Reviewerly/exHarmony huggingface dataset:
Radar signals recognition based on attention and denoising residual network

To solve the problem that complex radar emitter signals are difficult to identify under low signal-to-noise ratio, this paper proposes a novel radar signal recognition method based on an improved deep residual network. In this method, two IQ signals are used as the input of the method, which saves time for generating time-frequency images, and then the signal features are extracted through an improved deep residual network. A nonlinear transform layer is inserted into the network to automatically confirm the threshold value, and then the soft threshold method is used to denoise. The importance of features is weighted by attention unit, and then classified by softmax classifier. The experiments based on five kinds of radar signal datasets show higher accuracy at low signal-to-noise ratio compared with other methods. The experiments also verified its overall accuracy can still exceed 90% even at extremely low signal-to-noise ratio of -16dB.
An Empirical Analysis of Inferences From Commit, Fork, and Branch Rates of Top GitHub Projects

GitHub repositories have been used to understand some important parameters related to quantitative growth of software projects. The rate at which fork, commits and branches have been done in different repositories which are supposed to be the topmost ones when it comes to the number of contributors is the main point of study in this paper. The desirable goal is to find out some sort of similarity in the trends of all these software projects. Similar trends tend to lead to interesting inferences regarding the quantitative growth and even the quality of these software projects. Is the rate of forking, branching, committing anything to do with the success of software projects? The authors have taken up this question in the current study.
hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for Tamil TrollMeme Classification

Social media platforms often act as breeding grounds for various forms of trolling or malicious content targeting users or communities.One way of trolling users is by creating memes, which in most cases unites an image with a short piece of text embedded on top of it.The situation is more complex for multilingual(e.g., Tamil) memes due to the lack of benchmark datasets and models.We explore several models to detect Troll memes in Tamil based on the shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022.We observe while the text-based model MURIL performs better for Non-troll meme classification, the image-based model VGG16 performs better for Troll-meme classification.Further fusing these two modalities help us achieve stable outcomes in both classes.Our fusion model achieved a 0.561 weighted average F1 score and ranked second in this task.
Towards Generalizable Methods for Automating Risk Score Calculation

Clinical risk scores enable clinicians to tabulate a set of patient data into simple scores to stratify patients into risk categories.Although risk scores are widely used to inform decisionmaking at the point-of-care, collecting the information necessary to calculate such scores requires considerable time and effort.Previous studies have focused on specific risk scores and involved manual curation of relevant terms or codes and heuristics for each data element of a risk score.To support more generalizable methods for risk score calculation, we annotate 100 patients in MIMIC-III with elements of CHA 2 DS 2 -VASc and PERC scores, and explore using question answering (QA) and off-the-shelf tools.We show that QA models can achieve comparable or better performance for certain risk score elements as compared to heuristic-based methods, and demonstrate the potential for more scalable risk score automation without the need for expert-curated heuristics.Our annotated dataset will be released to the community to encourage efforts in generalizable methods for automating risk scores.
Logical Reasoning for Task Oriented Dialogue Systems

In recent years, large pretrained models have been used in dialogue systems to improve successful task completion rates. However, lack of reasoning capabilities of dialogue platforms make it difficult to provide relevant and fluent responses, unless the designers of a conversational experience spend a considerable amount of time implementing these capabilities in external rule based modules. In this work, we propose a novel method to fine-tune pretrained transformer models such as Roberta and T5, to reason over a set of facts in a given dialogue context.Our method includes a synthetic data generation mechanism which helps the model learn logical relations, such as comparison between list of numerical values, inverse relations (and negation), inclusion and exclusion for categorical attributes, and application of a combination of attributes over both numerical and categorical values, and spoken form for numerical values, without need for additional training data. We show that the transformer based model can perform logical reasoning to answer questions when the dialogue context contains all the required information, otherwise it is able to extract appropriate constraints to pass to downstream components (e.g. a knowledge base) when partial information is available. We observe that transformer based models such as UnifiedQA-T5 can be fine-tuned to perform logical reasoning (such as numerical and categorical attributes’ comparison) over attributes seen at training time (e.g., accuracy of 90%+ for comparison of smaller than kmax=5 values over heldout test dataset).
Semeful Sociabilities: Socially Networked Photography as Embodied Relationships of Touch

Abstract In this chapter, I define cultural semes in response to academic discussions of technological seams. Discussions of technological seams are divided by aspirations of seamlessness and seamfulness. Seamlessness aims towards technology being seamlessly integrated into our everyday relationships. Arguments of seam fulness are driven by a focus on user empowerment and how seamlessness acts as a form of social control. In this context, seme fulness focuses explicitly on the significance and meanings of Touch in human–technology physical relationships. Identifying social media and the technological self as embodied and drawing on the findings throughout this book, I propose the term “ seme ful sociabilities” to describe networked social media practices of the technological self, recognising visual social relationships as culturally meaningful practices of Touch.
Method for Evaluating Information Security Level in Organisations

Abstract This paper introduces a method for evaluating information security levels of organisations using a developed framework. The framework is based on Estonian Information Security Standard categories which is compatible with ISO 27001 standard. The framework covers both technical and organisational aspects of information security. The results provide an overview of security to the organisation’s management, compare different organisations across the region, and support strategic decision-making on a national level.
Triangular Transfer: Freezing the Pivot for Triangular Machine Translation

Triangular machine translation is a special case of low-resource machine translation where the language pair of interest has limited parallel data, but both languages have abundant parallel data with a pivot language.Naturally, the key to triangular machine translation is the successful exploitation of such auxiliary data.In this work, we propose a transfer-learningbased approach that utilizes all types of auxiliary data.As we train auxiliary source-pivot and pivot-target translation models, we initialize some parameters of the pivot side with a pre-trained language model and freeze them to encourage both translation models to work in the same pivot language space, so that they can be smoothly transferred to the source-target translation model.Experiments show that our approach can outperform previous ones.
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization

While the prompt-based fine-tuning methods had advanced few-shot natural language understanding tasks, self-training methods are also being explored.This work revisits the consistency regularization in self-training and presents explicit and implicit consistency regularization enhanced language model (EICO).By employing both explicit and implicit consistency regularization, EICO advances the performance of prompt-based few-shot text classification.For implicit consistency regularization, we generate pseudo-label from the weaklyaugmented view and predict pseudo-label from the strongly-augmented view.For explicit consistency regularization, we minimize the difference between the prediction of the augmentation view and the prediction of the original view.We conducted extensive experiments on six text classification datasets and found that with sixteen labeled examples, EICO achieves competitive performance compared to existing self-training few-shot learning methods.
Evaluating the Security Posture and Protection of Critical Assets of Industrial Control Systems in Zambia

The number of successful attacks on vital infrastructure has increased, as has the sophistication of the attacks. Many cybersecurity strategies include traditional best practices, but they frequently overlook organizational circumstances and unique critical infrastructure protection requirements. The goal of this qualitative multiple case research was to look into the cybersecurity tactics employed by IT managers and compliance officers to protect critical infrastructure from cyber threats. The participants in this study were IT managers and compliance officials from four Zambian case organizations. The conceptual framework was based on the routine activity theory published by criminologists Cohen and Felson in 1979. Interviews with two IT managers, three compliance officers, and 25 papers relating to cybersecurity and policy governance were used to gather data. Four significant themes emerged from data triangulation: the need for a robust worker training program, prioritizing infrastructure resiliency, the importance of security awareness, and the importance of organizational leadership support and investment. This research uncovered essential tactics that can help OT and compliance professionals enhance their cybersecurity strategy, which can help reduce successful assaults on critical infrastructure. The study findings will contribute to positive social change through an exploration and contextual analysis of cybersecurity strategy with situational awareness of OT practices to enhance cyber threat mitigation and inform business processes.
Characters Segmentation from Arabic Handwritten Document Images: Hybrid Approach

Character segmentation in Unconstrained Arabic handwriting is a complex and challenging task due to the overlapping and touching of words or letters.Such issues have not been widely investigated in the literature.Addressing these issues in the segmentation stage reduces errors in the segmentation process, which plays a significant role in enhancing the accuracy of the Arabic optical character recognition.Therefore, this paper proposes a hybrid approach to improve the accuracy for interconnection, overlapping or touching character segmentation.The proposed method includes several stages: removing extra shapes such as signatures from the document.Using morphological operations, connected components and bounding box detection, detect and extract individual words directly from the document.Finally, the touching characters segmentation is achieved based on background thinning and computational analysis of the word's region.The proposed method has been tested on KHATT, IFN/ENIT database and our own collected dataset.The experimental results showed that the proposed method obtained high performance and improved the accuracy compared to other methods.
A Comparative Performance of Optimizers and Tuning of Neural Networks for Spoof Detection Framework

The breakthroughs in securing speaker verification systems have been challenging and yet are explored by many researchers over the past five years.The compromise in security of these systems is due to naturally sounding synthetic speech and handiness of the recording devices.For developing a spoof detection system, the back-end classifier plays an integral role in differentiating spoofed speech from genuine speech.This work conducts the experimental analysis and comparison of up-to-date optimization techniques for a modified form of Convolutional Neural Network (CNN) architecture which is Light CNN (LCNN).The network is standardized by exploring various optimizers such as Adaptive moment estimation, and other adaptive algorithms, Root Mean Square propagation and Stochastic Gradient Descent (SGD) algorithms for spoof detection task.Furthermore, the activation functions and learning rates are also tested to investigate the hyperparameter configuration for faster convergence and improving the training accuracy.The counter measure systems are trained and validated on ASV spoof 2019 dataset with Logical (LA) and Physical Access (PA) attack data.The experimental results show optimizers perform better for LA attack in contrast to PA attack.Additionally, the lowest Equal Error Rate (EER) of 9.07 is obtained for softmax activation with SGD with momentum wrt LA attack and 9.951 for SGD with nestrov wrt PA attack.
Investigating Educational and Noneducational Answer Selection for Educational Question Generation

Educational automatic question generation (AQG) is often unable to realize its full potential in educational applications due to insufficient training data. For this reason, current research relies on noneducational question answering datasets for system training and evaluation. However, noneducational training data may comprise different language patterns than educational data. Consequently, the research question of whether models trained on noneducational datasets transfer well to the educational AQG task arises. In this work, we investigate the AQG subtask of answer selection, which aims to extract meaningful answers for the questions to be generated. We train and evaluate six modern and well-established BERT-based machine learning model architectures on two widely used noneducational datasets. Furthermore, we introduce a novel, midsized educational dataset for answer selection called TQA-A, which is used to investigate the transfer capabilities of the noneducational models to the educational domain. In terms of phrase-level evaluation metrics, noneducational models perform similar to models trained directly on the novel educational TQA-A dataset, although trained with considerably more training data. Moreover, models trained directly on TQA-A select fewer named entity-based and more verb-based answers than noneducational models. This provides evidence for differences in noneducational and educational answer selection tasks.
Leveraging Relaxed Equilibrium by Lazy Transition for Sequence Modeling

In sequence modeling, certain tokens are usually less ambiguous than others, and representations of these tokens require fewer refinements for disambiguation.However, given the nature of attention-based models like Transformer and UT (universal transformer), all tokens are equally processed towards depth.Inspired by the equilibrium phenomenon, we present a lazy transition, a mechanism to adjust the significance of iterative refinements for each token representation.Our lazy transition is deployed on top of UT to build LT (lazy transformer), where all tokens are processed unequally towards depth.Eventually, LT is encouraged to oscillate around a relaxed equilibrium.Our experiments show that LT outperforms baseline models on several tasks of machine translation, pre-training, Learning to Execute, and LAMBADA.
Intelligent Mechatronic Systems Applications in Engineering

This paper presents some experimental applications using intelligent systems such as sensors, controller, robotic systems, and crane systems. A Mobile robot design, path planning, and applications in real-time is proposed for blind people. A proposed A* algorithm is used for path planning of the mobile robot. Moreover, an image processing structure predicts the path without touching obstacles. An industrial robot is used with image processing to classify universal materials. On the other hand, a new walking performance six-legged mobile robot control is proposed to analyze the stability and walking conditions of the robot by using an open dynamic engine solution. Furthermore, a designed and controlled crane system is proposed for vibration and amplitude analysis of the double bridge crane system. On the system, a prosed neural predictor is used to analyze performance. Analysis and control of the welding position of an industrial robot are also utilized to compare with operator-based welding process structure. The results improved that all the proposed analyze has superior performance in real-time applications for all approaches.
Promoting a Pedagogical Shift from Didactic Teacher-Centered to Participatory Student-Centered Learning by Harnessing the Portability and Versatility of Mobile Technology

Mobile learning promotes a pedagogical shift from didactic teacher-centered to participatory student-centered learning.The aim of this study was to examine how physical settings and human behaviour within an environment affect user productivity in a designed environment.A case study that emphasized social interactions and comprised a design interaction module was developed.Students evaluated a designed place using mobile technology alongside traditional teaching resources through lectures and design studio sessions.The research methodology comprised a case study that emphasized social interaction using five intended learning outcomes.The case was assessed using tools that measured its congruency with overall as well as specific learning outcomes.The design interaction module was developed and implemented through mobile and face-to-face deliveries.Over a one-week block, five mobile lectures, the minimum required for impacting students' learning experience, were delivered.Mobile learning was the most suitable approach for teaching the design interaction module.By combining the experiential and instructional learning modes that provided students with more opportunities for understanding the impact of design on the users of an environment, the goal of encouraging students to develop a critical understanding of industrial design was achieved.
Review of Research on Markowitz Model in Portfolios

In modern portfolio theory, the Markowitz model is also known as the mean-variance model, which is the core of modern portfolio theory.The Markowitz model explains well the process of calculating the optimal portfolio solution and its principles.In addition to this, it is clear that the model also describes various situations in a portfolio by constructing the horizontal axis as the variance and the vertical axis as the mean.It is also explained in the Markowitz model that this uses the variance to represent the risk of the portfolio and the mean to represent the expected return of the portfolio.This paper aims to analyze the study of the Markowitz model in the context of investment portfolios.Through analysis, it is found that Markowitz though includes advantages like the ones just mentioned, the model may still have some disadvantages as well as limitations.For example, the model is only valid if the financial markets are strong-form efficient.Further, strong-form efficiency implies that financial markets are free from insider information and irrational investors and investment behavior.Finally, based on this model, the paper will conclude with future perspectives and related recommendations.
CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment

Pretrained language models (PLMs) have achieved superhuman performance on many benchmarks, creating a need for harder tasks.We introduce CoDA21 (Context Definition Alignment), a challenging benchmark that measures natural language understanding (NLU) capabilities of PLMs: Given a definition and a context each for k words, but not the words themselves, the task is to align the k definitions with the k contexts.CoDA21 requires a deep understanding of contexts and definitions, including complex inference and world knowledge.We find that there is a large gap between human and PLM performance, suggesting that CoDA21 measures an aspect of NLU that is not sufficiently covered in existing benchmarks. 1
WatClaimCheck: A new Dataset for Claim Entailment and Inference

We contribute a new dataset 1 for the task of automated fact checking and an evaluation of state of the art algorithms.The dataset includes claims (from speeches, interviews, social media and news articles), review articles published by professional fact checkers and premise articles used by those professional fact checkers to support their review and verify the veracity of the claims.An important challenge in the use of premise articles is the identification of relevant passages that will help to infer the veracity of a claim.We show that transferring a dense passage retrieval model trained with review articles improves the retrieval quality of passages in premise articles.We report results for the prediction of claim veracity by inference from premise articles.
Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue

Standard conversational semantic parsing maps a complete user utterance into an executable program, after which the program is executed to respond to the user.This could be slow when the program contains expensive function calls.We investigate the opportunity to reduce latency by predicting and executing function calls while the user is still speaking.We introduce the task of online semantic parsing for this purpose, with a formal latency reduction metric inspired by simultaneous machine translation.We propose a general framework with first a learned prefix-to-program prediction module, and then a simple yet effective thresholding heuristic for subprogram selection for early execution.Experiments on the SMCalFlow and TreeDST datasets show our approach achieves large latency reduction with good parsing quality, with a 30%-63% latency reduction depending on function execution time and allowed cost.
Going “Deeper”: Structured Sememe Prediction via Transformer with Tree Attention

Sememe knowledge bases (SKBs), which annotate words with the smallest semantic units (i.e., sememes), have proven beneficial to many NLP tasks.Building an SKB is very time-consuming and labor-intensive.Therefore, some studies have tried to automate the building process by predicting sememes for the unannotated words.However, all existing sememe prediction studies ignore the hierarchical structures of sememes, which are important in the sememebased semantic description system.In this work, we tackle the structured sememe prediction problem for the first time, which is aimed at predicting a sememe tree with hierarchical structures rather than a set of sememes.We design a sememe tree generation model based on Transformer with an adjusted attention mechanism, which shows its superiority over the baseline methods in experiments.We also conduct a series of quantitative and qualitative analyses of the effectiveness of our model.