OpenRaiser/Intern-Atlas huggingface dataset:
Towards Boosting the Open-Domain Chatbot with Human Feedback
Venue: arXiv cs.CL 2022
Year: 2022

Many open-domain dialogue models pre-trained with social media comments can generate coherent replies but have difficulties producing engaging responses when interacting with real users. This phenomenon might mainly result from the deficiency of annotated human-human conversations and the misalignment with human preference. In this paper, we propose a novel and efficient approach Diamante to boost the open-domain chatbot, where two kinds of human feedback (including explicit demonstration and implicit preference) are collected and leveraged. By asking annotators to select or amend the model-generated candidate responses, Diamante efficiently collects the human demonstrated responses and constructs a Chinese chit-chat dataset. To enhance the alignment with human preference, Diamante leverages the implicit preference in the data collection process and introduces the generation-evaluation joint training. Comprehensive experiments indicate that the Diamante dataset and joint training paradigm can significantly boost the performance of Chinese pre-trained dialogue models.
Consecutive k-out-of-n:F Systems Have Unbounded Roots
Venue: arXiv cs.DM 2022
Year: 2022

This article is studying the roots of the reliability polynomials of linear consecutive-\textit{k}-out-of-\textit{n}:\textit{F} systems. We are able to prove that these roots are unbounded in the complex plane, for any fixed $k\ge2$. In the particular case $k=2$, we show that the reliability polynomials have only real roots and highlight the closure of these roots by establishing their explicit formulas. We also point out that in this case, for any fixed \textit{n} the nonzero roots of the reliability polynomial are distinct numbers.
Approximation Algorithm for Minimum $p$ Union Under a Geometric Setting
Venue: arXiv cs.CG 2022
Year: 2022

In a minimum $p$ union problem (Min$p$U), given a hypergraph $G=(V,E)$ and an integer $p$, the goal is to find a set of $p$ hyperedges $E'\subseteq E$ such that the number of vertices covered by $E'$ (that is $|\bigcup_{e\in E'}e|$) is minimized. It was known that Min$p$U is at least as hard as the densest $k$-subgraph problem. A question is: how about the problem in some geometric settings? In this paper, we consider the unit square Min$p$U problem (Min$p$U-US) in which $V$ is a set of points on the plane, and each hyperedge of $E$ consists of a set of points in a unit square. A $(\frac{1}{1+\varepsilon},4)$-bicriteria approximation algorithm is presented, that is, the algorithm finds at least $\frac{p}{1+\varepsilon}$ unit squares covering at most $4opt$ points, where $opt$ is the optimal value for the Min$p$U-US instance (the minimum number of points that can be covered by $p$ unit squares).
Faithful Reasoning Using Large Language Models
Venue: arXiv cs.AI 2022
Year: 2022

Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model. This entails an unwelcome degree of opacity and compromises performance, especially on problems that are inherently multi-step. To address these limitations, we show how LMs can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem. Our approach works by chaining together reasoning steps, where each step results from calls to two fine-tuned LMs, one for selection and one for inference, to produce a valid reasoning trace. Our method carries out a beam search through the space of reasoning traces to improve reasoning quality. We demonstrate the effectiveness of our model on multi-step logical deduction and scientific question-answering, showing that it outperforms baselines on final answer accuracy, and generates humanly interpretable reasoning traces whose validity can be checked by the user.
Long-term variation of population exposure to PM2.5 in Eastern China: A perspective from SDG 11.6.2
Venue: arXiv cs.CY 2022
Year: 2022

Air pollution (e.g., PM2.5) has a negative effect on human health. Recently, the population-weighted annual mean PM2.5 concentration (PWAM) has been selected as an indicator 11.6.2 in Sustainable Development Goals (SDGs), for various countries to perfrom a long-term monitoring of population exposure to PM2.5 in cities. However, few studies have employed this indicator for a city-level analysis and also in a long-time series (e.g., for decades). To fill this research gap, this study investigates the long-term (2000-2020) variation of population exposure to PM2.5 in Eastern China (including 318 prefecture-level cities). Three categories of open geospatial data (including high-resolution and long-term PM2.5 and population data, and administrative boundary data of cities) are involved for analysis. We found that: 1) A considerable decrease has been observed for the PWAM during 2014-2020. 2) In 2020, the PWAM is for the first time lower than the interim target-1 (35 μg/m3) defined by the World Health Organization for 214 prefecture-level cities in Eastern China, which accounts for 67% of the total population. The results indicates a considerable improvement of air quality in Eastern China. More important, this study illustrates the feasibility of using open geospatial data to monitor the SDG indicator 11.6.2.
Learning Representations for Hyper-Relational Knowledge Graphs
Venue: arXiv cs.LG 2022
Year: 2022

Knowledge graphs (KGs) have gained prominence for their ability to learn representations for uni-relational facts. Recently, research has focused on modeling hyper-relational facts, which move beyond the restriction of uni-relational facts and allow us to represent more complex and real-world information. However, existing approaches for learning representations on hyper-relational KGs majorly focus on enhancing the communication from qualifiers to base triples while overlooking the flow of information from base triple to qualifiers. This can lead to suboptimal qualifier representations, especially when a large amount of qualifiers are presented. It motivates us to design a framework that utilizes multiple aggregators to learn representations for hyper-relational facts: one from the perspective of the base triple and the other one from the perspective of the qualifiers. Experiments demonstrate the effectiveness of our framework for hyper-relational knowledge graph completion across multiple datasets. Furthermore, we conduct an ablation study that validates the importance of the various components in our framework. The code to reproduce our results can be found at \url{https://github.com/HarryShomer/QUAD}.
Extracting Relations Between Sectors
Venue: arXiv cs.IR 2022
Year: 2022

The term "sector" in professional business life is a vague concept since companies tend to identify themselves as operating in multiple sectors simultaneously. This ambiguity poses problems in recommending jobs to job seekers or finding suitable candidates for open positions. The latter holds significant importance when available candidates in a specific sector are also scarce; hence, finding candidates from similar sectors becomes crucial. This work focuses on discovering possible sector similarities through relational analysis. We employ several algorithms from the frequent pattern mining and collaborative filtering domains, namely negFIN, Alternating Least Squares, Bilateral Variational Autoencoder, and Collaborative Filtering based on Pearson's Correlation, Kendall and Spearman's Rank Correlation coefficients. The algorithms are compared on a real-world dataset supplied by a major recruitment company, Kariyer.net, from Turkey. The insights and methods gained through this work are expected to increase the efficiency and accuracy of various methods, such as recommending jobs or finding suitable candidates for open positions.
HPPNet: Modeling the Harmonic Structure and Pitch Invariance in Piano Transcription
Venue: arXiv cs.SD 2022
Year: 2022

While neural network models are making significant progress in piano transcription, they are becoming more resource-consuming due to requiring larger model size and more computing power. In this paper, we attempt to apply more prior about piano to reduce model size and improve the transcription performance. The sound of a piano note contains various overtones, and the pitch of a key does not change over time. To make full use of such latent information, we propose HPPNet that using the Harmonic Dilated Convolution to capture the harmonic structures and the Frequency Grouped Recurrent Neural Network to model the pitch-invariance over time. Experimental results on the MAESTRO dataset show that our piano transcription system achieves state-of-the-art performance both in frame and note scores (frame F1 93.15%, note F1 97.18%). Moreover, the model size is much smaller than the previous state-of-the-art deep learning models.
Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes
Venue: arXiv cs.CV 2022
Year: 2022

We propose a methodology to predict the cardiac epicardial and mediastinal fat volumes in computed tomography images using regression algorithms. The obtained results indicate that it is feasible to predict these fats with a high degree of correlation, thus alleviating the requirement for manual or automatic segmentation of both fat volumes. Instead, segmenting just one of them suffices, while the volume of the other may be predicted fairly precisely. The correlation coefficient obtained by the Rotation Forest algorithm using MLP Regressor for predicting the mediastinal fat based on the epicardial fat was 0.9876, with a relative absolute error of 14.4% and a root relative squared error of 15.7%. The best correlation coefficient obtained in the prediction of the epicardial fat based on the mediastinal was 0.9683 with a relative absolute error of 19.6% and a relative squared error of 24.9%. Moreover, we analysed the feasibility of using linear regressors, which provide an intuitive interpretation of the underlying approximations. In this case, the obtained correlation coefficient was 0.9534 for predicting the mediastinal fat based on the epicardial, with a relative absolute error of 31.6% and a root relative squared error of 30.1%. On the prediction of the epicardial fat based on the mediastinal fat, the correlation coefficient was 0.8531, with a relative absolute error of 50.43% and a root relative squared error of 52.06%. In summary, it is possible to speed up general medical analyses and some segmentation and quantification methods that are currently employed in the state-of-the-art by using this prediction approach, which consequently reduces costs and therefore enables preventive treatments that may lead to a reduction of health problems.
Improving Datacenter Utilization through Containerized Service-Based Architecture
Venue: arXiv cs.DC 2022
Year: 2022

The modern datacenter's computing capabilities have far outstripped the applications running within and have become a hidden cost of doing business due to how software is architected and deployed. Resources are over-allocated to monolithic applications that sit idle for large parts of the day. If applications were architected and deployed differently, shared services could be used for multiple applications as needed. When combined with powerful orchestration software, containerized microservices can both deploy and dynamically scale applications from very small to very large within moments scaling the application not only across a single datacenter but across all datacenters where the application(s) are deployed. In this paper, we analyze data from an application(s) deployed both as a single monolithic codebase and as a containerized application using microservice-based architecture to calculate the performance and computing resource waste are both architected and deployed. A modern approach is offered as a solution as a path from how to go from a monolithic codebase to a more efficient, reliable, scalable, and less costly deployment model.
abstractPIM: A Technology Backward-Compatible Compilation Flow for Processing-In-Memory
Venue: arXiv cs.ET 2022
Year: 2022

The von Neumann architecture, in which the memory and the computation units are separated, demands massive data traffic between the memory and the CPU. To reduce data movement, new technologies and computer architectures have been explored. The use of memristors, which are devices with both memory and computation capabilities, has been considered for different processing-in-memory (PIM) solutions, including using memristive stateful logic for a programmable digital PIM system. Nevertheless, all previous work has focused on a specific stateful logic family, and on optimizing the execution for a certain target machine. These solutions require new compiler and compilation when changing the target machine, and provide no backward compatibility with other target machines. In this chapter, we present abstractPIM, a new compilation concept and flow which enables executing any function within the memory, using different stateful logic families and different instruction set architectures (ISAs). By separating the code generation into two independent components, intermediate representation of the code using target independent ISA and then microcode generation for a specific target machine, we provide a flexible flow with backward compatibility and lay foundations for a PIM compiler. Using abstractPIM, we explore various logic technologies and ISAs and how they impact each other, and discuss the challenges associated with it, such as the increase in execution time.
Do language models make human-like predictions about the coreferents of Italian anaphoric zero pronouns?
Venue: arXiv cs.CL 2022
Year: 2022

Some languages allow arguments to be omitted in certain contexts. Yet human language comprehenders reliably infer the intended referents of these zero pronouns, in part because they construct expectations about which referents are more likely. We ask whether Neural Language Models also extract the same expectations. We test whether 12 contemporary language models display expectations that reflect human behavior when exposed to sentences with zero pronouns from five behavioral experiments conducted in Italian by Carminati (2005). We find that three models - XGLM 2.9B, 4.5B, and 7.5B - capture the human behavior from all the experiments, with others successfully modeling some of the results. This result suggests that human expectations about coreference can be derived from exposure to language, and also indicates features of language models that allow them to better reflect human behavior.
The Glass Ceiling of Automatic Evaluation in Natural Language Generation
Venue: arXiv cs.CL 2022
Year: 2022

Automatic evaluation metrics capable of replacing human judgments are critical to allowing fast development of new methods. Thus, numerous research efforts have focused on crafting such metrics. In this work, we take a step back and analyze recent progress by comparing the body of existing automatic metrics and human metrics altogether. As metrics are used based on how they rank systems, we compare metrics in the space of system rankings. Our extensive statistical analysis reveals surprising findings: automatic metrics -- old and new -- are much more similar to each other than to humans. Automatic metrics are not complementary and rank systems similarly. Strikingly, human metrics predict each other much better than the combination of all automatic metrics used to predict a human metric. It is surprising because human metrics are often designed to be independent, to capture different aspects of quality, e.g. content fidelity or readability. We provide a discussion of these findings and recommendations for future work in the field of evaluation.
Few-shot Adaptive Object Detection with Cross-Domain CutMix
Venue: arXiv cs.CV 2022
Year: 2022

In object detection, data amount and cost are a trade-off, and collecting a large amount of data in a specific domain is labor intensive. Therefore, existing large-scale datasets are used for pre-training. However, conventional transfer learning and domain adaptation cannot bridge the domain gap when the target domain differs significantly from the source domain. We propose a data synthesis method that can solve the large domain gap problem. In this method, a part of the target image is pasted onto the source image, and the position of the pasted region is aligned by utilizing the information of the object bounding box. In addition, we introduce adversarial learning to discriminate whether the original or the pasted regions. The proposed method trains on a large number of source images and a few target domain images. The proposed method achieves higher accuracy than conventional methods in a very different domain problem setting, where RGB images are the source domain, and thermal infrared images are the target domain. Similarly, the proposed method achieves higher accuracy in the cases of simulation images to real images.
A topic-aware graph neural network model for knowledge base updating
Venue: arXiv cs.IR 2022
Year: 2022

The open domain knowledge base is very important. It is usually extracted from encyclopedia websites and is widely used in knowledge retrieval systems, question answering systems, or recommendation systems. In practice, the key challenge is to maintain an up-to-date knowledge base. Different from Unwieldy fetching all of the data from the encyclopedia dumps, to enlarge the freshness of the knowledge base as big as possible while avoiding invalid fetching, the current knowledge base updating methods usually determine whether entities need to be updated by building a prediction model. However, these methods can only be defined in some specific fields and the result turns out to be obvious bias, due to the problem of data source and data structure. The users' query intentions are often diverse as to the open domain knowledge, so we construct a topic-aware graph network for knowledge updating based on the user query log. Our methods can be summarized as follow: 1. Extract entities through the user's log and select them as seeds 2. Scrape the attributes of seed entities in the encyclopedia website, and self-supervised construct the entity attribute graph for each entity. 3. Use the entity attribute graph to train the GNN entity update model to determine whether the entity needs to be synchronized. 4.Use the encyclopedia knowledge to match and update the filtered entity with the entity in the knowledge base according to the minimum edit times algorithm.
How Readable is Model-generated Code? Examining Readability and Visual Inspection of GitHub Copilot
Venue: arXiv cs.SE 2022
Year: 2022

Background: Recent advancements in large language models have motivated the practical use of such models in code generation and program synthesis. However, little is known about the effects of such tools on code readability and visual attention in practice. Objective: In this paper, we focus on GitHub Copilot to address the issues of readability and visual inspection of model generated code. Readability and low complexity are vital aspects of good source code, and visual inspection of generated code is important in light of automation bias. Method: Through a human experiment (n=21) we compare model generated code to code written completely by human programmers. We use a combination of static code analysis and human annotators to assess code readability, and we use eye tracking to assess the visual inspection of code. Results: Our results suggest that model generated code is comparable in complexity and readability to code written by human pair programmers. At the same time, eye tracking data suggests, to a statistically significant level, that programmers direct less visual attention to model generated code. Conclusion: Our findings highlight that reading code is more important than ever, and programmers should beware of complacency and automation bias with model generated code.
PBAG: A Privacy-Preserving Blockchain-based Authentication Protocol with Global-updated Commitment in IoV
Venue: arXiv cs.CR 2022
Year: 2022

Internet of Vehicles(IoV) is increasingly used as a medium to propagate critical information via establishing connections between entities such as vehicles and infrastructures. During message transmission, privacy-preserving authentication is considered as the first line of defence against attackers and malicious information. To achieve a more secure and stable communication environment, ever-increasing numbers of blockchain-based authentication schemes are proposed. At first glance, existing approaches provide robust architectures and achieve transparent authentication. However, in these schemes, verifiers must connect to the blockchain network in advance and accomplish the authentication with smart contracts, which prolongs the latency. To remedy this limit, we propose a privacy-preserving blockchain-based authentication protocol(PBAG), where Root Authority(RA) generates a unique evaluation proof corresponding to the issued certificate for each authorized vehicle. Meanwhile, RA broadcasts a public global commitment based on all valid certificates. Instead of querying certificates stored in the blockchain, the vehicle will be efficiently proved to be an authorized user by utilizing the global commitment through bilinear pairing. Moreover, our scheme can prevent vehicles equipped with invalid certificates from accomplishing the authentication, thus avoiding the time-consuming for checking Certificate Revocation List (CRL). Finally, our scheme provides privacy properties such as anonymity and unlinkability. It allows anonymous authentication based on evaluation proofs and achieves traceability of identity in the event of a dispute. The simulation demonstrates that the average time of verification is 0.36ms under the batch-enabled mechanism, outperforming existing schemes by at least 63.7%.
Optimality Analysis and Block Sparse Algorithm for Complex Compressed Sensing
Venue: arXiv cs.IT 2022
Year: 2022

Recently, many new challenges in Compressed Sensing (CS), such as block sparsity, arose. In this paper, we present a new algorithm for solving CS with block sparse constraints (BSC) in complex fields. Firstly, based on block sparsity characteristics, we propose a new model to deal with CS with BSC and analyze the properties of the functions involved in this model. We then present a new $τ$-stationary point and analyze corresponding first-order sufficient and necessary conditions. That ensures we to further develop a block Newton hard-thresholding pursuit (BNHTP) algorithm for efficiently solving CS with BSC. Finally, preliminary numerical experiments demonstrate that the BNHTP algorithm has superior performance in terms of recovery accuracy and calculation time when compared with the classical AMP algorithm.