Reviewerly/exHarmony huggingface dataset:
Numeracy enhances the Literacy of Language Models

Specialized number representations in NLP have shown improvements on numerical reasoning tasks like arithmetic word problems and masked number prediction. But humans also use numeracy to make better sense of world concepts, e.g., you can seat 5 people in your ‘room’ but not 500. Does a better grasp of numbers improve a model’s understanding of other concepts and words? This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy. To support this investigation, we develop Wiki-Convert, a 900,000 sentence dataset annotated with numbers and units, to avoid conflating nominal and ordinal number occurrences. We find a significant improvement in MWP for sentences containing numbers, that exponent embeddings are the best number encoders, yielding over 2 points jump in prediction accuracy over a BERT baseline, and that these enhanced literacy skills also generalize to contexts without annotated numbers. We release all code at https://git.io/JuZXn.
MAESSTRO 1.0.0

MAESSTRO is a software for sound synthesis of piano tones based on a complete vibroacoustic modeling of the piano. The main goal is to provide piano makers a set of tools that allow them to predict the acoustic impacts of structural modifications of piano soundboards on the resulting sounds. The interest of the physical modeling that drives the numerical computations embedded in the software is to be able to reproduce the vibroacoustics of the piano in a realistic fashion: the acoustic variations between tones produced by different pianos faithfully reflect their structural differences. For that purpose, MAESSTRO gathers and coordinates several numerical and physical models that numerically simulate all of the physical phenomena involved in the production of piano tones, from the hammer impulsion upto the acoustic radiation.MAESSTRO includes several functionalities which are the following* entering the geometry and the materials of the virtual soundboard thanks to a Graphical User Interface (GUI), * feeding MAESSTRO with MIDI files to be synthesized,* simulating numerically the physical phenomena involved in the production of piano tones, * post-processing the software outputs, * creating audio files of synthesized piano tones.The package contains three subpackages :* maesstro-ihm contains the source codes of the GUI, implemented in TypeScript* maesstro-matlab is a Matlab toolbox that contains the source codes for the computation of the modal basis of the soundboard, including an automatic mesh generator using GMSH. Matlab is only needed to modify the source files and generate updated executables. In order to launch the executables, only Matlab Runtime is needed* maesstro-python contains the python modules which is used to call the different modules via a command line interface
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis

Autoregressive models and their sequential factorization of the data likelihood have recently demonstrated great potential for image representation and synthesis. Nevertheless, they incorporate image context in a linear 1D order by attending only to previously synthesized image patches above or to the left. Not only is this unidirectional, sequential bias of attention unnatural for images as it disregards large parts of a scene until synthesis is almost complete. It also processes the entire image on a single scale, thus ignoring more global contextual information up to the gist of the entire scene. As a remedy we incorporate a coarse-to-fine hierarchy of context by combining the autoregressive formulation with a multinomial diffusion process: Whereas a multistage diffusion process successively removes information to coarsen an image, we train a (short) Markov chain to invert this process. In each stage, the resulting autoregressive ImageBART model progressively incorporates context from previous stages in a coarse-to-fine manner. Experiments show greatly improved image modification capabilities over autoregressive models while also providing high-fidelity image generation, both of which are enabled through efficient training in a compressed latent space. Specifically, our approach can take unrestricted, user-provided masks into account to perform local image editing. Thus, in contrast to pure autoregressive models, it can solve free-form image inpainting and, in the case of conditional models, local, text-guided image modification without requiring mask-specific training.
Secure Privacy-Preserving Association Rule Mining With Single Cloud Server

To preserve the privacy of data uploaded on the cloud, it is widely accepted to encrypt the data before uploading it. This leads to the challenge of data analysis, especially association rule mining while protecting data privacy. As one of the solutions, homomorphic encryption is presented allowing encrypted data processing without decryption. In particular, the twin-cloud structure is frequently applied in the privacy-preserving association rule mining schemes based on asymmetric homomorphic encryption, which contradicts the reality that most of the practical applications applied the single cloud server. However, the existing related single cloud server schemes suffer from privacy leakage problems. To fill this gap in the literature, in this paper, we first present a universal secure multiplication protocol with the single cloud server using the garbled circuit and additive homomorphic encryption. Based on this multiplication protocol, we construct the inner product protocol, comparison protocol, frequent itemset protocol, and the final association rule mining protocol that is secure against privacy leakage. Finally, we give the theoretical security analysis of the proposed protocols and show its performance analysis.
Learning Space Partitions for Path Planning

Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function. Popular approaches like CEM and CMA-ES greedily focus on promising regions of the search space and may get trapped in local maxima. DOO and VOOT balance exploration and exploitation, but use space partitioning strategies independent of the reward function to be optimized. Recently, LaMCTS empirically learns to partition the search space in a reward-sensitive manner for black-box optimization. In this paper, we develop a novel formal regret analysis for when and why such an adaptive region partitioning scheme works. We also propose a new path planning method PlaLaM which improves the function value estimation within each sub-region, and uses a latent representation of the search space. Empirically, PlaLaM outperforms existing path planning methods in 2D navigation tasks, especially in the presence of difficult-to-escape local optima, and shows benefits when plugged into model-based RL with planning components such as PETS. These gains transfer to highly multimodal real-world tasks, where we outperform strong baselines in compiler phase ordering by up to 245% and in molecular design by up to 0.4 on properties on a 0-1 scale.
RANCANG BANGUN APLIKASI POINT OF SALES (POS) TERINTEGRASI SISTEM INFORMASI AKUNTANSI PADA CV MANTU SEDANA

CV Mantu Sedana is a company engaged in the culinary business, in which the product sold is Balinese chili sauce. CV Mantu Sedana still uses the conventional method in their sales transactions and accounting records, where consumers who make purchases will get written notes and their accounting recording processes still use the manual method, namely by recording on books and agendas. Based on the writer’s explanation above and the needs of CV Mantu Sedana, the writer feels that it is necessary to build an application system or platform that can be used in the sales transaction process as well as accounting records at CV Mantu Sedana, which aims to make it easier for owners to monitor their business in real-time and increase consumer satisfaction. Design of Integrated Point Of Sales (POS) Applications for Accounting Information Systems at CV Mantu Sedana. The writer used the agile method in designing this application. The Agile method is one of the software development methodologies used in software development. In this study, the writer used an interview and observation research instruments. The instrument was addressed to the owner of CV Mantu Sedana which aimed to determine the needs for the system being designed. In this study, the writer used qualitative data types in which the data used were in the form of words, sentences, schemes, and images, in the form of descriptive non-numerical data collection. The results of this study indicated that in making application design at CV Mantu Sedana, it can provide solutions to companies in developing services to customers and problems the company faced. After conducting interviews on user satisfaction in using this point of sales application, thus the writer found that this application was able to overcome the problems and objectives of using this application itself.
Pendekatan Remote Auditing Untuk Internal Audit Dalam Mendeteksi Kecurangan (Fraud) Pada Masa Pandemi Covid-19

The worsening economic conditions and weak supervision and the distorted focus of companies in facing the Covid-19 pandemic crisis open opportunities for fraudsters in companies that take advantage of the conditions to take actions that harm the company. The Covid-19 pandemic currently occurring has also affected work activities and the internal audit audit process that have been previously determined due to the imposition of PSBB and the prohibition of face-to-face activities so that it has an impact on obtaining audit evidence from restrictions on travel access to checkpoints in several areas. This research is expected to provide insights and solutions related to the application of a remote auditing approach to internal audit in detecting fraud during the Covid-19 pandemic. During the Covid-19 pandemic, remote auditing was the right choice to implement with the various advantages and disadvantages of remote auditing itself, remote auditing became an alternative so that work activities and the inspection process could continue and the role of internal audit in realizing added value for the company to assist management and directors in improving the internal control process and optimizing the detection of fraud.
Research Guides: Open Access: Importance of OA

This subject guide is intended to serve as a learning tool to educate our users on open access and the changing world of scholarly communications.
TAG: Gradient Attack on Transformer-based Language Models

Although distributed learning has increasingly gained attention in terms of effectively utilizing local devices for data privacy enhancement, recent studies show that publicly shared gradients in the training process can reveal the private training data (gradient leakage) to a third-party. We have, however, no systematic understanding of the gradient leakage mechanism on the Transformer based language models. In this paper, as the first attempt, we formulate the gradient attack problem on the Transformer-based language models and propose a gradient attack algorithm, TAG, to reconstruct the local training data. Experimental results on Transformer, TinyBERT4, TinyBERT6 BERT_BASE, and BERT_LARGE using GLUE benchmark show that compared with DLG, TAG works well on more weight distributions in reconstructing training data and achieves 1.5x recover rate and 2.5x ROUGE-2 over prior methods without the need of ground truth label. TAG can obtain up to 90% data by attacking gradients in CoLA dataset. In addition, TAG is stronger than previous approaches on larger models, smaller dictionary size, and smaller input length. We hope the proposed TAG will shed some light on the privacy leakage problem in Transformer-based NLP models.
ORIS - On Road Intelli-Sense

We present ORIS, a holistic autonomous traversal assist system. ORIS follows a multimodal architecture which includes navigation, environment sense, and on road vehicle and object detection. Introducing Level 4 autonomy within vehicles in India has been a challenge, with ambiguous road signs to unkept road markings, developing an autonomous system is hurdled with countless challenges. ORIS tackles the aforementioned challenges by introducing an efficient and minimalistic image-input based tagging solution over three different facets: Lane Segmentation and Centering, Road Sign Detection and Recognition, and Vehicles and On Road Object Detection.
Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning

Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited. In this paper, we propose a novel contrastive learning framework that trains sentence embeddings to encode the relations in a graph structure. Given a sentence (unstructured text) and its graph, we use contrastive learning to impose relation-related structure on the token level representations of the sentence obtained with a CharacterBERT (El Boukkouri et al., 2020) model. The resulting relation-aware sentence embeddings achieve state-of-the-art results on the relation extraction task using only a simple KNN classifier, thereby demonstrating the success of the proposed method. Additional visualization by a tSNE analysis shows the effectiveness of the learned representation space compared to baselines. Furthermore, we show that we can learn a different space for named entity recognition, again using a contrastive learning objective, and demonstrate how to successfully combine both representation spaces in an entity-relation task.
Abstract 9220: Employing a Convolutional Neural Network Facilitates Unsupervised Clustering of Aortic Outflow Velocity Profiles from Patients With Severe Aortic Stenosis

Introduction: The aortic outflow velocity profile is key to diagnose severe aortic stenosis (AS), and its shape reflects both aortic valve obstruction and left ventricular contractility. Hypothesis: Hypothesizing that aortic outflow velocity profiles contain more valuable information than can be captured by the human eye, features of the complex geometry of Doppler tracings were extracted by a convolutional neural network (CNN) pre-trained on big data (transfer learning). Methods: After pre-training a CNN (VGG-16) on a large data set (ImageNet data set; 14 million images belonging to 1,000 classes), the convolutional part was employed to transform Doppler tracings to 1D arrays. Principal component analysis (PCA) and k-means clustering of those 1D arrays were performed, and association between cluster and 2-year all-cause mortality was hereinafter assessed. Among 366 eligible patients with pre-procedural echocardiography and right heart catheterization prior to transcatheter aortic valve replacement (TAVR) for severe AS, good quality Doppler tracings from 101 patients were analyzed. Results: Among 101 patients (mean age: 79.3 ± 6.78 years; 49 [48.5%] women), estimated 2-year survival was 83.0% (95% CI: 75.1-91.7%). The convolutional part of the pre-trained VGG-16 model in conjunction with PCA and k-means clustering distinguished 2 shapes of aortic outflow velocity profiles. Kaplan-Meier analysis revealed that mortality in patients from cluster 2 (n = 40 [39.6%]) was significantly increased (HR for 2-year mortality: 3 [95% CI: 1-8.9]). Apart from reduced cardiac output and mean aortic valve gradient (AVG mean ), patients from cluster 2 were also characterized by signs of pulmonary hypertension, impaired right ventricular function and right atrial enlargement. Conventional dichotomization according to AVG mean confirmed that patients with AVG mean &lt; 40 mmHg (n = 54 [53.5%]) decease earlier, but no statistical significance was reached (HR for 2-year mortality: 1.8 [95% CI: 0.59-5.2]). Conclusions: This is a proof-of-principle that transfer learning aids in agnostic interpretation of aortic outflow velocity profiles, thereby holding the promise to refine prognostic resolution for survival after TAVR.
A Webshell Detection Algorithms via Multi-parameter Comprehensive Evaluation Based on Text Recognition

Webshell refers to the malicious backdoor that hackers can obtain the execution authority of arbitrary commands on the network servers. However, Webshell uses a variety of encryption and obfuscation technologies, which increase the difficulty of Webshell detection. To solve the current problems with Webshell detection, We propose a multi-parameter comprehensive evaluation Webshell detection algorithm based on text recognition, called Webshell-TR, and build a Webshell detection system WebShellKing according to this algorithm. The innovation of our method is to build a multi-parameter comprehensive evaluation detection algorithm by integrating seven parameters of Webshell detection, and improve the Webshell detection ability through the fusion judgment of multiple parameters. In order to verify the proposed algorithm, we build a benchmark data set WebSHBDB for Webshell detection, and carry out a controlled experiment. The experimental results show that WebShellKing has better recall rate than other popular Webshell detection tools, and can identify more covert Webshells.
THE DEVELOPMENT OF SOFT SKILLS FOR PROFESSIONAL ACTIVITY (TREND OR REALITY)

Abstract Nowadays it?s hard to distinguish between hard and soft skills requirements. A potential candidate can be requested for having both of them. The article provides the summarized overview of both skill kinds. Keywords: soft-skills, hard-skills, human recruitment.
ИЗ ИСТОРИИ ЭЛЕКТРОМАГНИТНОЕ РЕЛЕ

The stories of electrical work are outlined: on the magnetic field; compass; the first electromagnet; telegraph; electric motor; electric road
Learning to Draw: Emergent Communication through Sketching

Evidence that visual communication preceded written language and provided a basis for it goes back to prehistory, in forms such as cave and rock paintings depicting traces of our distant ancestors. Emergent communication research has sought to explore how agents can learn to communicate in order to collaboratively solve tasks. Existing research has focused on language, with a learned communication channel transmitting sequences of discrete tokens between the agents. In this work, we explore a visual communication channel between agents that are allowed to draw with simple strokes. Our agents are parameterised by deep neural networks, and the drawing procedure is differentiable, allowing for end-to-end training. In the framework of a referential communication game, we demonstrate that agents can not only successfully learn to communicate by drawing, but with appropriate inductive biases, can do so in a fashion that humans can interpret. We hope to encourage future research to consider visual communication as a more flexible and directly interpretable alternative of training collaborative agents.
Rich User Model Applications

Rich user models (RUMs) are computationally congenial representations of user session behaviors derived from session interaction history. They are user-centered and take a cognitive perspective of user interaction at several time scales: within session, session level, and long term use of the system, i.e. work career. This talk will present RUMs derived from legal research session history and show potential applications. One example is customer service intervention effects on future user search behaviors. This work looked at LexisAdvance legal research customers who contacted customer service search experts for in-session query formulation advice. A RUM analysis shows the effects on the user's query formulation bias in subsequent sessions, and also any changes in their search behavior patterns. Other potential applications of RUM analysis include quantification of user behavior changes before and after introduction of product/service features and capabilities. Examples in LexisAdvance include presentation of answer cards along with search results, and behavior effects for different types of automated query suggestions, e.g. token variation by similarity to the user's query vs. suggesting queries with conceptual diversification. RUMs can represent a deeper perspective of customer experience because they describe knowledge use in the context of specific tasks. RUMs enable behavior analysis capacities for exploratory user research and provide input to business and product hypotheses. They can also be used to monitor system performance changes, provide metrics for A/B search feature testing, and quantify the impact of customer education and support investments. Some speculative future applications include real time prediction of user interaction, implicit teaching of users, and other forms of personalization and interaction support. The presentation will also demo Persistent User Model Acquisition (PUMA), an automated system that acquires and maintains RUMs, and exposes them for interactive analysis.
Core Self-Evaluation, Writing Motivation, and Business Writing Performance of College Third Year Students of Occidental Mindoro State College

This predictive cross-sectional study explored core self-evaluation, level of writing motivation, and their influence on the business writing performance of junior business students at Occidental Mindoro State College. One hundred fifty junior business students of Occidental Mindoro State College Main Campus in Academic Year 2020-2021 selected through a simple random sampling technique were the respondents in the study. It was found that the student’s level of core self-evaluation was moderate considering their results in the locus of control, self-esteem, and self-efficacy. However, the students are highly motivated in performing their writing activities. Therefore, the improvement in core self-evaluation may slightly improve writing motivation and writing performance. Likewise, improving writing motivation may slightly improve the writing performance of business students. The positive beta coefficients indicate that if the indicators of writing motivation are improved, the students’ writing performance becomes better. Thus, core self-evaluation and writing motivation affect students’ business writing performance.
A deep Learning-based approach for UAV's distance and height estimation through a monocular camera

This paper offers a visual-based approach that detects the presence of Unmanned Aerial Vehicles (UAVs), or drones, and estimates their distance and height using a monocular camera. The proposed method consists of two fused models, the first model is the YOLOv4 objects detector which is trained on a new custom dataset to detect the UAVs presence and creates a box around the detected UAV followed by a deep feedforward neural network (DFNN)  model which is trained to take advantage of the box dimensions to estimate the UAV's distance and height. In other words, the estimation is based on the UAV's size and position in each frame. The trained YOLOv4 achieves a 99.79% mAP and an IoU of 79.61% and the proposed DFNN obtains an R2 score of 97.1% and an RMSE equal to 2.03m.
Learning to Augment Operations Research Algorithms

This chapter introduces how genetic programming can be integrated into generic optimisation solvers and boost their performance for production scheduling. A simple genetic programming algorithm is introduced to evolve variable selectors for optimisation solvers to reduce the computational efforts required to obtain high-quality or optimal solutions for production scheduling. The optimisation solver enhanced by the evolved variable selectors can find optimal solutions much faster when tested with small/medium instances and can find much better solutions for large instances within a limited run time. Experiments with a wide range of unseen benchmark datasets also demonstrate the effectiveness of the evolved variable selectors as compared with the specialised optimisation algorithms proposed in the literature.