Reviewerly/exHarmony huggingface dataset:
Consistency Regularization for Variational Auto-Encoders

Variational auto-encoders (VAEs) are a powerful approach to unsupervised learning. They enable scalable approximate posterior inference in latent-variable models using variational inference (VI). A VAE posits a variational family parameterized by a deep neural network called an encoder that takes data as input. This encoder is shared across all the observations, which amortizes the cost of inference. However the encoder of a VAE has the undesirable property that it maps a given observation and a semantics-preserving transformation of it to different latent representations. This inconsistency of the encoder lowers the quality of the learned representations, especially for downstream tasks, and also negatively affects generalization. In this paper, we propose a regularization method to enforce consistency in VAEs. The idea is to minimize the Kullback-Leibler (KL) divergence between the variational distribution when conditioning on the observation and the variational distribution when conditioning on a random semantic-preserving transformation of this observation. This regularization is applicable to any VAE. In our experiments we apply it to four different VAE variants on several benchmark datasets and found it always improves the quality of the learned representations but also leads to better generalization. In particular, when applied to the Nouveau Variational Auto-Encoder (NVAE), our regularization method yields state-of-the-art performance on MNIST and CIFAR-10. We also applied our method to 3D data and found it learns representations of superior quality as measured by accuracy on a downstream classification task.
The effect of maximum iteration using 3 dimensional limit equilibrium method in open pit mine

Abstract Nickel ore mines have a high potential of landslides due to their weak material base, which consists of soil. It is caused by the increase of soil density in rain conditions, leading to decreased soil shear strength (c) and internal friction angle (ϕ). This research aims to determine the optimum value of the maximum iteration number based on the Cuckoo Search and Particle Swarm Optimization search method. In this research, the slope is analyzed using the 3 Dimensional limit equilibrium method “Simplified Bishop,” a slope stability analysis method that uses the principle of static equilibrium. Alongside this method, the Cuckoo Search and Particle Swarm Optimization is adopted. The Cuckoo Search and Particle Swarm Optimization are metaheuristic optimization techniques used as the slipped surface search method. Series of 3-dimensional limit equilibrium computation is performed using different amounts of nests in the cuckoo search method and different particle values and maximum iteration number. Cuckoo Search method to achieve optimal nest 100 and iteration of 80 with the fastest compute time of 3 minutes 49 seconds. While the Particle Swarm Optimization to achieve optimal on particles 60, iteration as much as 480 with a compute time of 6 minutes 46 second, with a factor of safety value of 1,12.
Application of the video correlation method to measure the velocity on the surface of a liquid metal

Abstract In this work, the applicability of the video correlation method proposed by the authors for measuring the velocity on the surface of a liquid metal in an electrovortex flow in a hemisphere is investigated. The method is based on calculating the cross-correlation of the brightness signals of two closely spaced points in the video. It is shown that the method allows quite simply determining the velocity of the liquid metal.
Domain Invariant Representation Learning with Domain Density Transformations

Domain generalization refers to the problem where we aim to train a model on data from a set of source domains so that the model can generalize to unseen target domains. Naively training a model on the aggregate set of data (pooled from all source domains) has been shown to perform suboptimally, since the information learned by that model might be domain-specific and generalize imperfectly to target domains. To tackle this problem, a predominant approach is to find and learn some domain-invariant information in order to use it for the prediction task. In this paper, we propose a theoretically grounded method to learn a domain-invariant representation by enforcing the representation network to be invariant under all transformation functions among domains. We also show how to use generative adversarial networks to learn such domain transformations to implement our method in practice. We demonstrate the effectiveness of our method on several widely used datasets for the domain generalization problem, on all of which we achieve competitive results with state-of-the-art models.
Learning Mathematical Properties of Integers

Embedding words in high-dimensional vector spaces has proven valuable in many natural language applications. In this work, we investigate whether similarly-trained embeddings of integers can capture concepts that are useful for mathematical applications. We probe the integer embeddings for mathematical knowledge, apply them to a set of numerical reasoning tasks, and show that by learning the representations from mathematical sequence data, we can substantially improve over number embeddings learned from English text corpora.
Towards Code-Mixed Hinglish Dialogue Generation

Code-mixed language plays a crucial role in communication in multilingual societies. Though the recent growth of web users has greatly boosted the use of such mixed languages, the current generation of dialog systems is primarily monolingual. This increase in usage of code-mixed language has prompted dialog systems in a similar language. We present our work in Code-Mixed Dialog Generation, an unexplored task in code-mixed languages, generating utterances in code-mixed language rather than a single language that is more often just English. We present a new synthetic corpus in code-mix for dialogs, CM-DailyDialog, by converting an existing English-only dialog corpus to a mixed Hindi-English corpus. We then propose a baseline approach where we show the effectiveness of using mBART like multilingual sequence-to-sequence transformers for code-mixed dialog generation. Our best performing dialog models can conduct coherent conversations in Hindi-English mixed language as evaluated by human and automatic metrics setting new benchmarks for the Code-Mixed Dialog Generation task.
The Effect of Using Snowball Throwing Method and SQ4R Method for Students’ Reading Comprehension Skill at SMA Negeri 1 Kuala Stabat

The objectives of this research are to identify the effect of the Snowball Throwing Method for students’ reading comprehension skill, to identify the effect of SQ4R Method for the students’ reading comprehension and to find out which method is the more significant between The Snowball Trowing Method and SQ4R Method in teaching reading comprehension. This research is an experimental quantitative research. The sample of this research were taken from grade twelve of SMA Negeri 1 Kuala students.  The data were gathered by using reading text multiple- choice test and were analysed by using t-test. The findings showed Snowball Throwing Method is significantly affecting the students’ reading comprehension skill in reading News Item Text. Since the t obs > t table  (α= 0,05) df (58), or 9.44 >1.67155 (α =0,05) and SQ4R Method is also significantly affecting the students’ reading comprehension skill in reading recount text. Since the t obs > t table  (α = 0,05) df (58), or 4.08 >1.67155 (α =0,05). In conclusion, the using of Snowball Throwing Method and SQ4R are significantly affect the students’ reading comprehension skill. And Snowball Throwing Method is the more significanlty effect the students’ reading comprehension skill than SQ4R method  in teaching n ews i tem t ext.
How Deep Learning Model Architecture and Software Stack Impacts Training Performance in the Cloud

Choosing the right instance on a public cloud for model training is not an easy task. There are hundreds of different virtual machines available with a wide variety of CPU core counts (i.e., how many tasks can be performed in parallel), memory, disk type, network speed, and of course graphics card (GPU). The latter has often become a differentiating factor for choosing one virtual machine (VM) over another. On top of that, containerization technology has greatly simplified GPU computations for machine learning, wrapping the software stack above the Kernel level in containers and allowing to juggle with different combinations of frameworks, lower lever libraries, and hardware drivers. Technologies like Nvidia-docker has even unlocked new stack combinations (driver plus low lever Compute Unified Device Architecture libraries, most of the time referred as CUDA) that were not feasible before. This, however, adds another dimension to the performance optimization problem, and now you not only need to choose an optimal hardware for your machine learning task, but also a variate driver-CUDA combination to fine-tune the performance further.The goal of this work is to scrutinize how neural network architecture, different versions of NVIDIA drivers, and CUDA libraries will influence training performance and cost on different cloud VMs. We compared BERT, Mask R-CNN, and DLRM architectures using instances available on Amazon Cloud Services via Elastic Compute service (AWS EC2 for short) and showed that architecture, model implementation, and software stack can cause significant variation in training time and cost with a different optimal configuration for different architectures and software stacks.
Multiclass optimal classification trees with SVM-splits

Abstract In this paper we present a novel mathematical optimization-based methodology to construct tree-shaped classification rules for multiclass instances. Our approach consists of building Classification Trees in which, except for the leaf nodes, the labels are temporarily left out and grouped into two classes by means of a SVM separating hyperplane. We provide a Mixed Integer Non Linear Programming formulation for the problem and report the results of an extended battery of computational experiments to assess the performance of our proposal with respect to other benchmarking classification methods.
Study of Manifestation of Civil Unrest on Twitter.

Twitter is commonly used for civil unrest detection and forecasting tasks, but there is a lack of work in evaluating how civil unrest manifests on Twitter across countries and events. We present two in-depth case studies for two specific large-scale events, one in a country with high (English) Twitter usage (Johannesburg riots in South Africa) and one in a country with low Twitter usage (Burayu massacre protests in Ethiopia). We show that while there is event signal during the events, there is little signal leading up to the events. In addition to the case studies, we train Ngram-based models on a larger set of Twitter civil unrest data across time, events, and countries and use machine learning explainability tools (SHAP) to identify important features. The models were able to find words indicative of civil unrest that generalized across countries. The 42 countries span Africa, Middle East, and Southeast Asia and the events range occur between 2014 and 2019.
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object. We interpret this phenomenon using the information bottleneck principle: the final layer of a deep neural network, activated by the sigmoid or softmax activation functions, causes an information bottleneck, and as a result, only a subset of the task-relevant information is passed on to the output. We first support this argument through a simulated toy experiment and then propose a method to reduce the information bottleneck by removing the last activation function. In addition, we introduce a new pooling method that further encourages the transmission of information from non-discriminative regions to the classification. Our experimental evaluations demonstrate that this simple modification significantly improves the quality of localization maps on both the PASCAL VOC 2012 and MS COCO 2014 datasets, exhibiting a new state-of-the-art performance for weakly supervised semantic segmentation. The code is available at: this https URL
Dynamic Route Planning for UAV Using Receding Horizon Control Towards Moving Target

Aiming at the task of detecting moving targets for unmanned aerial vehicle (UAV), this paper proposes a dynamic route planning algorithm based on receding horizon control. In order to satisfy the flight dynamics constraints of the planned trajectory, the quadratic Bezier curve is introduced. In view of the weak detection and processing capabilities of UAV, receding horizon control is used for implementation planning to reduce the computational burden of UAV. Considering the characteristics of the moving target, the threat of the mission area, the minimum turn radius of UAV, and the connection of path segments, the optimization model is established with the shortest range as the optimization objective. According to the characteristics of the model, the sequence quadratic program (SQP) method is selected to solve the problem. In order to verify the effectiveness of the algorithm, three ways of moving targets are designed. Simulation results show that the route generated by this method can excellently avoid threats and reach the moving target, and the route is smooth enough for the actual flight.
A Survey on Securing Medical Data in Cloud Using Blockchain

Security is a major concern in every technology that is introduced newly to facilitate the existing mechanism for better maintenance and handling. This is also the case in electronic health records. The data of the hospitals and the associated patients gets digital in the past few decades. The data is stored in the cloud for various reasons such as convenience of the participating entities to access it, easy maintenance. But, with this there also arises various security concerns. It has been observed from the reason studies that blockchain is used as the means of securing the healthcare data in the cloud environment.This study discusses the following. 1) Applications of blockchain in cloud environment, 2) Applications of blockchain in securing healthcare data 3) General issues and security concerns in blockchain technology and what features of block chain makes it suitable for securing health care a nd what features restricts it from using.This work helps the future researchers in getting a deep understanding of the in and out of applying blockchain in cloud and healthcare environment.
Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification

This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset. We experiment with both a multi-task learning paradigm to jointly train a graph attention network for both the task of evidence extraction and veracity prediction, as well as a single objective graph model for solely learning veracity prediction and separate evidence extraction. In both instances, we employ a framework for per-cell linearization of tabular evidence, thus allowing us to treat evidence from tables as sequences. The templates we employ for linearizing tables capture the context as well as the content of table data. We furthermore provide a case study to show the interpretability our approach. Our best performing system achieves a FEVEROUS score of 0.23 and 53% label accuracy on the blind test data.
Are we summarizing the right way? : a survey of dialogue summarization data sets

Dialogue summarization is a long-standing task in the field of NLP, and several data sets with dialogues and associated human-written summaries of different styles exist. However, it is unclear for which type of dialogue which type of summary is most appropriate. For this reason, we apply a linguistic model of dialogue types to derive matching summary items and NLP tasks. This allows us to map existing dialogue summarization data sets into this model and identify gaps and potential directions for future work. As part of this process, we also provide an extensive overview of existing dialogue summarization data sets.
How to Obtain Reliable Labels for MBTI Classification from Texts?

Automatic detection of the Myers-Briggs Type Indicator (MBTI) from short posts attracted noticeable attention in the last few years.Recent studies showed that this is quite a difficult task, especially on commonly used Twitter data.Obtaining MBTI labels is also difficult, as human annotation requires trained psychologists, and automatic way of obtaining them is through long questionnaires of questionable usability for the task.In this paper, we present a method for collecting reliable MBTI labels via only four carefully selected questions that can be applied to any type of textual data.
Participatory Action Research on Gender-Based Hate Speech Online with a Karnataka-Based Youth Group

This report aims to contextualize a Participatory Action Research (PAR) exercise undertaken by IT for Change in collaboration with Samvada Baduku, an organization that empowers young people to be part of, and lead, social change. The initiative was undertaken as a part of the project Recognize, Resist, Remedy, which focuses on combating sexist hate speech online through legal and policy interventions by working with various stakeholders. The PAR component of the project aimed to co-evolve a proof-of-concept of a viable intervention model for young people that can promote a new online culture of zero tolerance for sexist speech.Working with 11 young people, the model sought to build awareness among the cohort about misogyny, and develop and implement strategies to counter it.Through training sessions, discussions, surveys, and a participant-led digital media campaign against sexist hate speech, the PAR reaffirmed the many established insights about the intersectional nature of gender power structures in the digital publics. It also enabled the participating collective to develop a high degree of self awareness about the nature of sexist hate, build informed perspectives, assume leadership, and confidently claim online spaces through local language materials for deepening feminist political discourse. At the same time, it brought home the challenges in confronting patriarchal tropes about permissible discourse, the visceral pushbacks that neutralize counterspeech, the corporeal consequences of being silenced, and the presence of a lurking gender police in familiar and stranger networks online.By the end of the study, the participants identified the need for systemic change, pointing to why a paradigm shift with respect to intimate interactions as well as institutional changes is necessary. They also began discovering their own political voice and laid claim to the digital publics, thereby enabling a meaningful contribution to locally responsive feminist leadership and activism.
Genre as Weak Supervision for Cross-lingual Dependency Parsing

Recent work has shown that monolingual masked language models learn to represent data-driven notions of language variation which can be used for domain-targeted training data selection. Dataset genre labels are already frequently available, yet remain largely unexplored in cross-lingual setups. We harness this genre metadata as a weak supervision signal for targeted data selection in zero-shot dependency parsing. Specifically, we project treebank-level genre information to the finer-grained sentence level, with the goal to amplify information implicitly stored in unsupervised contextualized representations. We demonstrate that genre is recoverable from multilingual contextual embeddings and that it provides an effective signal for training data selection in cross-lingual, zero-shot scenarios. For 12 low-resource language treebanks, six of which are test-only, our genre-specific methods significantly outperform competitive baselines as well as recent embedding-based methods for data selection. Moreover, genre-based data selection provides new state-of-the-art results for three of these target languages.
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies

Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space. In this work, we investigate the colloquially known phenomenon that trained agents often prefer actions at the boundaries of that space. We draw theoretical connections to the emergence of bang-bang behavior in optimal control, and provide extensive empirical evaluation across a variety of recent RL algorithms. We replace the normal Gaussian by a Bernoulli distribution that solely considers the extremes along each action dimension - a bang-bang controller. Surprisingly, this achieves state-of-the-art performance on several continuous control benchmarks - in contrast to robotic hardware, where energy and maintenance cost affect controller choices. Since exploration, learning,and the final solution are entangled in RL, we provide additional imitation learning experiments to reduce the impact of exploration on our analysis. Finally, we show that our observations generalize to environments that aim to model real-world challenges and evaluate factors to mitigate the emergence of bang-bang solutions. Our findings emphasize challenges for benchmarking continuous control algorithms, particularly in light of potential real-world applications.
Using of educational technologies in a common digital space of scientific knowledge

The role of the common digital space of scientific knowledge (CDSSK) in the development of educational technologies is considered. CDSSK is implemented by a number of Russian organizations as an information and technological environment, support for scientific research, education, popularization of science and preservation of scientific heritage. The structure of the CDSSK provides general principles for building links between individual components related to school and university education. One of the elements related to education in the structure of the CDSSK is a scientific virtual exhibition. Such an exhibition can be dedicated to both a specific person and a scientific event in general. Virtual exhibitions bring together a variety of resources related to the topic of the project (biographies of scientists, related museum and archival materials, publications, copies of films, interactive multimedia resources). The work presents examples of realized virtual exhibitions used in the educational process. The exhibitions were created on the platform of the Scientific Heritage of Russia digital library.
GNSS-based UAV detection

Chapter Contents: 8.1 Introduction 8.2 GNSS-based PR coverage 8.2.1 Back and forward scattering RCS of UAVs 8.2.2 Passive bistatic radar equation 8.2.3 Case studies 8.2.3.1 Case Study I: Heathrow Airport 8.2.3.2 Case Study II: Newark Liberty International Airport 8.3 Source signal reconstruction 8.3.1 Signal model 8.3.2 GNSS signal characteristics 8.3.3 Signal reconstruction algorithm 8.4 Target parameters estimation 8.4.1 Target localisation 8.4.2 Velocity estimation 8.5 Experimental analysis 8.5.1 Scenario 1: crossing in front of the receiver 8.5.2 Scenario 2: descending away from the receiver 8.6 Conclusion References