Reviewerly/exHarmony huggingface dataset:
A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer’s Type

In recent years there has been a burgeoning interest in the use of computational methods to distinguish between elicited speech samples produced by patients with dementia, and those from healthy controls.The difference between perplexity estimates from two neural language models (LMs) -one trained on transcripts of speech produced by healthy participants and the other trained on transcripts from patients with dementia -as a single feature for diagnostic classification of unseen transcripts has been shown to produce state-of-the-art performance.However, little is known about why this approach is effective, and on account of the lack of case/control matching in the most widely-used evaluation set of transcripts (De-mentiaBank), it is unclear if these approaches are truly diagnostic, or are sensitive to other variables.In this paper, we interrogate neural LMs trained on participants with and without dementia using synthetic narratives previously developed to simulate progressive semantic dementia by manipulating lexical frequency.We find that perplexity of neural LMs is strongly and differentially associated with lexical frequency, and that a mixture model resulting from interpolating control and dementia LMs improves upon the current state-of-the-art for models trained on transcript text exclusively.
Demystifying Self-Supervised Learning: An Information-Theoretical Framework.

Self-supervised representation learning adopts self-defined signals as supervision and uses the learned representation for downstream tasks, such as masked language modeling (e.g., BERT) for natural language processing and contrastive visual representation learning (e.g., SimCLR) for computer vision applications. In this paper, we present a theoretical framework explaining that self-supervised learning is likely to work under the assumption that only the shared information (e.g., contextual information or content) between the input (e.g., non-masked words or original images) and self-supervised signals (e.g., masked-words or augmented images) contributes to downstream tasks. Under this assumption, we demonstrate that self-supervisedly learned representation can extract task-relevant and discard task-irrelevant information. We further connect our theoretical analysis to popular contrastive and predictive (self-supervised) learning objectives. In the experimental section, we provide controlled experiments on two popular tasks: 1) visual representation learning with various self-supervised learning objectives to empirically support our analysis; and 2) visual-textual representation learning to challenge that input and self-supervised signal lie in different modalities.
A Recent Survey of Arabic Named Entity Recognition on Social Media

In the last few years, significant amounts of text data have emerged on the different social media platforms.A tendency to extract valuable information from these data for useful purposes has been created and developed.The Named Entity Recognition (NER), as a subtask of the Natural Language Processing (NLP), remains primordial in order to perform these extractions and the classification of entity names from the text regardless of its structure "formal or informal".Nevertheless, the most recent solutions for NER are confronted with the difficulty of adapting to the informal texts used on social media platforms.This work aims at providing a literature review of the various papers published in the field of NER on social media starting from 2014 until now, by identifying the particular characteristics surrounding the Arabic dialect compared to the English language.
Efficient Strategies for Hierarchical Text Classification: External Knowledge and Auxiliary Tasks

In hierarchical text classification, we perform a sequence of inference steps to predict the category of a document from top to bottom of a given class taxonomy.Most of the studies have focused on developing novels neural network architectures to deal with the hierarchical structure, but we prefer to look for efficient ways to strengthen a baseline model.We first define the task as a sequence-to-sequence problem.Afterwards, we propose an auxiliary synthetic task of bottom-up-classification.Then, from external dictionaries, we retrieve textual definitions for the classes of all the hierarchy's layers, and map them into the word vector space.We use the class-definition embeddings as an additional input to condition the prediction of the next layer and in an adapted beam search.Whereas the modified search did not provide large gains, the combination of the auxiliary task and the additional input of classdefinitions significantly enhance the classification accuracy.With our efficient approaches, we outperform previous studies, using a drastically reduced number of parameters, in two well-known English datasets.
A Corpus for Large-Scale Phonetic Typology

A major hurdle in data-driven research on typology is having sufficient data in many languages to draw meaningful conclusions.We present VoxClamantis V1.0, the first largescale corpus for phonetic typology, with aligned segments and estimated phonemelevel labels in 690 readings spanning 635 languages, along with acoustic-phonetic measures of vowels and sibilants.Access to such data can greatly facilitate investigation of phonetic typology at a large scale and across many languages.However, it is nontrivial and computationally intensive to obtain such alignments for hundreds of languages, many of which have few to no resources presently available.We describe the methodology to create our corpus, discuss caveats with current methods and their impact on the utility of this data, and illustrate possible research directions through a series of case studies on the 48 highest-quality readings.Our corpus and scripts are publicly available for non-commercial use at https:// voxclamantisproject.github.io.
Revisiting the Notion of Extension over Incomplete Abstract Argumentation Frameworks

We revisit the notion of i-extension, i.e., the adaption of the fundamental notion of extension to the case of incomplete Abstract Argumentation Frameworks. We show that the definition of i-extension raises some concerns in the "possible" variant, e.g., it allows even conflicting arguments to be collectively considered as members of an (i-)extension. Thus, we introduce the alternative notion of i*-extension overcoming the highlighted problems, and provide a thorough complexity characterization of the corresponding verification problem. Interestingly, we show that the revisitation not only has beneficial effects for the semantics, but also for the complexity: under various semantics, the verification problem under the possible perspective moves from NP-complete to P.
Automatic Detection of Masses From Mammographic Images via Artificial Intelligence Techniques

A novel computer-aided tool for automated sensing of normal tissue and abnormal masses from mammographic X-ray images is described. The pre-processing technique was firstly adopted for noise elimination on mammographic images. The automatic initialization of active contour was then placed on the pre-processed image for segmentation followed by deep convolutional neural networks to extract the features. Principal component analysis was then applied to choose the most significant features as input to the support vector machine classifier. Lastly, k-fold cross-validation techniques were executed for results validation. The developed tool was tested on public available datasets, namely Mammographic Image Analysis Society, and Digital Database for Screening Mammogram, based on eight evaluation methods: accuracy, sensitivity, specificity, receiver operating characteristic curve, area under curve (AUC), F1-score, precision, and recall. The outcome demonstrated the proposed system as a competitive tool in assisting radiologists as it attains an average of 95.24%, 93.94%, 96.61%, 94.66, 93.00%, 94.34%, and 0.98 for accuracy, sensitivity, specificity, precision, recall, F1-score, and AUC, respectively for testing on a combination of the aforementioned two datasets.
A Penny for Your Thoughts: The Value of Communication in Ad Hoc Teamwork

In ad hoc teamwork, multiple agents need to collaborate without having knowledge about their teammates or their plans a priori. A common assumption in this research area is that the agents cannot communicate. However, just as two random people may speak the same language, autonomous teammates may also happen to share a communication protocol. This paper considers how such a shared protocol can be leveraged, introducing a means to reason about Communication in Ad Hoc Teamwork (CAT). The goal of this work is enabling improved ad hoc teamwork by judiciously leveraging the ability of the team to communicate. We situate our study within a novel CAT scenario, involving tasks with multiple steps, where teammates' plans are unveiled over time. In this context, the paper proposes methods to reason about the timing and value of communication and introduces an algorithm for an ad hoc agent to leverage these methods. Finally, we introduces a new multiagent domain, the tool fetching domain, and we study how varying this domain's properties affects the usefulness of communication. Empirical results show the benefits of explicit reasoning about communication content and timing in ad hoc teamwork.
Enhanced encryption technique for secure iot data transmission

&lt;div class="page" title="Page 1"&gt;&lt;div class="section"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;&lt;span&gt;Internet of things is the latest booming innovation in the current period, which lets the physical entity to process and intervene with the virtual entities. As all the entities are connected with each other, it generates load of data, which lacks proper security and privacy standards. Cryptography is one of the domains of Network Security, which is one such mechanism that helps the data transmission process to be secure enough over the wireless or wired channel and along with that, it provides authenticity, confidentiality, integrity of data and prevents repudiation. In this paper, we have proposed an alternate enhanced cryptographic solution combing the characteristic of symmetric, asymmetric encryption algorithms and Public Key Server. Here, the key pairs of end points (User’s Device and IoT device) are generated using Elliptic Curve Cryptography and the respective public keys are registered in Public Key Server along with their unique MAC address. Thereafter, both the ends will agree on one common private secret key, which will be the base for further cryptographic process using AES algorithm. This model can be called as multi-phase protection mechanism. It will make the process of data transmission secure enough that no intermediate can tamper the data.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
Learning Interpretable Models in the Property Specification Language

We address the problem of learning human-interpretable descriptions of a complex system from a finite set of positive and negative examples of its behavior. In contrast to most of the recent work in this area, which focuses on descriptions expressed in Linear Temporal Logic (LTL), we develop a learning algorithm for formulas in the IEEE standard temporal logic PSL (Property Specification Language). Our work is motivated by the fact that many natural properties, such as an event happening at every n-th point in time, cannot be expressed in LTL, whereas it is easy to express such properties in PSL. Moreover, formulas in PSL can be more succinct and easier to interpret (due to the use of regular expressions in PSL formulas) than formulas in LTL. The learning algorithm we designed, builds on top of an existing algorithm for learning LTL formulas. Roughly speaking, our algorithm reduces the learning task to a constraint satisfaction problem in propositional logic and then uses a SAT solver to search for a solution in an incremental fashion. We have implemented our algorithm and performed a comparative study between the proposed method and the existing LTL learning algorithm. Our results illustrate the effectiveness of the proposed approach to provide succinct human-interpretable descriptions from examples.
TEACHING VOCABULARY TO YOUNG LEARNER USING VIDEO ON YOUTUBE AT ENGLISH COURSE

Vocabulary is one of the important elements of the English language. There are many media which can be used in teaching vocabulary to young learners. In this research, the researcher used video on YouTube to teach vocabulary. The research focuses on how students’ engagement during the implementation of video on YouTube in teaching vocabulary at an English course. In this research, the researcher used descriptive–qualitative research, and observation and documentation were used for collecting the data. This research was conducted at an English course in Bogor, in which the participants are seven young learners at grade two. The findings of this research were classified into two themes; students’ response to the classroom activities and the teacher instruction, and student’s engagement to the use of video on YouTube. It is revealed that most young learners at an English course were engaged during the implementation of the video on YouTube in teaching vocabulary. They also gave positive responses to the learning activity and preferred learning English vocabulary by using video on YouTube.
Emotional Expression of #body on Instagram

Our aim was to explore emotions in Instagram images marked with hashtags referring to body image–related components using an artificial intelligence–based discrete emotional analysis. A total of 500 Instagram photos marked by specific hashtags related to body image components were analyzed and specific discrete emotions expressed in each picture were detected using the Emotion application program interface API from Microsoft Azure Cognitive Service. Results showed that happiness and neutrality were the most intense and recognizable emotions expressed in all images. Happiness intensity was significantly higher in images with #bodyimage and #bodyconfidence and higher levels of neutral emotion were found in images tagged with #body, #bodyfitness, and #thininspirational. This study integrated a discrete emotional model with the conventional dimensional one, and offered a higher degree of granularity in the analysis of emotions–body link on Instagram through an artificial intelligence technology. Future research should deepen the use of discrete emotions on Instagram and the role of neutrality in body image representation.
Natural Scene Text Detection and Segmentation Using Phase-Based Regions and Character Retrieval

Multioriented text detection and recognition in natural scene images are still challenges in the document analysis and computer vision communities. In particular, character segmentation plays an important role in the complete end-to-end recognition system performance. In this work, a robust multioriented text detection and segmentation method based on a biological visual system model is proposed. The proposed method exploits the local energy model instead of a common approach based on variations of local image pixel intensities. Features such as lines and edges are obtained by searching for the maximum local energy utilizing the scale-space monogenic signal framework. The candidate text components are extracted from maximally stable extremal regions of the local phase information of the image. The candidate regions are filtered by their phase congruency and classified as text and nontext components by the AdaBoost classifier. Finally, misclassified characters are restored, and all final characters are grouped into words. Experimental results show that the proposed text detection and segmentation method is invariant to scale and rotation changes and robust to perspective distortions, blurring, low resolution, and illumination variations (low contrast, high brightness, shadows, and nonuniform illumination). Besides, the proposed method achieves often a better performance compared with state-of-the-art methods on typical natural scene datasets.
The COVID cyborg: Protecting data status

This article examines the increasing tendency towards governance of people through their representation via data. In its most contemporary iteration, the COVID-19 pandemic has seen the release of contact tracing apps – in Australia, COVIDSafe. While public discourse about the apps has focused principally on the important issue of data privacy, there are other possible effects whereby participation in such schemes might become a prerequisite to accessing services or basic rights – either from government or from corporations. The pathway to acceptability of applying our data in this way is already paved, through fitness monitors and other technologies by which we represent ourselves. This article sets out the foundation of such technologies and their application, before outlining their effect on the recognised boundaries of governance and the conception of the holder of rights and the substance of those rights.
How to Document Computer Networks

Documenting networks is an essential tool for troubleshooting network problems.The documentation details a network's structure and context, serves as a reference and makes network management more effective.Complex network diagrams are hard to document and maintain and are not guaranteed to reflect reality.They contain many superficial icons (e.g., wall, screen and tower).Defining a single coherent network architecture and topology, similar to engineering schematics, has received great interest.We propose a fundamental approach for methodically specifying a network architecture using a diagramming method to conceptualize the network's structure.The method is called a thinging (abstract) machine, through which the network world is viewed as a single unifying element called the thing/machine (thimac), providing the ontology for modeling the network.To test its viability, the thinging-machine-based methodology was applied to an existing computer network to produce a single integrated, diagrammatic representation that incorporates communication, software and hardware.The resultant description shows a viable, coherent depiction that can replace the current methods.
Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections

Adversarial interactions against politicians on social media such as Twitter have significant impact on society. In particular they disrupt substantive political discussions online, and may discourage people from seeking public office. In this study, we measure the adversarial interactions against candidates for the US House of Representatives during the run-up to the 2018 US general election. We gather a new dataset consisting of 1.7 million tweets involving candidates, one of the largest corpora focusing on political discourse. We then develop a new technique for detecting tweets with toxic content that are directed at any specific candidate. Such technique allows us to more accurately quantify adversarial interactions towards political candidates. Further, we introduce an algorithm to induce candidate-specific adversarial terms to capture more nuanced adversarial interactions that previous techniques may not consider toxic. Finally, we use these techniques to outline the breadth of adversarial interactions seen in the election, including offensive name-calling, threats of violence, posting discrediting information, attacks on identity, and adversarial message repetition.
Curves with Fast Computations in the First Pairing Group

Pairings are a powerful tool to build advanced cryptographic schemes. The most efficient way to instantiate a pairing scheme is through Pairing-Friendly Elliptic Curves. Because a randomly picked elliptic curve will not support an efficient pairing (the embedding degree will usually be too large to make any computation practical), a pairing-friendly curve has to be carefully constructed. This has led to famous curves, e.g. Barreto-Naehrig curves. However, the computation of the Discrete Logarithm Problem on the finite-field side has received much interest and its complexity has recently decreased. Hence the need to propose new curves has emerged. In this work, we give one new curve that is specifically tailored to be fast over the first pairing-group, which is well suited for several cryptographic schemes, such as group signatures, and their variants, or accumulators.
Probabilistic Robust Multi-Agent Path Finding

In a multi-agent path finding (MAPF) problem, the task is to move a set of agents to their goal locations without conflicts. In the real world, unexpected events may delay some of the agents. In this paper, we therefore study the problem of finding a p-robust solution to a given MAPF problem, which is a solution that succeeds with probability at least p, even though unexpected delays may occur. We propose two methods for verifying that given solutions are p-robust. We also introduce an optimal CBS-based algorithm, called pR-CBS, and a fast suboptimal algorithm, called pR-GCBS, for finding such solutions. Our experiments show that a p-robust solution reduces the number of conflicts compared to optimal, non-robust solutions.
Design of a self-organizing reciprocal modular neural network for nonlinear system modeling

Aiming to improve the model’s generalization performance for nonlinear system modeling, a self-organizing reciprocal modular neural network (SORMNN) is proposed in the present study, which imitates the modular structure with inter-module connections observed in human brains. The inter-module connections in SORMNN are built by inputting the output of each subnetwork to other subnetworks. All subnetworks work in parallel to process the allocated features, and the structure of each subnetwork is designed to be self-organized by using a growing and pruning algorithm based on the contribution of hidden neurons. An improved Levenberg-Marquardt (LM) algorithm using a sliding window is conducted to update the parameters of SORMNN, which makes SORMNN available for solving online problems. To validate the effectiveness of the proposed model, SORMNN is tested on chaotic benchmark time series prediction, four UCI benchmark problems and a practical problem for biochemical oxygen demand prediction in wastewater treatment process. Experimental results demonstrate that SORMNN exhibits both a higher training accuracy and a better generalization ability for nonlinear system modeling than other modular neural networks, and the inter-module connections have a positive effect on the superior performance of the proposed model and can make the network structure compact.
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding

We present MT-DNN 1 , an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models.Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM).A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm.To enable efficient production deployment, MT-DNN supports multitask knowledge distillation, which can substantially compress a deep neural model without significant performance drop.We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains.The software and pretrained models will be publicly available at https://github.com/namisan/mt-dnn. * Equal Contribution. 1 The complete name of our toolkit is M T 2 -DNN (The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding), but we use MT-DNN for sake of simplicity.