Reviewerly/exHarmony huggingface dataset:
Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions

Automated scoring of student responses to open-ended questions, including short-answer questions, has great potential to scale to a large number of responses. Recent approaches for automated scoring rely on supervised learning, i.e., training classifiers or fine-tuning language models on a small number of responses with human-provided score labels. However, since scoring is a subjective process, these human scores are noisy and can be highly variable, depending on the scorer. In this paper, we investigate a collection of models that account for the individual preferences and tendencies of each human scorer in the automated scoring task. We apply these models to a short-answer math response dataset where each response is scored (often differently) by multiple different human scorers. We conduct quantitative experiments to show that our scorer models lead to improved automated scoring accuracy. We also conduct quantitative experiments and case studies to analyze the individual preferences and tendencies of scorers. We found that scorers can be grouped into several obvious clusters, with each cluster having distinct features, and analyzed them in detail.
Digital contact tracing/notification for SARS-CoV-2: navigating six points of failure

Digital contact tracing/notification was initially hailed as a promising strategy to combat SARS-CoV-2, but in most jurisdictions it did not live up to its promise. To avert a given transmission event, both parties must have adopted the tech, it must detect the contact, the primary case must be promptly diagnosed, notifications must be triggered, and the secondary case must change their behavior to avoid the focal tertiary transmission event. If we approximate these as independent events, achieving a 26% reduction in R(t) would require 80% success rates at each of these six points of failure. Here we review the six failure rates experienced by a variety of digital contact tracing/notification schemes, including Singapore's TraceTogether, India's Aarogya Setu, and leading implementations of the Google Apple Exposure Notification system. This leads to a number of recommendations, e.g. that tracing/notification apps be multi-functional and integrated with testing, manual contact tracing, and the gathering of critical scientific data, and that the narrative be framed in terms of user autonomy rather than user privacy.
Form and Function of Connectives in Chinese Conversational Speech

Connectives convey discourse functions that provide textual and pragmatic information in speech communication on top of canonical, sentential use. This paper proposes an applicable scheme with illustrative examples for distinguishing Sentential, Conclusion, Disfluency, Elaboration, and Resumption uses of Mandarin connectives, including conjunctions and adverbs. Quantitative results of our annotation works are presented to gain an overview of connectives in a Mandarin conversational speech corpus. A fine-grained taxonomy is also discussed, but it requires more empirical data to approve the applicability. By conducting a multinomial logistic regression model, we illustrate that connectives exhibit consistent patterns in positional, phonetic, and contextual features oriented to the associated discourse functions. Our results confirm that the position of Conclusion and Resumption connectives orient more to positions in semantically, rather than prosodically, determined units. We also found that connectives used for all four discourse functions tend to have a higher initial F0 value than those of sentential use. Resumption and Disfluency uses are expected to have the largest increase in initial F0 value, followed by Conclusion and Elaboration uses. Durational cues of the preceding context enable distinguishing Sentential use from discourse uses of Conclusion, Elaboration, and Resumption of connectives.
An Enhanced Data Transmission in Mobile Banking Using LSB-AES Algorithm

The advent of mobile banking applications has transformed the way customers’ access banking services from brick-and-mortar to remote banking. The ubiquitous nature of this innovation has encouraged its adoption. This is because of improved banking services and accessibility to the services on a 24/7 basis using the internet. However, mobile banking applications are susceptible to numerous security threats and vulnerabilities that adversaries take advantage of to siphon money from bank customers. The aim of this study is to design and evaluate least significant bit and advanced encryption standard cryptography (LSB-AES) hybrid algorithm to protect data on transmission in mobile banking. This study employs data science research design, and performance evaluations demonstrated through simulations using MATLAB. Findings of this study can be applied to banks offering mobile banking across the world. This study utilized six color images from University of Southern California’s Signal and Image Processing Institute (USC-SIPI) dataset which were stored in Tagged Image File Format (TIFF). Contemporary steganographic systems utilize a minimum of 2 to 10 images for test simulations. Visual quality analysis of cover images and stego images was done using the following evaluation metrics: Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and histogram analysis. Security of the proposed algorithm was done using entropy analysis. MSE values of cover and stego images should be closer to zero and indicate that cover image is of good quality for embedding data. On the other hand, PSNR values should be more than 40 decibel (dB) which indicates good imperceptibility. Histogram analysis should demonstrate no visible distortions between the cover and stego image for it to be free from statistical attacks. Entropy analysis should yield values close to 8 for the algorithm to be robust. Results from the proposed LSB-AES hybrid algorithm evaluation metrics reveals that Mean Squared Error (MSE) values ranges from 0.0001297 to 0.0005646 while Peak Signal-to-Noise Ratio (PSNR) values ranges from 80.65 to 87.71 and entropy values ranges from 6.295 to 7.762. Histogram analysis reveals that the cover and stego images are almost similar. These results infer that the proposed algorithm has good quality images with good imperceptibility and that the proposed algorithm is reliable, robust and secure for mobile banking. Entropy and histogram analysis results show that the proposed algorithm is resistant to Man-in-the-Middle attacks. This study recommends that legislation evaluates and amends security of mobile banking policies so that the proposed LSB-AES hybrid algorithm can be adopted as a secure solution for mobile banking.
An Evolutionary Quantum Scrambling Scheme for Medical Image with NEQR Representation

Image scrambling methods are necessary for original image processes in applications of quantum image processing like quantum image encryption, which increases the strength of the encryption process, and the resulting image is difficult to identify and detect its details.As well as obtain a high entropy value and a histogram with a uniform peak for the encrypted image.Most researches focus on scrambling the position only or the value only; however, the quantum image scrambling researches focused on scrambling both position and value together is few.The idea of the study is developing a genetic algorithm to generate different schemes of scrambling based on the fast and elementary schemes with changing the quantum logical gate (NOT / C-Not) to get the best scheme that meets the requirements such as the cost, the complexity or the type of the image.One of the essential benefits of the proposed work is developing a general framework for the automatic generation of a suitable scrambling scheme based on image type, method, and logical circuit.The tests are simulated using MATLAB.The result confirms that hybrid value/position schemes with a C-Not gate have an entropy value close to 8 and a flat histogram.It is implicated that this framework will benefit many researchers in selecting the most appropriate scrambling method for their works.
Digital Image Steganography Utilizing Database Identification

This paper aims to apply digital image steganography based on a database through some proposed steps, first is converting the stego image(colored image) and covered (original) image into there 24-bit binary representation form, and the second step is segmenting the derived representation from the previous step into regions (sub-images) with size 10 (10*10), the third step is constructing a number of databases, each consists of 100 records and each record contains 24-bit pixel representation, next step is applying shifting process starting from the least significant bit LSB, the number of shifting times is depending on a proposed equation for each record, we repeat this process for each region's database in order to get a more secured information, next step is hiding (embedding) these databases into the covered image depending on a proposed method, finally, we apply a proposed method in order to eliminate any distortion derived after embedding the stego image into the original one after applying the proposed steganography method.
A new spell-checking approach based on the user profile

This paper presents a new approach for spell-checking based on the user profile and that can be applied for any language.For this purpose and for the specific case of Arabic, spelling errors are studied and divided into 18 types.Then, a relationship model between users and their errors is obtained.The proposed architecture initially gives apposite values for a current user, then corrects misspelled words by applying the spelling rules, and the remaining words are corrected based on the probability given by an adopted model of the profile values.To show the efficiency of our profile-based approach, we conducted an experiment with a corpus of 11,908 words containing 1,888 errors.It showed that our approach suggests the correct word in 88.43% times and ranks it in the first four positions in 75.14% times.Moreover, using the same corpus we compared our implemented tool with two existing ones where ours ranked better in 69.79% times than Sahehly and 77.63% times than MS word.
"Let's not Quote out of Context": Unified Vision-Language Pretraining for Context Assisted Image Captioning

Well-formed context aware image captions and tags in enterprise content such as marketing material are critical to ensure their brand presence and content recall. Manual creation and updates to ensure the same is non trivial given the scale and the tedium towards this task. We propose a new unified Vision-Language (VL) model based on the One For All (OFA) model, with a focus on context-assisted image captioning where the caption is generated based on both the image and its context. Our approach aims to overcome the context-independent (image and text are treated independently) nature of the existing approaches. We exploit context by pretraining our model with datasets of three tasks: news image captioning where the news article is the context, contextual visual entailment, and keyword extraction from the context. The second pretraining task is a new VL task, and we construct and release two datasets for the task with 1.1M and 2.2K data instances. Our system achieves state-of-the-art results with an improvement of up to 8.34 CIDEr score on the benchmark news image captioning datasets. To the best of our knowledge, ours is the first effort at incorporating contextual information in pretraining the models for the VL tasks.
Correlation analysis between talent training quality and regional economic development based on multivariate statistical analysis model

Abstract Talent training has a strong social constraint and economic dominance, and is closely related to regional economic development with two-way interaction. Context is any information that can be used to describe the situation and characteristics of an object, including time, location, social relationships, natural conditions, and project characteristics. Regional economic development is influenced by the process of multiple types of contextual elements, but traditional development models do not consider or only take into account a single contextual element, ignoring the combined influence of multiple contextual elements. To this end, the paper proposes a model for analyzing the correlation between talent training quality and regional economic development that integrates context-awareness and random forest algorithms, modeling contextual elements as feature attributes to be considered when splitting decision trees in random forests. The experimental results show that when conducting the analysis, assigning corresponding weights to various contextual elements according to the degree of importance can improve the accuracy of the recommendations. The prediction accuracy of the random forest model is higher under different data sampling ratios.
Will Working from Home Stick in Developing Economies?

In developing economies, a shift to working from home during the COVID-19 pandemic varies substantially.An increase in teleworking days per week ranges from 0.7 to 17.6 percentage points across 10 developing countries covered by an online survey to about 500 respondents per country.An estimated income discount associated with telework disappeared temporarily at the onset of the pandemic.A calibrated model indicates that workers' preferences to telework may largely depend on their educational attainments.Whether telework will sustain in these countries could depend on obstacles to telework, particularly for workers with less education, and a degree of economy-wide externality.
AMR4NLI: Interpretable and robust NLI measures from semantic graphs

The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis. NLI benchmarks contain human ratings of entailment, but the meaning relationships driving these ratings are not formalized. Can the underlying sentence pair relationships be made more explicit in an interpretable yet robust fashion? We compare semantic structures to represent premise and hypothesis, including sets of contextualized embeddings and semantic graphs (Abstract Meaning Representations), and measure whether the hypothesis is a semantic substructure of the premise, utilizing interpretable metrics. Our evaluation on three English benchmarks finds value in both contextualized embeddings and semantic graphs; moreover, they provide complementary signals, and can be leveraged together in a hybrid model.
Data Information Security Algorithm Based on Chaos and Hash Function

Abstract Chaotic systems are characterized by unidirectional, diffusive and initial value sensitivity of hash. Academia can use it to optimize algorithms for mathematical and computer encryption keys. This paper focuses on a hash function mixed chaotic system with a key. Then the state value and chaotic mapping relationship of the chaotic system are modified, and hash conclusions are obtained. Then the optimal design of messy technology with key hash is introduced briefly. A chaotic dynamic model with improved dynamic parameters is proposed to prevent chaos from affecting the speed and security of the algorithm. The results show that this method can effectively resist the attack of forging and peer keys. Moreover, the computation required by this algorithm is almost negligible.
Vocabulary-free Image Classification

Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categories, a.k.a. the vocabulary, is assumed at test time for composing the textual prompts. However, such assumption can be impractical when the semantic context is unknown and evolving. We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary. VIC is a challenging task as the semantic space is extremely large, containing millions of concepts, with hard-to-discriminate fine-grained categories. In this work, we first empirically verify that representing this semantic space by means of an external vision-language database is the most effective way to obtain semantically relevant content for classifying the image. We then propose Category Search from External Databases (CaSED), a method that exploits a pre-trained vision-language model and an external vision-language database to address VIC in a training-free manner. CaSED first extracts a set of candidate categories from captions retrieved from the database based on their semantic similarity to the image, and then assigns to the image the best matching candidate category according to the same vision-language model. Experiments on benchmark datasets validate that CaSED outperforms other complex vision-language frameworks, while being efficient with much fewer parameters, paving the way for future research in this direction.
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Large language models (LLMs) have shown excellent performance on various tasks, but the astronomical model size raises the hardware barrier for serving (memory size) and slows down token generation (memory bandwidth). In this paper, we propose Activation-aware Weight Quantization (AWQ), a hardware-friendly approach for LLM low-bit weight-only quantization. Our method is based on the observation that weights are not equally important: protecting only 1% of salient weights can greatly reduce quantization error. We then propose to search for the optimal per-channel scaling that protects the salient weights by observing the activation, not weights. AWQ does not rely on any backpropagation or reconstruction, so it can well preserve LLMs' generalization ability on different domains and modalities, without overfitting to the calibration set. AWQ outperforms existing work on various language modeling and domain-specific benchmarks. Thanks to better generalization, it achieves excellent quantization performance for instruction-tuned LMs and, for the first time, multi-modal LMs. Alongside AWQ, we implement an efficient and flexible inference framework tailored for LLMs on the edge, offering more than 3x speedup over the Huggingface FP16 implementation on both desktop and mobile GPUs. It also democratizes the deployment of the 70B Llama-2 model on mobile GPU (NVIDIA Jetson Orin 64GB).
OPTIMALISASI INOVASI TRANSPORTASI LITERASI

The lack of awareness of the people of Medan city about the importance of literacy in the current era of digitalization of information.The minimum number of mobile library cars provided by PEMKO Medan or PEMPROVSU requires donations to allocate CSR (Corporate Social Responsibility) funds from private institutions.Almira library as one of today's literacy transportation innovations, has a blue city car concept which is modified into a mobile library so that it looks good looking during literacy visits.Almira library activities are adjusted to the age of the target readers in literacy locations, not only coming with hundreds of reading books, but also participating in spreading educational activities.Each location has different literacy problems and solutions, such as the economic level of the community, the lack of available literacy facilities (books), low interest in reading.The Almira library hopes that more and more book donations will come and agencies (government/private) will be willing to donate their CSR (Corporate Social Responsibility) funds for almira library literacy transportation, so that the almira library will continue to run as it should, with students as drivers of today's literacy.Care about the growth and development of the nation's generation.In this study, the authors used a qualitative descriptive method.
Logistic Regression Using Hyperparameter Optimization on COVID-19 Patients’ Vital Status

This study aims to classify COVID-19 patients based on the results of their hematology tests. Hematology test results have been shown to be useful in identifying the severity and risk of COVID-19 patients. Specifically, this study focuses on classifying COVID-19 patients based on their vital status, namely Deceased and Alive. The dataset used in this study contains four variables: white blood cells (WBC), neutrophils (NEU), lymphocytes (LYM), and Neutrophil Lymphocyte Ratio (NLR). Logistic Regression algorithm was used to solve the problem, and hyperparameter optimization was implemented to obtain the best model performance. The objective of this study was to build the best parameter in classifying the patients’ vital status. The proposed model achieved an accuracy score of 78%, which is the best performance among the tested models. The results of this study provide a key component for decision making in hospitals, as it provides a way to quickly and accurately identify the vital status of COVID-19 patients. This study has important implications for managing the COVID-19 pandemic and should be of interest to researchers and practitioners in the field.&#x0D;
A Posit based Handwritten Digits Recognition System

The automated handwritten digit recognition system has numerous applications.It is required to be performed for address interpretation in postal services, bank cheque processing, or digitization of paper documents.But, for computers to recognize the handwritten numeral images is a challenging task.Various techniques have been utilized for this purpose, like convolutional neural networks architectures.This paper presents a novel design of a Posit-based handwritten digits recognition system, one of the convolutional neural network applications.Posit, a universal number system is a substitute of floating point arithmetic format and is hardware friendly.Herein, LeNet and ResNet-18 based HDRS (Handwritten Digits Recognition System) architecture is used for training and inference of model.The parameters obtained after training were converted to (8,0) Posit number system.Training of LeNet and ResNet-18 based HDRS has been done over the MNIST database, an open-source database for handwritten digits recognition.The proposed Posit (8, 0) based HDRS provides comparable accuracy to traditional floating point and fixed point based HDRS.
Qualitative and Quantitative Risk Assessment through IISRA Framework

Abstract: Information and communications technology (ICT) has expanded commercial potential beyond comprehension and has merged completely with organizations. In addition to its many advantages, ICT has disadvantages, such as cybersecurity risks, vulnerabilities, and a lack of adequate administrative access control that cybercriminals might take advantage of. Organizations are becoming more dependent on information security as a result of potential hazards brought on by advances in information technology, each of which has a different critical level based on its likelihood of happening and potential consequences. Methodologies for evaluating information security threats can be either quantitative or qualitative, depending on the Information outcome of their assessment. It is clear that both of the aforementioned choices have a number of fundamental flaws. In order to overcome them, this research paper focused on developing an Integrated Information Security Risk Assessment (IISRA) Framework that would be both more accurate and adaptable because existing approaches are frequently inappropriate and ineffectual due to the ongoing appearance of new sources of risks.