Reviewerly/exHarmony huggingface dataset:
Post-selection-free preparation of high-quality physical qubits

Rapidly improving gate fidelities for coherent operations mean that errors in state preparation and measurement (SPAM) may become a dominant source of error for fault-tolerant operation of quantum computers. This is particularly acute in superconducting systems, where tradeoffs in measurement fidelity and qubit lifetimes have limited overall performance. Fortunately, the essentially classical nature of preparation and measurement enables a wide variety of techniques for improving quality using auxiliary qubits combined with classical control and post-selection. In practice, however, post-selection greatly complicates the scheduling of processes such as syndrome extraction. Here we present a family of quantum circuits that prepare high-quality |0&gt; states without post-selection, instead using CNOT and Toffoli gates to non-linearly permute the computational basis. We find meaningful performance enhancements when two-qubit gate fidelities errors go below 0.2%, and even better performance when native Toffoli gates are available.
An Improved Online Folk Sports Culture Exchange Model Based on Multimedia Data Analysis and Edge Computing

In order to improve the effect of folk sports cultural exchange, this study proposes an improved online folk sports cultural exchange model based on multimedia data analysis and edge computing. Moreover, this study explores the factors that affect the local popularity of media content and establishes a relationship model between user request volume and edge node location characteristics. In addition, this study designs customized edge storage strategies under different conditions, which effectively increases the amount of user requests processed by edge nodes and enhances the agility of edge nodes to respond to user content requests. Finally, in order to improve the effectiveness of content crowdsourcing collection, the edge platform needs to select the optimal set of participants to complete the content collection task under a given task budget. The experimental research shows that the improved online folk sports culture exchange model based on multimedia data analysis and edge computing can realize the effective exchange of folk culture.
Optimizing Percentile Criterion Using Robust MDPs

We address the problem of computing reliable policies in reinforcement learning problems with limited data. In particular, we compute policies that achieve good returns with high confidence when deployed. This objective, known as the \emph{percentile criterion}, can be optimized using Robust MDPs~(RMDPs). RMDPs generalize MDPs to allow for uncertain transition probabilities chosen adversarially from given ambiguity sets. We show that the RMDP solution's sub-optimality depends on the spans of the ambiguity sets along the value function. We then propose new algorithms that minimize the span of ambiguity sets defined by weighted $L_1$ and $L_\infty$ norms. Our primary focus is on Bayesian guarantees, but we also describe how our methods apply to frequentist guarantees and derive new concentration inequalities for weighted $L_1$ and $L_\infty$ norms. Experimental results indicate that our optimized ambiguity sets improve significantly on prior construction methods.
Complexity Controlled Generative Adversarial Networks

One of the issues faced in training Generative Adversarial Nets (GANs) and their variants is the problem of mode collapse, wherein the training stability in terms of the generative loss increases as more training data is used. In this paper, we propose an alternative architecture via the Low-Complexity Neural Network (LCNN), which attempts to learn models with low complexity. The motivation is that controlling model complexity leads to models that do not overfit the training data. We incorporate the LCNN loss function for GANs, Deep Convolutional GANs (DCGANs) and Spectral Normalized GANs (SNGANs), in order to develop hybrid architectures called the LCNN-GAN, LCNN-DCGAN and LCNN-SNGAN respectively. On various large benchmark image datasets, we show that the use of our proposed models results in stable training while avoiding the problem of mode collapse, resulting in better training stability. We also show how the learning behavior can be controlled by a hyperparameter in the LCNN functional, which also provides an improved inception score.
Dense Peelable Random Uniform Hypergraphs

We describe a new family of $k$-uniform hypergraphs with independent random edges. The hypergraphs have a high probability of being peelable, i.e. to admit no sub-hypergraph of minimum degree $2$, even when the edge density (number of edges over vertices) is close to $1$. In our construction, the vertex set is partitioned into linearly arranged segments and each edge is incident to random vertices of $k$ consecutive segments. Quite surprisingly, the linear geometry allows our graphs to be peeled "from the outside in". The density thresholds $f_k$ for peelability of our hypergraphs ($f_3 \approx 0.918$, $f_4 \approx 0.977$, $f_5 \approx 0.992$, ...) are well beyond the corresponding thresholds ($c_3 \approx 0.818$, $c_4 \approx 0.772$, $c_5 \approx 0.702$, ...) of standard $k$-uniform random hypergraphs. To get a grip on $f_k$, we analyse an idealised peeling process on the random weak limit of our hypergraph family. The process can be described in terms of an operator on functions and $f_k$ can be linked to thresholds relating to the operator. These thresholds are then tractable with numerical methods. Random hypergraphs underlie the construction of various data structures based on hashing. These data structures frequently rely on peelability of the hypergraph or peelability allows for simple linear time algorithms. To demonstrate the usefulness of our construction, we used our $3$-uniform hypergraphs as a drop-in replacement for the standard $3$-uniform hypergraphs in a retrieval data structure by Botelho et al. This reduces memory usage from $1.23m$ bits to $1.12m$ bits ($m$ being the input size) with almost no change in running time.
PAC-Bayes Analysis Beyond the Usual Bounds

We focus on a stochastic learning model where the learner observes a finite set of training examples and the output of the learning process is a data-dependent distribution over a space of hypotheses. The learned data-dependent distribution is then used to make randomized predictions, and the high-level theme addressed here is guaranteeing the quality of predictions on examples that were not seen during training, i.e. generalization. In this setting the unknown quantity of interest is the expected risk of the data-dependent randomized predictor, for which upper bounds can be derived via a PAC-Bayes analysis, leading to PAC-Bayes bounds. Specifically, we present a basic PAC-Bayes inequality for stochastic kernels, from which one may derive extensions of various known PAC-Bayes bounds as well as novel bounds. We clarify the role of the requirements of fixed 'data-free' priors, bounded losses, and i.i.d. data. We highlight that those requirements were used to upper-bound an exponential moment term, while the basic PAC-Bayes theorem remains valid without those restrictions. We present three bounds that illustrate the use of data-dependent priors, including one for the unbounded square loss.
Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality

Statistical analysis is the tool of choice to turn data into information, and then information into empirical knowledge. To be valid, the process that goes from data to knowledge should be supported by detailed, rigorous guidelines, which help ferret out issues with the data or model, and lead to qualified results that strike a reasonable balance between generality and practical relevance. Such guidelines are being developed by statisticians to support the latest techniques for Bayesian data analysis. In this article, we frame these guidelines in a way that is apt to empirical research in software engineering. To demonstrate the guidelines in practice, we apply them to reanalyze a GitHub dataset about code quality in different programming languages. The dataset's original analysis (Ray et al., 2014) and a critical reanalysis (Berger at al., 2019) have attracted considerable attention -- in no small part because they target a topic (the impact of different programming languages) on which strong opinions abound. The goals of our reanalysis are largely orthogonal to this previous work, as we are concerned with demonstrating, on data in an interesting domain, how to build a principled Bayesian data analysis and to showcase some of its benefits. In the process, we will also shed light on some critical aspects of the analyzed data and of the relationship between programming languages and code quality. The high-level conclusions of our exercise will be that Bayesian statistical techniques can be applied to analyze software engineering data in a way that is principled, flexible, and leads to convincing results that inform the state of the art while highlighting the boundaries of its validity. The guidelines can support building solid statistical analyses and connecting their results, and hence help buttress continued progress in empirical software engineering research.
Quasi Error-free Text Classification and Authorship Recognition in a large Corpus of English Literature based on a Novel Feature Set

The Gutenberg Literary English Corpus (GLEC) provides a rich source of textual data for research in digital humanities, computational linguistics or neurocognitive poetics. However, so far only a small subcorpus, the Gutenberg English Poetry Corpus, has been submitted to quantitative text analyses providing predictions for scientific studies of literature. Here we show that in the entire GLEC quasi error-free text classification and authorship recognition is possible with a method using the same set of five style and five content features, computed via style and sentiment analysis, in both tasks. Our results identify two standard and two novel features (i.e., type-token ratio, frequency, sonority score, surprise) as most diagnostic in these tasks. By providing a simple tool applicable to both short poems and long novels generating quantitative predictions about features that co-determe the cognitive and affective processing of specific text categories or authors, our data pave the way for many future computational and empirical studies of literature or experiments in reading psychology.
Ball k-means

This paper presents a novel accelerated exact k-means algorithm called the Ball k-means algorithm, which uses a ball to describe a cluster, focusing on reducing the point-centroid distance computation. The Ball k-means can accurately find the neighbor clusters for each cluster resulting distance computations only between a point and its neighbor clusters' centroids instead of all centroids. Moreover, each cluster can be divided into a stable area and an active area, and the later one can be further divided into annulus areas. The assigned cluster of the points in the stable area is not changed in the current iteration while the points in the annulus area will be adjusted within a few neighbor clusters in the current iteration. Also, there are no upper or lower bounds in the proposed Ball k-means. Furthermore, reducing centroid-centroid distance computation between iterations makes it efficient for large k clustering. The fast speed, no extra parameters and simple design of the Ball k-means make it an all-around replacement of the naive k-means algorithm.
Evaluation of Job Satisfaction and Work Performance in EHRM via UTAUT

Background & Aim:The purpose of this study is to evaluate the job satisfaction and work performance in EHRM practices through the UTAUT and Viswesvaran Models as well as the relationship between job satisfaction and employee performance. Method:The study was carried out in Northern Indian Banks with 80 Sample size, and the data was collected using a quantitative research approach and a stratified sample method via an online survey questionnaire.Result: To meet the objectives, the data were analyzed using descriptive statistics and Spearman correlation.Job satisfaction and job performance were both assessed as moderate and high, respectively, and an important, favorable, and powerful correlation between both the E-HRM and worker job satisfaction and performance was discovered, with coefficients of 0.81 and 0.75, respectively. Conclusion:Before deploying an E-HRM system, human resource managers in the banking industry might use the findings of this study to give suitable training, information, or advice to their staff.It will help HR Managers in developing HR Strategies and achieving organizational goals by making the work environment easy and fruitful.
Knowledge Management in IT Sector: Review of literature

This paper summarizes and identifies the knowledge practices followed by organisations in various industries.The types of organisations studied under this study are SMEs, IT -Sector Organisations, Manufacturing Sector Organisations, Public and Private Sector Organisations.This paper analyses and reviews KM practices across the industries, SMEs and MNCs.The paper focuses on relevance of KM and best practices that are currently incorporated by them.
Consistent Accelerated Inference via Confident Adaptive Transformers

We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now ubiquitous in natural language processing (NLP). Amortized or approximate computational methods increase efficiency, but can come with unpredictable performance costs. In this work, we present CATs -- Confident Adaptive Transformers -- in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence. Our method trains additional prediction heads on top of intermediate layers, and dynamically decides when to stop allocating computational effort to each input using a meta consistency classifier. To calibrate our early prediction stopping rule, we formulate a unique extension of conformal prediction. We demonstrate the effectiveness of this approach on four classification and regression tasks.
Idris 2: Quantitative Type Theory in Practice

Dependent types allow us to express precisely what a function is intended to do. Recent work on Quantitative Type Theory (QTT) extends dependent type systems with linearity, also allowing precision in expressing when a function can run. This is promising, because it suggests the ability to design and reason about resource usage protocols, such as we might find in distributed and concurrent programming, where the state of a communication channel changes throughout program execution. As yet, however, there has not been a full-scale programming language with which to experiment with these ideas. Idris 2 is a new version of the dependently typed language Idris, with a new core language based on QTT, supporting linear and dependent types. In this paper, we introduce Idris 2, and describe how QTT has influenced its design. We give examples of the benefits of QTT in practice including: expressing which data is erased at run time, at the type level; and, resource tracking in the type system leading to type-safe concurrent programming with session types.
Awareness Logic: Kripke Lattices as a Middle Ground between Syntactic and Semantic Models

The literature on awareness modeling includes both syntax-free and syntax-based frameworks. Heifetz, Meier \& Schipper (HMS) propose a lattice model of awareness that is syntax-free. While their lattice approach is elegant and intuitive, it precludes the simple option of relying on formal language to induce lattices, and does not explicitly distinguish uncertainty from unawareness. Contra this, the most prominent syntax-based solution, the Fagin-Halpern (FH) model, accounts for this distinction and offers a simple representation of awareness, but lacks the intuitiveness of the lattice structure. Here, we combine these two approaches by providing a lattice of Kripke models, induced by atom subset inclusion, in which uncertainty and unawareness are separate. We show our model equivalent to both HMS and FH models by defining transformations between them which preserve satisfaction of formulas of a language for explicit knowledge, and obtain completeness through our and HMS' results. Lastly, we prove that the Kripke lattice model can be shown equivalent to the FH model (when awareness is propositionally determined) also with respect to the language of the Logic of General Awareness, for which the FH model where originally proposed.
ADAPTATION OF WINDOW-BASED TECHNIQUES FOR DETECTING SEISMIC EVENTS FOR LOW BANDWIDTH NETWORKS

This work is devoted to the development of the algorithm for detecting seismic events, which will be adapted to the computational resource of a seismic recorder. This modi-fication must ensure proper pre-processing on board the recorder during continuous data re-cording. This will reduce the amount of transmitted data to server, which will make it possible to use low bandwidth communication channels. The first test compared the classic STA/LTA with the modified one using reference seismic signal. The result showed that the arrival times of the detecting functions coincide.
MODELING OF A METHOD FOR DETERMINING THE CONTENT OF URANIUM AND POTASSIUM THORIUM IN A ROCK BY PASSIVE GAMMA-SPECTROMETRY

The present work is devoted to modeling the determination of the content of uranium and potassium thorium in the rock by the method of passive gamma-spectrometry used in radiometric surveys when searching for hydrocarbon deposits.
NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs

Deep Neural Networks (DNNs) may be partitioned across the edge and the cloud to improve the performance efficiency of inference. DNN partitions are determined based on operational conditions such as network speed. When operational conditions change DNNs will need to be repartitioned to maintain the overall performance. However, repartitioning using existing approaches, such as Pause and Resume, will incur a service downtime on the edge. This paper presents the NEUKONFIG framework that identifies the service downtime incurred when repartitioning DNNs and proposes approaches for reducing edge service downtime. The proposed approaches are based on 'Dynamic Switching' in which, when the network speed changes and given an existing edge-cloud pipeline, a new edge-cloud pipeline is initialised with new DNN partitions. Incoming inference requests are switched to the new pipeline for processing data. Two dynamic switching scenarios are considered: when a second edge-cloud pipeline is always running and when a second pipeline is only initialised when the network speed changes. Experimental studies are carried out on a lab-based testbed to demonstrate that Dynamic Switching reduces the downtime by at least an order of magnitude when compared to a baseline using Pause and Resume that has a downtime of 6 seconds. A trade-off in the edge service downtime and memory required is noted. The Dynamic Switching approach that requires the same amount of memory as the baseline reduces the edge service downtime to 0.6 seconds and to less than 1 millisecond in the best case when twice the amount of memory as the baseline is available.
On the Metaphysics of Digitalization

The danger in dealing with artificial intelligence is that simulation is confused with realization. This already leads us to use an inappropriate vocabulary, according to which software systems have “perceptions” and “make decisions.” For example, when the navigation system commands “Turn right,” we attribute an intention to the system, namely, to make us turn right. But as things stand, that is, at the current state of digital technologies, this would be a mystification. Software systems do not will, feel, think, decide. Humans however do. It is them who are not determined by mechanical processes. Thanks to their capacity for insight as well as their ability to have feelings, they can determine their actions themselves, and they do this by deciding to act in this way and not in another. This is what discerns us from robots.
Digital Education

The digital possibilities create new freedom, trigger an enormous dynamic of change not only in economic but also in cultural conditions, and therefore are able to strengthen the autonomy potential of individuals. At the same time however the digital transformation has put us under the permanent stress of a growing need for orientation. As a result of digitalization, we do not live in a knowledge society, but at best in a data society, or better: in a data economy. Only the evaluation, classification, and interpretation of data can constitute knowledge. The great challenge of education in times of digital transformation is to transform the current trend toward a data economy into a development toward a knowledge society. Therefore, the development of personality is more relevant today than ever before, and its importance will continue to increase due to the digitalization of our communications and interactions, transfers of data and services and their production.
Effects of Mind Mapping and Pre-Questioning on Reading Comprehension

This study was conducted to investigate the effects of mind mapping and pre-questioning on the students’ reading comprehension and on the students’ reading comprehension levels: literal comprehension, inferential comprehension, evaluation, and appreciation. This study was a quasi-experimental design, which involved 30 Diploma Nursing Student Stikes RS Baptis Kediri. Mind mapping was used for the experimental group and pre- questioning was for the control group. The instrument was 25 item reading comprehension test incorporating the four reading comprehension levels based on Barrett taxonomy. Independent Sample t-test and Manova test were used to analyze the data, which results revealed that there was no significant difference between the students who received mind mapping and those who received pre-questioning in their overall reading comprehension and in their literal comprehension, evaluation, and appreciation level. However, there was a significant difference between the students who received mind mapping and those who received pre-questioning in their inferential comprehension
The Effect of Motivation and Learning Effectiveness and the Quality of Economic Education Study Program Graduates

This research was conducted to find out information and learning learning to the quality of study program Economic Education University of Jambi. This research uses survey method with correlational approach. The in place of this research is the study program Economic Education University of Jambi with the number of affordable population as much as 325 graduates. Data collection using questionnaire instrument. The sampling technique is done randomly with the following steps: First, calculate the target population of all Economic Education courses in Jambi University; second, the number of people reachable by lottery gives the order number; third, selecting 20 random people in statistics to try; Fourth, select 50 random people in the sample to sample the research. This research produces; (a) There are factors influencing the feasibility of Economic Education Universitas Jambi; (b) There are many things that affect the quality of graduates. The implication of this research is the effort to improve the quality that can be done by improving the motivation and study of the students of Economic Education Universitas Jambi.
A Simple Lossless Algorithm (SLA) for on-board Satellite Hyperspectral Data Compression

&lt;p&gt;A novel algorithm for on-board satellite hyperspectral data compression is proposed. Computational complexity is targetted with comparable compression performance with state-ot-the-art on-board satellite hyperspectral data compression methods. &lt;/p&gt;