Reviewerly/exHarmony huggingface dataset:
Towards Making the Most of ChatGPT for Machine Translation

ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g, low-resource and distant-language-pairs translation. However, they usually adopt simple prompts which can not fully elicit the capability of ChatGPT. In this report, we aim to further mine ChatGPT’s translation ability by revisiting several aspects: temperature, task information, and domain information, and correspondingly propose two (simple but effective) prompts: Task-Specific Prompts (TSP) and Domain-Specific Prompts (DSP). We show that: 1) The performance of ChatGPT depends largely on temperature, and a lower temperature usually can achieve better performance; 2) Emphasizing the task information further improves ChatGPT’s performance, particularly in complex MT tasks; 3) Introducing domain information can elicit ChatGPT’s generalization ability and improve its performance in the specific domain; 4) ChatGPT tends to generate hallucinations for non-English-centric MT tasks, which can be partially addressed by our proposed prompts but still need to be highlighted for the MT/NLP community. We also explore the effects of advanced in-context learning strategies and find a (negative but interesting) observation: the powerful chain-of-thought prompt leads to word-by-word translation behavior, thus bringing significant translation degradation.
CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models

We propose CHiLL (Crafting High-Level Latents), an approach for natural-language specification of features for linear models. CHiLL prompts LLMs with expert-crafted queries to generate interpretable features from health records. The resulting noisy labels are then used to train a simple linear classifier. Generating features based on queries to an LLM can empower physicians to use their domain expertise to craft features that are clinically meaningful for a downstream task of interest, without having to manually extract these from raw EHR. We are motivated by a real-world risk prediction task, but as a reproducible proxy, we use MIMIC-III and MIMIC-CXR data and standard predictive tasks (e.g., 30-day readmission) to evaluate this approach. We find that linear models using automatically extracted features are comparably performant to models using reference features, and provide greater interpretability than linear models using “Bag-of-Words” features. We verify that learned feature weights align well with clinical expectations.
NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails

Traian Rebedea, Razvan Dinu, Makesh Narsimhan Sreedhar, Christopher Parisien, Jonathan Cohen. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2023.
Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System

Scoring systems are commonly seen for platforms in the era of Big Data. From credit scoring systems in financial services to membership scores in E-commerce shopping platforms, platform managers use such systems to guide users towards the encouraged activity pattern, and manage resources more effectively and efficiently. To establish such scoring systems, several “empirical criteria” are first determined, followed by a dedicated top-down design for each score factor, which usually requires enormous effort to adjust and tune the scoring function in the new application scenario. What's worse, many fresh projects usually have no ground truth or any experience to evaluate a reasonable scoring system, making the designing even harder. To reduce the effort of manual adjustment of the scoring function in every new scoring system, we innovatively study the scoring system from the preset empirical criteria without any ground truth and propose a novel framework to improve the system from scratch. In this paper, we propose a “counter-empirical attacking” mechanism that can generate “attacking” behavior traces and try to break the empirical rules of the scoring system. Then an adversarial “enhancer” is applied to evaluate the scoring system and find the improvement strategy. By training the adversarial learning problem, a proper scoring function can be learned to be robust to the attacking activity traces that are trying to violate the empirical criteria. Extensive experiments have been conducted on two scoring systems, including a shared computing resource platform and a financial credit system. The experimental results have validated the effectiveness of our proposed framework.
Building spatial symmetries into parameterized quantum circuits for faster training

Abstract Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates employed and by the correlations of their parameters. While much research has been devoted to devising adequate gate-sets, typically respecting some symmetries of the problem, very little is known about how their parameters should be structured. In this work, we show that an ideal parameter structure naturally&amp;#xD;emerges when carefully considering spatial symmetries (i.e., the symmetries that are permutations of parts of the system under study). Namely, we consider the automorphism group of the problem Hamiltonian, leading us to develop a circuit construction that is equivariant under this symmetry group. The benefits of our novel circuit structure, called ORB, are numerically probed in several ground-state problems. We find a consistent improvement (in terms of circuit depth, number of parameters required, and gradient magnitudes) compared to literature circuit constructions.
Generalization despite overfitting in quantum machine learning models

The widespread success of deep neural networks has revealed a surprise in classical machine learning: very complex models often generalize well while simultaneously overfitting training data. This phenomenon of benign overfitting has been studied for a variety of classical models with the goal of better understanding the mechanisms behind deep learning. Characterizing the phenomenon in the context of quantum machine learning might similarly improve our understanding of the relationship between overfitting, overparameterization, and generalization. In this work, we provide a characterization of benign overfitting in quantum models. To do this, we derive the behavior of a classical interpolating Fourier features models for regression on noisy signals, and show how a class of quantum models exhibits analogous features, thereby linking the structure of quantum circuits (such as data-encoding and state preparation operations) to overparameterization and overfitting in quantum models. We intuitively explain these features according to the ability of the quantum model to interpolate noisy data with locally "spiky" behavior and provide a concrete demonstration example of benign overfitting.
Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception

Multi-agent collaborative perception as a potential application for vehicle-to-everything communication could significantly improve the perception performance of autonomous vehicles over single-agent perception. However, several challenges remain in achieving pragmatic information sharing in this emerging research. In this paper, we propose SCOPE, a novel collaborative perception frame-work that aggregates the spatio-temporal awareness characteristics across on-road agents in an end-to-end manner. Specifically, SCOPE has three distinct strengths: i) it considers effective semantic cues of the temporal context to enhance current representations of the target agent; ii) it aggregates perceptually critical spatial information from heterogeneous agents and overcomes localization errors via multi-scale feature interactions; iii) it integrates multi-source representations of the target agent based on their complementary contributions by an adaptive fusion paradigm. To thoroughly evaluate SCOPE, we consider both real-world and simulated scenarios of collaborative 3D object detection tasks on three datasets. Extensive experiments show the superiority of our approach and the necessity of the proposed components. The project link is https://ydk122024.github.io/SCOPE/.
AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception

Driver distraction has become a significant cause of severe traffic accidents over the past decade. Despite the growing development of vision-driven driver monitoring systems, the lack of comprehensive perception datasets restricts road safety and traffic security. In this paper, we present an AssIstive Driving pErception dataset (AIDE) that considers context information both inside and outside the vehicle in naturalistic scenarios. AIDE facilitates holistic driver monitoring through three distinctive characteristics, including multi-view settings of driver and scene, multi-modal annotations of face, body, posture, and gesture, and four pragmatic task designs for driving understanding. To thoroughly explore AIDE, we provide experimental benchmarks on three kinds of baseline frameworks via extensive methods. Moreover, two fusion strategies are introduced to give new insights into learning effective multi-stream/modal representations. We also systematically investigate the importance and rationality of the key components in AIDE and benchmarks. The project link is https://github.com/ydk122024/AIDE.
On Finding Optimal Polytrees

Inferring probabilistic networks from data is a notoriously difficult task. Under various goodness-of-fit measures, finding an optimal network is NP-hard, even if restricted to polytrees of bounded in-degree. Polynomial-time algorithms are known only for rare special cases, perhaps most notably for branchings, that is, polytrees in which the in-degree of every node is at most one. Here, we study the complexity of finding an optimal polytree that can be turned into a branching by deleting some number of arcs or nodes, treated as a parameter. We show that the problem can be solved via a matroid intersection formulation in polynomial time if the number of deleted arcs is bounded by a constant. The order of the polynomial time bound depends on this constant, hence the algorithm does not establish fixed-parameter tractability when parameterized by the number of deleted arcs. We show that a restricted version of the problem allows fixed-parameter tractability and hence scales well with the parameter. We contrast this positive result by showing that if we parameterize by the number of deleted nodes, a somewhat more powerful parameter, the problem is not fixed-parameter tractable, subject to a complexity-theoretic assumption.
Human Activity Recognition using Smartphone

This paper consists of all the actions that are done by the people by using mobile phone. We used to record the actions by using the mobile phone. The main aim of this paper is to construct a classification model that is required for identifying actions of the people. This paper is used mainly to get solution of multi-classification problems. By using in a theoretic way, we can understand what the problem is, but we can only solve the problem by performing it in mathematical way. We can get very perfect result by solving in the mathematical way. Here we are deriving the actions of the persons using mobile phone, in a phone there are many sensors present in it. The sensors used in this paper are Accelerometer, Gyroscope. They are required for determining the actions of the person. The output obtained in this paper is used to compare the values in the term’s accuracy and precision. It uses a 3-dimension based accelerometer in order to collect the values obtained; there we determined that 31 values we contained in it. All the actions that are present are derived by using machine learning algorithms, they are, Naïve Bayes Classifiers, support vector machine, and neural networks. The output of the action determination by using the dataset required is used to determine a decrease of marking work to accomplish similar execution with machine learning.
Detection and Prediction of Adverse and Anomalous Events in Medical Robots

Adverse and anomalous (A&amp;A) events are a serious concern in medical robots. We describe a system that can rapidly detect such events and predict their occurrence. As part of this system, we describe simulation, data collection and user interface tools we build for a robot for small animal biopsies. The data we collect consists of both the hardware state of the robot and variables in the software controller. We use this data to train dynamic Bayesian network models of the joint hardware-software state-space dynamics of the robot. Our empirical evaluation shows that (i) our models can accurately model normal behavior of the robot, (ii) they can rapidly detect anomalous behavior once it starts, (iii) they can accurately predict a future A&amp;A event within a time window of it starting and (iv) the use of additional software variables beyond the hardware state of the robot is important in being able to detect and predict certain kinds of events.
Recurrent Natural Language Parsing 1

A recurrent network was trained from sentence examples to construct symbolic parses of sentence forms. Hundreds of sentences, representing significant syntactic complexity, were formulated and then divided into training and testing sets to evaluate the ability of a recurrent network to learn their structure. The network is shown to generalize well over test sentences and the errors that do remain are found to be of a single type and related to human limitations of sentence processing.
A SCIENTIFIC APPROACH TO MICROPHONE PLACEMENT FOR CYMBALS IN LIVE SOUND

The paper begins with an overview of the key research in the area of the physics of cymbals and overhead microphone placement. This is followed by a description of the experimental methods used in this research and then the presentation and analysis of results. Examples of best placement diagrams are given and the paper is concluded with an emphasis of relevant future work in this area.
Is everyone using technology? : addressing the social and cultural needs of newcomer youth

This mixed methodological study involving newcomer youth aged 17-24 and settlement service staff explores the role of technology in the process of settlement and the youth newcomer services. The findings indicate that youth prefer direct engagement with services and resources as they navigate the barriers that impact their access to mainstream and settlement service resources. They want to improve the quality of their engagement in communication as they strive to become part of their community. Participants involved in the study did not see technology as central to delivering youth settlement services. Youth did see the internet as necessary for schooling and in bridging to friends and family back home. The findings reflect the paucity of resources and capacity of mainstream and settlement specific services to meet the needs of youth, a lack of focus on integrated and accountable realm of settlement services specific to the needs of newcomer youth.
Feature Correlation Measure Based Real Time Discrimination Prevention with Transactional Data Sets using Social Networks

The effect of social network in anti-discrimination has been analyzed in detail.Various approaches towards anti-discrimination on transactional data have been identified, but does not produced expected performance level. To improve the performance, a feature correlation measure based approach has been presented in this article. The method reads the transactional data set and generates number of patterns based on the purchase details. For each pattern generated, the method estimate pattern impact measure towards the data set. Based on the value of PIM (Pattern Impact Measure), a subset of patterns is selected. With the selected pattern set, the method reads the social network user data and identifies the list of items being discussed. According to identified items, the method estimates the feature correlation measure (FCM) for each items identified. According to the value of FCM, a subset of items with higher value has been selected. The selected items are identified as more sensitive and being sanitized with the publishing data set. The sanitization is performed using probabilistic sanitization algorithm. The FCM algorithm has leverage the discrimination prevention and sanitization performance.
Integration of general education and vocational training on the example of phenomenon-based learning

This article discusses the possibilities of using phenomenon-oriented learning in institutions of secondary vocational education, the impact of learning based on phenomena on the integration of general education and vocational training, as well as the importance of the phenomenon-based approach for the development of students’ personality. The author prepared a brief overview of the existing experience in the implementation of phenomenon-based learning in education, including making a comparison with the integrated approach in education widely used in the 20s of the last century in our country, identified the differences between the integrated approach and phenomenon-based learning, and also conducted an experiment on the introduction of elements of phenomenon-based learning in the process of studying the discipline “Information technology in professional activity” for college students. The results of the study showed that the introduction of phenomenon-based learning into the educational process contributes to the development of a harmonious personality of the student, the development of creativity skills and the ability to independently work with information, and increases the motivation of students to study.
Forms and methods of educational work with cadets of educational organizations of the Federal Penitentiary Service of Russia in the context of distance learning

The article examines the system of forms and methods of educational work with cadets of educational institutions of the Federal Penitentiary Service of Russia, used in specific conditions of distance learning. The subject of the article is the statistical data obtained by the author of the article through a survey of full-time cadets of 1 and 2 courses of the SLI of the FPS of Russia, domestic legislation, as well as scientific literature on the topic of the work. The purpose of the study is to reveal the specifics of using various forms and methods of educational work with cadets of educational institutions of the Federal Penitentiary Service of Russia in the conditions of distance learning, to determine the most effective forms and methods of educational work in the indicated conditions. The methodological basis of the research wassis, induction, system-structural and formallogical methods. As a result of the work carried out, the concepts of «e-learning» and «distance lear-ning technologies» were studied as regulated by the current legislation. The main advantages and disadvantages of organizing educational work with cadets of educational institutions of the Federal Penitentiary Service of Russia in the context of distance learning are analyzed. The main methods and forms of educational work with cadets of educational institutions of the Federal Penitentiary Service of Russia are highlighted and compared. It has been established that in the conditions of distance learning, cadets of educational organizations of the Federal Penitentiary Service of Russia regard the fulfillment of personal instructions and assignments with the provision of individual assistance in their preparation as the most effective and interesting forms of educational work. The data obtained as a result of the study should be taken into account when organizing educational work with cadets in the conditions of distance learning in order to increase the effectiveness of educational work. The conclusion is made about the importance of distance learning in the modern system of education. At the same time, regardless of the specifics of the conditions for organizing the educational work with cadets, its effectiveness largely depends not on technology and technics, but on the competence and professionalism of the mentors who carry out this work. Key words: cadets, students, distance learning, forms and methods of education. formed by the statistical method, analysis, synthe
Make Virtual Training a Part of Volunteer Education

The Volunteer Management ReportVolume 25, Issue 11 p. 3-3 Training and Education Procedures Make Virtual Training a Part of Volunteer Education First published: 12 October 2020 https://doi.org/10.1002/vmr.31551Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume25, Issue11November 2020Pages 3-3 RelatedInformation
Region based Facial Expression Recognition using Gradient Directions

Facial Expression Recognition (FER) has gained significant importance in the research field of Affective Computing in different extents. As a part of the different dimensional thinking, aiming at improving the accuracy of the recognition system and reducing the computational load, region based FER is proposed in this paper. The system is an emotion identifying system among the basic emotions, through subject independent template matching based on gradient directions. The model designed is tested on the Enhanced Cohn-Kanade (CK+) dataset. Another important contribution of the work is using only eye (including eyebrows and the nose portion near eyes) and mouth regions in the emotion recognition. The emotion classification result is 94.3% (CK+ dataset) for 6-class FER.
Dual Image Watermarking using Symmetrical IB Algorithm

The technique of watermarking is used to safeguard the copyright protection and used to prove the proprietorship of certain images, documents, audio and video files. This paper presents a new scheme where the final digital image is created after embedding one watermark inside another watermark (dual watermark), scrambled the image to safeguard from intruders and then embedded into the main digital image. The dual watermark mechanism improves the capacity of the data to be watermarked and more importantly the dual watermarked image is encrypted using symmetrical encryption algorithm (IB algorithm) an improved version of the blowfish algorithm to increase the security and makes it harder for the intruders to crack the watermark.
Evaluating Load Balancing Algorithms for Performance Optimization in Cloud Computing

Cloud computing is a new sort of computing over internet. It has many advantages along with several issues. These issues are related to load management, security of data in cloud. In this paper, the most important concern is to prevent bottleneck in cloud computing. The load can be (CPU load, memory capacity, delay or network-load). Load balancing is the process of distributing the load among various servers so that none of the servers is underloaded. Load-balancing also prevents the situation where some servers are heavily loaded while others are idle. This process of Load balancing ensures that load is distributed equally among the servers. In this paper, some algorithms of load balancing is discussed along with its benefits and drawbacks and also tested these algorithms on some performance parameters.