Reviewerly/exHarmony huggingface dataset:
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding

Representing text in tables is essential for many business intelligence tasks such as semantic retrieval, data exploration and visualization, and question answering.Existing methods that leverage pretrained Transformer encoders range from a simple construction of pseudosentences by concatenating text across rows or columns to complex parameter-intensive models that encode table structure and require additional pretraining.In this work, we introduce a novel encoding strategy for Transformer encoders that preserves the critical property of permutation invariance across rows or columns.Unlike existing state-of-the-art methods for Table Understanding, our proposed approach does not require any additional pretraining and still substantially outperforms existing methods in almost all instances.We demonstrate the effectiveness of our proposed approach on three table interpretation tasks: column type annotation, relation extraction, and entity linking through extensive experiments on existing tabular datasets.
Residue-Based Natural Language Adversarial Attack Detection

Deep learning based systems are susceptible to adversarial attacks, where a small, imperceptible change at the input alters the model prediction. However, to date the majority of the approaches to detect these attacks have been designed for image processing systems. Many popular image adversarial detection approaches are able to identify adversarial examples from embedding feature spaces, whilst in the NLP domain existing state of the art detection approaches solely focus on input text features, without consideration of model embedding spaces. This work examines what differences result when porting these image designed strategies to Natural Language Processing (NLP) tasks - these detectors are found to not port over well. This is expected as NLP systems have a very different form of input: discrete and sequential in nature, rather than the continuous and fixed size inputs for images. As an equivalent model-focused NLP detection approach, this work proposes a simple sentence-embedding "residue" based detector to identify adversarial examples. On many tasks, it out-performs ported image domain detectors and recent state of the art NLP specific detectors.
Jam or Cream First? Modeling Ambiguity in Neural Machine Translation with SCONES

The softmax layer in neural machine translation is designed to model the distribution over mutually exclusive tokens.Machine translation, however, is intrinsically uncertain: the same source sentence can have multiple semantically equivalent translations.Therefore, we propose to replace the softmax activation with a multi-label classification layer that can model ambiguity more effectively.We call our loss function Single-label Contrastive Objective for Non-Exclusive Sequences (SCONES).We show that the multi-label output layer can still be trained on single reference training data using the SCONES loss function.SCONES yields consistent BLEU score gains across six translation directions, particularly for mediumresource language pairs and small beam sizes.By using smaller beam sizes we can speed up inference by a factor of 3.9x and still match or improve the BLEU score obtained using softmax.Furthermore, we demonstrate that SCONES can be used to train NMT models that assign the highest probability to adequate translations, thus mitigating the "beam search curse".Additional experiments on synthetic language pairs with varying levels of uncertainty suggest that the improvements from SCONES can be attributed to better handling of ambiguity.
Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection

Counterfactually Augmented Data (CAD) aims to improve out-of-domain generalizability, an indicator of model robustness.The improvement is credited to promoting core features of the construct over spurious artifacts that happen to correlate with it.Yet, over-relying on core features may lead to unintended model bias.Especially, constructdriven CAD-perturbations of core featuresmay induce models to ignore the context in which core features are used.Here, we test models for sexism and hate speech detection on challenging data: non-hateful and nonsexist usage of identity and gendered terms.On these hard cases, models trained on CAD, especially construct-driven CAD, show higher false positive rates than models trained on the original, unperturbed data.Using a diverse set of CAD-construct-driven and constructagnostic-reduces such unintended bias.
Explaining Dialogue Evaluation Metrics using Adversarial Behavioral Analysis

There is an increasing trend in using neural methods for dialogue model evaluation.Lack of a framework to investigate these metrics can cause dialogue models to reflect their biases and cause unforeseen problems during interactions.In this work, we propose an adversarial test-suite which generates problematic variations of various dialogue aspects, e.g.logical entailment, using automatic heuristics.We show that dialogue metrics for both opendomain and task-oriented settings are biased in their assessments of different conversation behaviors and fail to properly penalize problematic conversations, by analyzing their assessments of these problematic examples.We conclude that variability in training methodologies and data-induced biases are some of the main causes of these problems.We also conduct an investigation into the metric behaviors using a black-box interpretability model which corroborates our findings and provides evidence that metrics pay attention to the problematic conversational constructs signaling a misunderstanding of different conversation semantics.
Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages

We leverage pre-trained language models to solve the task of complex NER for two lowresource languages: Chinese and Spanish.We use the technique of Whole Word Masking (WWM) to boost the performance of masked language modeling objective on large and unsupervised corpora.We experiment with multiple neural network architectures, incorporating CRF, BiLSTMs, and Linear Classifiers on top of a fine-tuned BERT layer.All our models outperform the baseline by a significant margin and our best performing model obtains a competitive position on the evaluation leaderboard for the blind test set.
Indigenous Language Revitalization and the Dilemma of Gender Bias

Natural Language Processing (NLP), through its several applications, has been considered as one of the most valuable field in interdisciplinary researches, as well as in computer science.However, it is not without its flaws.One of the most common flaws is bias.This paper examines the main linguistic challenges of Inuktitut, an indigenous language of Canada, and focuses on gender bias identification and mitigation.We explore the unique characteristics of this language to help us understand the right techniques that can be used to identify and mitigate implicit biases.We use some methods to quantify the gender bias existing in Inuktitut word embeddings; then we proceed to mitigate the bias and evaluate the performance of the debiased embeddings.Next, we explain how approaches for detecting and reducing bias in English embeddings may be transferred to Inuktitut embeddings by properly taking into account the language's particular characteristics.We compare the effect of the debiasing techniques on Inuktitut and English.Finally, we highlight some future research directions which will further help to push the boundaries.
SGLB: Stochastic Gradient Langevin Boosting

This paper introduces Stochastic Gradient Langevin Boosting (SGLB) - a powerful and efficient machine learning framework that may deal with a wide range of loss functions and has provable generalization guarantees. The method is based on a special form of the Langevin diffusion equation specifically designed for gradient boosting. This allows us to theoretically guarantee the global convergence even for multimodal loss functions, while standard gradient boosting algorithms can guarantee only local optimum. We also empirically show that SGLB outperforms classic gradient boosting when applied to classification tasks with 0-1 loss function, which is known to be multimodal.
PatentTransformer-2: Controlling Patent Text Generation by Structural Metadata

PatentTransformer is our codename for patent text generation based on Transformer-based models. Our goal is "Augmented Inventing." In this second version, we leverage more of the structural metadata in patents. The structural metadata includes patent title, abstract, and dependent claim, in addition to independent claim previously. Metadata controls what kind of patent text for the model to generate. Also, we leverage the relation between metadata to build a text-to-text generation flow, for example, from a few words to a title, the title to an abstract, the abstract to an independent claim, and the independent claim to multiple dependent claims. The text flow can go backward because the relation is trained bidirectionally. We release our GPT-2 models trained from scratch and our code for inference so that readers can verify and generate patent text on their own. As for generation quality, we measure it by both ROUGE and Google Universal Sentence Encoder.
Manifold Learning for Accelerating Coarse-Grained Optimization

Algorithms proposed for solving high-dimensional optimization problems with no derivative information frequently encounter the "curse of dimensionality," becoming ineffective as the dimension of the parameter space grows. One feature of a subclass of such problems that are effectively low-dimensional is that only a few parameters (or combinations thereof) are important for the optimization and must be explored in detail. Knowing these parameters/ combinations in advance would greatly simplify the problem and its solution. We propose the data-driven construction of an effective (coarse-grained, "trend") optimizer, based on data obtained from ensembles of brief simulation bursts with an "inner" optimization algorithm, that has the potential to accelerate the exploration of the parameter space. The trajectories of this "effective optimizer" quickly become attracted onto a slow manifold parameterized by the few relevant parameter combinations. We obtain the parameterization of this low-dimensional, effective optimization manifold on the fly using data mining/manifold learning techniques on the results of simulation (inner optimizer iteration) burst ensembles and exploit it locally to "jump" forward along this manifold. As a result, we can bias the exploration of the parameter space towards the few, important directions and, through this "wrapper algorithm," speed up the convergence of traditional optimization algorithms.
Paraphrase Generation with Latent Bag of Words

Paraphrase generation is a longstanding important problem in natural language processing. In addition, recent progress in deep generative models has shown promising results on discrete latent variables for text generation. Inspired by variational autoencoders with discrete latent structures, in this work, we propose a latent bag of words (BOW) model for paraphrase generation. We ground the semantics of a discrete latent variable by the BOW from the target sentences. We use this latent variable to build a fully differentiable content planning and surface realization model. Specifically, we use source words to predict their neighbors and model the target BOW with a mixture of softmax. We use Gumbel top-k reparameterization to perform differentiable subset sampling from the predicted BOW distribution. We retrieve the sampled word embeddings and use them to augment the decoder and guide its generation search space. Our latent BOW model not only enhances the decoder, but also exhibits clear interpretability. We show the model interpretability with regard to \emph{(i)} unsupervised learning of word neighbors \emph{(ii)} the step-by-step generation procedure. Extensive experiments demonstrate the transparent and effective generation process of this model.\footnote{Our code can be found at \url{https://github.com/FranxYao/dgm_latent_bow}}
Future Direction of AI in Block-chain for security systems – A Comprehensive Report

Currently, blockchain is a game-changing technology that's revolutionizing the way applications are built because it eliminates the requirement for trust between network peers. Global and immutable repositories created by blockchain technology provide non-repudiation and accountability of the stored data. Because of this, processing and maintaining enormous volumes of data with ever-decreasing latencies are becoming more difficult. Therefore, artificial intelligence and machine learning approaches have made substantial advancements, paving the way for next-generation network infrastructure. The decentralization and tamper-proof nature of blockchain technology make it ideal for data exchange and privacy protection. This study paradigm may improve computer network reliability while also allowing new distributed and knowledge-driven security services and applications. Numerous issues are addressed in this work, including new cryptographic models for healthcare applications, intelligent threat-detection systems and novel approaches to consensus building in blockchains.
PROTECTION OF THE PROFESSIONAL HONOR AND DIGNITY OF A POLICE OFFICER IN WAR

The purpose of the research is a comprehensive analysis of theoretical and applied issues related to the protection of professional honor and dignity of police officers, development of recommendations aimed at improving the mechanism of legal protection of personal non-property rights of police officers, and substantiation of proposal for improving the legislation.The methodological basis of the study is a dialectical approach to the knowledge of theoretical and legal categories of honor and dignity.The scientific tools of the work comprise the principles of objectivity, systematicity, integrity, pluralism of knowledge of the civil doctrine on the protection of personal intangible assets.Results.A comparative legal analysis of the legal categories of honor, dignity, professional honor and dignity of a police officer was conducted.The mechanism of responsibility for insulting a police officer in foreign countries was studied.It was established that the essence of protecting the professional honor and dignity of a police officer is not only the ethical rehabilitation of the victim but also the creation of a positive image of the police officer.Conclusions.The article notes that the professional honor and dignity of a police officer acts both as an object of private legal protection and an object of public legal protection.It was proved that under declared martial law, it is inadmissible to limit the protection of professional honor and dignity of police officers only by civil and administrative means.It is proposed to provide for criminal liability for insulting a representative of law enforcement agencies in the performance of their official duties, in particular, in wartime.Criminalization of the relevant offense, along with civil and administrative liability, will be an adequate way to protect the honor and dignity of a police officer and an effective means of preventing such offenses.
The use of the mobile application Actionbound in the teaching of local history

Technology in the 21st century forms an indispensable part of the everyday life of all ages. Today, portable devices are used daily for multiple purposes (entertainment, recreation, health, productivity, physical exercise etc.), while games found on portable devices based on a player's location have started to burgeon as well. Education could not remain uninvolved in these developments. Over the last few years, technology has been used more to foster students' education through programs and applications. The gamification of education looks to maximize student engagement. This current study aims to plan and create an educational teaching scenario as part of the teaching of the History subject. Using the multimedia tool Actionbound, students are guided into the place of interest (monastery). The school subject of history becomes more entertaining as students move into space through GPS locations, questions, tests, and rewards. Therefore, students are no longer passive receptors of new knowledge and information but explorers who discover their new knowledge by taking an active role in the teaching process.
A Bachelor Degree Program in IoT Engineering

In the era of the fourth industrial revolution, technology is advancing at a pace much faster than what individuals, societies and nations can cope with. Internet of Things (IoT) is one such technology that is sweeping the economic, business and service landscapes with its disruption mechanisms that influence areas such as smart homes, hospitals, cities, as well as transportation and energy utilization with many exciting and novel applications. Additionally, the job market is evolving at a rate that makes it difficult for universities to sustain a stable programme structure, especially in engineering and information and communication technology disciplines as well as specializations and domains associated with technology. While several universities worldwide have established IoT degree programs at bachelor and master levels, institutions in developing countries are still contemplating the idea of pioneering such venture due to a number of reasons related to technology vacillation as well as market and social acceptance. Princess Sumaya University for Technology has therefore designed a bachelor degree programme on IoT Engineering in order to bridge the gap in the Jordanian ICT market and to meet demands for specialized engineers in this rapidly evolving area. The university adapted an optimization design process of the program including a cycle of consultation with stakeholders, followed by a formal procedure of rectification and accreditation. The proposed program was successfully a launched at the beginning of the academic year 2021/2022 with an intake cohort of 50 students.
Improved SURF in Color Difference Scale Space for Color Image Matching

This paper presents an improved SURF (Speeded Up Robust Features) for image matching which considers color information. Firstly, a new color difference scale space is constructed based on color information to detect feature point. Then we extracted a 192-dimensional vector to describe feature point, which includes a 64-dimensional vector representing the brightness information and a 128-dimensional vector representing the color information in a color image. Finally, in the process images matching, a new weighted Murkovski distance is used to measure the distance between two descriptors. From the experiment results, we can know that, compared the other methods, the feature points detection method proposed is more robust. The matching scores and precision of our method are dominant among different methods of color image matching. Compared with SURF, the number of feature points detected by the proposed method increases by 163%, the average matching scores and matching precision increase by 16% and 15.81% respectively.
The Dark Web – A Review

The internet consists of or can be divided into three parts. They are known or referred to as surface web, deep web and dark web. The dark web also known as the ‘dark net’, represents part of the deep web. TOR, 12P, and Freenet are some specialized tools needed to access the dark web as they cannot be accessed ordinarily. These tools anonymize the internet protocol address of the user. This web level is characterized by notoriety and the operation of illegal markets and activities such as the sale of illicit drugs, firearms, and hitman services amongst others. This review seeks to find out the usage of the dark web, findings in related literature, the ethical and unethical sides of the dark web as well as its involvement in cybercrime. Keywords: Dark Web, Darknet, TOR, Internet, Cybercrime BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: (2022): Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 209-214 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P34
Parameters of hulls of primitive binary narrow-sense BCH codes and their subcodes

<p style='text-indent:20px;'>The (Euclidean) hull of a linear code is defined to be the intersection of the code and its Euclidean dual. It is clear that the hulls are self-orthogonal codes, which are an important type of linear codes due to their wide applications in communication and cryptography. Let <inline-formula><tex-math id="M1">\begin{document}$ \mathcal C_{(2,n,\delta)} $\end{document}</tex-math></inline-formula> be the binary primitive narrow-sense BCH code, where <inline-formula><tex-math id="M2">\begin{document}$ n = 2^m-1 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M3">\begin{document}$ m $\end{document}</tex-math></inline-formula> is a positive integer. In this paper, we will investigate the parameters of the hulls of <inline-formula><tex-math id="M4">\begin{document}$ \mathcal C_{(2,n,\delta)} $\end{document}</tex-math></inline-formula>. The dimension of <inline-formula><tex-math id="M5">\begin{document}$ \text{Hull}(\mathcal C_{(2,n,\delta)}) $\end{document}</tex-math></inline-formula> will be presented when <inline-formula><tex-math id="M6">\begin{document}$ 2 \le \delta \le 2^{\lfloor \frac{m}{2} \rfloor+1}+2^{\lceil \frac{m}{2} \rceil-1}-1 $\end{document}</tex-math></inline-formula> with <inline-formula><tex-math id="M7">\begin{document}$ m \ge 5 $\end{document}</tex-math></inline-formula>. Furthermore, we give an improvement on lower bounds of the minimum distances of <inline-formula><tex-math id="M8">\begin{document}$ \text{Hull}(\mathcal C_{(2,n,\delta)}) $\end{document}</tex-math></inline-formula>. We also construct a self-orthogonal subcode of <inline-formula><tex-math id="M9">\begin{document}$ \text{Hull}(\mathcal C_{(2,n,\delta)}) $\end{document}</tex-math></inline-formula> and investigate the parameters of the self-orthogonal code.