Reviewerly/exHarmony huggingface dataset:
Minimal Port-based Teleportation

There are two types of port-based teleportation (PBT) protocols: deterministic -- when the state always arrives to the receiver but is imperfectly transmitted and probabilistic -- when the state reaches the receiver intact with high probability. We introduce the minimal set of requirements that define a feasible PBT protocol and construct a simple PBT protocol that satisfies these requirements: it teleports an unknown state of a qubit with success probability $p_{succ}=1-\frac{N+2}{2^{N+1}}$ and fidelity $1-O(\frac{1}{N})$ with the resource state consisting of $N$ maximally entangled states. This protocol is not reducible from either the deterministic or probabilistic PBT protocol. We define the corresponding efficient superdense coding protocols which transmit more classical bits with fewer maximally entangled states. Furthermore, we introduce rigorous methods for comparing and converting between different PBT protocols.
QR Code as the Delivery of agent to Schools

Divider or magazine magazines are a method for conveying data on Schools grounds. In the conveyance of data, the magazine has been broadly spread in a few utilizations of Schools, iRan and GreenDoc. Be that as it may, this is as yet not ideal in light of the fact that the strategy utilized is as yet utilizing paper. And furthermore the absence of consciousness of understudies will peruse the data on the magazine. QR codes are a sort of two-layered grid code or scanner tag created by Denso Wave in 1994. With that, the type of data is decreased to a QR code that can store nitty gritty data showed on a screen accessible at a few vital corners of the grounds and output the code on the principle page of the internet based magazine site. The strategy utilized is the cascade technique, perception, and writing study and diary references as supporting recorded as a hard copy. This exploration was intended to make it more straightforward to get to data and draw in perusing interest to understudies of Schools.
Enhanced optimal quantum communication by a generalized phase-shift-keying coherent signal

It is well known that the maximal success probability of the binary quantum communication can be improved by using a sub-Poissonian non-standard coherent state as an information carrier. In the present article, we consider the quantum communication with $N$-ary phase shift keying ($N$-PSK) signal for an arbitrary positive integer $N>1$. By using non-standard coherent state, we analytically provide the maximal success probability of the quantum communication with $N$-PSK. Unlike the binary case, we show that even super-Poissonianity of non-standard coherent state can improve the maximal success probability of $N$-PSK quantum communication.
Initialization of Latent Space Coordinates via Random Linear Projections for Learning Robotic Sensory-Motor Sequences

Robot kinematics data, despite being a high dimensional process, is highly correlated, especially when considering motions grouped in certain primitives. These almost linear correlations within primitives allow us to interpret the motions as points drawn close to a union of low-dimensional linear subspaces in the space of all motions. Motivated by results of embedding theory, in particular, generalizations of Whitney embedding theorem, we show that random linear projection of motor sequences into low dimensional space loses very little information about structure of kinematics data. Projected points are very good initial guess for values of latent variables in generative model for robot sensory-motor behaviour primitives. We conducted series of experiments where we trained a recurrent neural network to generate sensory-motor sequences for robotic manipulator with 9 degrees of freedom. Experimental results demonstrate substantial improvement in generalisation abilities for unobserved samples in the case of initialization of latent variables with random linear projection of motor data over initialization with zero or random values. Moreover, latent space is well-structured wherein samples belonging to different primitives are well separated from the onset of training process.
Gaussian theory for estimating fluctuating perturbations with back action evasive oscillator variables

We apply a Gaussian state formalism to track fluctuating perturbations that act on the position and momentum quadrature variables of a harmonic oscillator. Following a seminal proposal by Tsang and Caves [Phys. Rev. Lett. 105, 123601 (2010)], Einstein-Podolsky-Rosen correlations with the quadrature variables of an ancillary harmonic oscillator are leveraged to significantly improve the estimates as relevant sensor variables can be arbitrarily squeezed while evading adverse effects from the conjugate, anti-squeezed variables. Our real-time analysis of the continuous monitoring of the system employs a hybrid quantum-classical description of the quantum probe and the unknown classical perturbations, and it provides a general formalism to establish the achievements of the sensing scheme and how they depend on different parameters.
An User Experience Analysis of UNG E-Learning Using User Experience Questionnaire Tool

The user experience measurement can reveal the strengths and weaknesses of an application.One of the tools used to measure the user experience of an application is the User Experience Questionnaire (UEQ).UEQ can provide feedback to developers on the Attractiveness, Efficiency, Perspicuity, Dependability, Stimulation, and Novelty aspects of the application, whether it needs improvement or not.This study aims to measure the user experience of UNG E-learning using the User Experience Questionnaire (UEQ) on these six aspects.This research is quantitative research with a survey method approach.The tool used in analyzing the user experience of e-learning is UEQ.This study showed that the UNG e-learning was seen from the average respondents' answers were between -0.8 and 0.8, which means that the respondents gave neutral answers.Compared to other products in the benchmark, this e-learning gives poor results in Attractiveness, Efficiency, Perspicuity, Dependability, Stimulation, and Novelty.Therefore, this e-learning requires quality improvement in user experience in these six aspects.
Language modeling via stochastic processes

Modern language models can generate high-quality short texts. However, they often meander or are incoherent when generating longer texts. These issues arise from the next-token-only language modeling objective. Recent work in self-supervised learning suggests that models can learn good latent representations via contrastive learning, which can be effective for discriminative tasks. Our work analyzes the application of contrastive representations for generative tasks, like long text generation. We propose one approach for leveraging constrastive representations, which we call Time Control (TC). TC first learns a contrastive representation of the target text domain, then generates text by decoding from these representations. Compared to domain-specific methods and fine-tuning GPT2 across a variety of text domains, TC performs competitively to methods specific for learning sentence representations on discourse coherence. On long text generation settings, TC preserves the text structure both in terms of ordering (up to $+15\%$ better) and text length consistency (up to $+90\%$ better).
Antecedent and Consequence of ICT Utilization in Education: Voices from Private University Lecturers In Indonesia

Antecedent and Consequence of ICT Utilization in Education: Voices from Private University Lecturers In Indonesia. Objective: This research tested the factors empirically as antecedent in utilization of information technology such as social factor, affect, complexity, job fit, long term consequence, facilitating condition, and consequence of utilization of information technology to performance. Methods: Sampels in this research were private university’s economics lecturers in Indonesia. The lecturers of private universities in Maluku considered to represent private university’s lectures outside of Java and private university’s lecturers in Malang represented private university’s lecturers who was created to a control group. Findings: The result of this research found that private university’s lecturers in Indonesia perceived that utilization of information technology significantly gave positive influence to their performance. While utilization of information technology for half of lecturers were not influenced by six tested factors, and the others perceived that utilization of information technology were influenced by social factor, affect and facilitating condition. Keywords: education, ICT utilization, private university. Abstrak: Anteseden dan Konsukensi Pemanfaatan TIK Dalam Pendidikan: Pandangan Dosen Perguruan Tinggi Swasta (PTS) di Indonesia. Tujuan: Penelitian ini ingin menguji secara empiris faktor-faktor yang merupakan anteseden dalam pemanfaatan teknologi informasi yaitu, faktor sosial, affect, kompleksitas, kesesuaian tugas, konsekuensi jangka panjang dan kondisi yang memfasilitasi, serta konsekuensi pemanfaatan teknologi informasi terhadap kinerja. Metode: Sampel dalam penelitian ini adalah dosen-dosen PTS bidang ilmu ekonomi di Indonesia. Dosen PTS di Maluku di anggap mewakili dosen PTS di luar pulau Jawa dan dosen PTS di Malang mewakili dosen PTS di Indonesia yang di jadi kan kelompok kontrol. Temuan: Hasil penelitian menemukan, dosen-dosen PTS di Indonesia mempersepsikan pemanfaatan teknologi informasi berpengaruh positif secara signifikan terhadap kinerja mereka. Tetapi pemanfaatan teknologi informasi bagi sebagian dosen tidak di pengaruhi oleh keenam faktor yang di uji, sementara sebagian lainnya mempersepsikan pemanfaatan teknologi informasi di pengaruhi oleh faktor sosial, dampak dan kondisi yang memfasilitasi. Kata kunci: pendidikan, pemanfaatan TIK, universitas swasta. DOI: http://dx.doi.org/10.23960/jpp.v12.i2.202203
Revisiting Game Representations: The Hidden Costs of Efficiency in Sequential Decision-making Algorithms

Recent advancements in algorithms for sequential decision-making under imperfect information have shown remarkable success in large games such as limit- and no-limit poker. These algorithms traditionally formalize the games using the extensive-form game formalism, which, as we show, while theoretically sound, is memory-inefficient and computationally intensive in practice. To mitigate these challenges, a popular workaround involves using a specialized representation based on player specific information-state trees. However, as we show, this alternative significantly narrows the set of games that can be represented efficiently. In this study, we identify the set of large games on which modern algorithms have been benchmarked as being naturally represented by Sequential Bayesian Games. We elucidate the critical differences between extensive-form game and sequential Bayesian game representations, both theoretically and empirically. We further argue that the impressive experimental results often cited in the literature may be skewed, as they frequently stem from testing these algorithms only on this restricted class of games. By understanding these nuances, we aim to guide future research in developing more universally applicable and efficient algorithms for sequential decision-making under imperfect information.
Illuminating the Space of Dungeon Maps, Locked-door Missions and Enemy Placement Through MAP-Elites

Procedural Content Generation (PCG) methods are valuable tools to speed up the game development process. Moreover, PCG may also present in games as features, such as the procedural dungeon generation (PDG) in Moonlighter (Digital Sun, 2018). This paper introduces an extended version of an evolutionary dungeon generator by incorporating a MAP-Elites population. Our dungeon levels are discretized with rooms that may have locked-door missions and enemies within them. We encoded the dungeons through a tree structure to ensure the feasibility of missions. We performed computational and user feedback experiments to evaluate our PDG approach. They show that our approach accurately converges almost the whole MAP-Elite population for most executions. Finally, players' feedback indicates that they enjoyed the generated levels, and they could not indicate an algorithm as a level generator.
Hierarchical Reinforcement Learning with Timed Subgoals

Hierarchical reinforcement learning (HRL) holds great potential for sample-efficient learning on challenging long-horizon tasks. In particular, letting a higher level assign subgoals to a lower level has been shown to enable fast learning on difficult problems. However, such subgoal-based methods have been designed with static reinforcement learning environments in mind and consequently struggle with dynamic elements beyond the immediate control of the agent even though they are ubiquitous in real-world problems. In this paper, we introduce Hierarchical reinforcement learning with Timed Subgoals (HiTS), an HRL algorithm that enables the agent to adapt its timing to a dynamic environment by not only specifying what goal state is to be reached but also when. We discuss how communicating with a lower level in terms of such timed subgoals results in a more stable learning problem for the higher level. Our experiments on a range of standard benchmarks and three new challenging dynamic reinforcement learning environments show that our method is capable of sample-efficient learning where an existing state-of-the-art subgoal-based HRL method fails to learn stable solutions.
Smart Contracts for Incentivized Outsourcing of Computation

Outsourcing computation allows a resource limited client to expand its computational capabilities by outsourcing computation to other computing nodes or clouds. A basic requirement of outsourcing is providing assurance that the computation result is correct. We consider a smart contract based outsourcing system that achieves assurance by replicating the computation on two servers, and accepts the computation result if the two responses match. Correct computation result is obtained by using incentivization to instigate correct behaviour in servers. We show that all previous replication based incentivized outsourcing protocols with proven correctness fail when automated by a smart contract, because of the copy attack where a contractor simply copies the submitted response of the other contractor. We then design an incentivization mechanism that uses two lightweight challenge-response protocols that are used when the submitted results are compared, and employs monetary rewards, fines, and bounties to incentivize correct computation. We use game theory to model and analyze our mechanism, and prove that with appropriate choices of the mechanism parameters, there is a single Nash equilibrium corresponding to the contractors’ strategy of correctly computing the result. Our work provides a foundation for replicated incentivized computation in smart contract setting, and opens new research directions.
Transformer-based Korean Pretrained Language Models: A Survey on Three  Years of Progress

With the advent of Transformer, which was used in translation models in 2017, attention-based architectures began to attract attention. Furthermore, after the emergence of BERT, which strengthened the NLU-specific encoder part, which is a part of the Transformer, and the GPT architecture, which strengthened the NLG-specific decoder part, various methodologies, data, and models for learning the Pretrained Language Model began to appear. Furthermore, in the past three years, various Pretrained Language Models specialized for Korean have appeared. In this paper, we intend to numerically and qualitatively compare and analyze various Korean PLMs released to the public.
But that's not why: Inference adjustment by interactive prototype revision

Despite significant advances in machine learning, decision-making of artificial agents is still not perfect and often requires post-hoc human interventions. If the prediction of a model relies on unreasonable factors it is desirable to remove their effect. Deep interactive prototype adjustment enables the user to give hints and correct the model's reasoning. In this paper, we demonstrate that prototypical-part models are well suited for this task as their prediction is based on prototypical image patches that can be interpreted semantically by the user. It shows that even correct classifications can rely on unreasonable prototypes that result from confounding variables in a dataset. Hence, we propose simple yet effective interaction schemes for inference adjustment: The user is consulted interactively to identify faulty prototypes. Non-object prototypes can be removed by prototype masking or a custom mode of deselection training. Interactive prototype rejection allows machine learning na\"{i}ve users to adjust the logic of reasoning without compromising the accuracy.
Predict and Optimize: Through the Lens of Learning to Rank

In the last years predict-and-optimize approaches (Elmachtoub and Grigas 2021; Wilder, Dilkina, and Tambe 2019) have received increasing attention. These problems have the settings where the predictions of predictive machine learning (ML) models are fed to downstream optimization problems for decision making. Predict-and-optimize approaches propose to train the ML models, often neural network models, by directly optimizing the quality of decisions made by the optimization solvers. However, one major bottleneck of predict-and-optimize approaches is solving the optimization problem for each training instance at every epoch. To address this challenge, Mulamba et al. (2021) propose noise contrastive estimation by caching feasible solutions. In this work, we show the noise contrastive estimation can be considered a case of learning to rank the solution cache. We also develop pairwise and listwise ranking loss functions, which can be differentiated in closed form without the need of solving the optimization problem. By training with respect to these surrogate loss function, we empirically show that we are able to minimize the regret of the predictions.
Graph Neural Networks in IoT: A Survey

The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at https://github.com/GuiminDong/GNN4IoT.
Video Question Answering: Datasets, Algorithms and Challenges

Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos. It has earned increasing attention with recent research trends in joint vision and language understanding. Yet, compared with ImageQA, VideoQA is largely underexplored and progresses slowly. Although different algorithms have continually been proposed and shown success on different VideoQA datasets, we find that there lacks a meaningful survey to categorize them, which seriously impedes its advancements. This paper thus provides a clear taxonomy and comprehensive analyses to VideoQA, focusing on the datasets, algorithms, and unique challenges. We then point out the research trend of studying beyond factoid QA to inference QA towards the cognition of video contents, Finally, we conclude some promising directions for future exploration.
A Survey Towards Labor-saving the Corporate Recruitment

Abstract: Traditional recruiting approaches are becoming insufficient due to the fast expansion of employment markets. This is due to the fact that the corporate business is expanding at a rapid pace, and each organisation receives a large number of applications. On the practical side, reading and analysing each and every resume manually is an impossible and time-consuming task. To address this issue, this research offers a detailed and repeatable evaluation of the performance of Automatic Interviewing software, providing the framework for future benchmarking and relevant comparison studies. We also discovered that SpaCy consistently outperforms for Information Extraction and Named Entity Recognition. Keywords: Automated recruitment; deep learning; convolutional neural networks (CNN), LSTM, RNN;
Dynamic Multimodal Fusion

Deep multimodal learning has achieved great progress in recent years. However, current fusion approaches are static in nature, i.e., they process and fuse multimodal inputs with identical computation, without accounting for diverse computational demands of different multimodal data. In this work, we propose dynamic multimodal fusion (DynMM), a new approach that adaptively fuses multimodal data and generates data-dependent forward paths during inference. To this end, we propose a gating function to provide modality-level or fusion-level decisions on-the-fly based on multimodal features and a resource-aware loss function that encourages computational efficiency. Results on various multimodal tasks demonstrate the efficiency and wide applicability of our approach. For instance, DynMM can reduce the computation costs by 46.5% with only a negligible accuracy loss (CMU-MOSEI sentiment analysis) and improve segmentation performance with over 21% savings in computation (NYU Depth V2 semantic segmentation) when compared with static fusion approaches. We believe our approach opens a new direction towards dynamic multimodal network design, with applications to a wide range of multimodal tasks.
Mobile Guide for Museum in Context of International Mountain Museum, Nepal

This paper aims to utilization of information technology at museum to provide the information about the museum and its artifacts properly in easier way with the help of digital devices such as mobile to the visitors.