Reviewerly/exHarmony huggingface dataset:
Together in cyberspace

A recent musical practice that has emerged as a result of the twenty-first century’s rapidly developing technological landscape is live coding. This is the act of writing computer code for generating music in front of an audience while the performer projects their screen. As the number of live coders performing together increases, so too does the number of screens required to project all of the ensemble’s code. This well-documented problem is addressed in this chapter, which introduces a live coding editor built for collaborative improvisation and reflects on its impact on group creativity and ensemble interaction. The editor Troop displays all performer’s code in one window, simplifying technical setup, and shares inter-performer communication with audiences. This case study explores technological design parameters that allow live-coding composers to collaboratively compose music in real time and discuss what means of interaction and collaboration these afford.
Storchastic: A Framework for General Stochastic Automatic Differentiation

Modelers use automatic differentiation (AD) of computation graphs to implement complex Deep Learning models without defining gradient computations. Stochastic AD extends AD to stochastic computation graphs with sampling steps, which arise when modelers handle the intractable expectations common in Reinforcement Learning and Variational Inference. However, current methods for stochastic AD are limited: They are either only applicable to continuous random variables and differentiable functions, or can only use simple but high variance score-function estimators. To overcome these limitations, we introduce Storchastic, a new framework for AD of stochastic computation graphs. Storchastic allows the modeler to choose from a wide variety of gradient estimation methods at each sampling step, to optimally reduce the variance of the gradient estimates. Furthermore, Storchastic is provably unbiased for estimation of any-order gradients, and generalizes variance reduction techniques to higher-order gradient estimates. Finally, we implement Storchastic as a PyTorch library.
V2AnomalyVec: Deep Discriminative Embeddings for Detecting Anomalous Activities in Surveillance Videos

Understanding patterns in surveillance videos is always challenging due to the rapid movement of the crowd, occlusions, cluttered backgrounds, intraclass variations, and interclass similarities existing among normal and abnormal event classes. In the case of autoencoder-based normality model trained using segments of normal events only, there may be chances of anomalous events to be reconstructed well, leading to anomalous events detected as normal events. In this work, we propose deep discriminative embedding (DDE)-based framework using residual spatiotemporal auotencoders (R-STAEs) that learn V2AnomalyVec embeddings. In place of R-STAEs, any promising deep architectures to extract deep features from video segments of normal and abnormal events can also be used to form V2AnomalyVec. Results on four benchmark datasets demonstrate that the V2AnomalyVec-based approach performs significantly better than the normality model-based approaches and other state-of-the-art approaches. Studies with a reduced number of abnormal video segments to train the model prove that the proposed approach is also appropriate for detecting anomalous events even with less amount of abnormal training data.
Algorithmic Composition by Autonomous Systems with Multiple Time-Scales

Dynamic systems have found their use in sound synthesis as well as score synthesis. These levels can be integrated in monolithic autonomous systems in a novel approach to algorithmic composition that shares certain aesthetic motivations with some work with autonomous music systems, such as the search for emergence. We discuss various strategies for achieving variation on multiple time-scales by using slow-fast, hybrid dynamic systems, and statistical feedback. The ideas are illustrated with a case study.
Exploration of Chinese Characteristics of Innovation and Entrepreneurship Education Ecosystem in Colleges and Universities

The construction of university innovation and entrepreneurship education ecosystem is a hot concern for international entrepreneurship education theory and practice.However, China's specific national conditions and path of characteristics determine that the Western model cannot be copied. It must, on the basis of comprehensive experience of ancient, modern and abroad, based on China's national conditions, make Chinese choices and reflect Chinese characteristics.We should focus on building the “Chinese consensus” on the construction of the innovation and entrepreneurship education ecosystem in universities, building a solid “Chinese confidence”, highlighting the “Chinese style”, and building a “Chinese model”.
How does a Neural Network's Architecture Impact its Robustness to Noisy Labels?

Noisy labels are inevitable in large real-world datasets. In this work, we explore an area understudied by previous works -- how the network's architecture impacts its robustness to noisy labels. We provide a formal framework connecting the robustness of a network to the alignments between its architecture and target/noise functions. Our framework measures a network's robustness via the predictive power in its representations -- the test performance of a linear model trained on the learned representations using a small set of clean labels. We hypothesize that a network is more robust to noisy labels if its architecture is more aligned with the target function than the noise. To support our hypothesis, we provide both theoretical and empirical evidence across various neural network architectures and different domains. We also find that when the network is well-aligned with the target function, its predictive power in representations could improve upon state-of-the-art (SOTA) noisy-label-training methods in terms of test accuracy and even outperform sophisticated methods that use clean labels.
The effectiveness of the Electronic archiving system at the Documentation and Administrative Communication Center at Umm Al-Qura University: A case study: فعالية نظام الأرشفة الإلكترونية بمركز الوثائق والاتصالات الإدارية بجامعة أم القرى: دراسة حالة

The study aimed at identifying the electronic archiving system used in the Documentation and Administrative Communication Center at Umm Al-Qura University. It is a laser fiche system for document management and workflow solutions, which is the main goal of the study; revealing the reality of the use of the electronic archiving system in the center, its effectiveness in meeting its needs, the reasons for its selection of the system, its most important characteristics, and the identification of the methods used for the security and safety of documents. The study also reviews advantages and disadvantages of the used technique in order to conclude a number of results and recommendations which contribute to improvement and development of the system at the documentation and administrative communication center. This is because it is an essential pillar for achieving progress and development in conducting and archiving transactions in addition to raising the level of the university among other universities.Methodology of The Study: This study relied on the descriptive approach using the case study method as it fits with the subject of the study. Data was collected through an interview form directed to the Director of the Documentation and Administrative Communications Center. In addition, preparing a questionnaire including a set of multiple-choice questions sent via e-mail and communication to be completed by phone calls.Results of The Study: The study concluded a number of results, the most important of which are: that the electronic archiving system; Laser Fiche was able to achieve many advantages for the Documentation and Administrative Communication Center at Umm Al-Qura University. It also achieved the desired goals of its application. The study also proved the efficiency of the system in addition to the presence of Work Flow Masirat in order for the system to do some points that help the user.Recommendations: The researcher concluded a set of recommendations, the most important of which are: the necessity of continuous updating of the system and activating all its applications, and its developing to solve problems that were monitored through the results.
How Hateful are Movies? A Study and Prediction on Movie Subtitles

In this research, we investigate techniques to detect hate speech in movies. We introduce a new dataset collected from the subtitles of six movies, where each utterance is annotated either as hate, offensive or normal. We apply transfer learning techniques of domain adaptation and fine-tuning on existing social media datasets, namely from Twitter and Fox News. We evaluate different representations, i.e., Bag of Words (BoW), Bi-directional Long short-term memory (Bi-LSTM), and Bidirectional Encoder Representations from Transformers (BERT) on 11k movie subtitles. The BERT model obtained the best macro-averaged F1-score of 77%. Hence, we show that transfer learning from the social media domain is efficacious in classifying hate and offensive speech in movies through subtitles.
Learning from Limited Labels for Long Legal Dialogue

We study attempting to achieve high accuracy information extraction of case factors from a challenging dataset of parole hearings, which, compared to other legal NLP datasets, has longer texts, with fewer labels. On this co pus, existing work directly applying pretrained neural models has failed to extract all but a few relatively basic items with little improvement over rule-based extraction. We address two challenges posed by existing work: training on long documents and reasoning over complex speech patterns. We use a similar approach to the two-step open-domain question answering approach by using a Reducer to extract relevant text segments and a Producer to generate both extractive answers and non-extractive classifications. In a context like ours, with limited labeled data, we show that a superior approach for strong performance within limited development time is to use a combination of a rule-based Reducer and a neural Producer. We study four representative tasks from the parole dataset. On all four, we improve extraction from the previous benchmark of 0.41–0.63 to 0.83–0.89 F1.
Not All Models Localize Linguistic Knowledge in the Same Place: A Layer-wise Probing on BERToids' Representations.

Most of the recent works on probing representations have focused on BERT, with the presumption that the findings might be similar to the other models. In this work, we extend the probing studies to two other models in the family, namely ELECTRA and XLNet, showing that variations in the pre-training objectives or architectural choices can result in different behaviors in encoding linguistic information in the representations. Most notably, we observe that ELECTRA tends to encode linguistic knowledge in the deeper layers, whereas XLNet instead concentrates that in the earlier layers. Also, the former model undergoes a slight change during fine-tuning, whereas the latter experiences significant adjustments. Moreover, we show that drawing conclusions based on the weight mixing evaluation strategy—which is widely used in the context of layer-wise probing—can be misleading given the norm disparity of the representations across different layers. Instead, we adopt an alternative information-theoretic probing with minimum description length, which has recently been proven to provide more reliable and informative results.
Implementation of Cloud Accreditation as Accreditation Assessment Simulation in Lancang Kuning University

SIM Accreditation Cloud is a media accreditation simulation facility that is used by the Study Program and BPM in implementing and monitoring the feasibility of the study program whether it is in accordance with the standards that should be in creating a quality culture. In the face of the Regulation of the Minister of Education and Culture (PERMENDIKBUD) No. 5 of 2020 and the regulation of the National Accreditation Board for Higher Education (BANPT) No. 1 of 2020, related to the mechanism for extending the accreditation of universities and study programs that already use the Higher Education Accreditation Instrument (IAPT 3.0) and Study Program Accreditation Instrument (IAPS 4.0), so We need a system that can help Lancang Kuning University in the preparation and implementation of accreditation assessment simulations for both universities and study programs in order to be able to adjust the implementation and preparation of the mechanism for extending the validity period of accreditation for colleges and study programs. However, the fact is that until now there has been no study program that has proposed an extension of the accreditation period using the Study Program accreditation instrument. Lancang Kuning University has 21 Study Programs consisting of 9 Faculties, in implementing an internal quality assurance system, Lancang Kuning University has a Quality Assurance Agency in charge of managing a quality culture, but in the implementation of accreditation with IAPS 4.0 this has not been effective, this is proven to be absent from the study program. who are ready to apply with IAPS 4.0, because there are still obstacles in the preparation of accreditation instruments, while the need for accreditation ratings is very much needed by study programs so that they are able to compete and realize the vision and mission of the study program.
Evaluating Efficient Performance Estimators of Neural Architectures

Conducting efficient performance estimations of neural architectures is amajor challenge in neural architecture search (NAS). To reduce the architecturetraining costs in NAS, one-shot estimators (OSEs) amortize the architecturetraining costs by sharing the parameters of one supernet between allarchitectures. Recently, zero-shot estimators (ZSEs) that involve no trainingare proposed to further reduce the architecture evaluation cost. Despite thehigh efficiency of these estimators, the quality of such estimations has notbeen thoroughly studied. In this paper, we conduct an extensive and organizedassessment of OSEs and ZSEs on three NAS benchmarks: NAS-Bench-101/201/301.Specifically, we employ a set of NAS-oriented criteria to study the behavior ofOSEs and ZSEs and reveal that they have certain biases and variances. Afteranalyzing how and why the OSE estimations are unsatisfying, we explore how tomitigate the correlation gap of OSEs from several perspectives. For ZSEs, wefind that current ZSEs are not satisfying enough in these benchmark searchspaces, and analyze their biases. Through our analysis, we give out suggestionsfor future application and development of efficient architecture performanceestimators. Furthermore, the analysis framework proposed in our work could beutilized in future research to give a more comprehensive understanding of newlydesigned architecture performance estimators. All codes and analysis scriptsare available at https://github.com/walkerning/aw_nas.
FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset

While significant advancements have been made in the generation of deepfakes using deep learning technologies, its misuse is a well-known issue now. Deepfakes can cause severe security and privacy issues as they can be used to impersonate a person's identity in a video by replacing his/her face with another person's face. Recently, a new problem of generating synthesized human voice of a person is emerging, where AI-based deep learning models can synthesize any person's voice requiring just a few seconds of audio. With the emerging threat of impersonation attacks using deepfake audios and videos, a new generation of deepfake detectors is needed to focus on both video and audio collectively. A large amount of good quality datasets is typically required to capture the real-world scenarios to develop a competent deepfake detector. Existing deepfake datasets either contain deepfake videos or audios, which are racially biased as well. Hence, there is a crucial need for creating a good video as well as an audio deepfake dataset, which can be used to detect audio and video deepfake simultaneously. To fill this gap, we propose a novel Audio-Video Deepfake dataset (FakeAVCeleb) that contains not only deepfake videos but also respective synthesized lip-synced fake audios. We generate this dataset using the current most popular deepfake generation methods. We selected real YouTube videos of celebrities with four racial backgrounds (Caucasian, Black, East Asian, and South Asian) to develop a more realistic multimodal dataset that addresses racial bias and further help develop multimodal deepfake detectors. We performed several experiments using state-of-the-art detection methods to evaluate our deepfake dataset and demonstrate the challenges and usefulness of our multimodal Audio-Video deepfake dataset.
Information Directed Reward Learning for Reinforcement Learning

For many reinforcement learning (RL) applications, specifying a reward is difficult. In this paper, we consider an RL setting where the agent can obtain information about the reward only by querying an expert that can, for example, evaluate individual states or provide binary preferences over trajectories. From such expensive feedback, we aim to learn a model of the reward function that allows standard RL algorithms to achieve high expected return with as few expert queries as possible. For this purpose, we propose Information Directed Reward Learning (IDRL), which uses a Bayesian model of the reward function and selects queries that maximize the information gain about the difference in return between potentially optimal policies. In contrast to prior active reward learning methods designed for specific types of queries, IDRL naturally accommodates different query types. Moreover, by shifting the focus from reducing the reward approximation error to improving the policy induced by the reward model, it achieves similar or better performance with significantly fewer queries. We support our findings with extensive evaluations in multiple environments and with different types of queries.
Finnish Dialect Identification: The Effect of Audio and Text

Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recording in a dataset consisting of 23 different dialects. Our results show that the best accuracy is received by combining both of the modalities, as text only reaches to an overall accuracy of 57%, where as text and audio reach to 85%. Our code, models and data have been released openly on Github and Zenodo.
Hidden Service Circuit Reconstruction Attacks Based on Middle Node Traffic Analysis

Traffic analysis is widely considered as an attack posing a threat to anonymity of the communication and may reveal the real identity of the users. In this paper, a novel anonymous circuit reconstruction attack method that correlates the circuit traffic is proposed. This method then reconstructs a complete communication tunnel using the location of middle nodes found between the hidden and client services. The attack process includes independent determination of the location of the malicious nodes. A traffic correlation framework of AutoEncoder + CNN + BiLSTM is established, based on the Generative Adversarial Networks (GAN) model. BiLSTM applies the packet size and packet interval features of bidirectional traffic and combines the reconstruction loss function with the discrimination loss function to achieve correlated traffic evaluation. After balancing the reconstruction loss and discrimination loss scores, the simulation results confirm that the identification performance of the proposed system is higher than the advanced models.
Exploring the Limits of Out-of-Distribution Detection

Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can significantly improve the state-of-the-art (SOTA) on a range of near OOD tasks across different data modalities. For instance, on CIFAR-100 vs CIFAR-10 OOD detection, we improve the AUROC from 85% (current SOTA) to more than 96% using Vision Transformers pre-trained on ImageNet-21k. On a challenging genomics OOD detection benchmark, we improve the AUROC from 66% to 77% using transformers and unsupervised pre-training. To further improve performance, we explore the few-shot outlier exposure setting where a few examples from outlier classes may be available; we show that pre-trained transformers are particularly well-suited for outlier exposure, and that the AUROC of OOD detection on CIFAR-100 vs CIFAR-10 can be improved to 98.7% with just 1 image per OOD class, and 99.46% with 10 images per OOD class. For multi-modal image-text pre-trained transformers such as CLIP, we explore a new way of using just the names of outlier classes as a sole source of information without any accompanying images, and show that this outperforms previous SOTA on standard vision OOD benchmark tasks.
Open Knowledge Graphs Canonicalization using Variational Autoencoders

Noun phrases and Relation phrases in open knowledge graphs are not canonicalized, leading to an explosion of redundant and ambiguous subject-relation-object triples. Existing approaches to solve this problem take a two-step approach. First, they generate embedding representations for both noun and relation phrases, then a clustering algorithm is used to group them using the embeddings as features. In this work, we propose Canonicalizing Using Variational AutoEncoders and Side Information (CUVA), a joint model to learn both embeddings and cluster assignments in an end-to-end approach, which leads to a better vector representation for the noun and relation phrases. Our evaluation over multiple benchmarks shows that CUVA outperforms the existing state-of-the-art approaches. Moreover, we introduce CanonicNell, a novel dataset to evaluate entity canonicalization systems.
GeneSis: A Generative Approach to Substitutes in Context

The lexical substitution task aims at generating a list of suitable replacements for a target word in context, ideally keeping the meaning of the modified text unchanged. While its usage has increased in recent years, the paucity of annotated data prevents the finetuning of neural models on the task, hindering the full fruition of recently introduced powerful architectures such as language models. Furthermore, lexical substitution is usually evaluated in a framework that is strictly bound to a limited vocabulary, making it impossible to credit appropriate, but out-of-vocabulary, substitutes. To assess these issues, we proposed GeneSis (Generating Substitutes in contexts), the first generative approach to lexical substitution. Thanks to a seq2seq model, we generate substitutes for a word according to the context it appears in, attaining state-of-the-art results on different benchmarks. Moreover, our approach allows silver data to be produced for further improving the performances of lexical substitution systems. Along with an extensive analysis of GeneSis results, we also present a human evaluation of the generated substitutes in order to assess their quality. We release the fine-tuned models, the generated datasets, and the code to reproduce the experiments at https://github.com/SapienzaNLP/genesis.