Reviewerly/exHarmony huggingface dataset:
Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support.

Confidential multi-stakeholder machine learning (ML) allows multiple parties to perform collaborative data analytics while not revealing their intellectual property, such as ML source code, model, or datasets. State-of-the-art solutions based on homomorphic encryption incur a large performance overhead. Hardware-based solutions, such as trusted execution environments (TEEs), significantly improve the performance in inference computations but still suffer from low performance in training computations, e.g., deep neural networks model training, because of limited availability of protected memory and lack of GPU support. To address this problem, we designed and implemented Perun, a framework for confidential multi-stakeholder machine learning that allows users to make a trade-off between security and performance. Perun executes ML training on hardware accelerators (e.g., GPU) while providing security guarantees using trusted computing technologies, such as trusted platform module and integrity measurement architecture. Less compute-intensive workloads, such as inference, execute only inside TEE, thus at a lower trusted computing base. The evaluation shows that during the ML training on CIFAR-10 and real-world medical datasets, Perun achieved a 161x to 1560x speedup compared to a pure TEE-based approach.
Factorized Gaussian Process Variational Autoencoders

Variational autoencoders often assume isotropic Gaussian priors and mean-field posteriors, hence do not exploit structure in scenarios where we may expect similarity or consistency across latent variables. Gaussian process variational autoencoders alleviate this problem through the use of a latent Gaussian process, but lead to a cubic inference time complexity. We propose a more scalable extension of these models by leveraging the independence of the auxiliary features, which is present in many datasets. Our model factorizes the latent kernel across these features in different dimensions, leading to a significant speed-up (in theory and practice), while empirically performing comparably to existing non-scalable approaches. Moreover, our approach allows for additional modeling of global latent information and for more general extrapolation to unseen input combinations.
Learning to measure: adaptive informationally complete POVMs for near-term quantum algorithms

Variational quantum algorithms stand as the most promising approaches towards practical applications of near-term quantum computers. However, these methodologies usually require a large number of measurements, which represents an important roadblock for future real-world use cases. We introduce a novel approach to tackle this problem: a variational measurement scheme. We present an algorithm that optimises informationally complete POVMs on the fly in order to minimise the statistical fluctuations in the estimation of relevant cost functions. We use it in combination with the Variational Quantum Eigensolver to calculate ground-state energies of molecular Hamiltonians in numerical simulations and show that it is competitive with state-of-the-art measurement reduction approaches. We also highlight the potential of the informational completeness of the measurement outcomes by reusing the ground-state energy estimation data to perform high-fidelity reduced state tomography.
Hierarchical Complementary Learning for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) is a challenging problem which aims to localize objects with only image-level labels. Due to the lack of ground truth bounding boxes, class labels are mainly employed to train the model. This model generates a class activation map (CAM) which activates the most discriminate features. However, the main drawback of CAM is the ability to detect just a part of the object. To solve this problem, some researchers have removed parts from the detected object \cite{b1, b2, b4}, or the image \cite{b3}. The aim of removing parts from image or detected parts of the object is to force the model to detect the other features. However, these methods require one or many hyper-parameters to erase the appropriate pixels on the image, which could involve a loss of information. In contrast, this paper proposes a Hierarchical Complementary Learning Network method (HCLNet) that helps the CNN to perform better classification and localization of objects on the images. HCLNet uses a complementary map to force the network to detect the other parts of the object. Unlike previous works, this method does not need any extras hyper-parameters to generate different CAMs, as well as does not introduce a big loss of information. In order to fuse these different maps, two different fusion strategies known as the addition strategy and the l1-norm strategy have been used. These strategies allowed to detect the whole object while excluding the background. Extensive experiments show that HCLNet obtains better performance than state-of-the-art methods.
Literature Review of Lean Methodology and Research Issues for Identifying and Eliminating Waste in Software Development

Lean principles is a methodology that focuses on identifying and eliminating activities or tasks that are not considered as important by the customer and do not add value, whether it is a manufacturing or software process. Be it manufacturing or a service industry, there are some components which can be identified as waste. Lean is a customer-centric concept; hence, activities which do not add value to the customer are considered as waste. An activity in a process which does not add value to customer consumes resources and adds cost or time can be can be called as waste or useless and hence can be eliminated. Lean is a widespread concept of manufacturing but seldom used in software industry. Literature contribution on Lean methodology in software and manufacturing industry are fragmented and show some significant limitations. Aim of this paper is to promote Lean concept in software industry.
GTCOM Neural Machine Translation Systems for WMT20

This paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT20 shared news translation task. We participate in four directions: English to (Khmer and Pashto) and (Khmer and Pashto) to English. Further, we get the best BLEU scores in the directions of English to Pashto, Pashto to English and Khmer to English (13.1, 23.1 and 25.5 respectively) among all the participants. Our submitted systems are unconstrained and focus on mBART (Multilingual Bidirectional and Auto-Regressive Transformers), back-translation and forward-translation. Also, we apply rules, language model and RoBERTa model to filter monolingual, parallel sentences and synthetic sentences. Besides, we validate the difference of the vocabulary built from monolingual data and parallel data.
TEACHING ME CHING METHODS A THODS AT OPTION T OPTIONAL MATHEMATICS

Background. The article discusses the problems of introducing optional classes into the secondary school — a fundamentally new mass form of education, examines the methods and techniques of teaching and their appropriate use in optional classes. It is necessary to take into account the fact that the choice of students in elective classes is based on the principle of voluntariness. Methods. The article expresses the importance of focusing mathematical electives on the development of teaching methods for topics and sections of mathematics that are included in high school programs. Results. 1. It should be noted that the students of mathematical electives themselves prefer creative forms and methods of teaching and strive not to be passive listeners. 2. Characterizing the whole complex of teaching methods used in elective classes in mathematics, it should be noted that in the proper sense of the word teaching methods are used here the same as in teaching the basic course of mathematics. 3. In the general course of mathematics, the teaching method is used only fragmentarily, the essence of which lies in the independent disclosure of new content by students with the unobtrusive help of the teacher. Conclusion. What is described in this article should not be understood as a decisive rejection of lectures in general. On the contrary, under certain conditions, a lecture method of presentation (say, a survey lecture on a complex problem) can be useful.
EXPLORING THE CONNECTION AMONG LANGUAGE, CULTURE, IDENTITY AND DIFFERENCE

This paper aims to explore the link among language, culture, identity and difference. It is beneficial to analyse because it contributes on the development of language education. The reason is that language learning and culture are inherent each other. This research’s method is the reflective inquiry as the part of qualitative research. This study scrutinises writer’s personal experiences concerning the link among language, culture, identity and difference. Hence, the research’s subject in this research is the writer’s personal experiences. Specifically, the writer’s experiences are intonation in expressing angriness and interacting with Jakarta people. The related experiences are written and collected by taking notes, and it is directly analysed when the data collected. The study’s result indicates that there has been a robust connection among language, culture, identity and difference. One’s culture and language can signify their identity and difference. Therefore, the connection between language, culture, identity and difference is required to explore in order to elicit significant impact on the development of language learning. Keywords : Language, Culture, Identity, Difference, Language learning
RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models

We describe a robotic learning system for autonomous navigation in diverse environments. At the core of our method are two components: (i) a non-parametric map that reflects the connectivity of the environment but does not require geometric reconstruction or localization, and (ii) a latent variable model of distances and actions that enables efficiently constructing and traversing this map. The model is trained on a large dataset of prior experience to predict the expected amount of time and next action needed to transit between the current image and a goal image. Training the model in this way enables it to develop a representation of goals robust to distracting information in the input images, which aids in deploying the system to quickly explore new environments. We demonstrate our method on a mobile ground robot in a range of outdoor navigation scenarios. Our method can learn to reach new goals, specified as images, in a radius of up to 80 meters in just 20 minutes, and reliably revisit these goals in changing environments. We also demonstrate our method's robustness to previously-unseen obstacles and variable weather conditions. We encourage the reader to visit the project website for videos of our experiments and demonstrations this https URL
Oracle Complexity in Nonsmooth Nonconvex Optimization

It is well-known that given a smooth, bounded-from-below, and possibly nonconvex function, standard gradient-based methods can find $\epsilon$-stationary points (with gradient norm less than $\epsilon$) in $\mathcal{O}(1/\epsilon^2)$ iterations. However, many important nonconvex optimization problems, such as those associated with training modern neural networks, are inherently not smooth, making these results inapplicable. In this paper, we study nonsmooth nonconvex optimization from an oracle complexity viewpoint, where the algorithm is assumed to be given access only to local information about the function at various points. We provide two main results (under mild assumptions): First, we consider the problem of getting near $\epsilon$-stationary points. This is perhaps the most natural relaxation of finding $\epsilon$-stationary points, which is impossible in the nonsmooth nonconvex case. We prove that this relaxed goal cannot be achieved efficiently, for any distance and $\epsilon$ smaller than some constants. Our second result deals with the possibility of tackling nonsmooth nonconvex optimization by reduction to smooth optimization: Namely, applying smooth optimization methods on a smooth approximation of the objective function. For this approach, we prove an inherent trade-off between oracle complexity and smoothness: On the one hand, smoothing a nonsmooth nonconvex function can be done very efficiently (e.g., by randomized smoothing), but with dimension-dependent factors in the smoothness parameter, which can strongly affect iteration complexity when plugging into standard smooth optimization methods. On the other hand, these dimension factors can be eliminated with suitable smoothing methods, but only by making the oracle complexity of the smoothing process exponentially large.
DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction

Distant supervision (DS) is a well established technique for creating large-scale datasets for relation extraction (RE) without using human annotations. However, research in DS-RE has been mostly limited to the English language. Constraining RE to a single language inhibits utilization of large amounts of data in other languages which could allow extraction of more diverse facts. Very recently, a dataset for multilingual DS-RE has been released. However, our analysis reveals that the proposed dataset exhibits unrealistic characteristics such as 1) lack of sentences that do not express any relation, and 2) all sentences for a given entity pair expressing exactly one relation. We show that these characteristics lead to a gross overestimation of the model performance. In response, we propose a new dataset, DiS-ReX, which alleviates these issues. Our dataset has more than 1.5 million sentences, spanning across 4 languages with 36 relation classes + 1 no relation (NA) class. We also modify the widely used bag attention models by encoding sentences using mBERT and provide the first benchmark results on multilingual DS-RE. Unlike the competing dataset, we show that our dataset is challenging and leaves enough room for future research to take place in this field.
Guided Table Structure Recognition through Anchor Optimization

This paper presents the novel approach towards table structure recognition by leveraging the guided anchors. The concept differs from current state-of-the-art approaches for table structure recognition that naively apply object detection methods. In contrast to prior techniques, first, we estimate the viable anchors for table structure recognition. Subsequently, these anchors are exploited to locate the rows and columns in tabular images. Furthermore, the paper introduces a simple and effective method that improves the results by using tabular layouts in realistic scenarios. The proposed method is exhaustively evaluated on the two publicly available datasets of table structure recognition i.e ICDAR-2013 and TabStructDB. We accomplished state-of-the-art results on the ICDAR-2013 dataset with an average F-Measure of 95.05$\%$ (94.6$\%$ for rows and 96.32$\%$ for columns) and surpassed the baseline results on the TabStructDB dataset with an average F-Measure of 94.17$\%$ (94.08$\%$ for rows and 95.06$\%$ for columns).
Calibrating Intermediate COCOMO Model Using Genetic Algorithm

Calculating the cost of programming is the most critical component used to measure effort and time to accomplish the task during software project management. Nowadays, there are several models for cost forecasting program development, supplying useful information to project management team to make the best choices. One such notable numerical model is the COCOMO model. The objective is to modify the coefficients of intermediate COCOMO model with a meta-heuristic algorithm. Estimated cost will be compared with the real data. The methodology is applied on COCOMO NASA software dataset.
Game Theory Approach for Trust Management in the Realm of IoT

The main purpose of game theory approach is to provide security- and confidence-based routing to find confidence-based secure route from initiator to destination which will satisfy some quality-of-service constraints. By applying this methodology, the utility capacity of each node is settled dependent on the verification and amassed throughput. By checking the past estimations, the utility capacity is redesigned with a punishment esteem and dependent on the refreshed utility capacity, the assault node is perceived. At the point when a middle of the road node gets a packet, it will choose its next jump dependent on the steering section. What is more, simultaneously, it will likewise affirm whether its potential next jump’s trust bigger than the trust esteem or not. If not, which indicates that the following jump is certainly not a dependable node (i.e., suspect or vindictive node) and a safe way trust-ready occasion will be enacted. The complete objective of the framework is in light of the abrupt or concealed noxious node in the steering way, so as to keep up the effectiveness of directing. Contrasted and other trust course support frameworks of this new proposed secure way trust-ready framework could diminish the directing overhead and course revelation recurrence. The fundamental reasons are: (1) this new framework is increasingly qualified to the “trust” standard, goal, way ID instead of just goal; (2) the previous bounce of every way in the forerunner list is utilized to oversee the engendering scope of the course blunder message.
EVMPatch: Timely and Automated Patching of Ethereum Smart Contracts

Recent attacks exploiting errors in smart contract code had devastating consequences thereby questioning the benefits of this technology. It is currently highly challenging to fix errors and deploy a patched contract in time. Instant patching is especially important since smart contracts are always online due to the distributed nature of blockchain systems. They also manage considerable amounts of assets, which are at risk and often beyond recovery after an attack. Existing solutions to upgrade smart contracts depend on manual and error-prone processes. This paper presents a framework, called EVMPatch, to instantly and automatically patch faulty smart contracts. EVMPatch features a bytecode rewriting engine for the popular Ethereum blockchain, and transparently/automatically rewrites common off-the-shelf contracts to upgradable contracts. The proof-of-concept implementation of EVMPatch automatically hardens smart contracts that are vulnerable to integer over/underflows and access control errors, but can be easily extended to cover more bug classes. Our extensive evaluation on 14,000 real-world (vulnerable) contracts demonstrate that our approach successfully blocks attack transactions launched on these contracts, while keeping the intended functionality of the contract intact. We perform a study with experienced software developers, showing that EVMPatch is practical, and reduces the time for converting a given Solidity smart contract to an upgradable contract by 97.6 %, while ensuring functional equivalence to the original contract.
A Weakly Supervised Model for Solving Math word Problems.

Solving math word problems (MWPs) is an important and challenging problem in natural language processing. Existing approaches to solve MWPs require full supervision in the form of intermediate equations. However, labeling every math word problem with its corresponding equations is a time-consuming and expensive task. In order to address this challenge of equation annotation, we propose a weakly supervised model for solving math word problems by requiring only the final answer as supervision. We approach this problem by first learning to generate the equation using the problem description and the final answer, which we then use to train a supervised MWP solver. We propose and compare various weakly supervised techniques to learn to generate equations directly from the problem description and answer. Through extensive experiment, we demonstrate that even without using equations for supervision, our approach achieves an accuracy of 56.0 on the standard Math23K dataset. We also curate and release a new dataset for MWPs in English consisting of 10227 instances suitable for training weakly supervised models.
Contextualized Offline Relevance Weighting for Efficient and Effective Neural Retrieval

Online search latency is a major bottleneck in deploying large-scale pre-trained language models, e.g. BERT, in retrieval applications. Inspired by the recent advances in transformer-based document expansion technique, we propose to trade offline relevance weighting for online retrieval efficiency by utilizing the powerful BERT ranker to weight the neighbour documents collected by generated pseudo-queries for each document. In the online retrieval stage, the traditional query-document matching is reduced to the much less expensive query to pseudo-query matching, and a document rank list is quickly recalled according to the pre-computed neighbour documents. Extensive experiments on the standard MS MARCO dataset with both passage and document ranking tasks demonstrate promising results of our method in terms of both online efficiency and effectiveness.
Big Data Technology and Deep Learning Algorithm in Civil Engineering Teaching

Abstract The advent of the era of big data is driving changes in teaching methods. This research mainly discusses the application of big data technology and deep learning algorithm in civil engineering teaching. From the perspective of students, provide guidance on learning and life, pass on the experience summary of graduating students to school students for reference, and combine real-time school data to provide real-time guidance to school students. From the perspective of faculty and staff, we can understand the basic situation of students in a short time to achieve the purpose of improving teaching quality. Teachers should choose and determine the teaching support based on the knowledge and skills that the civil engineering students have mastered, combined with the teaching content. School teachers need to improve their understanding of problems, pay attention to creating various problem scenarios for students, promote the collision of multiple thinking processes, and cultivate students’ creative thinking and ability. In the process of evaluating whether students can use the visual method of knowledge to memorize new knowledge, the average evaluation value is between 2.5-3. This research is helpful to promote the reform of civil engineering teaching.
Hate Speech Detector: Negator

In this contemporary world, diversity is far spread. In this background tolerance and moderate ideology are the only tools for peaceful survival. However, it is unfortunate to experience hate speeches quite often. In this background, an attempt has been made to develop a project to detect hate speech on the internet. The project has been developed using HTML, CSS, JavaScript and NLP (Natural Language Processing), all of which equip the Negator and make it an antidote for Hate Speech.