Reviewerly/exHarmony huggingface dataset:
The history of Slavonic clausal complementation: A corpus view

Syntactic annotation of historical text, with no access to native-speaker intuitions, poses a number of problems to the annotator who is faced with the task of giving a single analysis of each sentence. This article reports on the experiences from annotating complementation structures in Old Church Slavonic and Old East Slavonic in the PROIEL and TOROT treebanks. Two case studies are examined: complement clauses in Old Church Slavonic and the history of Russian čьto ‘what, which, that’. In the first case the annotation scheme is shown to work well in terms of interannotator agreement and retrievability. However, the price is that a large number of examples with jako ‘that’ are analysed as complement clauses with a subjunction, even though many of these examples are in fact ambiguous and jako can equally well be interpreted as a quotative particle followed by direct speech. The second case study looks at a development from situation where čьto could be taken to be an interrogative pronoun in all subordinated clauses, to a situation where a subjunction and a relative pronoun analysis are also available. This leads to a large number of ambiguous occurrences. The solution in TOROT is to analyse unambiguous interrogative pronoun and subjunction examples at face value, while all of the remaining occurrences are analysed as relative clauses. This makes the annotator’s job manageable, but causes retrievability problems, since individual researchers will have to sift through the relative clause examples themselves.
VaLiPro: Linear Programming Validator for Cluster Computing Systems

The article presents and evaluates a scalable algorithm for validating solutions of linear programming problems on cluster computing systems. The main idea of the method is to generate a regular set of points (validation set) on a small-radius hypersphere centered at the point of the solution under validation. The objective function is calculated for each point of the validation set that belongs to the feasible region. If all these values are less than or equal to the value of the objective function at the point under validation, then this point is the correct solution. The parallel implementation of the VaLiPro algorithm is performed in C++ through the parallel BSF-skeleton, which encapsulates all aspects related to the MPI-based parallelization of the program. We provide the results of large-scale computational experiments on a cluster computing system to study the scalability of the VaLiPro algorithm.
Virtualization Technology for Cloud-Based Services

Cloud computing is the most popular computing paradigm, which delivers computing services in multiple marketing options including Software, Infrastructure and Platforms over the Internet. Cloud allows businesses to pay only for services they use, which reduces overall operating costs, and also helps infrastructure run more efficiently by providing a scalable set of resources aligning with business's needs for change. This has led to a big shift of organizations from the traditional computing paradigm to cloud-based services. Virtualization plays a very crucial role in defining cloud computing services. This chapter provides a detailed overview of Cloud Computing along with Virtualization Technology. It also explains the role of Virtualization Technology in cloud resource management and its implementation-associated components. Further this chapter discusses the various characteristics and components of cloud computing which plays a key role in multiple areas like healthcare, agriculture, etc.
A N-gram based approach to auto-extracting topics from research articles1

A lot of manual work goes into identifying a topic for an article. With a large volume of articles, the manual process can be exhausting. Our approach aims to address this issue by automatically extracting topics from the text of large numbers of articles. This approach takes into account the efficiency of the process. Based on existing N-gram analysis, our research examines how often certain words appear in documents in order to support automatic topic extraction. In order to improve efficiency, we apply custom filtering standards to our research. Additionally, delete as many noncritical or irrelevant phrases as possible. In this way, we can ensure we are selecting unique keyphrases for each article, which capture its core idea1. For our research, we chose to center on the autonomous vehicle domain, since the research is relevant to our daily lives. We have to convert the PDF versions of most of the research papers into editable types of files such as TXT. This is because most of the research papers are only in PDF format. To test our proposed idea of automating, numerous articles on robotics have been selected. Next, we evaluate our approach by comparing the result with other models.
Cyclotomic matrices and hypergeometric functions over finite fields

With the help of hypergeometric functions over finite fields, we study some arithmetic properties of cyclotomic matrices involving characters and binary quadratic forms over finite fields. Also, we confirm some related conjectures posed by Zhi-Wei Sun.
DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation

We design, implement, and evaluate DeepEverest, a system for the efficient execution of interpretation by example queries over the activation values of a deep neural network. DeepEverest consists of an efficient indexing technique and a query execution algorithm with various optimizations. Experiments with our prototype implementation show that DeepEverest, using less than 20% of the storage of full materialization, significantly accelerates individual queries by up to 63x and consistently outperforms other methods on multi-query workloads that simulate DNN interpretation processes.
Excluding false negative error in certification of quantum channels

Certification of quantum channels is based on quantum hypothesis testing and involves also preparation of an input state and choosing the final measurement. This work primarily focuses on the scenario when the false negative error cannot occur, even if it leads to the growth of the probability of false positive error. We establish a condition when it is possible to exclude false negative error after a finite number of queries to the quantum channel in parallel, and we provide an upper bound on the number of queries. On top of that, we found a class of channels which allow for excluding false negative error after a finite number of queries in parallel, but cannot be distinguished unambiguously. Moreover, it will be proved that parallel certification scheme is always sufficient, however the number of steps may be decreased by the use of adaptive scheme. Finally, we consider examples of certification of various classes of quantum channels and measurements.
Spherical wavefront measurement on modified cyclic radial shearing interferometry

We propose a radial shearing interferometric approach to measure spherical wavefronts as both of the reflective and transmissive optical configurations. The modified cyclic radial shearing interferometer uses a single lens in the optical layout, which can conveniently adjust the radial shearing ratio between two shearing spherical wavefronts, and the use of a polarization camera enables to reconstruct the wavefront by a single image. The wavefront mapped onto the camera plane can be identified and quantified throughout an optimized wavefront reconstruction algorithm. In the experiments, plano-convex lenses and concave mirrors were used to generate spherical wavefronts, and the proposed system was able to reconstruct the surface figures after system characterization and calibration. Further investigations were performed to evaluate the system measurement accuracy by the radius of curvature comparison with design value and a commercial Shack-Hartmann wavefront sensor.
AugMax: Adversarial Composition of Random Augmentations for Robust Training

Data augmentation is a simple yet effective way to improve the robustness ofdeep neural networks (DNNs). Diversity and hardness are two complementarydimensions of data augmentation to achieve robustness. For example, AugMixexplores random compositions of a diverse set of augmentations to enhancebroader coverage, while adversarial training generates adversarially hardsamples to spot the weakness. Motivated by this, we propose a data augmentationframework, termed AugMax, to unify the two aspects of diversity and hardness.AugMax first randomly samples multiple augmentation operators and then learnsan adversarial mixture of the selected operators. Being a stronger form of dataaugmentation, AugMax leads to a significantly augmented input distributionwhich makes model training more challenging. To solve this problem, we furtherdesign a disentangled normalization module, termed DuBIN(Dual-Batch-and-Instance Normalization), that disentangles the instance-wisefeature heterogeneity arising from AugMax. Experiments show that AugMax-DuBINleads to significantly improved out-of-distribution robustness, outperformingprior arts by 3.03%, 3.49%, 1.82% and 0.71% on CIFAR10-C, CIFAR100-C, TinyImageNet-C and ImageNet-C. Codes and pretrained models are available:https://github.com/VITA-Group/AugMax.
Introducing a Framework to Enable Anonymous Secure Multi-Party Computation in Practice

Secure Multi-Party Computation (SMPC) allows a set of parties to securely compute a functionality in a distributed fashion without the need for any trusted external party. Usually, it is assumed that the parties know each other and have already established authenticated channels among each other. However, in practice the parties sometimes must stay anonymous. In this paper, we conceptualize a framework that enables the repeated execution of an SMPC protocol for a given functionality such that the parties can keep their participation in the protocol executions private and at the same time be sure that only authorized parties may take part in a protocol execution. We identify the security properties that an implementation of our framework must meet and introduce a first implementation of the framework that achieves these properties.
ANDROID-BASED DIGITAL BOOK DEVELOPMENT FOR BASIC NETWORK STUDENTS IN SMK DIAN INDONESIA

This research is a development research that aims to develop a renewable learning media based on android in basic network lessons for class X Computer and Network Engineering. The research and development method used is to use the R & D research developed by Borg and Gall. This study uses a 4-D development model (Define, Design, Development and Disseminate). This research was conducted at SMK Dian Indonesia with the subject of class X TKJ. Data collection instruments were observation and interviews. The feasibility of this learning media was assessed by media experts and material experts. Based on the results of the analysis, the percentage of 86% was obtained with the very feasible category by the media expert, and 85% with the very feasible category by the material expert. That can be concluded that the development of digital books based on android is very suitable for use in basic network learning in class X Vocational High Schools.
Performance Analysis of Weather Forecasting using Machine Learning Algorithms (Analisis Performansi Prakiraan Cuaca Menggunakan Algoritma Machine Learning)

Weather forecasting are very important in various fields of human life, including in big cities. The need for accurate weather forecasts will be effective and efficient in managing the quality of civilization flexibly. In many cases it is found that the results of weather forecasts in the same city differ depending on the radius. This of course requires a precise and accurate algorithm to determine it. The algorithm used is based on machine learning type of artificial neural network which compares backpropagation and bayessian regularization. The results obtained show that bayessian regularization outperforms backpropagation with the smallest MSE and the highest accuracy and the shortest computation time to determine sunny, cloudy, light rain and heavy rain forecasts. The unbalanced distribution of data causes fluctuations in the MSE calculation and accuracy. The addition of training will improve system performance which is indicated by a significant increase in accuracy. Likewise, decreasing the MSE can increase the accuracy of the system to reach the point of convergence. This is an indicator that the performance of Bayessian regularization is the recommended algorithm for forecasting weather in cities and their surroundings, even between provinces or between countries.
A Framework of Controlled Robot Language for Reliable Human-Robot Collaboration

Effective and efficient communication is critical for human-robot collaboration and human-agent teaming. This paper presents the design of a Controlled Robot Language (CRL) and its formal grammar for instruction interpretation and automated robot planning. The CRL framework defines a formal language domain that deterministically maps linguistic commands to logical semantic expressions. As compared to Controlled Natural Language, which aims for general knowledge representation, CRL expressions are particularly designed to parse human instructions in automated robot planning. The grammar of CRL is developed in accordance with the IEEE CORA ontology, which defines the majority of formal English domain, accepting large range of intuitive instructions. For sentences outside the grammar coverage, CRL checker is used to detect linguistic patterns, which can be further processed by CRL translator to recover back an equivalent expression in CRL grammar. The final output is formal semantic representation using first-order logic in large discourse. The CRL framework was evaluated on various corpora and it outperformed CRL in balancing coverage and specificity.
Differentially Private Federated Bayesian Optimization with Distributed Exploration

Bayesian optimization (BO) has recently been extended to the federated learning (FL) setting by the federated Thompson sampling (FTS) algorithm, which has promising applications such as federated hyperparameter tuning. However, FTS is not equipped with a rigorous privacy guarantee which is an important consideration in FL. Recent works have incorporated differential privacy (DP) into the training of deep neural networks through a general framework for adding DP to iterative algorithms. Following this general DP framework, our work here integrates DP into FTS to preserve user-level privacy. We also leverage the ability of this general DP framework to handle different parameter vectors, as well as the technique of local modeling for BO, to further improve the utility of our algorithm through distributed exploration (DE). The resulting differentially private FTS with DE (DP-FTS-DE) algorithm is endowed with theoretical guarantees for both the privacy and utility and is amenable to interesting theoretical insights about the privacy-utility trade-off. We also use real-world experiments to show that DP-FTS-DE achieves high utility (competitive performance) with a strong privacy guarantee (small privacy loss) and induces a trade-off between privacy and utility.
Real-Time Instance Segmentation for Low-Cost Mobile Robot Systems Based on Computation Offloading

Instance segmentation can enable mobile robots to obtain the environmental semantic information and accomplish more complex interaction with environments, such as navigation, grasping, and virtual reality. However, low-cost mobile robots have limited onboard resources, and can not afford the massive computation of instance segmentation methods. This paper proposes a real-time instance segmentation framework for mobile robot systems based on computation offloading, which offloads part of computation of the instance segmentation network to the cloud, and leverages the powerful computation resources and sufficient memories on the cloud platform to accelerate the network. First, we formulate the instance segmentation network as the directed acyclic graph, and present its time cost model and energy consumption model. Then, a computation offloading strategy is proposed to reduce the time cost of the whole instance segmentation and the energy consumption on the mobile robot. Our framework has been verified on the representative one-stage method, Yolact, and two-stage method, Mask R-CNN. The results show that our framework can accelerate the execution of instance segmentation network on mobile robots, and achieve the speed of around one second per frame.
Measuring Data Collection Diligence for Community Healthcare

Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of high-quality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of the word non-diligence here is to emphasize that poor data collection is often not a deliberate action by CHW but arises due to a myriad of factors, sometime beyond the control of the CHW. In this work, we define and test a data collection diligence score. This challenging unlabeled data problem is handled by building upon domain expert’s guidance to design a useful data representation of the raw data, using which we design a simple and natural score. An important aspect of the score is relative scoring of the CHWs, which implicitly takes into account the context of the local area. The data is also clustered and interpreting these clusters provides a natural explanation of the past behavior of each data collector. We further predict the diligence score for future time steps. Our framework has been validated on the ground using observations by the field monitors of our partner NGO in India. Beyond the successful field test, our work is in the final stages of deployment in the state of Rajasthan, India. This system will be helpful in providing non-punitive intervention and necessary guidance to encourage CHWs.
Learning to Communicate and Correct Pose Errors

Learned communication makes multi-agent systems more effective by aggregating distributed information. However, it also exposes individual agents to the threat of erroneous messages they might receive. In this paper, we study the setting proposed in V2VNet, where nearby self-driving vehicles jointly perform object detection and motion forecasting in a cooperative manner. Despite a huge performance boost when the agents solve the task together, the gain is quickly diminished in the presence of pose noise since the communication relies on spatial transformations. Hence, we propose a novel neural reasoning framework that learns to communicate, to estimate potential errors, and finally, to reach a consensus about those errors. Experiments confirm that our proposed framework significantly improves the robustness of multi-agent self-driving perception and motion forecasting systems under realistic and severe localization noise.
Data Analytics and Mining

The proliferation of the internet and advancement in technology have prompted the generation of humongous amounts of data at breakneck speeds from multitudinous sources such as IoT devices, clickstreams, social media, video surveillance systems, and various kinds of sensors. Such kind of data updates with high frequency and loses its value and relevance in a brief timeframe. Therefore, it becomes imperative to process and analyze such data on-the-fly enabling time-critical decision making in numerous applications. Real-time querying and big data streams are the new requirements. The earlier batch processing frameworks such as Hadoop endow valuable insight into what has happened in the past but aren’t able to deal with what is happening currently. Due to slower response time and high latency, it isn’t completely suitable to handle the dynamic real-time data; furthermore, the ability to make the right decisions and take proper actions at the opportune time can’t be achieved. Hence, we need additional tools to cope with these new demands. This chapter covers new technologies such as in-memory computing and stream processing, and a brief overview of real-time architectures is also presented. This chapter discusses the state-of-the-art real-time analytics platforms such as Apache Storm, Apache Spark, Apache Flink, and so on that can be applied to real-time applications, and finally this chapter concludes with the comparison of these platforms based on various essential features such as latency, throughput, delivery guarantees, and so on that can help in choosing a particular platform for certain applications.
Batch-Constraint Inverse Reinforcement Learning

We consider a completely offline inverse reinforcement learning setup, i.e., where the reward function is unknown and the interaction with the environment is not possible. This typically occurs in situations where data collection is risky or costly, such as healthcare or industrial controls. Establishing explainable rewards for decision-making is critical to quantifying and adapting policy. However, existing methods hardly learn an interpretable reward in offline setting. In this paper, we introduce an offline inverse reinforcement learning algorithm, BCIRL, to recover the implicit reward function and optimal policy from expert demonstrations in off-policy model-free settings. To address challenges in offline settings, we restrict the action space to behave close to the policy on the given data. We demonstrate that BCIRL performs strongly on control environment, that recovered rewards provide useful insights on experts’ preferences.
Relation Extraction between Medical Entities using Deep Learning Approach

Medical discharge summaries or patient prescriptions contain variety of medical terms. The semantic relation extraction between medical terms is essential for discovery of significant medical knowledge. The relation classification is one of the imperative tasks of biomedical information extraction. The automatic identification of relations between medical diseases, tests and treatments can improve the quality of patient care. This paper presents the deep learning based proposed system for relation extraction between medical entities. In this paper, convolution neural network is used for relation classification. The system is divided into four modules: word embedding, feature extraction, convolution and softmax classifier. The output contains classified relations between medical entities. In this work, data set provided by I2b2 2010 challenge is used for relation detection which consisted of total 9070 relations in test data and 5262 total relations in train data. The performance evaluation of relation extraction task is done using precision and recall. The system achieved average 75% precision and 72% recall. The performance of the system is compared with awarded i2b2 participated systems. Keywords: Convolution Neural Network;Feature Extraction;Relation Classification;Word Embedding.
Use of Gesture Recognition for Differently Abled Persons

In recent times, there are more number of people prone to disabilities due to various factors. To alleviate the life of differently abled people, we propose a project which uses hand gestures to operate devices in the surrounding. The need for human assistance is reduced which also reduces the financial burden on the patients. We have used the concept of computer vision to recognize hand gestures and perform the function of operating devices. Each hand gesture is assigned with a predefined function to execute a certain task.