Reviewerly/exHarmony huggingface dataset:
Effect of Vision-and-Language Extensions on Natural Language Understanding in Vision-and-Language Models.

Extending language models with structural modifications and vision-and-language (V&L) pretraining are successful ways of making V&L models that can ground vision and language. Potential applications of these advanced models include multi-modal machine reading comprehension models and multi-modal dialogue models, which require language ability upon grounding. Although language capability is crucial for such applications, the impact of extending their visual capabilities on their language capabilities is not fully understood. This paper investigates how visual extension affects the language capability of V&L models using the GLUE benchmark. We found that visual extension causes some decreases in language capability and that V&L pretraining has a greater impact than structural modifications on the decreases. Our results suggest the need for further study on pretraining that can maintain or, if possible, improve a model's language capability.
Fine-tuning Deep RL with Gradient-Free Optimization

Deep reinforcement learning makes it possible to train control policies that map high-dimensional observations to actions. These methods typically use gradient-based optimization techniques to enable relatively efficient learning, but are notoriously sensitive to hyperparameter choices and do not have good convergence properties. Gradient-free optimization methods, such as evolutionary strategies, can offer a more stable alternative but tend to be much less sample efficient. In this work we propose a combination, using the relative strengths of both. We start with a gradient-based initial training phase, which is used to quickly learn both a state representation and an initial policy. This phase is followed by a gradient-free optimization of only the final action selection parameters. This enables the policy to improve in a stable manner to a performance level not obtained by gradient-based optimization alone, using many fewer trials than methods using only gradient-free optimization. We demonstrate the effectiveness of the method on two Atari games, a continuous control benchmark and the CarRacing-v0 benchmark. On the latter we surpass the best previously reported score while using significantly fewer episodes.
Evaluating language models for the retrieval and categorization of lexical collocations

Lexical collocations are idiosyncratic combinations of two syntactically bound lexical items (e.g., “heavy rain” or “take a step”). Understanding their degree of compositionality and idiosyncrasy, as well their underlying semantics, is crucial for language learners, lexicographers and downstream NLP applications. In this paper, we perform an exhaustive analysis of current language models for collocation understanding. We first construct a dataset of apparitions of lexical collocations in context, categorized into 17 representative semantic categories. Then, we perform two experiments: (1) unsupervised collocate retrieval using BERT, and (2) supervised collocation classification in context. We find that most models perform well in distinguishing light verb constructions, especially if the collocation’s first argument acts as subject, but often fail to distinguish, first, different syntactic structures within the same semantic category, and second, fine-grained semantic categories which restrict the use of small sets of valid collocates for a given base.
Predicting hidden structure in dynamical systems

The dynamical properties of a nonlinear system can be learned from its time-series data, but is it possible to predict what happens when the system is tuned far away from its training values?
Organizational justice and job performance of academic staff at public universities in Iraq

Job performance is one of the most critical subjects in literature due to its crucial role in the success of organizations. The purpose of this study is to examine the impact of organizational justice on job performance among academic staff. The study employed a quantitative method to analyse the data collected at three Iraqi public universities located in Baghdad by using questionnaires. Stratified sampling technique was used to ensure the equal distribution of the samples among the three universities’ academic staff. Only 197 responses were valid to be analysed by using Smart PLS. The findings showed that distributive justice and interactional justice positively impacted on academic performance and ability to increase the performance, while procedural justice did not impact on academic performance. However, distributive justice had a higher impact on the job performance of academic staff compared to interactional justice. The findings of this study imply that decision-makers at universities should pay more attention to the fair distribution of resources, payment, promotion and training to increase job performance.
Optimization forecasting using back-propagation algorithm

The purpose of this study was to evaluate the back-propagation model by optimizing the parameters for the prediction of broiler chicken populations by provinces in Indonesia. Parameter optimization is changing the learning rate (lr) of the backpropagation prediction model. Data sourced from the Directorate General of Animal Husbandry and Animal Health processed by the Central Statistics Agency (BPS). Data is the population of Broiler Chickens from 2017 to 2019 (34 records). The analysis process uses the help of RapidMiner software. Data is divided into 2 parts, namely training data (2017-2018) and testing data (2018-2019). The backpropagation model used is 1-2-1; 1-25-1 and 1-45-1 with a learning rate (0.1; 0.01; 0.001; 0.2; 0.02; 0.002; 0.3; 0.03; 0.003). From the three models tested, the 1-45-1 model (lr = 0.3) is the best model with Root Mean Squared Error = 0.028 in the training data. With this model, the prediction results obtained with an accuracy value of 91% and Root Mean Squared Error = 0.00555 in the testing data.
Prodigal Heirs and Their Social Networks in Early Modern English Drama, 1590–1640

This article combines computational social network analysis and contextualized close reading to study 20 English plays from 1590 to 1640 that draw for their main plot on the Biblical parable of the prodigal son. Often viewed as an inherently conservative comic subgenre that ends with the restoration of patriarchal authority, recent literary critics argue that many plays portray the prodigal heirs in a positive light, linking prodigality to risky economic speculation and the improvisational skills needed to navigate London’s expanding commercial economy. For the earlier plays in our corpus (ca. 1590-1610), we find evidence for this view in our analysis of two clusters of understudied minor characters related to the pleasures and punishment of prodigality. However, these clusters all but disappear in the later plays (ca. 1610-1640). Instead, we see a group of weightier, more individualized servants and stewards rise to structural prominence. Informed by close reading of plays like Thomas Heywood’s The English Traveller (1624) and Phillip Massinger’s A New Way to Pay Old Debts (1626), we interpret this as a shift towards a more moralistic censure of prodigality as a threat to the patrilineal succession of elite households and the social hierarchy it buttressed.
Investigating Sharing of Cyber Threat Intelligence and Proposing A New Data Model for Enabling Automation in Knowledge Representation and Exchange

For a strong, collective defense in the digital domain, we need to produce, consume, analyze, and share cyber threat intelligence. With an increasing amount of available information, we need automation to ensure adequate efficiency. We present the results from a questionnaire investigating the use of standards and standardization and how practitioners share and use cyber threat intelligence (CTI). We propose a strict data model for CTI that enables consumption of all relevant data, data validation, and analysis of consumed content. The main contribution of this article is insight into how CTI is shared and used by practitioners, and the strictness of the data model that enforces input of information and enables automation and deduction of new knowledge.
RIDS

The expansion of the Internet-of-Things (IoT) market is visible in homes, factories, public places, and smart cities. While the massive deployment of connected devices offers opportunities to improve quality of life and to develop new services, the impact of such devices on the security of the users in a context where the level of malicious threat continues to increase is a major concern. One of the challenges is the heterogeneity and constant evolution of wireless technologies and protocols used. To overcome this problem, we propose RIDS, a Radio Intrusion Detection System that is based on the monitoring and profiling of radio communications at the physical layer level using autoencoder neural networks. RIDS is independent of the wireless protocols and modulation technologies used. Besides, it is designed to provide a threefold diagnosis of the detected anomalies: temporal (start and end date of the detected anomaly), frequential (main frequency of the anomaly), and spatial (location of the origin of the anomaly). To demonstrate the relevance and the efficiency of our approach, we collected a large dataset of radio-communications recorded with three different probes deployed in an experimental room. Multiple real-world attacks involving a wide variety of communication technologies are also injected to assess the detection and diagnosis efficiency. The results demonstrate the efficiency of RIDS in detecting and diagnosing anomalies that occurred in the 400–500 Mhz and 800–900 Mhz frequency bands. It is noteworthy that compromised devices and attacks using these communication bands are generally not easily covered by traditional solutions.
CURE: A Security Architecture with CUstomizable and Resilient Enclaves

Security architectures providing Trusted Execution Environments (TEEs) have been an appealing research subject for a wide range of computer systems, from low-end embedded devices to powerful cloud servers. The goal of these architectures is to protect sensitive services in isolated execution contexts, called enclaves. Unfortunately, existing TEE solutions suffer from significant design shortcomings. First, they follow a one-size-fits-all approach offering only a single enclave type, however, different services need flexible enclaves that can adjust to their demands. Second, they cannot efficiently support emerging applications (e.g., Machine Learning as a Service), which require secure channels to peripherals (e.g., accelerators), or the computational power of multiple cores. Third, their protection against cache side-channel attacks is either an afterthought or impractical, i.e., no fine-grained mapping between cache resources and individual enclaves is provided. In this work, we propose CURE, the first security architecture, which tackles these design challenges by providing different types of enclaves: (i) sub-space enclaves provide vertical isolation at all execution privilege levels, (ii) user-space enclaves provide isolated execution to unprivileged applications, and (iii) self-contained enclaves allow isolated execution environments that span multiple privilege levels. Moreover, CURE enables the exclusive assignment of system resources, e.g., peripherals, CPU cores, or cache resources to single enclaves. CURE requires minimal hardware changes while significantly improving the state of the art of hardware-assisted security architectures. We implemented CURE on a RISC-V-based SoC and thoroughly evaluated our prototype in terms of hardware and performance overhead. CURE imposes a geometric mean performance overhead of 15.33% on standard benchmarks.
Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection.

Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task “Offensive Language Identification in Dravidian Languages” at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.
Perception of Undergraduate Medical and Dental Students Towards Learning Anatomy in Google Classroom

Background: Google Classroom is one of the online learning system developed by Google which enables both the teachers and learners to engage in 'beyond the classroom' learning in an innovative way.The present study mainly focus to assess the perception of students towards learning Anatomy in Google Classroom Methodology: A study was conducted in undergraduate medical and dental students of ACS Medical College and Thai Moogambigai Dental College, Chennai who are residing in their homes due to Covid 19 lock down.Anatomy virtual class was created in Google classroom individually for medical and dental students and students were asked to join the class by means of class code.The materials were uploaded on a daily basis based on the syllabus.Assignment and assessment were also given and graded.After two months of learning experience in Google Classroom, perception of students and challenges faced by them towards this learning system was collected by structured questionnaire in Google form and it was statistically analysed Results: About 163 students responded to the questionnaire.79 (48.2%) students were males and 85(51.8%)students were females.Overall the students gave positive response towards their learning experience in Google classroom and few problems also have been found out like issues regarding accessing course materials and navigating the system, difficulty in submission of assignments, poor internet connection and they also want to make the classes more interactive Conclusion: Sensitisation and proper training should be given to the students regarding the use of Google Classroom.Quizzes, mind mapping, clinical based questions can be given to make the participants more engaged and interactive.Hence Google classroom is definitely a student centered online learning platform with the teacher as facilitators which helps to make the students competent.
A Data Structure Perspective to the RDD-based Apriori Algorithm on Spark

During the recent years, a number of efficient and scalable frequent itemset mining algorithms for big data analytics have been proposed by many researchers. Initially, MapReduce-based frequent itemset mining algorithms on Hadoop cluster were proposed. Although, Hadoop has been developed as a cluster computing system for handling and processing big data, but the performance of Hadoop does not meet the expectation for the iterative algorithms of data mining, due to its high I/O, and writing and then reading intermediate results in the disk. Consequently, Spark has been developed as another cluster computing infrastructure which is much faster than Hadoop due to its in-memory computation. It is highly suitable for iterative algorithms and supports batch, interactive, iterative, and stream processing of data. Many frequent itemset mining algorithms have been re-designed on the Spark, and most of them are Apriori-based. All these Spark-based Apriori algorithms use Hash Tree as the underlying data structure. This paper investigates the efficiency of various data structures for the Spark-based Apriori. Although, the data structure perspective has been investigated previously, but for MapReduce-based Apriori, and it must be re-investigated in the distributed computing environment of Spark. The considered underlying data structures are Hash Tree, Trie, and Hash Table Trie. The experimental results on the benchmark datasets show that the performance of Spark-based Apriori with Trie and Hash Table Trie are almost similar but both perform many times better than Hash Tree in the distributed computing environment of Spark.
Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation

In this paper, we describe our efforts on the shared task of sarcasm and sentiment detection in Arabic (Abu Farha et al., 2021). The shared task consists of two sub-tasks: Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2). Our experiments were based on fine-tuning seven BERT-based models with data augmentation to solve the imbalanced data problem. For both tasks, the MARBERT BERT-based model with data augmentation outperformed other models with an increase of the F-score by 15% for both tasks which shows the effectiveness of our approach.
HUB@DravidianLangTech-EACL2021: Identify and Classify Offensive Text in Multilingual Code Mixing in Social Media

This paper introduces the system description of the HUB team participating in DravidianLangTech - EACL2021: Offensive Language Identification in Dravidian Languages. The theme of this shared task is the detection of offensive content in social media. Among the known tasks related to offensive speech detection, this is the first task to detect offensive comments posted in social media comments in the Dravidian language. The task organizer team provided us with the code-mixing task data set mainly composed of three different languages: Malayalam, Kannada, and Tamil. The tasks on the code mixed data in these three different languages can be seen as three different comment/post-level classification tasks. The task on the Malayalam data set is a five-category classification task, and the Kannada and Tamil language data sets are two six-category classification tasks. Based on our analysis of the task description and task data set, we chose to use the multilingual BERT model to complete this task. In this paper, we will discuss our fine-tuning methods, models, experiments, and results.
Delivering cultural heritage and historical events to people through virtual reality

Abstract The concept of Society 5.0 promotes a human-centered society and uses advance technology as its core. In other words, human uses technology to improve and support their daily life. One of the best inventions in technology is digitalizing data. This means human found a new way in storing information, including information about culture and history. The technology in converting physical data into digital data is beneficial for historical and cultural archiving. People can see and watch historical events as if they had lived in that era of the event with extreme immersiveness. One technology that can provide the possibilities is called Virtual Reality animation. This research aims to promote the idea of using VR animation to recreate historical or traditional events for educational material and entertainment material. Methods in this research is mainly observation on today’s state of Virtual Reality animation and people interest on history and tradition. The results shows that advanced technology Virtual Reality and Artificial Intelligence allows people to learn histories from the past and explore new things in the present to live in the better future.
XORBoost: Tree Boosting in the Multiparty Computation Setting

We present a novel protocol XORBoost for both training gradient boosted tree models and for using these models for inference in the multiparty computation (MPC) setting. Our protocol supports training for generically split datasets (vertical and horizontal splitting, or combination of those) while keeping all the information about features, thresholds, and evaluation paths private; only tree depth and the number of the binary trees are public parameters of the model. By using novel optimization techniques that reduce the number of oblivious permutation evaluations as well as sorting operations, we further speedup the algorithm. The protocol is agnostic to the underlying MPC framework or implementation.
StyleMapGAN: Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing.

Generative adversarial networks (GANs) synthesize realistic images fromrandom latent vectors. Although manipulating the latent vectors controls thesynthesized outputs, editing real images with GANs suffers from i)time-consuming optimization for projecting real images to the latent vectors,ii) or inaccurate embedding through an encoder. We propose StyleMapGAN: theintermediate latent space has spatial dimensions, and a spatially variantmodulation replaces AdaIN. It makes the embedding through an encoder moreaccurate than existing optimization-based methods while maintaining theproperties of GANs. Experimental results demonstrate that our methodsignificantly outperforms state-of-the-art models in various image manipulationtasks such as local editing and image interpolation. Last but not least,conventional editing methods on GANs are still valid on our StyleMapGAN. Sourcecode is available at https://github.com/naver-ai/StyleMapGAN.
Joint Action Storyboards

Building and maintaining common ground is vital for effective collaboration in CSCW. Moreover, subtle changes in a CSCW user interface can significantly impact grounding and collaborative processes. Yet, researchers and technology designers lack tools to understand how specific user interface designs may hinder or facilitate communication grounding. In this work, we leverage the well-established theory of communication grounding to develop a visual framework, called Joint Action Storyboards (JASs), to analyze and articulate how interaction minutiae impact the costs of communication grounding. JASs can depict an integrated view of mental actions of collaborators, their physical interactions with each other and the CSCW environment, and the corresponding grounding costs incurred. We present the development of JASs and discuss its various benefits for HCI and CSCW research. Through a series of case studies, we demonstrate how JASs provide an analysis tool for researchers and technology designers and serve as a tool to articulate the impact of interaction minutiae on communication grounding.
Algorithm for Locating the Vertices of a QR Code and Removing Perspective

Scanning QR Codes from cellular phone cameras has made reading this type of two-dimensional code more accessible. However, QR Code photos may show geometric distortions caused by camera positioning in relation to the image to be read and thereby, resulting in a perspective image. These deformations make it difficult to decode QR Codes and thinking about it, this article proposes an algorithm that, first, finds the vertices of the polygon that delimits the area of a QR Code and from the coordinates of these vertices, the algorithm removes the perspective from the image.