Reviewerly/exHarmony huggingface dataset:
IoT Security Risk Management Strategy Reference Model (IoTSRM2)

Nowadays, Internet of Things (IoT) adoptions are burgeoning and deemed the lynchpin towards achieving ubiquitous connectivity. In this context, defining and leveraging robust IoT security risk management strategies are paramount for secure IoT adoptions. Thus, this study aims to support IoT adopters from any sector to formulate or reframe their IoT security risk management strategies to achieve robust strategies that effectively address IoT security issues. In a nutshell, this article relies on a mixed methods research methodology and proposes a reference model for IoT security risk management strategy. The proposed IoT security risk management strategy reference model (IoTSRM2) relies on the 25 selected IoT security best practices which are outlined using a proposed taxonomic hierarchy, and on the proposed three-phased methodology that consists of nine steps and outputs. The main contribution of this work is the proposed IoTSRM2 which consists of six domains, 16 objectives, and 30 prioritized controls. Furthermore, prior to providing the related work, this article provides a critical evaluation of selected informative references of IoTSRM2 based on their percentage-wise linkage to the IoTSRM2 domains and to the entire IoTSRM2. The findings of the critical evaluation illustrate, inter alia, the selected informative references that are the top three most and least linked to the entire IoTSRM2.
Multi-Modal Image Captioning for the Visually Impaired

One of the ways blind people understand their surroundings is by clicking images and relying on descriptions generated by image captioning systems.Current work on captioning images for the visually impaired do not use the textual data present in the image when generating captions.This problem is critical as many visual scenes contain text.Moreover, up to 21% of the questions asked by blind people about the images they click pertain to the text present in them (Bigham et al., 2010).In this work, we propose altering AoANet, a state-of-the-art image captioning model, to leverage the text detected in the image as an input feature.In addition, we use a pointer-generator mechanism to copy the detected text to the caption when tokens need to be reproduced accurately.Our model outperforms AoANet on the benchmark dataset VizWiz, giving a 35% and 16.2% performance improvement on CIDEr and SPICE scores, respectively..
Unsupervised Label Refinement Improves Dataless Text Classification

Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description.While promising, it crucially relies on accurate descriptions of the label set for each downstream task.This reliance causes dataless classifiers to be highly sensitive to the choice of label descriptions and hinders the broader application of dataless classification in practice.In this paper, we ask the following question: how can we improve dataless text classification using the inputs of the downstream task dataset?Our primary solution is a clustering based approach.Given a dataless classifier, our approach refines its set of predictions using k-means clustering.We demonstrate the broad applicability of our approach by improving the performance of two widely used classifier architectures, one that encodes text-category pairs with two independent encoders and one with a single joint encoder.Experiments show that our approach consistently improves dataless classification across different datasets and makes the classifier more robust to the choice of label descriptions. 1
Pasta: A Case for Hybrid Homomorphic Encryption

The idea of hybrid homomorphic encryption (HHE) is to drastically reduce bandwidth requirements when using homomorphic encryption (HE) at the cost of more expensive computations in the encrypted domain. To this end, various dedicated schemes for symmetric encryption have already been proposed. However, it is still unclear if those ideas are already practically useful, because (1) no cost-benefit analysis was done for use cases and (2) very few implementations are publicly available. We address this situation in several ways. We build an open-source benchmarking r framework, we explore properties of the respective HHE proposals. It turns out that even medium-sized use cases are infeasible, especially when involving integer arithmetic. Next, we propose Pasta, a cipher thoroughly optimized for integer HHE use cases. Pasta is designed to minimize the multiplicative depth, while also leveraging the structure of two state-of-the-art integer HE schemes (BFV and BGV) to minimize the homomorphic evaluation latency. Using our new benchmarking environment, we extensively evaluate Pasta in SEAL and HElib and compare its properties to 8 existing ciphers in two use cases. Our evaluations show that Pasta outperforms its competitors for HHE both in terms of homomorphic evaluation time and noise consumption, showing its efficiency for applications in real-world HE use cases. Concretely, Pasta outperforms Agrasta by a factor of up to 82, Masta by a factor of up to 6 and Hera up to a factor of 11 when applied to the two use cases.
Blockchain adoption factors for SMEs in supply chain management

Blockchain-based applications can enhance the sharing of information in processes involving multiple types of stakeholders, as in Supply Chain Management (SCM). A supply chain network can benefit from the visibility of the flow of goods, money, and information enabled by blockchain technology. So far, only limited evidence is available on the perspective of Small and Medium-sized Enterprises (SMEs) on the adoption of blockchain-based applications in their business processes. This may lead to a heavily diminished role for SMEs in SCM if large consortia unilaterally decide to adopt a blockchain-based architecture to improve the performance of the entire supply chain. Therefore, we conducted a study to explore which factors influence SMEs’ intention to adopt blockchain technology. Based on a literature review into technology adoption frameworks, we derived the technological, organizational, and environmental (TOE) factors that can play a role in their decision-making process. We distributed a survey amongst European SMEs using the multi-criteria decision-making method of the Best-Worst Method (BWM) to elicit the relative weights of these factors. The data analyses show that in contrast to other studies into technology adoption, the SMEs’ intention to adopt blockchain-based applications is primarily influenced by organizational rather than by technological and environmental factors. This implies that SMEs are best supported by showing blockchain’s organizational benefits and by training senior executives at their company.
PASS: Perturb-and-Select Summarizer for Product Reviews

The product reviews summarization task aims to automatically produce a short summary for a set of reviews of a given product.Such summaries are expected to aggregate a range of different opinions in a concise, coherent and informative manner.This challenging task gives rise to two shortcomings in existing work.First, summarizers tend to favor generic content that appears in reviews for many different products, resulting in template-like, less informative summaries.Second, as reviewers often disagree on the pros and cons of a given product, summarizers sometimes yield inconsistent, self-contradicting summaries.We propose the PASS system (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based model (T5 in our case), which follows a few-shot fine-tuning scheme.A key component of the PASS system relies on applying systematic perturbations to the model's input during inference, which allows it to generate multiple different summaries per product.We develop a method for ranking these summaries according to desired criteria, coherence in our case, enabling our system to almost entirely avoid the problem of selfcontradiction.We compare our system against strong baselines on publicly available datasets, and show that it produces summaries which are more informative, diverse and coherent.
Review on Natural Language Processing Based on Different Techniques

With the exponential growth of the information on the Internet, there is a high demand for making this information readable and processable by machines. For this purpose, there is a need for the Natural Language Processing (NLP) pipeline. Natural language analysis is a tool used by computers to grasp, perceive, and control human language. This paper discusses various techniques addressed by different researchers on NLP and compares their performance. The comparison among the reviewed researches illustrated that good accuracy levels haved been achieved. Adding to that, the researches that depended on the Sentiment Analysis and ontology methods achieved small prediction error.&#x0D; The syntactic analysis or parsing or syntax analysis is the third stage of the NLP as a conclusion to use NLP technology. This step aims to accurately mean or, from the text, you may state a dictionary meaning. Syntax analysis analyzes the meaning of the text in comparison with the formal grammatical rules.
Attention Flows are Shapley Value Explanations

Shapley Values, a solution to the credit assignment problem in cooperative game theory, are a popular type of explanation in machine learning, having been used to explain the importance of features, embeddings, and even neurons.In NLP, however, leave-oneout and attention-based explanations still predominate.Can we draw a connection between these different methods?We formally prove that -save for the degenerate case -attention weights and leave-one-out values cannot be Shapley Values.Attention flow is a post-processed variant of attention weights obtained by running the max-flow algorithm on the attention graph.Perhaps surprisingly, we prove that attention flows are indeed Shapley Values, at least at the layerwise level.Given the many desirable theoretical qualities of Shapley Values -which has driven their adoption among the ML community -we argue that NLP practitioners should, when possible, adopt attention flow explanations alongside more traditional ones.
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation

We study the task of long-form opinion text generation, which faces at least two distinct challenges.First, existing neural generation models fall short of coherence, thus requiring efficient content planning.Second, diverse types of information are needed to guide the generator to cover both subjective and objective content.To this end, we propose DY-PLOC, a generation framework that conducts dynamic planning of content while generating the output based on a novel design of mixed language models.To enrich the generation with diverse content, we further propose to use large pre-trained models to predict relevant concepts and to generate claims.We experiment with two challenging tasks on newly collected datasets: (1) argument generation with Reddit ChangeMyView, and (2) writing articles using New York Times' Opinion section.Automatic evaluation shows that our model significantly outperforms competitive comparisons.Human judges further confirm that our generations are more coherent with richer content.
Hierarchical Macro Discourse Parsing Based on Topic Segmentation

Hierarchically constructing micro (i.e., intra-sentence or inter-sentence) discourse structure trees using explicit boundaries (e.g., sentence and paragraph boundaries) has been proved to be an effective strategy. However, it is difficult to apply this strategy to document-level macro (i.e., inter-paragraph) discourse parsing, the more challenging task, due to the lack of explicit boundaries at the higher level. To alleviate this issue, we introduce a topic segmentation mechanism to detect implicit topic boundaries and then help the document-level macro discourse parser to construct better discourse trees hierarchically. In particular, our parser first splits a document into several sections using the topic boundaries that the topic segmentation detects. Then it builds a smaller and more accurate discourse sub-tree in each section and sequentially forms a whole tree for a document. The experimental results on both Chinese MCDTB and English RST-DT show that our proposed method outperforms the state-of-the-art baselines significantly.
Comparative Study and Design Light Weight Data Security System for Secure Data Transmission in Internet of Things

Internet of things is shortened as IoT. Today IoT is a key and abrogating subject of the specialized and social importance. Results of buyers, things and vehicles, industry based and fundamental segments, sensors, and other everyday items are converged with network of internet and the solid information abilities which guarantee to change the sort in which we work and live. The proposed work demonstrates the implementation of symmetric key lightweight algorithm for secured data transmission of images and text using image encryption system as well as reversible data hiding system. In this paper, implemented symmetric key cryptography for various formats of images, as well as real time image acquisition system has been designed in the form of graphical user interface. Reversible data hiding system has also been designed for secure data transmission system.
Conditional Generation of Temporally-ordered Event Sequences

Models of narrative schema knowledge have proven useful for a range of event-related tasks, but they typically do not capture the temporal relationships between events.We propose a single model that addresses both temporal ordering, sorting given events into the order they occurred, and event infilling, predicting new events which fit into an existing temporally-ordered sequence.We use a BARTbased conditional generation model that can capture both temporality and common event co-occurrence, meaning it can be flexibly applied to different tasks in this space.Our model is trained as a denoising autoencoder: we take temporally-ordered event sequences, shuffle them, delete some events, and then attempt to recover the original event sequence.This task teaches the model to make inferences given incomplete knowledge about the events in an underlying scenario.On the temporal ordering task, we show that our model is able to unscramble event sequences from existing datasets without access to explicitly labeled temporal training data, outperforming both a BERT-based pairwise model and a BERT-based pointer network.On event infilling, human evaluation shows that our model is able to generate events that fit better temporally into the input events when compared to GPT-2 story completion models.
Improving Dialog Systems for Negotiation with Personality Modeling

In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent's high-level strategy in negotiation tasks.Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent's personality type during both learning and inference.We test our approach on the CRAIGSLISTBAR-GAIN dataset (He et al., 2018) and show that our method using ToM inference achieves a 20% higher dialog agreement rate compared to baselines on a mixed population of opponents.We also find that our model displays diverse negotiation behavior with different types of opponents.
You Don't Know How I Feel: Insider-Outsider Perspective Gaps in Cyberbullying Risk Detection

Cyberbullying is a prevalent concern within social computing research that has led to the development of several supervised machine learning (ML) algorithms for automated risk detection. A critical aspect of ML algorithm development is how to establish ground truth that is representative of the phenomenon of interest in the real world. Often, ground truth is determined by third-party annotators (i.e., “outsiders”) who are removed from the situational context of the interaction; therefore, they cannot fully understand the perspective of the individuals involved (i.e., “insiders”). To understand the extent of this problem, we compare “outsider” versus “insider” perspectives when annotating 2,000 posts from an online peer-support platform. We interpolate this analysis to a corpus containing over 2.3 million posts on bullying and related topics, and reveal significant gaps in ML models that use third-party annotators to detect bullying incidents. Our results indicate that models based on the insiders’ perspectives yield a significantly higher recall in identifying bullying posts and are able to capture a range of explicit and implicit references and linguistic framings, including person-specific impressions of the incidents. Our study highlights the importance of incorporating the victim’s point of view in establishing effective tools for cyberbullying risk detection. As such, we advocate for the adoption of human-centered and value-sensitive approaches for algorithm development that bridge insider-outsider perspective gaps in a way that empowers the most vulnerable.
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer

Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer.These techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence.In this paper, we propose a counterfactual-based method to modify the latent representation, by posing a 'what-if' scenario.This simple and disciplined approach also enables a fine-grained control on the transfer strength.We conduct experiments with the proposed methodology on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support our hypothesis.
Rationalization through Concepts

Automated predictions require explanations to be interpretable by humans.One type of explanation is a rationale, i.e., a selection of input features such as relevant text snippets from which the model computes the outcome.However, a single overall selection does not provide a complete explanation, e.g., weighing several aspects for decisions.To this end, we present a novel self-interpretable model called ConRAT.Inspired by how human explanations for high-level decisions are often based on key concepts, ConRAT extracts a set of text snippets as concepts and infers which ones are described in the document.Then, it explains the outcome with a linear aggregation of concepts.Two regularizers drive ConRAT to build interpretable concepts.In addition, we propose two techniques to boost the rationale and predictive performance further.Experiments on both single-and multi-aspect sentiment classification tasks show that ConRAT is the first to generate concepts that align with human rationalization while using only the overall label.Further, it outperforms state-of-the-art methods trained on each aspect label independently.
Diversifying Dialog Generation via Adaptive Label Smoothing

Neural dialogue generation models trained with the one-hot target distribution suffer from the over-confidence issue, which leads to poor generation diversity as widely reported in the literature.Although existing approaches such as label smoothing can alleviate this issue, they fail to adapt to diverse dialog contexts.In this paper, we propose an Adaptive Label Smoothing (AdaLabel) approach that can adaptively estimate a target label distribution at each time step for different contexts.The maximum probability in the predicted distribution is used to modify the soft target distribution produced by a novel light-weight bi-directional decoder module.The resulting target distribution is aware of both previous and future contexts and is adjusted to avoid over-training the dialogue model.Our model can be trained in an endto-end manner.Extensive experiments on two benchmark datasets show that our approach outperforms various competitive baselines in producing diverse responses.
Topic Modeling Based Extractive Text Summarization

Text summarization is an approach for identifying important information present within text documents. This computational technique aims to generate shorter versions of the source text, by including only the relevant and salient information present within the source text. In this paper, we propose a novel method to summarize a text document by clustering its contents based on latent topics produced using topic modeling techniques and by generating extractive summaries for each of the identified text clusters. All extractive sub-summaries are later combined to generate a summary for any given source document. We utilize the lesser used and challenging WikiHow dataset in our approach to text summarization. This dataset is unlike the commonly used news datasets which are available for text summarization. The well-known news datasets present their most important information in the first few lines of their source texts, which make their summarization a lesser challenging task when compared to summarizing the WikiHow dataset. Contrary to these news datasets, the documents in the WikiHow dataset are written using a generalized approach and have lesser abstractedness and higher compression ratio, thus proposing a greater challenge to generate summaries. A lot of the current state-of-the-art text summarization techniques tend to eliminate important information present in source documents in the favor of brevity. Our proposed technique aims to capture all the varied information present in source documents. Although the dataset proved challenging, after performing extensive tests within our experimental setup, we have discovered that our model produces encouraging ROUGE results and summaries when compared to the other published extractive and abstractive text summarization models.
An efficient and effective deep convolutional kernel pseudoinverse learner with multi-filter

The convolutional neural network is the most widely used deep neural network. However, it still has some disadvantages. First, the back-propagation method is usually used in the training of convolutional neural networks, but it has several inherent defects, such as the vanishing gradient problem and exploding gradient problem. Moreover, the training of a convolutional neural network often needs substantial computational resources and time. To solve the above problems, based on pseudoinverse learning (PIL), kernel pseudoinverse learning (KPIL) is proposed, showing improved performance. The number of hidden layer neurons of KPIL is equal to the number of input data and does not need to be set. KPIL uses the kernel method to calculate the output of hidden layer, avoiding the uncertainty of random input weights. Based on KPIL, a deep convolutional kernel pseudoinverse learner with a multi-filter design is proposed, named kernel pseudoinverse learning convolutional neural network (KPIL-CNN), which uses multiple fixed convolutional kernels: kernel pseudoinverse learning filters (KPIL filter), Gabor filters, and random filters. The features obtained by this multi-filter approach are combined into feature maps by learning weights. The experimental results show that the proposed KPIL-CNN performs better than other network models with the same scale in the task of natural image classification. The training of KPIL-CNN is efficient and effective. Furthermore, KPIL-CNN outperforms the existing methods in the classification of the ISIC 2017 skin lesion dataset.