Reviewerly/exHarmony huggingface dataset:
DIGITALLY TRANSFORMING THE PROFESSIONAL SCHOOL COUNSELOR

In the Internet Age, digital transformation has touched nearly every profession and industry, and K-12 school counseling is no exception.In the modern public school, counselors are expected to meet a variety of demands using evidence-based practices.Many school counselors, however, still struggle to meet the expectation to use data.In our current research, we surveyed 263 school counselors in the State of Utah to explore the challenges surrounding datadriven decision-making.Research revealed the most prominent barriers regarded data and a perceived lack of time.Computerized systems can automate many of the tasks required of counselors saving them time and can also process data in a structured way that facilitates analysis leading to the identification of essential patterns, opportunities, and risks in the student body.By using methods of quantitative analysis, we correlate the counselor's responses to possible technology tools that could assist them in making data-driven decisions.
Digital Module 04: Diagnostic Measurement: Modeling Checklists for Practitioners https://ncme.elevate.commpartners.com

Abstract In this ITEMS module, we provide a didactic overview of the specification, estimation, evaluation, and interpretation steps for diagnostic measurement/classification models (DCMs), which are a promising psychometric modeling approach. These models can provide detailed skill‐ or attribute‐specific feedback to respondents along multiple latent dimensions and hold theoretical and practical appeal for a variety of fields. We use a current unified modeling framework—the log‐linear cognitive diagnosis model (LCDM)—as well as a series of quality‐control checklists for data analysts and scientific users to review the foundational concepts, practical steps, and interpretational principles for these models. We demonstrate how the models and checklists can be applied in real‐life data‐analysis contexts. A library of macros and supporting files for Excel, SAS, and Mplus are provided along with video tutorials for key practices.
Morphology-based vs Unsupervised Word Clustering for Training Language Models for Serbian

When training language models (especially for highly inflective languages), some applications require word clustering in order to mitigate the problem of insufficient training data or storage space.The goal of word clustering is to group words that can be well represented by a single class in the sense of probabilities of appearances in different contexts.This paper presents comparative results obtained by using different approaches to word clustering when training class N-gram models for Serbian, as well as models based on recurrent neural networks.One approach is unsupervised word clustering based on optimized Brown's algorithm, which relies on bigram statistics.The other approach is based on morphology, and it requires expert knowledge and language resources.Four different types of textual corpora were used in experiments, describing different functional styles.The language models were evaluated by both perplexity and word error rate.The results show notable advantage of introducing expert knowledge into word clustering process.
Are We Summarizing the Right Way? A Survey of Dialogue Summarization Data Sets

Dialogue summarization is a long-standing task in the field of NLP, and several data sets with dialogues and associated human-written summaries of different styles exist.However, it is unclear for which type of dialogue which type of summary is most appropriate.For this reason, we apply a linguistic model of dialogue types to derive matching summary items and NLP tasks.This allows us to map existing dialogue summarization data sets into this model and identify gaps and potential directions for future work.As part of this process, we also provide an extensive overview of existing dialogue summarization data sets.
Physical layer security designs for 5G and beyond

Physical layer security (PLS) has emerged as a promising and powerful concept for securing future wireless technologies, including fifth generation (5G) and beyond networks, as it has the potential to solve many of the problems associated with conventional cryptography -based approaches. In this chapter, the principles of PLS as a complementary solution to cryptography for future networks are presented. The concepts, merits, and demerits for different types of PLS techniques are discussed and explained. Moreover, the recent applications of PLS to different emerging wireless technologies are also presented. Furthermore, the details about physical layer authentication methods against spoofing attacks and details about jamming attacks and related solutions are also included.
Knowledge Management for Rural Industries using Cloud Computing

About more than two decades ago, the practice of Knowledge Management (KM) was started. Also their importance was understood by the many growing and developed organizations. Now it becomes as an importantly integral and inherent component of any business units. Knowledge management (KM) is actually the process of creating, sharing, using and managing the knowledge and information of any organisation. Actually to achieving organisational objectives, it refers to a multidisciplinary approach. This is by making the best use of knowledge. KM efforts focus on organisational many of the objectives. Some of these are like improved performance, competitive advantage, innovation, the sharing of lessons learned, integration and continuous improvement of the organisation. “Rural industries” refers to the small-scale activities that took place in communities where the dominant economic pursuits revolved around agriculture or natural product extraction. Such industry supported these activities and processed their output. Cloud computing is defined as “the practice of using a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server/personal computer.” Because of Information and Computer Technologies (ICT), it is possible to put many innovative fruitful ideas into KM practices. In reality this expresses the new trends in KM. This also justifies that, various newly developed technologies have impacted on, how the knowledge is managed? One of advanced, most predominant technology is Cloud Computing, ofcourse within the ICT. The way IT services are provisioned, it has significantly impacted. The rural industries can now get the advantage of such hosted services for major IT activities. This is nothing but KM through the means of Cloud Computing. Before the advent of Cloud Computing, earlier it was beyond their budget.
A Genetic Method using Hybrid Crossover for Solving Travelling Salesman Problem

This paper proposes a Genetic approach using Hybrid Crossover for Solving the Travelling Salesman Problem. Proposed hybrid method generates an initial population using Nearest Neighbor (NN) approach which is modified using “Sub-Path Mutation” (SPM) process. Modified population undergoes Distance Preserving Crossover (DPX) [2] and 2-opt Optimal mutation (2-opt) [1] to check for possible refinement. SPM searches position for the minimum distant city within a given path. This work is motivated by the algorithm developed by [3] who performed DPX and 2-opt mutation on the initial population generated using NN. For performance comparison, standard TSPLIB data is taken. The proposed hybrid method performances better in terms of % best error. It performs better than methods reported in [3 - 11].
Computer Intelligent Proofreading Method for English Translation Based on Foreign Language Translation Model

With the development of computer technology, intelligent proofreading of English translation by computer has gradually become one of the research directions. Due to the influence of algorithm and matching degree, there are still bottlenecks in English translation. At present, machine translation is widely used in English translation. GLR algorithm plays an important role in the field of machine translation. However, the number of recognition results calculated by general GLR algorithm is accidental, which can not effectively guarantee the accuracy of recognition results. On this basis, in order to accurately recognize foreign languages, this paper designs an optimized GLR algorithm based on intelligent recognition. First, a foreign language corpus with a rating scale of about 600000 English and Chinese words is constructed to make the foreign language have retrieval function. Then, the foreign language structure is constructed by using the foreign language center points, and the GLR algorithm is optimized. Finally, according to the syntactic function of the linear table, the English Chinese structural ambiguity in the recognition results is corrected, and the effectiveness of intelligent proofreading is evaluated. The experimental results show that the highest score of the optimized GLR algorithm is 93.9, and the lowest score of the statistical algorithm is 76.7, which effectively verifies the advantages of high precision and context consistency of the proposed system. This fully shows that the algorithm in this paper meets the design requirements in all aspects. At the same time, it is of great value to study the English translation computer intelligent proofreading method based on the foreign language translation model.
Distributed Key Management for IT Infrastructure using Block Chain and Hash Graph

The Cloud Computing Paradigm simplified the IT infrastructure management thereby making the organiza-tions and enterprises to rapidly migrate from conventional model to service driven model. The service driven model allows the users to access, maintain and backup the files from remote location over the internet through the central-ized cloud service providers. Inspite of major advantages, there are some concerns for the safety and privacy risks of organizational and users sensitive information in cloud stor-age. The existing service oriented model uses distributed key management which encrypts the data and stores it in cloud servers to provide the authentication to data, but it addresses challenges in identifying ownership of data, boot-strapping problem and securing the keys. The proposed system uses block chain, a distributed ledger for user authentication in cloud server and Hash graph with asynchronous byzantine fault tolerance algorithm is proposed for replicat-ed state machines with guaranteed Byzantine fault-tolerance and it can be utilized in the IT Infrastructure envi-ronment which solves the hassles of maintaining security keys
Markle Hellman Knapsack Crypto-System Auditing Scheme for Privacy Preserving in Cloud Storage

Cloud computing is an on demand paradigm which provides a different kind of services to the cloud users. Cloud storage is the most popular service, as the data owners are free from the data management and storage overhead. However, the data owners’ concern about the security of the data. In order to address this issue, this paper presents an efficient security with an auditing scheme that guarantees the security of the data and preserve data integrity. In this paper, the cloud storage auditing model used efficient privacy preserving algorithm, namely Markle Hellman Knapsack Crypto-System (MHKCS) algorithm. This algorithm effectively improves the data integrity, confidentiality and security. Moreover, reduces the key generation time, encryption time and decryption time. The performance of MHKCS algorithm is calculated using evaluation metrics like encryption time, decryption time, key generation time and communication cost. The MHKCS algorithm achieved approximately 10% better performance in terms of encryption time than the existing methods RSA, MRSA, and MRSAC.
Cross-Validation and Blind Feature Analysis of 25 Percent Embedding on JPEG Image Format using SVM

This paper provides a result assessment of traditional JPEG picture extraction function steganalysis compared to a cross-validation picture. Four distinct algorithms are used as steganographic systems in the spatial and transform domain. They are LSB Matching, LSB Replacement, Pixel Value Differencing and F5.A 25 percentage of embedding with text embedding information is considered in this paper. The characteristics regarded for evaluation are the First Order, Second Order, Extended DCT characteristics, and Markov characteristics. Support Vector Machine is the classifier used here. In statistical recovery, six distinct kernels and four distinct sampling techniques are used for evaluation.
Learning-Based Robust Anomaly Detection in the Presence of Adversarial Attacks

To address the anomaly detection problem in the presence of noisy sensor observations and probing costs, we in this paper propose a soft actor-critic deep reinforcement learning framework. Moreover, considering adversarial jamming attacks, we design a generative adversarial network (GAN) based framework to identify the jammed sensors. To evaluate the proposed framework, we measure the performance in terms of detection accuracy, stopping time, and the total number of samples needed for detection. Via simulation results, we demonstrate the performances when soft actor-critic algorithms are sensitive to the probing cost and actively adapt to different environment settings. We analyze the impact of jamming attacks and identify the improvements achieved by GAN-based approach. We further provide comparisons between the performances of the proposed soft actor-critic and conventional actor-critic algorithms.
Consent-driven Data Reuse in Multi-tasking Crowdsensing Systems: A Privacy-by-Design Solution

Mobile crowdsensing allows gathering massive data across time and space to feed our environmental knowledge, and to link such knowledge to user behavior. However, a major challenge facing mobile crowdsensing is to guarantee privacy preservation to the contributing users. Privacy preservation in crowdsensing systems has led to two main approaches, sometimes combined, which are, respectively, to trade privacy for rewards, and to take advantage of privacy-enhancing technologies “anonymizing” the collected data. Although relevant, we claim that these approaches do not sufficiently take into account the users’ own tolerance to the use of the data provided, so that the crowdsensing system guarantees users the expected level of confidentiality as well as fosters the use of crowdsensing data for different tasks. To this end, we leverage the ℓ-Completeness property, which ensures that the data provided can be used for all the tasks to which their owners consent as long as they are analyzed with ℓ−1 other sources, and that no privacy violations can occur due to the related contribution of users with less stringent privacy requirements. The challenge, therefore, is to ensure ℓ-Completeness when analyzing the data while allowing the data to be used for as many tasks as possible, and promoting the accuracy of the resulting knowledge. This is achieved through a clustering algorithm sensitive to the data distribution, which optimizes data reuse and utility. Nevertheless, it is critical to allow the deployment of such a solution even in the presence of a malicious adversary able to act on the server side, for which we introduce a privacy-by-design architecture leveraging Trusted Execution Environments. The implementation of a prototype using SGX enclaves further allows running experiments that show that our system incurs a reasonable performance overhead, while providing strong security properties against a malicious adversary.
Joint Load balancing and Spatial-temporal Prediction Optimization for Ultra-Dense Network

To meet the further explosive capacity demand, network densification is a promising technology. The load imbalance between cells in an ultra-dense heterogeneous network is a major challenge, which seriously affects the performance of the system. Existing load balancing (LB) methods usually operate in reactive mode. The parameters of the cells are adjusted reactively according to the dynamic changes of network load. The inherent reactivity of existing LB schemes undermines the quality of experience (QoE) in 5G and beyond. To solve this problem, we propose a mobility management framework for load balancing, which turns the original reactive load balancing into forwardaware and active. The simulation results show that the proposed method can better optimize the network performance and realize the intelligent mobile management for the future network.
TDT: Teaching Detectors to Track without Fully Annotated Videos

Recently, one-stage trackers that use a joint model to predict both detections and appearance embeddings in one forward pass received much attention and achieved state-of-the-art results on the Multi-Object Tracking (MOT) bench-marks. However, their success depends on the availability of videos that are fully annotated with tracking data, which is expensive and hard to obtain. This can limit the model generalization. In comparison, the two-stage approach, which performs detection and embedding separately, is slower but easier to train as their data are easier to annotate. We propose to combine the best of the two worlds through a data distillation approach. Specifically, we use a teacher embedder, trained on Re-ID datasets, to generate pseudo appearance embedding labels for the detection datasets. Then, we use the augmented dataset to train a detector that is also capable of regressing these pseudo-embeddings in a fully-convolutional fashion. Our proposed one-stage solution matches the two-stage counterpart in quality but is 3 times faster. Even though the teacher embedder has not seen any tracking data during training, our proposed tracker achieves competitive performance with some popular trackers (e.g. JDE) trained with fully labeled tracking data.
An English Teaching Pronunciation Detection and Recognition Algorithm Based on Cluster Analysis and Improved SSD

The accuracy of English pronunciation is the key index to evaluate the quality of English teaching. Correct pronunciation and smooth language flow are the expectations of every student for English learning. Aiming at the poor effect and slow speed of the original SSD (Single Shot MultiBox Detector) algorithm in English teaching pronunciation detection, this paper proposes a clustering and improved SSD algorithm for English teaching pronunciation detection and recognition. The algorithm improves the concept module to enhance the feature extraction ability of the network and improve the detection speed. Meanwhile, it integrates multiscale features to realize multilayer multiplexing and equalization of features, so as to improve the detection effect of small target sound. This algorithm extracts more features by introducing channel attention mechanism, which increases the detection accuracy while reducing computation. In order to optimize the network’s ability to extract target location information, K-means clustering method is used to set the default parameters that are more in line with the characteristics of target samples. The experimental results showed that the proposed algorithm can accurately evaluate the pronunciation quality of reading aloud, so as to effectively reflect the oral English level of the reader.
Characterization of AES Implementations on Microprocessor-based IoT Devices

The increased proliferation of IoT devices and the emergence of 5G networks have necessitated increased security of data storage and communication in such connected devices. Thus, cryptography is used in IoT environments to provide secrecy and integrity to the data as well as both authentication and anonymity to the communications across the IoT network. However, IoT devices are resource-constrained devices; have limited memory, network bandwidth, power, and compute units. Since most of the existing cryptographic algorithms were designed to run on resource powerful devices (e.g., desktops or servers), many of these algorithms may not fit into resource-constrained devices. Therefore, in this work, we present a practical performance analysis of different implementations of the Advanced Encryption Standard (AES), which is the most widely used symmetric-key cryptosystem in the IoT environment. Specifically, we explore execution times, energy consumption, and memory usage of the different AES implementations across 4 different public libraries. Furthermore, our analysis is done using various modes, key sizes, plaintext sizes, and microprocessor-based IoT devices. Our results show that for the same combination of inputs and a given algorithm, different crypto library implementations give results with widely varying relative differences. As per the obtained results, the PyCryptodome library seems to be the most suitable one in terms of both execution time and energy on a resource-constrained IoT device and has the most efficient memory usage.
DETECTION AND CLASSIFICATION OF VEHICLES IN ULTRA-HIGH RESOLUTIONS IMAGES USING NEURAL NETWORKS

The paper proposes a deep neural network architecture based on the integration of the convolutional neural network Faster R-CNN with the Feature Pyramid Network module. Based on this approach, an algorithm for detecting and classifying vehicles in images and a corresponding model have been developed.&#x0D; A cross-platform environment ML.NET was used to train the proposed model. The results of comparing the effectiveness of the proposed approach and convolutional neural networks YOLO v4 and Faster R-CNN are presented. The improvement of the accuracy of detection and localization of different types of vehicles in ultra-high resolutions images is shown. Examples of processing ultra-high resolutions remote sensing images and appropriate recommendations are given.
Blockchain with cloud for handling healthcare data: A privacy-friendly platform

In addition to its unique characteristics, health care data make it an attractive target for criminals. Additionally, healthcare data is highly regulated by US & EU privacy and security laws and international laws governing data storage in the cloud. The value of healthcare data and these regulatory requirements have motivated organizations to use blockchain to protect data. Blockchain will facilitate effective data exchange while maintaining patient privacy and data protection. Cloud data has long been a draw for cyber attackers. Today, cloud health data has been their latest interest. The cloud-based application that theoretically transforms the operation and communication of existing healthcare networks is EHR. The EHR is a digital file comprising the personal details of citizens/patients (name, photo, address, age, etc.) and health (existing diseases, previous surgeries, allergies, blood type, allergies, vaccinations, etc.). Maximizing cost efficiency is one of the drivers for integrating cloud services in health care. The return on investment for healthcare services is better when using cloud solutions. The cloud infrastructure includes a centralized medical record entry point, allowing many clinicians to display laboratory reports or consult patient notes. Loud technologies would allow researchers to explore these data and analyses public health more accurately. Cloud technology is not the best way to handle the medical services and records of the healthcare system. Nevertheless, this technology allows patients and physicians access to software that will enhance patient care. The Cloud also provides an excellent opportunity to utilize patient data on a health computer scale to draw insights on health and allow patients to manage personal details quickly and efficiently. There are hurdles in introducing emerging technologies and threatening privacy violations, but health services will utilize this increasingly growing and valuable technology with the appropriate training and framework. As part of this paper, we present a patient-centric healthcare data management solution that uses blockchain technology as a storage mechanism that helps to ensure patient privacy.