Reviewerly/exHarmony huggingface dataset:
Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech

We introduce a generic, language-independent method to collect a large percentage of offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic information embedded in the emojis to collect a large number of offensive tweets. We apply the proposed method on Arabic tweets and compare it with English tweets - analysing key cultural differences. We observed a constant usage of these emojis to represent offensiveness throughout different timespans on Twitter. We manually annotate and publicly release the largest Arabic dataset for offensive, fine-grained hate speech, vulgar and violence content. Furthermore, we benchmark the dataset for detecting offensiveness and hate speech using different transformer architectures and perform in-depth linguistic analysis. We evaluate our models on external datasets - a Twitter dataset collected using a completely different method, and a multi-platform dataset containing comments from Twitter, YouTube and Facebook, for assessing generalization capability. Competitive results on these datasets suggest that the data collected using our method captures universal characteristics of offensive language. Our findings also highlight the common words used in offensive communications, common targets for hate speech, specific patterns in violence tweets; and pinpoint common classification errors that can be attributed to limitations of NLP models. We observe that even state-of-the-art transformer models may fail to take into account culture, background and context or understand nuances present in real-world data such as sarcasm.
Verifiable Quantum Advantage without Structure

We show the following hold, unconditionally unless otherwise stated, relative to a random oracle with probability 1: - There are NP search problems solvable by BQP machines but not BPP machines. - There exist functions that are one-way, and even collision resistant, against classical adversaries but are easily inverted quantumly. Similar separations hold for digital signatures and CPA-secure public key encryption (the latter requiring the assumption of a classically CPA-secure encryption scheme). Interestingly, the separation does not necessarily extend to the case of other cryptographic objects such as PRGs. - There are unconditional publicly verifiable proofs of quantumness with the minimal rounds of interaction: for uniform adversaries, the proofs are non-interactive, whereas for non-uniform adversaries the proofs are two message public coin. - Our results do not appear to contradict the Aaronson-Ambanis conjecture. Assuming this conjecture, there exist publicly verifiable certifiable randomness, again with the minimal rounds of interaction. By replacing the random oracle with a concrete cryptographic hash function such as SHA2, we obtain plausible Minicrypt instantiations of the above results. Previous analogous results all required substantial structure, either in terms of highly structured oracles and/or algebraic assumptions in Cryptomania and beyond.
Predicting the weekly COVID-19 new cases using multilayer perceptron: An evidence from west Java, Indonesia

COVID-19 is a contagious disease caused by the coronavirus (SARS-CoV-2) that attacks the respiratory tract. On August 14th, 2021, 653,741 persons had been proven positive for COVID-19. The number of patients tends to increase as the number of COVID-19 cases grows. The more infected people, the more cases of COVID-19 there will be. The Bed Occupancy Ratio (BOR) in West Java reached an all-time high of 91.6 percent in June 2021, far exceeding the WHO recommendation of 60 percent, before gradually declining to 30.69 percent in August. Because of the new cases mentioned, the rate of spread of COVID-19 in West Java, the forecast of new cases is very strategic. The number of new cases in this study was predicted using a Multilayer Perceptron (MLP). The data used in this study were sourced from the COVID-19 Task Force. The data is the number of positive and new cases from 34 provinces in Indonesia from March 2nd, 2020, to August 14th, 2021. The results of the evaluation using test data on the number of active cases in the last 19 weeks, namely April 10th - August 14th, 2021, The MLP is accurate in forecasting the number of new cases 18 times for both forecast periods with APE &lt; 15%, with the value MAPE, RMSE and MAE obtained were 5.52%, 1157,61, and 706.811. The results of this study can be helpful for the government as a reference in conditioning hospital bed capacity to deal with active COVID-19 cases in West Java in the next two weeks so that the hospital rejects no COVID-19 patients because the hospital is full.
Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks

Labelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are diverse valid beliefs about what the correct data labels should be. So far, dataset creators have acknowledged annotator subjectivity, but rarely actively managed it in the annotation process. This has led to partly-subjective datasets that fail to serve a clear downstream use. To address this issue, we propose two contrasting paradigms for data annotation. The descriptive paradigm encourages annotator subjectivity, whereas the prescriptive paradigm discourages it. Descriptive annotation allows for the surveying and modelling of different beliefs, whereas prescriptive annotation enables the training of models that consistently apply one belief. We discuss benefits and challenges in implementing both paradigms, and argue that dataset creators should explicitly aim for one or the other to facilitate the intended use of their dataset. Lastly, we conduct an annotation experiment using hate speech data that illustrates the contrast between the two paradigms.
Deep Speech Based End-to-End Automated Speech Recognition (ASR) for Indian-English Accents

Automated Speech Recognition (ASR) is an interdisciplinary application of computer science and linguistics that enable us to derive the transcription from the uttered speech waveform. It finds several applications in Military like High-performance fighter aircraft, helicopters, air-traffic controller. Other than military speech recognition is used in healthcare, persons with disabilities and many more. ASR has been an active research area. Several models and algorithms for speech to text (STT) have been proposed. One of the most recent is Mozilla Deep Speech, it is based on the Deep Speech research paper by Baidu. Deep Speech is a state-of-art speech recognition system is developed using end-to-end deep learning, it is trained using well-optimized Recurrent Neural Network (RNN) training system utilizing multiple Graphical Processing Units (GPUs). This training is mostly done using American-English accent datasets, which results in poor generalizability to other English accents. India is a land of vast diversity. This can even be seen in the speech, there are several English accents which vary from state to state. In this work, we have used transfer learning approach using most recent Deep Speech model i.e., deepspeech-0.9.3 to develop an end-to-end speech recognition system for Indian-English accents. This work utilizes fine-tuning and data argumentation to further optimize and improve the Deep Speech ASR system. Indic TTS data of Indian-English accents is used for transfer learning and fine-tuning the pre-trained Deep Speech model. A general comparison is made among the untrained model, our trained model and other available speech recognition services for Indian-English Accents.
DuoSearch: A Novel Search Engine for Bulgarian Historical Documents

Search in collections of digitised historical documents is hindered by a two-prong problem, orthographic variety and optical character recognition (OCR) mistakes. We present a new search engine for historical documents, DuoSearch, which uses ElasticSearch and machine learning methods based on deep neural networks to offer a solution to this problem. It was tested on a collection of historical newspapers in Bulgarian from the mid-19th to the mid-20th century. The system provides an interactive and intuitive interface for the end-users allowing them to enter search terms in modern Bulgarian and search across historical spellings. This is the first solution facilitating the use of digitised historical documents in Bulgarian.
Quasi-Negative Binomial: Properties, Parametric Estimation, Regression Model and Application to RNA-SEQ Data

Background: The Poisson and the Negative Binomial distributions are commonly used to model count data.The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial has a variance larger than the mean and therefore both models are appropriate to model over-dispersed count data.Objectives: A new two-parameter probability distribution called the Quasi-Negative Binomial Distribution (QNBD) is being studied in this paper, generalizing the well-known negative binomial distribution.This model turns out to be quite flexible for analyzing count data.Our main objectives are to estimate the parameters of the proposed distribution and to discuss its applicability to genetics data.As an application, we demonstrate that the QNBD regression representation is utilized to model genomics data sets.Results: The new distribution is shown to provide a good fit with respect to the "Akaike Information Criterion", AIC, considered a measure of model goodness of fit.The proposed distribution may serve as a viable alternative to other distributions available in the literature for modeling count data exhibiting overdispersion, arising in various fields of scientific investigation such as genomics and biomedicine.
Practical foundations of machine learning for addiction research. Part II. Workflow and use cases

In a continuum with applied statistics, machine learning offers a wide variety of tools to explore, analyze, and understand addiction data. These tools include algorithms that can leverage useful information from data to build models; these models can solve particular tasks to answer addiction scientific questions. In this second part of a two-part review on machine learning, we explain how to apply machine learning methods to addiction research. Like other analytical tools, machine learning methods require a careful implementation to carry out a reproducible and transparent research process with reliable results. This review describes a workflow to guide the application of machine learning in addiction research, detailing study design, data collection, data pre-processing, modeling, and results communication. How to train, validate, and test a model, detect and characterize overfitting, and determine an adequate sample size are some of the key issues when applying machine learning. We also illustrate the process and particular nuances with examples of how researchers in addiction have applied machine learning techniques with different goals, study designs, or data sources as well as explain the main limitations of machine learning approaches and how to best address them. A good use of machine learning enriches the addiction research toolkit.
Exposure to dialect variation in an artificial language prior to literacy training impairs reading of words with competing variants but does not affect decoding skills.

Many bidialectal children grow up speaking a variety (e.g., a regional dialect) that differs from the variety in which they subsequently acquire literacy. Previous computational simulations and artificial literacy learning experiments with adults have demonstrated lower accuracy in reading contrastive words for which dialect variants exist compared with noncontrastive words without dialect variants. At the same time, exposure to multiple varieties did not affect learners' ability to phonologically decode untrained words; in fact, longer literacy training resulted in a benefit from dialect exposure as competing variants in the input may have increased reliance on grapheme-phoneme conversion. However, these previous experiments interleaved word learning and reading/spelling training, yet children typically acquire substantial oral language knowledge prior to literacy training. Here we used artificial literacy learning with adults to examine whether the previous findings replicate in an ecologically more valid procedure where word learning precedes literacy training. We also manipulated training conditions to explore interventions thought to be beneficial for literacy acquisition, such as providing explicit social cues for variety use and literacy training in both varieties. Our findings replicated the reduced accuracy for reading contrastive words in those learners who had successfully acquired the dialect variants prior to literacy training. This effect was exacerbated when literacy training also included dialect variation. Crucially, although no benefits from the interventions were found, dialect exposure did not affect reading and spelling of untrained words suggesting that phonological decoding skills can remain unaffected by the existence of multiple word form variants in a learner's lexicon. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Pengembangan Modul Fisika Berbasis QR-CODE pada Pokok Bahasan Fisika Inti Kelas XII SMA

The development of this module is based on the learning process that has used internet media, but in fact its use during the learning process has not been maximized and the use of the internet for learning has not been able to direct students to achieve learning goals. The purpose of this research is to produce a product in the form of a QR Code-based module with core physics as a learning resource for class XII physics that is suitable for use by students. This development research uses the ADDIE model which only takes 4 stages, namely Analysis (Analysis), Design (Design), Development (Development), Implementation (Implementation). Based on the results of the analysis, the percentage of validity results obtained from media experts 75%, material experts 78.125%, linguists 74.5%, and trials to groups of students with 91.66% results. Based on the results of the validation that has been carried out, the QR Code-based physics module with the core physics subject of class XII is feasible and effective to be used as a physics learning medium.
Short-term memory ability of reservoir-based temporal difference learning model

A network model with temporal difference (TD) learning and reservoir computing (RC) has been proposed to control autonomous robots. RC is a framework for constructing a recurrent neural network that processes complex time series with less computational cost. TD learning is a framework of reinforcement learning, which realizes that an agent takes actions in an environment to maximize the cumulative reward. The control model using TD learning with RC realize the optimization of agent's action based on the sensory signal that is a continuous-valued time-varying signal. The model uses online reinforcement learning to train the connection weights between the reservoir and the output layer to represent the action value. In the present study, we evaluate the model with a task requiring short-term memory and clarify the reservoir's role in memorizing task-relevant sensory information. We show that the reservoir in the RC-based TD learning model enhances the performance in the memory-required task. The choice of parameter values that specify the reservoir dynamics is critical to ensure performance in the task.
Spatial auditory presentation of a partner’s presence induces the social Simon effect

Social presence is crucial for smooth communications in virtual reality (VR). Current telecommunication systems rarely submit spatial auditory information originating from remote people. However, such information may enhance social presence in VR. In this study, we constructed a dynamic binaural synthesis system and investigated the effect of spatial auditory information of a remote partner on a participant's behavior using the social Simon effect (SSE). The SSE is a spatial stimulus-response compatibility effect between two persons. The SSE occurs when one perceives that their partner is present. Several studies have confirmed the SSE in actual environments. We presented partner sounds diotically (i.e., without spatial information) to one group or binaurally (i.e., with spatial information) to another group through headphones without providing visual information about the partner. The results showed that the SSE was induced only in the binaural group in the current auditory VR (Experiment 1), whereas both groups exhibited the SSE in an actual environment (Experiment 2). These results suggest that the auditory spatial information of remote people is sufficient to induce the SSE and has a potential to enhance social presence.
Low Complexity Encoder with Multilabel Classification and Image Captioning燤odel

Due to the advanced development in the multimedia-on-demand traffic in different forms of audio, video, and images, has extremely moved on the vision of the Internet of Things (IoT) from scalar to Internet of Multimedia Things (IoMT).Since Unmanned Aerial Vehicles (UAVs) generates a massive quantity of the multimedia data, it becomes a part of IoMT, which are commonly employed in diverse application areas, especially for capturing remote sensing (RS) images.At the same time, the interpretation of the captured RS image also plays a crucial issue, which can be addressed by the multi-label classification and Computational Linguistics based image captioning techniques.To achieve this, this paper presents an efficient low complexity encoding technique with multi-label classification and image captioning for UAV based RS images.The presented model primarily involves the low complexity encoder using the Neighborhood Correlation Sequence (NCS) with a burrows wheeler transform (BWT) technique called LCE-BWT for encoding the RS images captured by the UAV.The application of NCS greatly reduces the computation complexity and requires fewer resources for image transmission.Secondly, deep learning (DL) based shallow convolutional neural network for RS image classification (SCNN-RSIC) technique is presented to determine the multiple class labels of the RS image, shows the novelty of the work.Finally, the Computational Linguistics based Bidirectional Encoder Representations from Transformers (BERT) technique is applied for image captioning, to provide a proficient textual description of the RS image.The performance of the presented technique is tested using the UCM dataset.The simulation outcome implied that the presented model has obtained effective compression performance, reconstructed image quality, classification results, and image captioning outcome.
Are E2E ASR models ready for an industrial usage?

The Automated Speech Recognition (ASR) community experiences a major turning point with the rise of the fully-neural (End-to-End, E2E) approaches. At the same time, the conventional hybrid model remains the standard choice for the practical usage of ASR. According to previous studies, the adoption of E2E ASR in real-world applications was hindered by two main limitations: their ability to generalize on unseen domains and their high operational cost. In this paper, we investigate both above-mentioned drawbacks by performing a comprehensive multi-domain benchmark of several contemporary E2E models and a hybrid baseline. Our experiments demonstrate that E2E models are viable alternatives for the hybrid approach, and even outperform the baseline both in accuracy and in operational efficiency. As a result, our study shows that the generalization and complexity issues are no longer the major obstacle for industrial integration, and draws the community's attention to other potential limitations of the E2E approaches in some specific use-cases.
Towards Safe, Explainable, and Regulated Autonomous Driving

There has been recent and growing interest in the development and deployment of autonomous vehicles, encouraged by the empirical successes of powerful artificial intelligence techniques (AI), especially in the applications of deep learning and reinforcement learning. However, as demonstrated by recent traffic accidents, autonomous driving technology is not fully reliable for safe deployment. As AI is the main technology behind the intelligent navigation systems of self-driving vehicles, both the stakeholders and transportation regulators require their AI-driven software architecture to be safe, explainable, and regulatory compliant. In this paper, we propose a design framework that integrates autonomous control, explainable AI (XAI), and regulatory compliance to address this issue, and then provide an initial validation of the framework with a critical analysis in a case study. Moreover, we describe relevant XAI approaches that can help achieve the goals of the framework.
Perfect state transfer in two dimensions and the bivariate dual-Hahn polynomials

A new solvable two-dimensional spin lattice model defined on a regular grid of triangular shape is proposed. The hopping amplitudes between sites are related to recurrence coefficients of certain bivariate dual-Hahn polynomials. For a specific choice of the parameters, perfect state transfer and fractional revival are shown to take place.
New Insights on Differential and Linear Bounds Using Mixed Integer Linear Programming

Mixed Integer Linear Programming (MILP) is a very common method of modelling differential and linear bounds for ciphers, as it automates the process of finding the best differential trail or linear approximation. The Convex Hull (CH) modelling, introduced by Sun et al. (Eprint 2013/Asiacrypt 2014), is a popular method in this regard, which can convert the conditions corresponding to a small (4-bit) SBox to MILP constraints efficiently. In our work, we study this modelling with CH in more depth and observe a previously unreported problem associated with it. Our analysis shows, there are SBoxes for which the CH modelling can yield incorrect modelling. As such, using the CH modelling may lead to incorrect differential or linear bounds. This arises from the observation that although the CH is generated for a certain set of points, there can be points outside this set which also satisfy all the inequalities of the CH. As apparently no variant of the CH modelling can circumvent this problem, we propose a new modelling for differential and linear bounds. Our modelling makes use of every points of interest individually. This modelling works for an arbitrary SBox, and is able to find the exact bound. Additionally, we also explore the possibility of using redundant constraints, such that the run time for an MILP solver can be reduced while keeping the optimal result unchanged. For this purpose, we revisit the CH modelling and use the CH constraints as redundant constraints (on top of our usual constraints, which ensure the aforementioned problem does not occur). In fact, we choose two heuristics from the convex hull modelling. The first uses all the inequalities of a convex hull, while second uses a reduced number of inequalities. Apart from that, we also propose to use the solutions for the smaller rounds as another heuristic to find the optimal bound for a higher round. With our experiments on round-reduced GIFT-128, we show it is possible to reduce the run time a few folds using a suitable choice of redundant constraints. Further, we observe the necessity to consider separate heuristics for the differential and linear cases. We also present the optimal linear bounds for 11- and 12-rounds of GIFT-128, extending from the best-known result of 10-rounds.