.
Attack Detection Using a Lightweight Blockchain Based Elliptic Curve Digital Signature Algorithm in Cyber Systems

Cyber-physical systems (CPSs) are highly susceptible to malicious cyberattacks due to their reliance on communication networks. For this reason, many different attack detection techniques have been developed to guarantee the safety of CPSs. This article introduces BlockChain (BC) to address CPS issues such as data security and privacy. Additionally, BC is not well suited for CPS due to its high computing complexity, limited scalability, significant bandwidth overhead, and latency. To meet the requirements of CPS, a light-weight blockchain-based signature algorithm (LWBSA) model is developed in this work. The concept's resource constraints are alleviated by having a single centrally managed manager generate shared keys for outward-bound data transmission requests. The LWBSA paradigm provided herein produces an overlay network where extremely equipped resources can merge into a community BC, hence ensuring both dedicated privileges. Lightweight consensus, the elliptic curve digital signature algorithm (ECDSA), and distributed throughput management (DTM) are the three optimizations implemented in the ELIB model discussed here. Extensive simulation is carried out to examine the implications of different situations on processing time, energy usage, and overhead. The experimental outcomes show that the LWBSA achieves the best possible performance across a wide variety of measures.
ASSET MANAGEMENT SYSTEM DESIGN OF VILLAGE BASED ON GEOGRAPHIC INFORMATION SYSTEM

Management of an asset by the government is a process that starts from planning to asset inventorying that have been pre-existing or obtained from legitimate assistance so that they can managed appropriately and beneficially for the community. For the government, especially in village regions, management of assets is very important, so that both government apparatus and village community get complete, accurate and real-time information about the assets owned by the village government so that the information can be used for activities of village government and communities optimally. The goal of this research is to design and build an asset management system based on geographic information system (GIS) for government in the village. The GIS-based asset management design system uses a waterfall-model approach with five stages, namely: 1) Analysis, 2) Design, 3) Implementation, 4) Integration Testing, and 5) Maintenance. This asset management application is built with web-based technology using the Leaflet framework that supports Web Map Service (WMS) layers, GeoJSON data, vectors and tile layers, while the database in this application uses MySQL. The results of this GIS-based asset management system design research can be used to store, collect, repair, process, control and monitoring assets so that asset management for activities that benefit the community can be optimally improved. For the maintenance and utilization of asset management applications, training is carried out for operators and supervisors, as well as system support personnel.
Reversible Data Hiding in Encrypted Image Using Multiple Data-Hiders Sharing Algorithm

Reversible Data Hiding in Encrypted Image (RDHEI) is a technology for embedding secret information in an encrypted image. It allows the extraction of secret information and lossless decryption and the reconstruction of the original image. This paper proposes an RDHEI technique based on Shamir's Secret Sharing technique and multi-project construction technique. Our approach is to let the image owner hide the pixel values in the coefficients of the polynomial by grouping the pixels and constructing a polynomial. Then, we substitute the secret key into the polynomial through Shamir's Secret Sharing technology. It enables the Galois Field calculation to generate the shared pixels. Finally, we divide the shared pixels into 8 bits and allocate them to the pixels of the shared image. Thus, the embedded space is vacated, and the generated shared image is hidden in the secret message. The experimental results demonstrate that our approach has a multi-hider mechanism and each shared image has a fixed embedding rate, which does not decrease as more images are shared. Additionally, the embedding rate is improved compared with the previous approach.
Low Latency Low Loss Media Delivery Utilizing In-Network Packet Wash

Abstract This paper presents new techniques and mechanisms for carrying streams of layered video using Scalable Video Coding (SVC) from servers to clients, utilizing the Packet Wash mechanism which is part of the Big Packet Protocol (BPP). BPP was designed to handle the transfer of packets for high-bandwidth, low-latency applications, aiming to overcome a number of issues current networks have with high precision services. One of the most important advantages of BPP is that it allows the dynamic adaption of packets during transmission. BPP uses Packet Wash to reduce the payload, and the size of a packet by eliminating specific chunks. For video, this means cutting out specific segments of the transferred video, rather than dropping packets, as happens with UDP based transmission, or retrying the transmission of packets, as happens with TCP. The chunk elimination approach is well matched with SVC video, and these techniques and mechanisms are utilized and presented. An evaluation of the performance is provided, plus a comparison of using UDP or TCP, which are the other common approaches for carrying media over IP. Our main contributions are the mapping of SVC video into BPP packets to provide low latency, low loss delivery, which provides better QoE performance than either UDP or TCP, when using those techniques and mechanisms. This approach has proved to be an effective way to enhance the performance of video streaming applications, by obtaining continuous delivery, while maintaining guaranteed quality at the receiver. In this work we have successfully used an H264 SVC encoded video for layered video transmission utilizing BPP, and can demonstrate video delivery with low latency and low loss in limited bandwidth environments.
Efficient learning of large sets of locally optimal classification rules

Conventional rule learning algorithms aim at finding a set of simple rules, where each rule covers as many examples as possible. In this paper, we argue that the rules found in this way may not be the optimal explanations for each of the examples they cover. Instead, we propose an efficient algorithm that aims at finding the best rule covering each training example in a greedy optimization consisting of one specialization and one generalization loop. These locally optimal rules are collected and then filtered for a final rule set, which is much larger than the sets learned by conventional rule learning algorithms. A new example is classified by selecting the best among the rules that cover this example. In our experiments on small to very large datasets, the approach's average classification accuracy is higher than that of state-of-the-art rule learning algorithms. Moreover, the algorithm is highly efficient and can inherently be processed in parallel without affecting the learned rule set and so the classification accuracy. We thus believe that it closes an important gap for large-scale classification rule induction.
The status of digital pathology and associated infrastructure within Alzheimer’s Disease Centers

Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders.However, infrastructure barriers may limit adoption.To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs).Questions covered infrastructure, funding sources, and data management related to digital pathology.Of the 35 ADCs to which the survey was sent, 33 responded.Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%).Approximately a third of respondents stated there were fees to utilize the scanner.For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC.For scanner purchasing and operations, 50% of respondents stated they received institutional support.Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%).Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships.These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.
Pentingnya Media Pembelajaran dalam Proses Belajar Mengajar

Learning media is one component in the learning process. Learning media is a tool that can help teachers convey learning material so that children can have an interest and interest in the learning material presented. It's just that in using learning media, teachers must be able to choose learning media that are appropriate to the material to be delivered and teachers must also adjust to the character of their students in choosing learning media. Some teachers still do not understand how important it is to use appropriate learning media so that learning does not feel monotonous and boring for students. Therefore, in this study the authors used the method of library research (library research) to examine how important appropriate instructional media is for the teaching and learning process in elementary schools. The results of the study state that appropriate learning media is very important in supporting student learning. Selection of appropriate learning can help students to understand the learning material delivered by the teacher. Learning media can provide concrete experience and as an intermediary that helps student learning.