Reviewerly/exHarmony huggingface dataset:
Abstractive meeting summarization based on an attentional neural model

Through the ages, in all nations, at all times, people spend a lot of their time on discussing new and important issues either on meetings or in conferences. With the evolution and the abundance of Automatic Speech Recognition (ASR) frameworks, automatic transcripts and even automatic meeting summarization are getting more and more interesting. Recently, automatic summarization faces deeper progresses on speech summarization. Neural models had been introduced to tackle with many difficulties of abstractive summarization. Our contribution in this paper focuses on these weaknesses of neural abstractive meeting summarization and suggests an encoder-decoder model based on an attentional algorithm on the decoding sequence. We proposed a deep encoder-decoder model based on attention mechanism (DEDA) for ASR transcripts. Experiments on the AMI Dataset demonstrates that our proposed method ensured competitive results with the state of the art even on extractive or abstractive models. The experimental analyses also put the stress on the performance of the summarized utterances as well as the reduction of the occurrence repetition in summaries.
Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences

With the increase in demand for video conferencing and IOT applications, efficient video coding standards are necessary. The performance of MPEG-4 coding scheme depends on the efficiency of the video object plane (VOP) generation methods. In head and shoulder video, such as news reading, video conferencing video sequences, the object has a very little movement in between two consecutive frames. Therefore, traditional segmentation methods could not extract the complete VOP efficiently. In this paper, we propose an efficient spatiotemporal segmentation method to extract the moving object for the generation of VOP in head and shoulder video sequences. First, a motion map of the object at each frame is generated based on the entropy of the temporal change of each pixel. Secondly, each frame is spatially segmented based on peak-means clustering approach. Finally, both motion map and spatial segmentation information are fused to extract the complete shape of the slow moving object. Experimental outcome depicts that the proposed method has highest detection accuracy with average intersection of union (IOU) score of 94.32% per frame and F1 measure of 97.75% per frame.
NLP Tools for Predictive Maintenance Records in MaintNet

Processing maintenance logbook records is an important step in the development of predictive maintenance systems. Logbooks often include free text fields with domain specific terms, abbreviations, and non-standard spelling posing challenges to off-the-shelf NLP pipelines trained on standard contemporary corpora. Despite the importance of this data type, processing predictive maintenance data is still an under-explored topic in NLP. With the goal of providing more datasets and resources to the community, in this paper we present a number of new resources available in MaintNet, a collaborative open-source library and data repository of predictive maintenance language datasets. We describe novel annotated datasets from multiple domains such as aviation, automotive, and facility maintenance domains and new tools for segmentation, spell checking, POS tagging, clustering, and classification.
Efficiency of Outsourcing and Outstaffing Mechanisms Based on MOOCs in the Market of Entrepreneurial Education Services

The purpose of this article is to analyze, based on new experimental data, the ef-fectiveness of entrepreneurship education using outsourcing and outstaffing (O&amp;O) mechanisms based on the implementation of a massive open online course (MOOC). The field study was conducted among 205 undergraduate stu-dents enrolled in entrepreneurial programs in Management and Economics at three Russian universities. As a result of the study, the following hypotheses were confirmed. Namely, H1 "Implementation of O&amp;O does not reduce student satisfaction with the educational process", H2 "Implementation of O&amp;O does not reduce the level of students' understanding of theoretical teaching materials". H3 "Implementation of O&amp;O does not reduce the level of students' understanding of practical learning tasks", H4 "The introduction of O&amp;O has a positive impact on the business environment of the university". H5 "The introduction of O&amp;O has a positive impact on the entrepreneurial intentions of students", H6 "The introduc-tion of O&amp;O does not reduce the level of students' achievements". At the same time, all O&amp;O elements were included in the educational process through online access to MOOCs based on EdX. The practical significance of the results ob-tained in the article is that the proposed approach allows one to effectively imple-ment outsourcing and outstaffing for the organization of entrepreneurial education based on MOOC, the creation and connection of which does not require signifi-cant costs from the university.
Brute Force and Buffer Overflow Attacks

This chapter covers two important attacks, the buffer overflow attack and the brute force attack, which are frequently employed against C++ applications and programs.
Improved Autoregressive Modeling with Distribution Smoothing

While autoregressive models excel at image compression, their sample quality is often lacking. Inspired by randomized smoothing for adversarial defense, we incorporate randomized smoothing techniques into autoregressive generative modeling. We first model a smoothed version of the data distribution and then recover the data distribution by learning to reverse the smoothing process. We demonstrate empirically on a 1-d dataset that by appropriately choosing the smoothing level, we can keep the proposed process relatively easier to model than directly learning a data distribution with a high Lipschitz constant. Since autoregressive generative modeling consists of a sequence of 1-d density estimation problems, we believe the same arguments can be generalized to an autoregressive model. This seemingly simple procedure drastically improves the sample quality of existing autoregressive models on several synthetic and real-world datasets while obtaining competitive likelihoods on synthetic datasets.
Evaluation of Neural Network Architectures for First Break Picking

Summary We have implemented a deep learning based first break picker and trained it on various land seismic datasets and evaluated a number of neural network architectures. A deep network with U-net architecture, pre-trained on coarse scale input data provided the most accurate results. Because we use the full shot gather at various scales, the impact of noisy traces is reduced. The neural network corrects random mispicks. This suggest a practical application, namely to train, or re-train a pre-trained network via transfer learning, on a single dataset after conventional FB picking.
Suspect AI: Vibraimage, Emotion Recognition Technology, and Algorithmic Opacity

Vibraimage is a digital system that quantifies a subject’s mental and emotional state by analysing video footage of the movements of their head. Vibraimage is used by police, nuclear power station operators, airport security and psychiatrists in Russia, China, Japan and South Korea, and has been deployed at an Olympic Games, FIFA World Cup, and G7 Summit. Yet there is no reliable evidence that the technology is actually effective; indeed, many claims made about its effects seem unprovable. What exactly does vibraimage measure, and how has it acquired the power to penetrate the highest profile and most sensitive security infrastructure across Russia and Asia?I first trace the development of the emotion recognition industry, before examining attempts by vibraimage’s developers and affiliates scientifically to legitimate the technology, concluding that the disciplining power and corporate value of vibraimage is generated through its very opacity, in contrast to increasing demands across the social sciences for transparency. I propose the term ‘suspect AI’ to describe the growing number of systems like vibraimage that algorithmically classify suspects / non-suspects, yet are themselves deeply suspect. Popularising this term may help resist such technologies’ reductivist approaches to ‘reading’ – and exerting authority over – emotion, intentionality and agency.
A profile of senior high school students’ science process skills on heat material

Abstract In physics learning, students need to be trained to develop Science Process Skills (SPS). This research is pre-experiment research. The main objective of this research is to analyze the profile of senior high school students’ SPS on heat material. The subjects of this research were senior high school students in Indonesia. The sample of this research was 110 students of class X senior high school, first year. Determination of the sample in this research using purposive sampling. The SPS Test Instrument (SPSTI) to determine the profile of senior high school students’ SPS on heat material. SPSTI consists of eight indicators: Formulating the problem; Formulate a hypothesis; Identifying experimental variables; Defines the operational definition of the experimental variable; Designing experimental procedures; Creating graph; Data analysis; Formulating conclusions. This instrument has been declared valid and reliable by the three experts. In the case of this research, the profile of senior high school students’ SPS on heat material is generally still at a low level. The implication of this research can be used as empirical evidence that senior high school students’ SPS on heat material still needs to be improved.
Verifying That a Compiler Preserves Concurrent Value-Dependent Information-Flow Security

It is common to prove by reasoning over source code that programs do not leak sensitive data. But doing so leaves a gap between reasoning and reality that can only be filled by accounting for the behaviour of the compiler. This task is complicated when programs enforce value-dependent information-flow security properties (in which classification of locations can vary depending on values in other locations) and complicated further when programs exploit shared-variable concurrency.Prior work has formally defined a notion of concurrency-aware refinement for preserving value-dependent security properties. However, that notion is considerably more complex than standard refinement definitions typically applied in the verification of semantics preservation by compilers. To date it remains unclear whether it can be applied to a realistic compiler, because there exist no general decomposition principles for separating it into smaller, more familiar, proof obligations.In this work, we provide such a decomposition principle, which we show can almost halve the complexity of proving secure refinement. Further, we demonstrate its applicability to secure compilation, by proving in Isabelle/HOL the preservation of value-dependent security by a proof-of-concept compiler from an imperative While language to a generic RISC-style assembly language, for programs with shared-memory concurrency mediated by locking primitives. Finally, we execute our compiler in Isabelle on a While language model of the Cross Domain Desktop Compositor, demonstrating to our knowledge the first use of a compiler verification result to carry an information-flow security property down to the assembly-level model of a non-trivial concurrent program.
Microseismic Hypocenter Location Using an Artificial Neural Network

Summary The sharp increase in the occurrence of human induced earthquakes globally requires real-time source location capabilities, particularly in areas where no prior seismic activity occurred. Recent advances in the field of machine learning coupled with available computational resources provide a great opportunity to address the challenge. Researchers have started looking into using convolutional neural networks (CNNs) for hypocenter determination by training on already located seismic events. We propose an alternate approach to the problem. We train a feed-forward neural network on synthetic P-wave arrival time data (based on a velocity model or empirical data). Once trained, the neural network can be deployed for real-time location of seismic events using observed P-wave arrival times. The use of a feed-forward neural network allows fast training compared to CNNs. We show sensitivity of the proposed method to the training dataset (density and distribution of the training sources), noise in the arrival times of the detected events, and size of the monitoring network.
Penerapan Laporan Keuangan Berbasis Excel pada Bengkel Las Bina Logam

The purpose of this research was to knowing how to apply the excel-based income statement at Bina Logam Welding Workshop and knowing what are the problems of Bina Logam Welding Workshop, so it doesn’t do excel based bookkeeping. The research method is Resource & Development. Data collection techniques carried out by interview and documentation. The results of this research found that the application of excel-based financial statements at Bina Logam Welding Workshop has helped companies to process avalaible data to create standard financial reports as well and see the company’s performance reflected in the income statements. The income statement at Bina Logam Welding Workshop hasn’t fully met the accounting standards. The obstacles that hamper the application of excel based financial statements at Bina Logam Welding Workshop are: lack of human resources who have the ability to apply the accounting and financial statements which is in accordance with standards because of the inadequate level of education to manage company finances, there are still mixed company accounts and owner’s personal accounts, making it difficult to determine financial activities for workshop and for personal, and recording transactions or bookkeeping that hasn’t been done well. Keywords : Accounting, Financial Statement, Ms. Excel
Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation

Many real-world applications such as robotics provide hard constraints on power and compute that limit the viable model complexity of Reinforcement Learning (RL) agents. Similarly, in many distributed RL settings, acting is done on un-accelerated hardware such as CPUs, which likewise restricts model size to prevent intractable experiment run times. These actor-latency constrained settings present a major obstruction to the scaling up of model complexity that has recently been extremely successful in supervised learning. To be able to utilize large model capacity while still operating within the limits imposed by the system during acting, we develop an (ALD) procedure that leverages a continual form of distillation that transfers learning progress from a large capacity learner model to a small capacity actor model. As a case study, we develop this procedure in the context of partially-observable environments, where transformer models have had large improvements over LSTMs recently, at the cost of significantly higher computational complexity. With transformer models as the learner and LSTMs as the actor, we demonstrate in several challenging memory environments that using Actor-Learner Distillation largely recovers the clear sample-efficiency gains of the transformer learner model while maintaining the fast inference and reduced total training time of the LSTM actor model.
Higher Education Enrolment Query Chatbot Using Machine Learning

The automation has taken over many of the works that were done by humans because of the accuracy of the machine which is not found in humans. One such technology is a chatbot, also recognized as a communications agent. Chatbots are widely used in industry, banking, consultancy, and many more sectors. Education is one of the areas a chatbot is being used in. By the use of techniques like Natural Language Processing (NLP), Natural Language Understanding (NLU), Pattern Matching Techniques, and Retrieval of Information (IR), a chatbot can be built faster, easier, and more effectively. Incorporating a bot in an educational institution can help make rendering information faster than normal, and processes such as registration, application for ID, etc., can be automated, so people who are anxious about talking to others can use the bot to access the information they need, and also prevent confusion.
Preference-based AI Planning for Web Service Composition

A web service is a technology that allows data to be exchanged between applications using open protocols and standards. Web services generally offer basic functions that solve specific problems. The composition of services occurs when more complex problems need to be solved using successive invocations to other services. The automatic composition of web services is a current problem. Due to the continuous creation and updating of services, the manual composition of new services becomes impractical. Planning algorithms are an alternative to automate the process of web services composition. In particular, the planning algorithms with preferences allow users to guide the algorithm to find a solution in line with their requirements (e.g. prioritizing the use of services according to properties such as availability, cost, response time, etc.). This paper introduces a Planning approach with preferences to compose and run complex services from a set of web services given as input. To evaluate the approach, different preferences about the input services were used to obtain composition of services. As a result, we measured the feasibility of the proposed approach and the impact of the use of preferences on the composition result.
Automatic Grading of Portuguese Short Answers Using a Machine Learning Approach

Short answers are routinely used in learning environments for students’ assessment. Despite its importance, teachers find the task of assessing discursive answers very time-consuming. Aiming at assisting in this problem, this work explores the Automatic Short Answer Grading (ASAG) field using a machine learning approach. The literature was reviewed and 44 papers using different techniques were analyzed considering many aspects. A Portuguese dataset was build with more than 7000 short answers. Different approaches were experimented and a final model was created with their combination. The model’s effectiveness showed to be satisfactory, with kappa scores indicating moderate/substantial agreement between the model and human grading.
Noise against noise: stochastic label noise helps combat inherent label noise

The noise in stochastic gradient descent (SGD) provides a crucial implicit regularization effect, previously studied in optimization by analyzing the dynamics of parameter updates. In this paper, we are interested in learning with noisy labels, where we have a collection of samples with potential mislabeling. We show that a previously rarely discussed SGD noise, induced by stochastic label noise (SLN), mitigates the effects of inherent label noise. In contrast, the common SGD noise directly applied to model parameters does not. We formalize the differences and connections of SGD noise variants, showing that SLN induces SGD noise dependent on the sharpness of output landscape and the confidence of output probability, which may help escape from sharp minima and prevent overconfidence. SLN not only improves generalization in its simplest form but also boosts popular robust training methods, including sample selection and label correction. Specifically, we present an enhanced algorithm by applying SLN to label correction. Our code is released.
The method of spatial-temporal reconstruction of dynamic images based on a geometric model with contour and texture analysis

Abstract The article presents a method for image restoration based on a geometric model. The object of research is methods of image removal and restoration. The research is aimed at creating a high-speed and highly efficient system for recovering the lost zones of the underlying surface map image. An algorithm for removing objects and restoring images of lost areas is investigated and its software implementation is developed. The efficiency of the developed algorithm was evaluated on a test example using a statistical criterion.
Attentive deep CNN for speaker verification

In this paper, an end-to-end speaker verification system based on attentive deep convolutional neural network (CNN) is highlighted. It takes log filter bank coefficients as input and measures speaker similarity between a test utterance and enrollment utterances by cosine similarity for verification. The approach utilizes the channel attention module of convolutional block attention module (CBAM) to increase representation power by giving different weights to feature maps. In addition, softmax is used to pre-train for initializing the weights of the network and tuple-based end-to-end (TE2E) loss function is responsible for fine-tune in evaluation stage, such a strategy not only results in notable improvements over the baseline model but also allows for direct optimization of the evaluation metric. Experimental results on VoxCeleb dataset indicates that proposed model achieves an equal error rate (EER) of 3.83%, which is slightly worse than x-vectors while outperforms i-vectors.
Selecting Stable Safe Configurations for Systems Modelled by Neural Networks with ReLU Activation

Combining machine learning with constraint solving and formal methods is an interesting new direction in research with a wide range of safety critical applications. Our focus in this work is on analyzing Neural Networks with Rectified Linear Activation Function (NN-ReLU). The existing, very recent research works in this direction describe multiple approaches to satisfiability checking for constraints on NN-ReLU output. Here we extend this line of work in two orthogonal directions: We propose an algorithm for finding configurations of NN-ReLU that are (1) safe and (2) stable. We assume that the inputs of the NN-ReLU are divided into existentially and universally quantified variables, where the former represent the parameters for configuring the NN-ReLU and the latter represent (possibly constrained) free inputs. We are looking for (1) values of the configuration parameters for which the NN-ReLU output satisfies a given constraint for any legal values of the input variables (the safety requirement); and (2) such that the entire family of configurations with configuration variable values close to a safe configuration is also safe (the stability requirement). To our knowledge this is the first work that proposes SMT-based algorithms for searching safe and stable configuration parameters for systems modelled using neural networks. We experimentally evaluate our algorithm on NN-ReLUs trained on a set of real-life datasets originating from an industrial CAD application at Intel.
Deep Learning Models For Word Sense Disambiguation: A Comparative Study

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Investigation of edge detection methods using the example of wave field pattern image

Abstract This article explores the methods of detecting boundaries in images of wave field patterns. As a result, an application for image processing was developed using Sobel, Prewitt, Roberts, Gabor and Canny filters and experiments were carried out to detect the edge on images of wave field patterns. In conclusion, the quantitative and qualitative characteristics of the filters were presented, and a conclusion was made on the selection of the boundary detection filter. The results of this study will be used in the study of the neural network neocognitron for the recognition of geological and physical models of environments (GPME). This work was supported by the RFBR grant No. 19-07-00170.