Reviewerly/exHarmony huggingface dataset:
Privacy, Free Speech and Content Moderation: A Literature Review and Constitutional Framework Analysis

Content moderation is one of the lifeblood of Internet platform companies. Concealment and innovation coexist. The United States and the European Union are leading the world in the field of human rights protection in content moderation. Both the text of the Constitution and the jurisprudence have formed a relatively complete framework of protection. Public and private sectors, online and offline communities, and the balance between ex-ante and ex-post, are at the intersection of content moderation with privacy and free speech. We need to further research exploring the design of the online moderation model, which will balance the arguments around policy, human rights law, and the need to make online spaces safer for a world-wide diverse population.
Analisis Kualitas Pelayanan Publik pada Dinas Perpustakaan dan Arsip Provinsi Sumatera Utara

This study aims to analyze the quality and factors that influence public services at the Library and Archives Service of North Sumatra Province. The services available at the North Sumatra Provincial Library and Archives Service are public services, children's services, deposit services, reference services, digital services and mobile library services. The research method used is descriptive research with a qualitative approach. Data collection techniques used observation, study documentation and interviews. Data analysis used descriptive analysis. The results of this study indicate that the quality of public services at the Library and Archives Service of North Sumatra Province refers to Zeithaml's theory, namely from the dimensions: 1) Reliability (reliability); 2) Responsiveness (responsiveness); 3) Assurance (guarantee); 4) Empathy (empathy); 5) Tangibles (physical evidence). Everything has been done well, but the Tangibles dimensions (physical evidence) have not been maximized in the form of the problematic INSLISLITE application, the room temperature is not cold enough, the room lacks lighting. Factors that affect Public Service Services at the Library and Archives Service of North Sumatra Province are: 1) Inadequate facilities; 2) Budget that has not been maximized.
Interactive Learning Content Using H5P in Pronunciation Course

In this blended learning, students experience offline and online learning. Sometimes students experience burnout in online learning. This can be caused by various things, such as signal interference, less interesting learning materials, and so on. In Pronunciation course, there are many symbols and transcripts on the pronunciation. More practice made the pronunciation of a word better. The purpose of this study is to developed interactive learning content using H5P in Pronunciation course. The method used is ADDIE model development method, the steps are analysis, design, development, implementation, and evaluation. The results are interactive video and interactive quizzes using H5P which are applied to students in Pronunciation course. Using interactive videos and interactive quizzes, students better understand the material and feel excited in the learning process.
Analisis Pengelolaan Arsip Dinamis di Dinas Perpustakaan dan Kearsipan Kabupaten Aceh Barat

The purpose of this study is twofold namely to analizy the management of dynamic archives in the Library and Archives Office of West Aceh District and to examine the obstacles faced in the process of managing the archives. This study uses management theory by Ricky W.Griffin a research foundation. Data collection techniques were carried out by means of observation, interviews and documentation. The results of the study indicate that records management includes the creation includes incoming and outgoing mail archives. The use of archives is their use in the continuity of work and archive storage arranged according to the order of the classification code, in order to facilitate the retrieval of archives. Archive maintenance is done by cleaning the dust using a duster and mothballs. Archive depreciation has never been carried out, but there is a depreciation plan in 2023 with preparation of archive retention schedule. Dynamic archive management has obstacles including the lack of archivist functional, the absence of archive depots and record centers, the absence of an archive list, archive management that is still done manually and the limited budget.
Perbedaan Motivasi Belajar Dan Minat Belajar Antara Siswa Yang Belajar Daring Dan Luring Pada Pelajaran Bahasa Indonesia Di SMP Swasta Katolik Budi Murni 3 Medan

This study uses a quantitative approach, with the sampling technique used is accidental sampling. The population in this study amounted to 216 and the sample from class VIII was 54 which was divided into 2 groups, namely 27 data from each group. The analytical method used to analyze the data obtained in this study uses the Mann Whitney U analysis method. This type of research uses the theoretical basis of learning motivation and interest in learning based on aspects of the theory of Martaniah (2006) and Slameto (2013). The results of this study indicate that the significance value of Mann Whitney U is 0.924, which means that the difference between learning motivation in students who study online and offline is rejected. Thus, the results of the study in the interest in learning group showed the significance value of Mann Whitney U was obtained at 0.808, which means that there is no difference between students' interest in learning when studying online and offline, meaning that this study was rejected because it was not in accordance with the initial hypothesis.
Politeness Strategies Analysis in "Two Distant Strangers"

The effort to satisfy someone else’s face wants is referred to as a politeness strategy in linguistics. These strategies are used in everyday communication. Aside from using politeness strategies in daily life dialogues, politeness strategies can also be seen in films. The object of this study is the script of a short film on Netflix entitled Two Distant Strangers. There are three key characters which are Carter, Merk, and Perri. This research aims to discover the types of politeness strategies utilized by the characters in Two Distant Strangers and the sociological factors that influence the usage of politeness strategies in the short film. This research employs the pragmatic approach and qualitative methodology. From the analysis, the researchers found that all characters in Two Distant Strangers implemented four types of politeness strategies. The researchers also discovered that the characters’ utterances demonstrated a relationship between politeness strategies and related sociological factors. Bald on-record is the most frequently used strategy because Merk, the character who acts as a police officer, believes he is more powerful than the other characters. The sociological factor of high to low power plays a part in why Merk is not hesitant to ignore other characters’ faces and command them as he likes.
BIGDATA ANALYTICS IN INSTAGRAM

The personalization of each user's feed on Instagram has been greatly aided by the usage of AI and Big Data. Because of this, the Instagram algorithm is designed to help every user locate material that they are interested in. Instagram utilizes AI to sort its feed and display articles that people are likely to like or like. This is due to the fact that technology gradually learns what content is valuable and pertinent for every user. Instagram has expanded beyond being a platform for only sharing images and videos with its estimated 1.1 billion monthly active users. Now, you may advertise using the photo-sharing software. Big Data and AI are used on Instagram to gather analytical information about user behavior. Following that, data on user interactions and search choices will be provided.
Time Governors for Safe Path-Following Control

Safe and smooth robot motion around obstacles is an essential skill for autonomous robots, especially when operating around people and other robots. Conventionally, due to real-time operation requirements and onboard computation limitations, many robot motion planning and control methods follow a two-step approach: first construct a (e.g., piecewise linear) collision-free reference path for a simplified robot model, and then execute the reference plan via path-following control for a more accurate and complex robot model. A challenge of such a decoupled robot motion planning and control method for highly dynamic robotic systems is ensuring the safety of path-following control as well as the successful completion of the reference plan. In this paper, we introduce a novel dynamical systems approach for online closed-loop time parametrization, called $\textit{a time governor}$, of a reference path for provably correct and safe path-following control based on feedback motion prediction, where the safety of robot motion under path-following control is continuously monitored using predicted robot motion. After introducing the general framework of time governors for safe path following, we present an example application for the fully actuated high-order robot dynamics using proportional-and-higher-order-derivative (PhD) path-following control whose feedback motion prediction is performed by Lyapunov ellipsoids and Vandemonde simplexes. In numerical simulations, we investigate the role of reference position and velocity feedback, and motion prediction on path-following performance and robot motion.
Learning and Blending Robot Hugging Behaviors in Time and Space

We introduce an imitation learning-based physical human-robot interaction algorithm capable of predicting appropriate robot responses in complex interactions involving a superposition of multiple interactions. Our proposed algorithm, Blending Bayesian Interaction Primitives (B-BIP) allows us to achieve responsive interactions in complex hugging scenarios, capable of reciprocating and adapting to a hugs motion and timing. We show that this algorithm is a generalization of prior work, for which the original formulation reduces to the particular case of a single interaction, and evaluate our method through both an extensive user study and empirical experiments. Our algorithm yields significantly better quantitative prediction error and more-favorable participant responses with respect to accuracy, responsiveness, and timing, when compared to existing state-of-the-art methods.
A Numerical Example of Russell's Paradox and Its Novel Geometric Properties

Abstract There are some paradoxes in the composition relationship between point-line-plane-space. I found that a numerical example of Russell's paradox- ∅ Pan -can solve these problems. ∅ Pan is a set group that all its sets are mutually included. Geometric ∅ Pan has remarkable properties that all its lines are mutually included and mutually iterated, and the same as planes and spaces. Visualization models for these geometric set groups were established through cross-composing timelines, cross-monitoring screens, and cross-sharing cyberspaces , respectively. These findings demonstrate that sets/objects can mutually include and geometries/objects can mutually iterate. I anticipate the present paper to be a starting point for a novel kind of set group and algebraic geometry.
High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems

Non-orthogonal multiple access (NOMA) technique is important for achieving a high data rate in next-generation wireless communications. A key challenge to fully utilizing the effectiveness of the NOMA technique is the optimization of the resource allocation (RA), e.g., channel and power. However, this RA optimization problem is NP-hard, and obtaining a good approximation of a solution with a low computational complexity algorithm is not easy. To overcome this problem, we propose the coherent Ising machine (CIM) based optimization method for channel allocation in NOMA systems. The CIM is an Ising system that can deliver fair approximate solutions to combinatorial optimization problems at high speed (millisecond order) by operating optimization algorithms based on mutually connected photonic neural networks. The performance of our proposed method was evaluated using a simulation model of the CIM. We compared the performance of our proposed method to simulated annealing, a conventional-NOMA pairing scheme, deep Q learning based scheme, and an exhaustive search scheme. Simulation results indicate that our proposed method is superior in terms of speed and the attained optimal solutions.
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field

Ultra-fine entity typing (UFET) aims to predict a wide range of type phrases that correctly describe the categories of a given entity mention in a sentence. Most recent works infer each entity type independently, ignoring the correlations between types, e.g., when an entity is inferred as a president, it should also be a politician and a leader. To this end, we use an undirected graphical model called pairwise conditional random field (PCRF) to formulate the UFET problem, in which the type variables are not only unarily influenced by the input but also pairwisely relate to all the other type variables. We use various modern backbones for entity typing to compute unary potentials, and derive pairwise potentials from type phrase representations that both capture prior semantic information and facilitate accelerated inference. We use mean-field variational inference for efficient type inference on very large type sets and unfold it as a neural network module to enable end-to-end training. Experiments on UFET show that the Neural-PCRF consistently outperforms its backbones with little cost and results in a competitive performance against cross-encoder based SOTA while being thousands of times faster. We also find Neural- PCRF effective on a widely used fine-grained entity typing dataset with a smaller type set. We pack Neural-PCRF as a network module that can be plugged onto multi-label type classifiers with ease and release it in https://github.com/modelscope/adaseq/tree/master/examples/NPCRF.
Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation

The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets. However, in realistic scenarios, finding an optimal source domain with identical label space is a challenging task. Partial domain adaptation alleviates this problem of procuring a labeled dataset with identical label space assumptions and addresses a more practical scenario where the source label set subsumes the target label set. This, however, presents a few additional obstacles during adaptation. Samples with categories private to the source domain thwart relevant knowledge transfer and degrade model performance. In this work, we try to address these issues by coupling variational information and adversarial learning with a pseudo-labeling technique to enforce class distribution alignment and minimize the transfer of superfluous information from the source samples. The experimental findings in numerous cross-domain classification tasks demonstrate that the proposed technique delivers superior and comparable accuracy to existing methods.
Generating Synthetic Data in a Secure Federated General Adversarial Networks for a Consortium of Health Registries

In this work, we review the architecture design of existing federated General Adversarial Networks (GAN) solutions and highlight the security and trust-related weaknesses in the existing designs. We then describe how these weaknesses make existing designs unsuitable for the requirements needed for a consortium of health registries working towards generating synthetic datasets for research purposes. Moreover, we propose how these weaknesses can be addressed with our novel architecture solution. Our novel architecture solution combines several building blocks to generate synthetic data in a decentralised setting. Consortium blockchains, secure multi-party computations, and homomorphic encryption are the core building blocks of our proposed architecture solution to address the weaknesses in the existing design of federated GANs. Finally, we discuss our proposed solution's advantages and future research directions.
Intermediate Entity-based Sparse Interpretable Representation Learning

Interpretable entity representations (IERs) are sparse embeddings that are "human-readable" in that dimensions correspond to fine-grained entity types and values are predicted probabilities that a given entity is of the corresponding type. These methods perform well in zero-shot and low supervision settings. Compared to standard dense neural embeddings, such interpretable representations may permit analysis and debugging. However, while fine-tuning sparse, interpretable representations improves accuracy on downstream tasks, it destroys the semantics of the dimensions which were enforced in pre-training. Can we maintain the interpretable semantics afforded by IERs while improving predictive performance on downstream tasks? Toward this end, we propose Intermediate enTity-based Sparse Interpretable Representation Learning (ItsIRL). ItsIRL realizes improved performance over prior IERs on biomedical tasks, while maintaining "interpretability" generally and their ability to support model debugging specifically. The latter is enabled in part by the ability to perform "counterfactual" fine-grained entity type manipulation, which we explore in this work. Finally, we propose a method to construct entity type based class prototypes for revealing global semantic properties of classes learned by our model.
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. Towards enabling QFL under various channel conditions, in this article we develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. Compared to the existing depth-fixed QNNs, training the depth-controllable eSQNN architecture is more challenging due to high entanglement entropy and inter-depth interference, which are mitigated by introducing entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, we optimize the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.
Automatic oracle generation in Microsoft's Quantum Development Kit using QIR and LLVM passes

Automatic oracle generation techniques can find optimized quantum circuits for classical components in quantum algorithms. However, most implementations of oracle generation techniques require that the classical component is expressed in terms of a conventional logic representation such as logic networks, truth tables, or decision diagrams. We implemented LLVM passes that can automatically generate QIR functions representing classical Q# functions into QIR code implementing such functions quantumly. We are using state-of-the-art logic optimization and oracle generation techniques based on XOR-AND graphs for this purpose. This enables not only a more natural description of the quantum algorithm on a higher level of abstraction, but also enables technology-dependent or application-specific generation of the oracles.
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder

The success of deep learning is partly attributed to the availability of massive data downloaded freely from the Internet. However, it also means that users' private data may be collected by commercial organizations without consent and used to train their models. Therefore, it's important and necessary to develop a method or tool to prevent unauthorized data exploitation. In this paper, we propose ConfounderGAN, a generative adversarial network (GAN) that can make personal image data unlearnable to protect the data privacy of its owners. Specifically, the noise produced by the generator for each image has the confounder property. It can build spurious correlations between images and labels, so that the model cannot learn the correct mapping from images to labels in this noise-added dataset. Meanwhile, the discriminator is used to ensure that the generated noise is small and imperceptible, thereby remaining the normal utility of the encrypted image for humans. The experiments are conducted in six image classification datasets, consisting of three natural object datasets and three medical datasets. The results demonstrate that our method not only outperforms state-of-the-art methods in standard settings, but can also be applied to fast encryption scenarios. Moreover, we show a series of transferability and stability experiments to further illustrate the effectiveness and superiority of our method.
Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation

Large pretrained language models can easily produce toxic or biased content, which is prohibitive for practical use. In order to detect such toxic generations, existing methods rely on templates, real-world data extraction, crowdsourcing workers, or automatic generation to construct adversarial contexts that are likely to induce toxic generations. However, what type of context is more likely to induce unsafe responses is still under-explored. In this paper, we identify that context toxicity and context category (e.g., \textit{profanity}, \textit{insult}, \textit{drugs}, etc.) are two important factors to cause safety issues in response generation. Hence, we propose a method called \emph{reverse generation} to construct adversarial contexts conditioned on a given response, with the flexibility to control category, toxicity level, and inductivity of the generated contexts. Via reverse generation, we augment the existing BAD dataset and construct a new dataset BAD+ which contains more than 120K diverse and highly inductive contexts in 12 categories. We test three popular pretrained dialogue models (Blender, DialoGPT, and Plato2) and find that BAD+ can largely expose their safety problems. Furthermore, we show that BAD+ can greatly enhance the safety of generation and reveal the key factors of safety improvement. Our code and dataset is available at \url{https://github.com/thu-coai/Reverse_Generation}.