Reviewerly/exHarmony huggingface dataset:
Exploiting Robust Unsupervised Video Person Re-identification.

Unsupervised video person re-identification (reID) methods usually depend on global-level features. And many supervised reID methods employed local-level features and achieved significant performance improvements. However, applying local-level features to unsupervised methods may introduce an unstable performance. To improve the performance stability for unsupervised video reID, this paper introduces a general scheme fusing part models and unsupervised learning. In this scheme, the global-level feature is divided into equal local-level feature. A local-aware module is employed to explore the poentials of local-level feature for unsupervised learning. A global-aware module is proposed to overcome the disadvantages of local-level features. Features from these two modules are fused to form a robust feature representation for each input image. This feature representation has the advantages of local-level feature without suffering from its disadvantages. Comprehensive experiments are conducted on three benchmarks, including PRID2011, iLIDS-VID, and DukeMTMC-VideoReID, and the results demonstrate that the proposed approach achieves state-of-the-art performance. Extensive ablation studies demonstrate the effectiveness and robustness of proposed scheme, local-aware module and global-aware module.
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking

Dialogue State Tracking is central to multi-domain task-oriented dialogue systems, responsible for extracting information from user utterances. We present a novel hybrid architecture that augments GPT-2 with representations derived from Graph Attention Networks in such a way to allow causal, sequential prediction of slot values. The model architecture captures inter-slot relationships and dependencies across domains that otherwise can be lost in sequential prediction. We report improvements in state tracking performance in MultiWOZ 2.0 against a strong GPT-2 baseline and investigate a simplified sparse training scenario in which DST models are trained only on session-level annotations but evaluated at the turn level. We further report detailed analyses to demonstrate the effectiveness of graph models in DST by showing that the proposed graph modules capture inter-slot dependencies and improve the predictions of values that are common to multiple domains.
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings

Recent advances in off-policy deep reinforcement learning (RL) have led to impressive success in complex tasks from visual observations. Experience replay improves sample-efficiency by reusing experiences from the past, and convolutional neural networks (CNNs) process high-dimensional inputs effectively. However, such techniques demand high memory and computational bandwidth. In this paper, we present Stored Embeddings for Efficient Reinforcement Learning (SEER), a simple modification of existing off-policy RL methods, to address these computational and memory requirements. To reduce the computational overhead of gradient updates in CNNs, we freeze the lower layers of CNN encoders early in training due to early convergence of their parameters. Additionally, we reduce memory requirements by storing the low-dimensional latent vectors for experience replay instead of high-dimensional images, enabling an adaptive increase in the replay buffer capacity, a useful technique in constrained-memory settings. In our experiments, we show that SEER does not degrade the performance of RL agents while significantly saving computation and memory across a diverse set of DeepMind Control environments and Atari games. Finally, we show that SEER is useful for computation-efficient transfer learning in RL because lower layers of CNNs extract generalizable features, which can be used for different tasks and domains.
Exploring multinomial naïve Bayes for Yorùbá text document classification

The recent increase in the emergence of Nigerian language text online motivates this paper in which the problem of classifying text documents written in Yorùbá language into one of a few pre-designated classes is considered. Text document classification/categorization research is well established for English language and many other languages; this is not so for Nigerian languages. This paper evaluated the performance of a multinomial Naive Bayes model learned on a research dataset consisting of 100 samples of text each from business, sporting, entertainment, technology and political domains, separately on unigram, bigram and trigram features obtained using the bag of words representation approach. Results show that the performance of the model over unigram and bigram features is comparable but significantly better than a model learned on trigram features. The results generally indicate a possibility for the practical application of NB algorithm to the classification of text documents written in Yorùbá language.&#x0D; Keywords: Supervised learning, text classification, Yorùbá language, text mining, BoW Representation
Testing for Signal-to-Noise Ratio in Linear Regression: A Test for Big Data Era

This paper proposes a test for the signal-to-noise ratio applicable to a range of significance tests and model diagnostics in a linear regression. It is particularly useful under a large or massive sample size, where a conventional test frequently rejects an economically negligible deviation from the null hypothesis. The test is conducted in the context of the traditional $F$-test, with its critical values increasing with sample size. It maintains desirable size properties under a large or massive sample size, when the null hypothesis is violated by a practically negligible margin.
Virtual education and student perception of teacher performance in the distance learning environment

This article aims to carry out a descriptive analysis of the performance of teachers qualified as researchers, in the distance education environment according to the student's perspective. The results will be a frame of reference for university authorities on the path of continuous improvement of virtual education. When carrying out the research, a general qualification of the teaching performance of 14.20 was determined, established the highest grade equal to 20, it can be indicated that there is a good performance of the teacher in the virtual education environment. In addition, the results show that the highest evaluation corresponds to the indicator management of the group and fulfillment of the objectives, which is directly related to the administration of the class, while the lowest rating is for the indicator Teacher effectiveness so that their students acquire relevant knowledge, skills and attitudes”, which is directly related to the didactic strategies used, that is, to the use of technological tools that today are more than just an option. Finally, it can be noted that of the total of 17 teaching, 23.5% present a very good performance, 35.3% present a good performance and 41.2% present a regular performance.
Situational involvement and footing shifts in mobile live video streams

Mobile live video streaming with smartphones is an everyday media practice in which the participants are in a specific multimodal constellation and streamers and viewers have access to various semiotic resources for interactionally establishing alignment. Based on the multimodal sequence analysis of a concise episode of a journalist's livestream coverage of a political event on the streaming platform Periscope, I will address the question of how participation and involvement in live video streams are achieved and organised by the participants. I will show that hosts in the media practice of live video streaming act in an interaction-dominant manner and involve the viewers in the situation through asymmetrical participation coordination via footing shifts.
ReLU Network Approximation in Terms of Intrinsic Parameters

This paper studies the approximation error of ReLU networks in terms of the number of intrinsic parameters (i.e., those depending on the target function $f$). First, we prove by construction that, for any Lipschitz continuous function $f$ on $[0,1]^d$ with a Lipschitz constant $\lambda>0$, a ReLU network with $n+2$ intrinsic parameters can approximate $f$ with an exponentially small error $5\lambda \sqrt{d}\,2^{-n}$ measured in the $L^p$-norm for $p\in [1,\infty)$. More generally for an arbitrary continuous function $f$ on $[0,1]^d$ with a modulus of continuity $\omega_f(\cdot)$, the approximation error is $\omega_f(\sqrt{d}\, 2^{-n})+2^{-n+2}\omega_f(\sqrt{d})$. Next, we extend these two results from the $L^p$-norm to the $L^\infty$-norm at a price of $3^d n+2$ intrinsic parameters. Finally, by using a high-precision binary representation and the bit extraction technique via a fixed ReLU network independent of the target function, we design, theoretically, a ReLU network with only three intrinsic parameters to approximate H\older continuous functions with an arbitrarily small error.
An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model

Multi-turn response selection models have recently shown comparable performance to humans in several benchmark datasets. However, in the real environment, these models often have weaknesses, such as making incorrect predictions based heavily on superficial patterns without a comprehensive understanding of the context. For example, these models often give a high score to the wrong response candidate containing several keywords related to the context but using the inconsistent tense. In this study, we analyze the weaknesses of the open-domain Korean Multi-turn response selection models and publish an adversarial dataset to evaluate these weaknesses. We also suggest a strategy to build a robust model in this adversarial environment.
The students' perception on the use of computer assisted language learning

While listening comprehension is the heart of language, but it is the least understood and researched skill. This is the reason why particular attention must be given to listening comprehension as a skill required in foreign language acquisition and especially in part played by listening skills. This study aims to see the effect of computer assisted language learning (CALL) applied to students` listening performance. This study adopts a qualitative approach. Data were collected using questionnaires. The findings in this study reveals Students`-perceptions and feedback on CALL. It shows that many of the students feel easier in understanding the listening material using CALL. It also shows that many of the teachers interested using CALL but not interested with the LMS provided. It can be concluded that most of the students have a positive impression about the use of CALL in English learning, especially for Listening.
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models

Recent works have shown that powerful pre-trained language models (PLM) can be fooled by small perturbations or intentional attacks. To solve this issue, various data augmentation techniques are proposed to improve the robustness of PLMs. However, it is still challenging to augment semantically relevant examples with sufficient diversity. In this work, we present Virtual Data Augmentation (VDA), a general framework for robustly fine-tuning PLMs. Based on the original token embeddings, we construct a multinomial mixture for augmenting virtual data embeddings, where a masked language model guarantees the semantic relevance and the Gaussian noise provides the augmentation diversity. Furthermore, a regularized training strategy is proposed to balance the two aspects. Extensive experiments on six datasets show that our approach is able to improve the robustness of PLMs and alleviate the performance degradation under adversarial attacks. Our codes and data are publicly available at \textcolor{blue}{\url{https://github.com/RUCAIBox/VDA}}.
Replay-Guided Adversarial Environment Design

Deep reinforcement learning (RL) agents may successfully generalize to new settings if trained on an appropriately diverse set of environment and task configurations. Unsupervised Environment Design (UED) is a promising self-supervised RL paradigm, wherein the free parameters of an underspecified environment are automatically adapted during training to the agent's capabilities, leading to the emergence of diverse training environments. Here, we cast Prioritized Level Replay (PLR), an empirically successful but theoretically unmotivated method that selectively samples randomly-generated training levels, as UED. We argue that by curating completely random levels, PLR, too, can generate novel and complex levels for effective training. This insight reveals a natural class of UED methods we call Dual Curriculum Design (DCD). Crucially, DCD includes both PLR and a popular UED algorithm, PAIRED, as special cases and inherits similar theoretical guarantees. This connection allows us to develop novel theory for PLR, providing a version with a robustness guarantee at Nash equilibria. Furthermore, our theory suggests a highly counterintuitive improvement to PLR: by stopping the agent from updating its policy on uncurated levels (training on less data), we can improve the convergence to Nash equilibria. Indeed, our experiments confirm that our new method, PLR$^{\perp}$, obtains better results on a suite of out-of-distribution, zero-shot transfer tasks, in addition to demonstrating that PLR$^{\perp}$ improves the performance of PAIRED, from which it inherited its theoretical framework.
Transcendence in the Play of Remembrance

Young children are continuously issued invitations to transcendence by the contexts in which they live. The shape of the communal play of remembrance is therefore anchored by the believers’ memories of mother-child interplay as well as by their adult conceptions of God’s relationship with them down through history. Ritualization is the positive term used by E. H. Erikson to describe this regularized and recurring kind of interplay. Hope is the sense that one can experience transcendence. Certain cultures are only capable of generating themes of silence, and then hope has been cut out, spirit crushed, and transcendence shut down in that culture. What is needed is someone who knows the story and who understands how to steward the boundaries of such space so that those gathered can, together, become the play of remembrance. Both the individual and the community engage in sacred play to the degree that they embrace the process of transcendence.
Graph Neural Network based scheduling : Improved throughput under a generalized interference model

In this work, we propose a Graph Convolutional Neural Networks (GCN) based scheduling algorithm for adhoc networks. In particular, we consider a generalized interference model called the $k$-tolerant conflict graph model and design an efficient approximation for the well-known Max-Weight scheduling algorithm. A notable feature of this work is that the proposed method do not require labelled data set (NP-hard to compute) for training the neural network. Instead, we design a loss function that utilises the existing greedy approaches and trains a GCN that improves the performance of greedy approaches. Our extensive numerical experiments illustrate that using our GCN approach, we can significantly ($4$-$20$ percent) improve the performance of the conventional greedy approach.
Research on Inductive Neuron Model and its Dynamic Characteristics

神经元的大小属于介观尺度范围，本文考虑神经元的电感特性，建立了由细胞膜电感、膜电容、钾离子忆阻器和氯离子电阻构成的神经元经典电路模型和介观电路模型。利用经典电路理论和介观电路的量子理论，推导了在外部冲击激励下神经元细胞膜电压响应的表达式。将枪乌贼神经元的电生理参数代入膜电压表达式并计算可知，两种模型下的膜电压均先增大后减小，最后达到零值的静息状态，且其能量主要集中在(0~30) Hz的脑电频率范围内。进一步比较发现，介观电路模型下膜电压的峰值及达到峰值所需的时间（达峰时间）均低于经典电路模型下的值，并与枪乌贼轴突受到刺激后的实验结果更接近，说明介观电路模型更能反应神经元受到刺激后的生理特征。基于介观电路模型，随着外部激励强度的增加，膜电压的峰值增加且达峰时间变短。膜电压峰值及达峰时间等参数更易受神经元膜电容的影响。神经元的介观电路模型对于理解神经元受到刺激后的兴奋性，推动受大脑功能启发的量子神经网络的发展等具有重要意义。
Human irrationality: both bad and good for reward inference

Assuming humans are (approximately) rational enables robots to infer reward functions by observing human behavior. But people exhibit a wide array of irrationalities, and our goal with this work is to better understand the effect they can have on reward inference. The challenge with studying this effect is that there are many types of irrationality, with varying degrees of mathematical formalization. We thus operationalize irrationality in the language of MDPs, by altering the Bellman optimality equation, and use this framework to study how these alterations would affect inference. We find that wrongly modeling a systematically irrational human as noisy-rational performs a lot worse than correctly capturing these biases -- so much so that it can be better to skip inference altogether and stick to the prior! More importantly, we show that an irrational human, when correctly modelled, can communicate more information about the reward than a perfectly rational human can. That is, if a robot has the correct model of a human's irrationality, it can make an even stronger inference than it ever could if the human were rational. Irrationality fundamentally helps rather than hinder reward inference, but it needs to be correctly accounted for.
ANALISIS SISTEM INFORMASI AKUNTANSI PENERIMAAN KAS PADA SULTAN ALAUDDIN HOTEL DAN CONVENTION MAKASSAR

An Analysis of Accounting Information System 0n Cash-receipt and Cash-payment at Sultan Alauddin Hotel and Convention Makassar. Supervised by Neng Indriyani, SE,.MM and Tamsi,SE,.MM. The research aimed to find out the accounting information system on cash-receipt and cash-flow at Sultan Alauddin Hotel and Convention Makassar. The research method was descriptive qualitative to describes the problem of a company related to cash-receipt and cash-payment. The technique of collecting data was through the library study, the interview, and the observation. The research result at Sultan Alauddin Hotel and Convention Makassar was not following the controlling elements which cover the managerial, the authority, and the recording system, the healthy practice, and the qualified employees. It showed that Sultan Alauddin Hotel and Convention Makassar was not following the elements off good internal control.
USE OF CONVOLUTIONAL NEURAL NETWORKS FOR X-RAY IMAGE ORIENTATION DETERMINATION

The fact that Artificial Intelligence (AI) based algorithms exhibit a high performance on image classification tasks has been shown many times. Still, certain issues exist with the application of machine learning (ML) artificial neural network (ANN) algorithms. The best known is the need for a large amount of statistically varied data, which can be addressed with expanded collection or data augmentation. Other issues are also present. Convolutional neural networks (CNNs) show extremely high performance on image-shaped data. Despite their performance, CNNs exhibit a large issue which is the sensitivity to image orientation. Previous research shows that varying the orientation of images may greatly lower the performance of the trained CNN. This is especially problematic in certain applications, such as X-ray radiography, an example of which is presented here. Previous research shows that the performance of CNNs is higher when used on images in a single orientation (left or right), as opposed to the combination of both. This means that the data needs to be differentiated before it enters the classification model. In this paper, the CNN-based model for differentiation between left and right-oriented images is presented. Multiple CNNs are trained and tested, with the highest performing being the VGG16 architecture which achieved an Accuracy of 0.99 (+/- 0.01), and an AUC of 0.98 (+/- 0.01). These results show that CNNs can be used to address the issue of orientation sensitivity by splitting the data in advance of being used in classification models.
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach

Reliable automatic evaluation of dialogue systems under an interactive environment has long been overdue. An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large-scale experiments. Though researchers have attempted to use metrics (e.g., perplexity, BLEU) in language generation tasks or some model-based reinforcement learning methods (e.g., self-play evaluation) for automatic evaluation, these methods only show a very weak correlation with the actual human evaluation in practice. To bridge such a gap, we propose a new framework named ENIGMA for estimating human evaluation scores based on recent advances of off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation, making automatic evaluations feasible. More importantly, ENIGMA is model-free and agnostic to the behavior policies for collecting the experience data (see details in Section 2), which significantly alleviates the technical difficulties of modeling complex dialogue environments and human behaviors. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
Chapter 6. gm/ID Technique for Analog Circuit Design

The world population is growing, and it is expected that in 2050 there will be 9.7 billion inhabitants on the Earth. According to FAO (United Nations, Food and Agriculture Organization) we need to increase the productivity of agriculture by between 50% and 70% to be able to feed the world population in 2050. Other researchers think that reducing the wastage of food may be enough to handle the 2050 population. Several factors must be considered to ensure that humanity is able to feed the world's population in 2050 and beyond. • Less arable land: As cities are growing, the space available for agriculture is shrinking. • Climate change: Dramatic impacts on agribusiness. • Role of agribusiness on GHG emissions. • Planetary boundaries and the role of agribusiness. • Availability of freshwater. • Soil degradation. The seasonal school presents and discusses the major problems that agribusiness is facing and the different technologies that can be applied to solve and improve such issues. Specific case studies are presented along with the technological solutions that have been applied to solve or minimize the impact. Agribusiness covers different topics such as arable farming, dairy farming, fruits, vegetables, meat, etc. Each of these domains has different needs that can be addressed through smart agriculture technologies such as circuits and systems. Also, these domains affect the sustainability of the planet as they impact at least 4 out of the 9 planetary boundaries.
Hierarchical Character Tagger for Short Text Spelling Error Correction

State-of-the-art approaches to spelling error correction problem include Transformer-based Seq2Seq models, which require large training sets and suffer from slow inference time; and sequence labeling models based on Transformer encoders like BERT, which involve token-level label space and therefore a large pre-defined vocabulary dictionary. In this paper we present a Hierarchical Character Tagger model, or HCTagger, for short text spelling error correction. We use a pre-trained language model at the character level as a text encoder, and then predict character-level edits to transform the original text into its error-free form with a much smaller label space. For decoding, we propose a hierarchical multi-task approach to alleviate the issue of long-tail label distribution without introducing extra model parameters. Experiments on two public misspelling correction datasets demonstrate that HCTagger is an accurate and much faster approach than many existing models.