Reviewerly/exHarmony huggingface dataset:
Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation

This paper is concerned with improving dialogue generation models through injection of knowledge, e.g., content relevant to the post that can increase the quality of responses.Past research extends the training of the generative models by incorporating statistical properties of posts, responses and related knowledge, without explicitly assessing the knowledge quality.In our work, we demonstrate the importance of knowledge relevance and adopt a two-phase approach.We first apply a novel method, Transformer & Post based Posterior Approximation (TPPA) to select knowledge, and then use the Transformer with Expanded Decoder (TED) model to generate responses from both the post and the knowledge.TPPA method processes posts, post related knowledge, and response related knowledge at both word and sentence level.Our experiments with the TED generative model demonstrate the effectiveness of TPPA as it outperforms a set of strong baseline models.Our TPPA method is extendable and supports further optimization of knowledge retrieval and injection.
QoS-driven scheduling in the cloud

Abstract Priority-based scheduling policies are commonly used to guarantee that requests submitted to the different service classes offered by cloud providers achieve the desired Quality of Service (QoS). However, the QoS delivered during resource contention periods may be unfair on certain requests. In particular, lower priority requests may have their resources preempted to accommodate resources associated with higher priority ones, even if the actual QoS delivered to the latter is above the desired level, while the former is underserved. Also, competing requests with the same priority may experience quite different QoS, since some of them may have their resources preempted, while others do not. In this paper we present a new scheduling policy that is driven by the QoS promised to individual requests. Benefits of using the QoS-driven policy are twofold: it maintains the QoS of each request as high as possible, considering their QoS targets and available resources; and it minimizes the variance of the QoS delivered to requests of the same class, promoting fairness. We used simulation experiments fed with traces from a production system to compare the QoS-driven policy with a state-of-the-practice priority-based one. In general, the QoS-driven policy delivers a better service than the priority-based one. Moreover, the equity of the QoS delivered to requests of the same class is much higher when the QoS-driven policy is used, particularly when not all requests get the promised QoS, which is the most important scenario. Finally, based on the current practice of large public cloud providers, our results show that penalties incurred by the priority-based scheduler in the scenarios studied can be, on average, as much as 193% higher than those incurred by the QoS-driven one.
Comparison of semiclassical and quantum models of a two-level atom-cavity QED system in the strong coupling regime

We present a numerical study comparing semiclassical and quantum models of a damped, strongly interacting cavity QED system composed of a single two-level atom interacting with a single quantized cavity mode driven externally by a tunable monochromatic field. We compute the steady state transmission spectrum of the coupled system under each model and show that in the strong coupling regime, the two models yield starkly different results. The fully quantum mechanical model of the system correctly yields the expected multiphoton transmission spectra while the semiclassical approach results in a bistable spectrum.
Impact of ASR on Alzheimer’s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others

Automatic Speech Recognition (ASR) is a critical component of any fully-automated speechbased dementia detection model.However, despite years of speech recognition research, little is known about the impact of ASR accuracy on dementia detection.In this paper, we experiment with controlled amounts of artificially generated ASR errors and investigate their influence on dementia detection.We find that deletion errors affect detection performance the most, due to their impact on the features of syntactic complexity and discourse representation in speech.We show the trend to be generalisable across two different datasets for cognitive impairment detection.As a conclusion, we propose optimising the ASR to reflect a higher penalty for deletion errors in order to improve dementia detection performance.
An Investigation of Potential Function Designs for Neural CRF

The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling.In this paper, we investigate a series of increasingly expressive potential functions for neural CRF models, which not only integrate the emission and transition functions, but also explicitly take the representations of the contextual words as input.Our extensive experiments show that the decomposed quadrilinear potential function based on the vector representations of two neighboring labels and two neighboring words consistently achieves the best performance.
A Method for Building a Commonsense Inference Dataset based on Basic Events

We present a scalable, low-bias, and low-cost method for building a commonsense inference dataset that combines automatic extraction from a corpus and crowdsourcing.Each problem is a multiple-choice question that asks contingency between basic events.We applied the proposed method to a Japanese corpus and acquired 104k problems.While humans can solve the resulting problems with high accuracy (88.9%), the accuracy of a highperformance transfer learning model is reasonably low (76.0%).We also confirmed through dataset analysis that the resulting dataset contains low bias.We released the datatset to facilitate language understanding research.
Self-Tuned Mirror Descent Schemes for Smooth and Nonsmooth High-Dimensional Stochastic Optimization

We consider randomized block coordinate stochastic mirror descent (RBSMD) methods for solving high-dimensional stochastic optimization problems with strongly convex objective functions. Our goal is to develop RBSMD schemes that achieve a rate of convergence with a minimum constant factor with respect to the choice of the stepsize sequence. To this end, we consider both subgradient and gradient RBSMD methods addressing nonsmooth and smooth problems, respectively. For each scheme, first, we develop self-tuned stepsize rules characterized in terms of problem parameters and algorithm settings; second, we show that the nonaveraging iterate generated by the underlying RBSMD method converges to the optimal solution both in an almost sure and a mean sense; third, we show that the mean squared error is minimized. When problem parameters are unknown, we develop a unifying self-tuned update rule that can be applied in both subgradient and gradient stochastic mirror descent (SMD) methods, and show that for any arbitrary and small enough initial stepsize, a suitably defined error bound is minimized. We provide constant factor comparisons with standard SMD and RBSMD methods. Our numerical experiments performed on a support vector machine (SVM) model display that the self-tuned schemes are significantly robust with respect to the choice of problem parameters and the initial stepsize.
High Performance Natural Language Processing

Scale has played a central role in the rapid progress natural language processing has enjoyed in recent years.While benchmarks are dominated by ever larger models, efficient hardware use is critical for their widespread adoption and further progress in the field.In this cutting-edge tutorial, we will recapitulate the state-of-the-art in natural language processing with scale in perspective.After establishing these foundations, we will cover a wide range of techniques for improving efficiency, including knowledge distillation, quantization, pruning, more efficient architectures, along with case studies and practical implementation tricks.
Correction to: Example‑dependent cost‑sensitive credit cards fraud detection using SMOTE and Bayes minimum risk

The article Example-dependent cost-sensitive credit cards fraud detection using SMOTE and Bayes minimum risk,
Magic cube puzzle approach for image encryption

In principle, the image encryption algorithm produces an encrypted image. The encrypted image is composed of arbitrary patterns that do not provide any clues about the plain image and its cipher key. Ideally, the encrypted image is entirely independent of its plain image. Many functions can be used to achieve this goal. Based on the functions used, image encryption techniques are categorized into: (1) Block-based; (2) Chaotic-based; (3) Transformation-based; (4) Conventional-based; and (5) Miscellaneous based. This study proposes a magic cube puzzle approach to encrypt an 8-bit grayscale image. This approach transforms a plain image into a particular size magic cube puzzle, which is consists of a set of blocks. The magic cube puzzle algorithm will diffuse the pixels of the plain image as in a Rubik’s Cube game, by rotating each block in a particular direction called the transposition orientation. The block’s transposition orientation is used as the key seed, while the generation of the cipher key uses a random permutation of the key seed with a certain key length. Several performance metrics have been used to assess the goals, and the results have been compared to several standard encryption methods. This study showed that the proposed method was better than the other methods, except for entropy metrics. For further studies, modification of the method will be carried out in such a way as to be able to increase its entropy value to very close to 8 and its application to true color images. In essence, the magic cube puzzle approach has a large space for pixel diffusion that is possibly supposed to get bigger as a series of data has transformed into several magic cubes. Then, each magic cube has transposed with a different technique. This proposed approach is expected to add to a wealth of knowledge in the field of data encryption.
Bootstrapping a DQN Replay Memory with Synthetic Experiences

An important component of many Deep Reinforcement Learning algorithms is the Experience Replay that serves as a storage mechanism or memory of experienced transitions.These experiences are used for training and help the agent to stably find the perfect trajectory through the problem space.The classic Experience Replay however makes only use of the experiences it actually made, but the stored transitions bear great potential in form of knowledge about the problem that can be extracted.The gathered knowledge contains state-transitions and received rewards that can be utilized to approximate a model of the environment.We present an algorithm that creates synthetic experiences in a nondeterministic discrete environment to assist the learner with augmented training data.The Interpolated Experience Replay is evaluated on the FrozenLake environment and we show that it can achieve a 17% increased mean reward compared to the classic version.
InVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles

Semantic Role Labeling (SRL) is deeply dependent on complex linguistic resources and sophisticated neural models, which makes the task difficult to approach for non-experts.To address this issue we present a new platform named Intelligible Verbs and Roles (InVeRo).This platform provides access to a new verb resource, VerbAtlas, and a state-of-the-art pretrained implementation of a neural, span-based architecture for SRL.Both the resource and the system provide human-readable verb sense and semantic role information, with an easy to use Web interface and RESTful APIs available at http://nlp.uniroma1.it/invero.
Understanding Procedural Text using Interactive Entity Networks

The task of procedural text comprehension aims to understand the dynamic nature of entities/objects in a process.Here, the key is to track how the entities interact with each other and how their states are changing along the procedure.Recent efforts have made great progress to track multiple entities in a procedural text, but usually treat each entity separately and ignore the fact that there are often multiple entities interacting with each other during one process, some of which are even explicitly mentioned.In this paper, we propose a novel Interactive Entity Network (IEN), which is a recurrent network with memory equipped cells for state tracking.In each IEN cell, we maintain different attention matrices through specific memories to model different types of entity interactions.Importantly, we can update these memories in a sequential manner so as to explore the causal relationship between entity actions and subsequent state changes.We evaluate our model on a benchmark dataset, and the results show that IEN outperforms stateof-the-art models by precisely capturing the interactions of multiple entities and explicitly leverage the relationship between entity interactions and subsequent state changes.
Experimental Test of Tracking the King Problem

In quantum theory, the retrodiction problem is not as clear as its classical counterpart because of the uncertainty principle of quantum mechanics. In classical physics, the measurement outcomes of the present state can be used directly for predicting the future events and inferring the past events which is known as retrodiction. However, as a probabilistic theory, quantum-mechanical retrodiction is a nontrivial problem that has been investigated for a long time, of which the Mean King Problem is one of the most extensively studied issues. Here, we present the first experimental test of a variant of the Mean King Problem, which has a more stringent regulation and is termed "Tracking the King". We demonstrate that Alice, by harnessing the shared entanglement and controlled-not gate, can successfully retrodict the choice of King's measurement without knowing any measurement outcome. Our results also provide a counterintuitive quantum communication to deliver information hidden in the choice of measurement.
Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

The multi-dividing ontology learning framework has been proven to have a higher efficiency for tree-structured ontology learning, and in this work, we consider a special setting of this learning framework in which ontology sample set for each rate is divided into two groups. This setting can be regarded as the classic two-sample learning problem associated with multi-dividing ontology framework. In this work, we mainly focus on the theoretical analysis of multi-dividing two-sample ontology learning algorithm, whose ontology objective function is proposed, and the generalization bounds in this setting is obtained in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> -statistics technique. The theoretical result given is of potential guiding significance in the field of ontology engineering applications.
Pembelajaran Daring Latsar CPNS From Home dalam Masa Pandemi Covid-19

This study analyzes the learning of the Whole of Government through online classes at Latsar CPNS from Home during the COVID-19 pandemic at the Yogyakarta Financial Training Center. The method used is quasi experiment. The results of the study are as follows: the average post-test score of the class is 92 or very satisfactory, the highest score per participant with a score of 100 or very satisfying and the lowest is 81 or satisfying. The learning action plan is able to convey material up to 81% -95%. The disadvantages of online learning are the lack of understanding of the material and the constraint of unstable internet connections, while the strengths are interesting, fun, sharpening your dexterity in searching and finding answers. Suggestions for improvement are to add to the discussion of learning material, the form of questions with right-wrong answer choices be replaced by multiple choices, and reduce the number of post-test questions. In general online learning is considered positive and effective by participants so that it can be continued both during the pandemic and the new normal.
Exploring the Online Learning Self-efficacy of Teacher Education Students at the Laguna State Polytechnic University: Basis for Transition to Flexible Learning System

The global pandemic due to the emergence of COVID-19 impacted the education sector causing the dramatic shift from conventional to flexible learning.To sustain learning continuity, online learning modality rises as a promising instructional response to the pandemic.This study utilized correlational research to explore the factors influencing the online learning self-efficacy of the 387 (57%) teacher education students.It also described the online learning self-efficacy, online readiness, coping strategies, and familiarity and capability of students to educational technology tools.The content-validated and researcher-made instrument which established an excellent reliability index (0.96), was administered via Google forms.Multiple regression analysis was employed to determine the significant predictors of the students' online learning self-efficacy.The results revealed that online readiness, coping strategies, and familiarity and capability significantly explain the variation in the student's self-efficacy to learn online.The administration and the teachers may consider the students' computer skills, reflect on their readiness for online learning, and understand their coping mechanisms in the digital classroom.These factors could be sourced out in developing instructional materials to meet the diverse needs of the students.
Open Ethnographic Archiving as Feminist, Decolonizing Practice

Dubbed Silicon Savannah, Nairobi has become a hot spot of tech development that promises to “save Africa.” Qualitative research—carried out by a tangle of private, academic, and non-profit organizations—is part of the work, promising to reveal how people in Kenya are building and benefiting from a dazzling array of digital products. Amidst the enthusiasm, longstanding problems with ways in which research data in Nairobi is conceived, collected, and shared are easily glossed over. This article advances thinking about the politics of qualitative data, unraveling normative concepts like ethics and transparency by both examining existing data practices and modeling alternatives. I describe the sociotechnical infrastructure underlying the ethnographic project, detailing tactics for deploying an instance of open source software—the Platform for Experimental, Collaborative Ethnography (PECE)—to draw research interlocutors into collaborative effort to understand and build decolonized qualitative data infrastructures. Through such processes I learned that collaborating on data not only refreshes the social contract of qualitative work; it can also enhance its robustness and validity. I advise scholars to better document our own knowing practices in order to attend to the inevitability of margins created through all data practices, including our own
Prediction model of algal blooms using logistic regression and confusion matrix

Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.
Blockchain-Based Diversion-Point System for Balancing Customer Flow in Shopping Mall

Changing the store layout of a shopping mall is usually costly in terms of time, resources, and money. Balancing customer flow is obviously an economical way to rationalize the store layout without displacing stores or changing their locations. However, it has long been a big challenge for managers of shopping malls, because it is difficult to build trust among stores for the sake of regulating customer flow. This trust depends on a multi-party cooperation model, of which the agreements are implemented on asymmetric information. Unfortunately, any form of endorsement with human intervention cannot support building trust on asymmetric information. To solve this problem technically, this paper proposes a diversion-point system to dynamically divert part of customer flow from popular stores to less popular ones. The system operates diversion-points and -vouchers on an asymmetric basis. It also employs a Blockchain subsystem to replace the centralized endorsement and preserve the information asymmetry, thereby building trust into the cooperation among customers, the shopping mall, and the stores therein. The evaluation shows that the proposed system is effective in remedying imperfect store layout of the shopping mall.
Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations

Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (the “knowledge acquisition” problem). To address these issues, we devise a novel logical reasoner called Braid, that supports probabilistic rules, and uses the notion of custom unification functions and dynamic rule generation to overcome the brittle matching and knowledge-gap problem prevalent in traditional reasoners. In this paper, we describe the reasoning algorithms used in Braid, and their implementation in a distributed task-based framework that builds proof/explanation graphs for an input query. We use a simple QA example from a children’s story to motivate Braid’s design and explain how the various components work together to produce a coherent logical explanation. Finally, we evaluate Braid on the ROC Story Cloze test and achieve close to state-of-the-art results while providing frame-based explanations.
Single Image Defogging Algorithm Based on Conditional Generative Adversarial Network

Outside the house, images taken using a phone in foggy weather are not suitable for automation due to low contrast. Usually, it is revised in the dark channel prior (DCP) method (K. He et al. 2009), but the non-sky bright area exists due to mistakes in the removal. In this paper, we propose an algorithm, defog-based generative adversarial network (DbGAN). We use generative adversarial network (GAN) for training and embed target map (TM) in the anti-network generator, only the part of bright area layer of image, in local attention model image training and testing in deep learning, and the effective processing of the wrong removal part is achieved, thus better restoring the defog image. Then, the DCP method obtains a good defog visual effect, and the evaluation index peak signal-to-noise ratio (PSNR) is used to make a judgment; the simulation result is consistent with the visual effect. We proved the DbGAN is a practical import of target map in the GAN. The algorithm is used defogging in the highlighted area is well realized, which makes up for the shortcomings of the DCP algorithm.